3Articles review and critique

RESEARCH ARTICLE

Mitochondrial fragmentation and network

architecture in degenerative diseases

Syed I. Shah, Johanna G. Paine, Carlos Perez, Ghanim UllahID*

Department of Physics, University of South Florida, Tampa, FL, United States of America

* [email protected]

Abstract

Fragmentation of mitochondrial network has been implicated in many neurodegenerative,

renal, and metabolic diseases. However, a quantitative measure of the microscopic parame-

ters resulting in the impaired balance between fission and fusion of mitochondria and conse-

quently the fragmented networks in a wide range of pathological conditions does not exist.

Here we present a comprehensive analysis of mitochondrial networks in cells with Alzhei-

mer’s disease (AD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), Parkin-

son’s disease (PD), optic neuropathy (OPA), diabetes/cancer, acute kidney injury, Ca 2+

overload, and Down Syndrome (DS) pathologies that indicates significant network fragmen-

tation in all these conditions. Furthermore, we found key differences in the way the micro-

scopic rates of fission and fusion are affected in different conditions. The observed

fragmentation in cells with AD, HD, DS, kidney injury, Ca 2+

overload, and diabetes/cancer

pathologies results from the imbalance between the fission and fusion through lateral inter-

actions, whereas that in OPA, PD, and ALS results from impaired balance between fission

and fusion arising from longitudinal interactions of mitochondria. Such microscopic differ-

ence leads to major disparities in the fine structure and topology of the network that could

have significant implications for the way fragmentation affects various cell functions in differ-

ent diseases.

Introduction

Mitochondrion is a ubiquitous organelle and powerhouse of the cell that exists in living cells as

a large tubular assembly, extending throughout the cytoplasm and in close apposition with

other key organelles such as nucleus, the endoplasmic reticulum, the Golgi network, and the

cytoskeleton [1–5]. Its highly flexible and dynamic network architecture ranging from a few

hundred nanometers to tens of micrometers with the ability to rapidly change from fully con-

nected to fragmented structures makes it suitable for diverse cytosolic conditions and cell

functions [6–8]. Cells continuously adjust the rate of mitochondrial fission and fusion in

response to changing energy and metabolic demands to facilitate the shapes and distribution

of mitochondria throughout the cell [9–11]. Similarly, stressors such as reactive oxygen species

(ROS) and Ca 2+

dysregulation interfere with various aspects of mitochondrial dynamics [12–

14]. This is probably why many neuronal, metabolic, and renal diseases have been linked to

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 1 / 21

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OPEN ACCESS

Citation: Shah SI, Paine JG, Perez C, Ullah G

(2019) Mitochondrial fragmentation and network

architecture in degenerative diseases. PLoS ONE

14(9): e0223014. https://doi.org/10.1371/journal.

pone.0223014

Editor: Hemachandra Reddy, Texas Technical

University Health Sciences Center, UNITED

STATES

Received: April 18, 2019

Accepted: September 11, 2019

Published: September 26, 2019

Copyright: © 2019 Shah et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All relevant data are

within the manuscript and its Supporting

Information files.

Funding: This works was supported by National

Institute of Health through grant R01 AG053988

(to GU). URL of funder website: https://www.nih.

gov. The funders had no role in study design, data

collection and analysis, decision to publish, or

preparation of the manuscript.

Competing interests: The authors have declared

that no competing interests exist.

primary or secondary changes in mitochondrial dynamics [9, 15–37]. Neuronal cells, due to

their complex morphology and extreme energy dependent activities such as synaptic transmis-

sion, vesicle recycling, axonal transport, and ion channels and pumps activity, are particularly

sensitive to changes in the topology of mitochondrial network [38–41].

The mitochondrial network organization makes a bidirectional relationship with the cell’s

bioenergetics and metabolic variables [11, 42]. For example, the morphological state of mito-

chondria has been linked to their energy production capacity [43–46], as well as cell health and

death [10, 46–49] on one hand, alterations in mitochondrial energy production caused by

genetic defects in respiratory chain complexes lead to fragmentation of mitochondrial network

[50, 51] on the other hand. Similarly, while ROS induces fragmentation of mitochondrial net-

work [12–14], overproduction of ROS in hyperglycemic conditions requires dynamic changes

in mitochondrial morphology and fragmentation of the network [52]. Furthermore, high cyto-

solic Ca 2+

induces mitochondrial fragmentation [14], whereas fragmentation blocks the propa-

gation of toxic intracellular Ca 2+

signals [53, 54] and can limit the local Ca 2+

uptake capacity of

mitochondria due to their smaller sizes. Thus dynamic changes in mitochondrial morphology

and fragmentation of its network can be part of the cycle that drives the progression of degen-

erative diseases [11–13, 18, 22, 52, 55–70].

Despite a clear association with many cell functions in physiological conditions, quantita-

tive measures of the microscopic fission and fusion rates leading to a given topology of the

mitochondrial network remain elusive. While fluorescence imagining has been instrumental

in providing biologically useful insights into the structure and function of mitochondria,

detailed description of the kinetics and the dynamical evolution of the complex mitochondrial

networks in health and disease are still out of reach of these techniques. Although it is difficult

to study such dynamics experimentally, computational techniques provide a viable alternative.

Various computational studies on the identification and analysis of network parameters from

experimental mitochondrial micrographs have been performed using either custom built

applications [71–76] or commercially available tools [77], depending upon the particular ques-

tion being asked. However, a comprehensive study quantifying the imbalance between fission

and fusion responsible for the network fragmentation observed in many diseases does not

exist.

In this paper, we adopt and extend the method developed in Refs. [75, 76] using a pipeline

of computational tools that process and extract a range of network parameters from mitochon-

drial micrographs recorded through fluorescence microscopy, and simulate mitochondrial

networks to determine microscopic rates of fission and fusion leading to the observed network

properties. We first demonstrate our approach by application to images of mitochondrial

networks in striatal cells from YAC128 Huntington’s disease (HD) transgenic mice (bearing a

111 polyglutamine repeat Q111/0 and Q111/1) and their control counterparts reported in

Ref. [78]. This is followed by the application of our technique to images of mitochondria in

cells with Alzheimer’s disease (AD) [79], amyotrophic lateral sclerosis (ALS) [80], Parkinson’s

disease (PD) [81], optic neuropathy (OPA) [66], diabetes/cancer [65], acute kidney injury [64],

Ca 2+

overload [14], and Down syndrome (DS) [36, 82] pathologies from the literature. The

images analyzed in this study were selected based on the following criteria. (1) The paper from

which the images were selected reported images of mitochondrial networks both in normal

and diseased cells from the same cell/animal model. (2) The images were of high enough qual-

ity so that they can be processed properly, making sure that the network extracted indeed

represented the actual mitochondrial network without introducing artifacts during the pro-

cessing. The cell/animal models used in these studies are listed in S1 Table in the Supplemen-

tary Information Text and detailed in the Results section below. Although we found

fragmented mitochondrial networks and imbalanced fission and fusion in all these pathologies

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in comparison to their respective control conditions, significant differences between the

microscopic properties underlying such fragmentation exist in different diseases.

Methods

Image analysis

Mitochondria in a cell can form networks of different topologies ranging from a fully disinte-

grated network with one mitochondrion per cluster to a well-connected network comprising

of clusters with several mitochondria per cluster to a fully connected network where all clusters

are connected to form a single giant cluster. These topologies can be uniquely distinguished by

various network parameters such as the mean degree <k> (the average number of nearest

neighbors), giant cluster Ng (the largest cluster in the network), giant cluster normalized with

respect to the total number of nodes (mitochondria) or edges (connections) Ng/N, and distri-

butions of various features such as the number of mitochondria in various linear branches,

cyclic loops, and clusters comprising both branches and loops.

To extract all this information from experimental images of mitochondrial networks, we

adopt and extend the procedure first reported in Ref. [75] using a pipeline of Matlab (The

MathWorks, Natick, MA) tools. Often, we are required to preprocess the images for removing

any legends or masking/removing areas that contain artifacts (Fig 1A). The colors representing

processes other than mitochondria are removed and the resulting image is converted to gray-

scale image (Fig 1B). Next, we take a series of steps to extract the underlying mitochondrial

network and the key information about the network.

Fig 1. Steps involved in the processing of the images and retrieval of various network features. (a) Original image, (b) the grayscale image containing mitochondrial

network only, (c) binary image, and (d) skeletonized image. Panel (e) shows a graph (partially shown) representation of the skeletonized image where red, green, and

blue colors represent nodes with degree 1, 2 and 3 respectively. Size distribution of cyclic loops (f) and linear branch lengths (g), and cumulative probability distribution

of cluster sizes (h) in mitochondrial network in striatal cells from wildtype (NL, red) and YAC128 HD (blue) transgenic mice. The image used for the mitochondrial

network extraction in panel (a) was adopted from Ref. [78] with permission.

https://doi.org/10.1371/journal.pone.0223014.g001

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Step 1: We use Matlab function im2bw to generate a binary image (Fig 1C) from the prepro- cessed gray scale image (Fig 1B) of the micrograph by applying appropriate threshold intensity

using Matlab function graythresh. Step 2: The resulting binary image is reduced to a trace of one-pixel thick lines called skele-

ton using Matlab function bwmorph, which represents mitochondrial network (Fig 1D). Step 3: To extract various features of the mitochondrial network from skeletonized image,

we first label different clusters using Matlab routine bwlabel. The labeled clusters are then con- verted to a graph (Fig 1R, only partial graph is shown for clarity) where the nodes are color-

coded according to their degree. The graph is then used to extract network parameters such as

<k>, Ng, and Ng/N. We also extracted size distribution of loops or cycles with no open ends

(Fig 1F), size distribution of branches with at least one open end (Fig 1G), and cumulative

probability distribution of individual cluster sizes (Fig 1H) in terms of number of edges, where

a single cluster could have both loops and branches and is disconnected from other clusters.

All the above properties are extracted for mitochondrial networks in the cells with different

pathologies and the corresponding control cells for comparison. For example, we compare the

size distributions of loops, branches, and clusters in striatal cells from YAC128 Huntington’s

disease (HD) transgenic mice (blue) and their control counterparts (NL, red) reported in

Ref. [78] in Fig 1F–1H. A clear leftward shift in these distributions can be seen in HD, indicat-

ing a fragmented mitochondrial network as compared to NL cells. The overall number of

loops and branches also decreases in HD.

Modeling and simulating mitochondrial network

To simulate mitochondrial network, we used the model described in Sukhorukov et al. [76], where the network results from two fusion and two fission reactions (Fig 2). In the model, a

dimer tip representing a single mitochondrion can fuse with other dimer tips, forming a net-

work node. At most three tips can merge. The two possible fusion and corresponding fission

reactions are termed as tip-to-tip and tip-to-side reactions. The biological equivalent of the

tip-to-tip reaction would be the fusion of two mitochondria moving along the same microtu-

bule track in the opposite directions and interacting longitudinally [83]. Similarly, tip-to-side

reaction mimics the merging of two mitochondria moving laterally [83]. These two types of

Fig 2. Experimentally observed mitochondrial network and the scheme to model it. (a) Color coded mitochondrial network retrieved from experimental image of a

striatal cell from a wildtype mice and (b) its zoomed in version. (c) Model scheme representing the tip-to-tip fusion of two X1 nodes into X2 and tip-to-side fusion of

one X1 node with one X2 node to make one X3 node, and their corresponding fission processes. The image used for the mitochondrial network extraction in panel (a)

was adopted from Ref. [78] with permission.

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interactions are explained further in section “Mitochondrial interactions” of Supplementary

Information text and sketched in S1 Fig. This way, the network can have nodes with degree 1

(isolated tip), degree 2 (two merged nodes), and degree 3 (three merged nodes). To each fusion

process, there is an associated fission process. Thus, the four possible processes can be repre-

sented by the following two reaction equations.

X1 þX1 ! a1

b1

X2;

X1 þX2 ! a2

b2

X3:

Where X1 (Fig 2A, red), X2 (Fig 2A, green), and X3 (Fig 2A, blue) represent nodes with

degree 1, 2, and 3 respectively. Nodes with degree 4 are not included because of their extremely

low probability [75, 76]. Network edges connecting the nodes define minimal (indivisible)

constituents of the organelle. Therefore, all parameters are calculated in terms of number of

edges in the network.

Next, we implement the model as an agent-based model using Gillespie algorithm [75, 76,

84]. We initialize the simulation with the number of edges (N) estimated from experimental

micrographs of the cell that we intend to model and all nodes initially in X1 form with their

number equal to the mitochondrial components representing the cell. The number of edges in

the images processed in this paper ranges from as few as 72 to as many as 19519. The network

is allowed to evolve through a sequence of fusion and fission processes according to their pro-

pensities at a given time step. In all cases, we run the algorithm for 5N time steps to reach the

steady state and extract various network features (<k>, Ng, branch lengths etc.) at the end of

the run using various Graph and Network algorithms in Matlab. Depending on the fusion (a1

& a2) and fission (b1 & b2) rates used, networks of varying properties ranging from mostly

consisting of isolated mitochondria or branched clusters to a fully connected one giant cluster

can be generated [76].

To search for a network with specific properties, we follow the procedure in [75, 76] and

vary the ratio of fusion and fission processes, i.e. C1 = a1/b1 and C2 = a2/b2 by fixing b1 and

b2 at 0.01 and 3b1/2 respectively, and allowing a1 and a2 to vary. For every set of (C1, C2) val-

ues, we repeat the simulations 100 times with different sequences of random numbers and

report different parameters/features of the network averaged over all 100 runs. Results from a

sample run with N = 3000 are shown in Fig 3A1–3A3, where we plot <k> (Fig 3A1) and Ng/N

(Fig 3A2) as functions of C2 at fixed C1 = 0.0007. Ng/N versus <k> from the same simulation

is shown in Fig 3A3. Increasing C1 shifts the curve to the right. We scan a wide range of C1

and C2 values and plot <k> and Ng/N obtained from experimental images on this two param-

eter phase space diagram. As an example, the red crosses in the inset in Fig 3A3 represent Ng/

N versus <k> retrieved from experimental images of mitochondria in striatal cells from NL

and HD transgenic mice [78]. The values from the image are mapped with the corresponding

C1 and C2 values on the phase space diagram and reported as the values for that cell.

Larger values of C1 and C2 mean more frequent tip-to-tip and tip-to-side fusion respec-

tively, and vice versa. A very small value of C2 (or C1) results in a network mainly consisting

of linear chains and isolated nodes (Fig 3B1) with small <k> and Ng/N (Fig 3A1 & 3A2).

Medium value of C2 leads to a network having clusters with both branches and loops (Fig

3B2), whereas large C2 value results in a network having one giant cluster (Fig 3B3) with large

Mitochondrial fragmentation in degenerative diseases

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<k> and Ng/N values. To demonstrate further that how low, intermediate, and large values of

C2 (or C1) affect the fine structure of the network, we show distributions of the loop, branch,

and cluster sizes from three simulations in Fig 3C1–3C3. We pick C2 values obtained for mito-

chondrial networks (details about C1 and C2 values for different conditions are given below)

in striatal cells with HD pathology (C2 = 0.22e-4, C1 = 4.9e-4), their corresponding NL cells

(C2 = 0.44e-4, C1 = 4.9e-4) [78], and NL cells from ALS experiments (C2 = 1.0e-4, C1 = 4.8e-

4) reported in Ref. [80] as representatives of the three cases. We also performed simulations

using C1 and C2 values representing mitochondrial networks in cells with DS pathology

(C2 = 0.32e-4 value) and their corresponding NL cells (C2 = 0.88e-4 value) [36, 82] and

observed a clear rightward shift in all three distributions at 0.88e-4 as compared to those at

C2 = 0.32e-4 (not shown). In addition to shifting to the right, the range of all three distribu-

tions widens as we increase the value of C2, indicating that both the sizes and diversity of the

network components increase.

Results

As pointed out above, we processed images of mitochondrial networks in cells with various

neurological pathologies including AD [79], ALS [80], PD [81], HD [78], OPA [66], Ca 2+

over-

load in astrocytes [14], and DS [36, 82] as well as other conditions such as kidney disease [64]

Fig 3. Model results at different C1 and C2 values. Mean degree (a1), Ng/N (a2), and Ng/N versus <k> (a3) as functions of C2 at a fixed value of C1. Inset in

(a3) shows a zoomed in version of the main plot in (a3) with superimposed Ng/N versus <k> from experimental images of mitochondria in striatal cells (red

cross) from wildtype (NL) and YAC128 HD transgenic mice [78]. Mitochondrial network changes from fragmented (b1) to physiologically viable, well-

connected (b2) to a fully connected network making one giant cluster (b3) as we increase C2 (or C1). Distribution of loop sizes (c1), branch lengths (c2), and

cluster sizes (c3) retrieved from simulated networks at two different C2 values corresponding to mitochondrial network in striatal cells from HD transgenic

mice (representative of low C2) (black bars) and striatal cells from wildtype mice in the same experiments (representative of intermediate C2) (red bars). The

insets in (c1) and (c2) and the blue bars in (c3) correspond to C2 value for the normal cells in ALS experiments (representative of high C2). The inset in (c3)

shows the tail of the blue distribution indicating the formation of a giant cluster at high C2. At smaller cluster sizes, the black, red, and blue bars in panel (c1) are

comparable and are skipped for clarity.

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and diabetes/cancer [65] from published literature. Details of the cell models analyzed are

given in the following paragraphs and tabulated in S1 Table. Key network parameters such as

<k>, Ng, Ng/N retrieved from the diseased cells and their normal counterparts are listed in

Table 1. A universal signature of all pathological conditions we analyzed in this study is that

mitochondrial networks in the diseased cells are fragmented as compared to normal cells. In

terms of network parameters, this translates into smaller <k>, average cluster size, Ng, and

Ng/N for mitochondrial networks in cells with pathological conditions as compared to control

cells.

Our observations are in agreement with previous studies investigating these diseases indi-

vidually. For example, it has been shown that mitochondrial dysfunction in fibroblasts from

human fetuses with trisomy of Hsa21 (DS-HFF) [82], human fibroblasts from subjects with

DS [36], and mouse embryonic fibroblasts derived from a DS mouse model [36] are correlated

with the significant fragmentation of the underlying mitochondrial network when compared

to healthy cells, in line with our results showing that <k> and Ng/N for the network in NL

cells are higher than those in DS affected cells. Another study investigating mitochondrial

dynamics in AD showed that neuroblastoma cells overexpressing APPswe mutant and amyloid

β display more fragmented mitochondrial networks as compared to control cells [79]. Along similar lines, cells with HD pathology were shown to be accompanied by mitochondrial frag-

mentation and cristae alterations in several cellular models of the disease. These alterations

were attributed to increased basal activity of the Ca 2+

-dependent phosphatase calcineurin that

dephosphorylates the pro-fission dynamin related protein 1 (Drp1) and mediates its transloca-

tion to mitochondria [85]. This study also showed that the upregulation of calcineurin activity

results from the higher Ca 2+

concentration in the cytoplasm in HD due to enhanced release

from intracellular stores such as the endoplasmic reticulum. Parkinson’s disease is another

complex multifactorial etiology, involving many genetic and environmental factors over the

Table 1. Network parameters obtained from images of cells with different pathologies. Column 1 lists the disease for which micrographs of normal (NL) and diseased

cells were analyzed (column 2). Column 3–8 lists the total number of edges, mean degree, total number of clusters (excluding isolated nodes), average cluster size (in terms

of number of edges), giant cluster size (in terms of number of edges), and the ratio of the giant cluster and network size.

Condition Normal vs diseased Number of edges Mean degree

<k>

Number of clusters Avg. cluster size Ng Ng/N

HD NL 2664 1.67 556 4.79 50 0.0188

HD 2150 1.63 512 4.20 40 0.0186

AD NL 642 1.64 144 4.46 27 0.042

AD 1061 1.62 258 4.11 40 0.038

DS NL 1916 1.52 623 3.08 34 0.017

DS 1365 1.47 502 2.72 14 0.010

PD NL 19519 1.72 3416 5.71 107 0.006

PD 8715 1.70 1691 5.15 45 0.005

ALS NL 103 1.75 19 5.42 38 0.369

ALS 72 1.69 13 5.54 16 0.222

Kidney injury NL 5038 1.66 1061 4.75 58 0.012

Kidney injury 5386 1.64 1207 4.64 59 0.011

Diabetes/Cancer NL 3546 1.67 715 4.96 82 0.023

Diabetes/Cancer 3504 1.65 769 4.55 35 0.010

OPA NL 5263 1.69 1045 5.04 49 0.0093

OP 7772 1.67 1656 4.69 43 0.0055

Ca 2+

NL 3195 1.59 903 3.54 126 0.039

Ca 2+

overload 2576 1.57 764 3.37 82 0.032

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course of time. An in-depth analysis of the human primary skin fibroblasts obtained from spo-

radic late-onset PD patients with those from healthy age-matched control subjects showed that

the diseased fibroblasts exhibit significantly compromised mitochondrial structure and func-

tion [81]–in line with the network parameters estimated in our study.

We also analyzed images of mitochondrial networks in mouse hippocampus-derived neu-

roblastoma cells, transduced with wildtype, R15L, and S59L mutations of Coiled-coil-helix-

coiled-coil-helix domain-containing protein 10 (CHCHD10) that were reported in Ref. [80].

Both <k> and Ng (and Ng/N) decrease in the presence of CHCHD10 mutations as compared

to wildtype CHCHD10. CHCHD10 mutations are associated with a spectrum of familial and

sporadic frontotemporal dementia-ALS diseases [86, 87], Charcot–Marie–Tooth disease type 2

[88], mitochondrial myopathy and spinal muscular atrophy Jokela type [89]. Recently, Woo

et al. [80] showed that CHCHD10 results in cytoplasmic accumulation of TAR DNA-binding protein 43 (TDP-43) that increases mitochondrial fission proteins Drp1 and Fis1, reduces

mitochondrial fusion protein Mfn1, and promotes mitochondrial fragmentation [90, 91].

TDP-43 pathology is associated with the vast majority of ALS and frontotemporal lobar degen-

erations [92] and plays a major role in other neurodegenerative diseases [93, 94] and cellular

toxicity in general [95, 96]. Overexpression of TDP-43 also promotes juxtanuclear aggregation

of mitochondria [90, 91]. The larger average cluster size we observe in cells with CHCHD10

mutations as compared to NL cells could reflect this behavior (Table 1, column 6).

Mitochondrial damage is also believed to be a key contributor to renal diseases like acute

kidney injury. By processing images of mitochondrial networks reported in Brooks et al. [64], we observe smaller <k> and Ng/N in rat proximal tubular cells and primary renal proximal

tubular cells treated with azide to induced ATP depletion and model in vivo ischemia. These values confirm the conclusions in Ref. [64], where a larger number of cells exhibited frag-

mented mitochondrial networks in cells treated with azide and cisplatin to induce nephrotoxi-

city as compared to control cells. The same study also reported that both ischemic acute

kidney injury and tubular apoptosis were observed to be ameliorated by Mdivi-1, a pharmaco-

logical inhibitor of Drp1.

A dimeric mitochondrial outer membrane protein, MitoNEET, is implicated in the etiology

of many pathologies including obesity, insulin resistance, diabetes, and cancer. Its downregula-

tion reduces cell proliferation and tumor growth in breast cancer adipocytes and in pancreatic

cells [97–100]. Our analysis of fluorescence images of MitoNEET knockout mouse embryonic

fibroblasts indicates that <k>, average cluster size, and Ng/N all decrease when compared with

control mouse embryonic fibroblasts. These results are in agreement with the observations sug-

gesting that the downregulation of MitoNEET in mouse embryonic fibroblasts and pancreatic β cells results in reduced connectivity of mitochondrial network and vice versa [99, 101].

Mitochondriopathies are also associated with many multisystemic diseases including infan-

tile-onset developmental delay, muscle weakness, ataxia, and optic nerve atrophy caused by a

homozygous mutation in the yeast mitochondrial escape 1-like 1 gene (YME1L1) [102].

YME1L1 plays a key role in mitochondrial morphology by mediating optic atrophy type 1

(OPA1) protein that is involved in mitochondrial fusion and remodeling, and is also believed

to be associated with hereditary Spastic Paraplegia 7 disease, Autosomal Recessive disorder,

obesity, and defective thermogenesis [73, 103–106]. We found that <k>, mean cluster size,

and Ng/N all decrease in cells expressing YME1L1 missense mutation R149W and YME1L1.

These results are in agreement with the observations of fragmented mitochondrial network in

HeLa cells and fibroblasts from mouse and patients with proliferation defect expressing

R149W or YME1L1 knockout cells [66] and SHSY5Y cells where YME1L1 is degraded in

response to distinct cellular stresses that depolarize mitochondria and deplete cellular ATP

[103].

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Interestingly, a common feature among the pathological conditions discussed in this paper

and several other degenerative diseases where mitochondrial fragmentation is observed, is that

intracellular Ca 2+

concentration in the cells affected by these pathologies is upregulated [107–

120]. Therefore, we analyzed images of mitochondrial networks in cells with higher intracellu-

lar Ca 2+

concentration. These images were reported in Ref. [14], where rat cortical astrocytes

were treated with Ca 2+

ionophore 4Br-A23187 that increases intracellular Ca 2+

concentration

in dose-dependent manner. We found that <k>, average cluster size, Ng, and Ng/N for mito-

chondrial network in astrocytes exposed to 4Br-A23187 are significantly lower than those

observed in control cells.

Next, we perform extensive stochastic simulations (see “Modeling and simulating mito-

chondrial network” section) to search for the tip-to-tip and tip-to-side fusion and fission rates

characterizing mitochondrial networks in cells with different pathologies and their respective

control conditions. Final results from these simulations are summarized in Table 2. As is evi-

dent from columns 7 and 8, in all cases the values of C1 or/and C2 for mitochondrial network

in diseased cells are smaller than those in control cells. This confirms the lower tip-to-tip or

tip-to-side fusion to fission ratios in the diseased cells.

In most cases, we identified C1 and C2 where the model gives the exact <k> and Ng/N val-

ues observed in the experiment. In some cases, the Ng/N value from simulation is slightly dif-

ferent than that retrieved from experimental images. However, it is possible to get C1 and C2

values that would result in the exact Ng/N values. This will require running the algorithm with

smaller C1 and C2 increments, which will significantly increase computational time. On aver-

age, simulating the network with one set of C1 and C2 values and 100 repetitions to minimize

the stochastic variability, takes 5 to 10 hours (depending on N). Thus, halving the increments

of one or both of C1 and C2 would double or quadruple the computational time respectively.

Table 2. Comparison of microscopic parameters of mitochondrial network obtained from simulations and experiments. Column 1 lists the condition for which

images of normal (NL) and diseased cells were analyzed (column 2). Columns 3 & 4 and 5 & 6 compare <k> and Ng/N respectively from experiment and theory. Columns

7 & 8 are the C1 (tip-to-tip fusion/fission) and C2 (tip-to-side fusion/fission) values obtained by fitting the model to the data and used in simulations.

Condition Normal vs diseased Mean degree <k>

Exp Theory

Ng/N

Exp Theory

C1 C2

HD NL 1.67 1.67 0.0188 0.022 4.9e-4 4.40e-5

HD 1.63 1.63 0.0186 0.0101 4.9e-4 2.20e-5

AD NL 1.64 1.64 0.042 0.108 7.0e-4 2.30e-4

AD 1.62 1.62 0.038 0.067 7.0e-4 1.90e-4

DS NL 1.52 1.52 0.017 0.017 5.0e-4 0.88e-4

DS 1.47 1.47 0.010 0.010 5.0e-4 0.32e-4

PD NL 1.72 1.72 0.006 0.008 1.2e-3 7.00e-6

PD 1.70 1.70 0.005 0.007 9.8e-4 7.00e-6

ALS NL 1.75 1.75 0.369 0.359 4.8e-4 1.00e-4

ALS 1.69 1.69 0.222 0.225 1.0e-4 1.00e-4

Kidney injury NL 1.66 1.66 0.012 0.016 9.1e-4 4.00e-5

Kidney injury 1.64 1.64 0.011 0.011 9.0e-4 0.25e-4

Diabetes/Cancer NL 1.67 1.67 0.023 0.020 9.8e-4 4.50e-5

Diabetes/Cancer 1.65 1.65 0.010 0.013 9.8e-4 2.50e-5

OPA NL 1.69 1.69 0.0093 0.0081 9.0e-4 1.00e-5

OPA 1.67 1.67 0.0055 0.0075 7.6e-4 1.00e-5

Ca 2+

NL 1.59 1.59 0.039 0.038 7.0e-4 1.46e-4

Ca 2+

overload 1.57 1.58 0.032 0.032 7.0e-4 1.13e-4

https://doi.org/10.1371/journal.pone.0223014.t002

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A close look at the values of C1 and C2 reveals two main trends (Table 3). In case of HD,

AD, DS, Ca 2+

overload, kidney disease, and diabetes/cancer the fusion to fission ratio for the

tip-to-tip reaction remains constant, while the fusion to fission ratio for the tip-to-side reaction

decreases when compared to the control conditions. As shown by an example from HD (Fig

4A1–4A4), this results in smaller number of X3 species with a gain in X1 and X2 species in the

diseased state (Fig 4A4 and Table 3). However, since the probability of cyclic loops depends on

both X2 and X3, the large decrease in X3 and moderate increase in X2 lead to smaller cyclic

loops and consequently smaller clusters in the diseased state (Fig 4A1 & 4A3). Larger number

of X2 species with no change in X1 would translate into longer and/or larger number of linear

branches. However, the simultaneous increase in the number of X1 species would result in

shorter branches (Fig 4A2) and higher number of isolated mitochondria. A relatively smaller

decrease in C2 leads to a smaller decrease in X3, and a smaller increase in X1 and X2, which

would lead to smaller but larger number of linear chains. The larger number of linear chains

could overcompensate for the small decrease in X3, resulting in a larger number of cyclic

loops. Such behavior is demonstrated by an example using network statistics for diabetes

(S2 Fig).

An opposite effect can be seen in case of OPA, PD, and ALS where C1 decreases and C2

remains constant when compared to normal cells. The lower fusion to fission ratio for the tip-

to-tip reaction leads to larger and smaller number of X1 and X2 mitochondrial species respec-

tively (Table 3). A larger decrease in C1 would lead to a larger increase in X1 and a larger

decrease in X2, and consequently shorter, fewer linear chains (and larger number of isolated

mitochondria) and vice versa. For example, the relatively smaller decrease in C1 in case of

OPA leads to shorter linear branches (leftward shift in Fig 4B2) but the number of branches

increases (taller bars) as compared to control conditions. Although the fusion to fission ratio

for the tip-to-side reaction does not change, the larger number of linear chains available to

make cyclic loops leads to a larger number of smaller loops (Fig 4B1). If the decrease in C1 is

larger, one would see a significant decrease in the number of loops and branches (and signifi-

cant increase in the number of isolated mitochondria) in addition to the leftward shift in the

diseased case. Such behavior is demonstrated by an example using network statistics for PD

(S3 Fig).

To see if the conclusions made above for a given disease holds when images of mitochon-

drial networks recorded from different cell/animal models or different experimental setup are

used, we analyzed two more examples each for AD [121], PD [122], and ALS [91]. As clear

from S2 Table, the results are largely consistent with our conclusions discussed above. The

Table 3. Comparison of the fusion to fission ratio for the tip-to-tip and tip-to-side reactions in the normal and diseased states predicted by the model. The sub-

scripts n and d indicate normal and diseased states respectively. The C1 and C2 values estimated for different conditions are used to estimate the fractions of X1, X2, and

X3 species in steady state and compare them with the diseased states.

Condition C1n/C1d C2n/C2d X1n X2n X3n X1n/X1d X2n/X2d X3n/X3d

HD 1.00 2.00 0.359 0.562 0.079 0.985 0.948 1.852

AD 1.00 1.21 0.432 0.429 0.140 0.992 0.969 1.143

DS 1.00 2.75 0.454 0.502 0.044 0.980 0.939 17.058

CA 1.00 1.29 0.441 0.459 0.100 0.990 0.969 1.238

Kidney 1.01 1.60 0.344 0.605 0.052 0.991 0.971 1.704

Diabetes/

Cancer

1.00 1.80 0.334 0.614 0.052 0.990 0.971 1.739

OPA 1.18 1.00 0.292 0.691 0.018 0.938 1.030 0.955

PD 1.22 1.00 0.260 0.728 0.012 0.922 1.032 0.954

ALS 4.80 1.00 0.341 0.468 0.191 0.875 1.129 0.976

https://doi.org/10.1371/journal.pone.0223014.t003

Mitochondrial fragmentation in degenerative diseases

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mean degree is higher for mitochondrial networks in NL cells as compared to their diseased

counterparts. The microscopic rates (C2/C1) given by the simulations are also consistent with

the above conclusions. With the exception of one example for AD and PD each, the normal-

ized giant cluster (Ng/N) for all cases from our simulations also follows a consistent trend. For

the two examples where Ng/N is slightly larger for NL cells than the diseased cells, we noticed

that the overall mitochondrial network (network size in terms of the total number of edges in

the cell) in the imaged area of the NL cells were significantly larger than those in the diseased

cells. We suspect that this contributed to this discrepancy. Nevertheless, the mean degree in

the same two examples is still consistent with our conclusions in the preceding paragraphs.

Despite the fact that the overall cumulative probability of the cluster sizes shifts to the left in

all cases (see for example Fig 4A3 & 4B3), the different microscopic mechanisms for fragmen-

tation lead to mitochondrial networks with significantly different fine structures. This is dem-

onstrated by the fraction of X1, X2, and X3 species at steady state (Table 3, columns 4–9)

obtained from simulations using C1 and C2 values for mitochondrial networks in cells with

different pathologies and their respective control conditions. In the first group of conditions

described above, the fraction of X3 species decreases significantly while X1 and X2 both

increase moderately in the diseased state. This would lead to smaller and fewer cyclic loops. In

the second group of diseases, X1 increases significantly while X2 decreases moderately. Since

the propensity of X1+X2 ! X3 reaction is given by a1 × X1 × X2, the relatively larger increase in X1 with the moderate decrease in X2 leads to a larger fraction of X3 species in the diseased

Fig 4. Two different types of microscopic changes in the fusion to fission processes leading to mitochondrial network fragmentation demonstrated

with examples from HD (striatal cells from mouse embryos bearing a 111 polyglutamine repeat Q111/0 and Q111/1) versus control [78] for the first

type (top row) and OPA (mouse embryonic fibroblasts with the pathogenic mutation R149W in human YME1L1) versus control [66] for the

second type (bottom row) of microscopic changes. Distributions of (a1) loop sizes, (a2) branch lengths, and (a3) cluster sizes (cumulative

probability) for NL (red) and diseased cells (blue) from experimental images. (a4) Fraction of X1 (NL: green, diseased: red), X2 (NL: magenta,

diseased: blue) and X3 (NL: black, diseased: cyan) species from the model as functions of the number of iterations using C1 and C2 values for HD

experiments. The model results show average of 100 runs. (b1-b4) shows the same mitochondrial network features as (a1-a4) for mouse embryonic

fibroblasts with OPA pathology and their normal counterparts. Note that the curves for X3 species in cells with OPA pathology and NL overlap

(b4).

https://doi.org/10.1371/journal.pone.0223014.g004

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 11 / 21

state. A larger increase in X3 and a smaller decrease in X2 would lead to a larger number of

cyclic loops (although still smaller in sizes) and vice versa.

The large variability in the fine structure of the mitochondrial network resulting from the

different microscopic origins of fragmentation is highlighted further in Fig 5. We simulate

mitochondrial networks in different diseases and their respective control conditions using

their corresponding C1 and C2 values in the model, and extract the size distributions for

branch lengths, cyclic loops, and clusters. The means of these distributions are shown in Fig 5,

where the relative decrease vary significantly from one disease to another. A similar variability

can also be seen in the variances of these three distributions while comparing different diseases

to their respective control conditions (not shown).

Discussion

A tight balance between fission and fusion of mitochondria is crucial for the normal cell func-

tion [20, 29, 123]. This is probably why many degenerative diseases have been linked to the pri-

mary or secondary changes in mitochondrial dynamics leading to fragmented mitochondrial

networks [9, 15–36]. Our analysis of images of mitochondrial networks from several previ-

ously reported experimental studies indicates that in general mitochondria in normal cells

form a well-connected network that can be described by larger mean degree, giant cluster,

branch lengths, clusters, and loops as compared to fragmented network characterized by

smaller values of all these parameters in cells with nine different types of pathologies. We ex-

ploit these differences and model mitochondrial network to gain a quantitative understanding

Fig 5. The differences in the microscopic changes leading to mitochondrial network fragmentation lead to significantly differences in

the way the fine structure and topology of the network is affected in different diseases. The mean of size distribution of (a) cyclic loops, (b)

branch lengths, and (c) clusters for normal (red) and diseased (blue) cells given by the model using the estimated C1 and C2 values from the

experimental micrographs of mitochondrial networks with the condition modeled. Each data point is averaged over 100 runs with error bars

showing the standard error of the mean. Simulation results for ALS are plotted separately in the insets for clarity.

https://doi.org/10.1371/journal.pone.0223014.g005

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 12 / 21

of the changes in the fission and fusion processes due to lateral and longitudinal interactions

in all these pathologies.

It is worth mentioning that the class (transient versus complete) of fusion depends on the

way two mitochondria interact with each other (see for example [83] for further details). Tran-

sient fusion where two mitochondria come into close apposition, remain fused for less than 4s

to 5 min with a mean duration of 45s, and re-separate, preserving their original topologies,

results from oblique or lateral interaction of two mitochondria associated with separate tracks.

Complete fusion on the other hand results from longitudinal merging of organelles moving

along a single track.

We show that the nine conditions can be divided into two main groups. The fragmentation in

cells with AD, HD, DS, Ca 2+

overload, diabetes/cancer, and acute kidney injury pathologies mainly

results from the decreased fusion in favor of fission due to lateral interaction between mitochon-

dria. In case of OPA, PD, and ALS on the other hand, the balance between fusion and fission due

to lateral interaction remains intact. However, the increased fission at the expense of fusion due to

longitudinal interaction leads to fragmented mitochondrial network in these diseases.

The differences in the microscopic properties of mitochondrial fission and fusion could

have key implications for the way fragmentation affects cell function depending on the mor-

phology and the region of the cell where fragmentation occurs. For example, impaired balance

between fission and fusion due to longitudinal interaction would lead to shorter linear chains

of mitochondria that could significantly affect signaling along neuronal processes and synap-

ses. Increased rate of fission at the expense of fusion due to lateral interaction on the other

hand would likely have a more significant effect on the functions in regions such as cell body

where a healthy mitochondrial network is key for the function of organelles such as nucleus

and Golgi network.

We remark that our conclusions are based on limited data available. Consolidating these

conclusions will need further future experiments and analysis of the mitochondrial networks

in the different diseases using the approach discussed in this paper. Nevertheless, we believe

that our framework provides a solid foundation for developing computational tools that could

use these indicators for inferring the extent and types of signaling disruptions in different

pathologies. While beyond the scope of this study, we believe that validating our predictions

about the disruption of lateral and/or longitudinal fission/fusion in different diseases, experi-

mental techniques similar to that used in Ref. [83] could be useful. In this technique, the

exchange of matrix contents between individual mitochondria is visualized in real time as the

two mitochondria fuse or detach by using mitochondrial matrix-targeted green-photoacti-

vated, red-fluorescent Kindling fluorescent protein in combination with green or yellow

fluorescence protein or the cyan-photoactivated mtPAGEP (mitochondria-targeted photoacti-

vatable green-fluorescence protein) in combination with red fluorescence protein [83].

Supporting information

S1 Text. Description of different types of mitochondrial interactions, cell models, and dis-

eases investigated in this study.

(DOCX)

S1 Fig. Longitudinal and lateral mitochondrial interactions (fusion/fission). (a) End-to-

end fusion of two mitochondria moving towards each other along a common microtubule

(not shown), (b) Side-to-side and end-to-side fusion of two mitochondria moving on two dif-

ferent microtubule tracks (not shown). Arrows indicate the direction of motion.

(TIFF)

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 13 / 21

S2 Fig. A smaller decrease in C2 leads to a smaller decrease in X3, and a smaller increase in

X1 and X2, which would lead to a smaller but larger number of linear chains and larger

number of cyclic loops. Here we compare mitochondrial network fragmentation in HD (stria-

tal cells from mouse embryos bearing a 111 polyglutamine repeat Q111/0 and Q111/1) versus

control [78] with C2n/C2d = 2.0 (top row) and diabetes (MitoNEET knockout mouse embry-

onic fibroblasts) versus control [65] with C2n/C2d = 1.8 (bottom row). Distributions of (a1)

loop sizes, (a2) branch lengths, and (a3) cluster sizes (cumulative probability) for NL (red) and

diseased cells (blue) from experimental images. (a4) Fraction of X1 (NL: green, diseased: red),

X2 (NL: magenta, diseased: blue) and X3 (NL: black, diseased: cyan) species from the model as

functions of the number of iterations using C1 and C2 values for HD experiments. The model

results show average of 100 runs. (b1-b4) shows the same mitochondrial network features as

(a1-a4) for MitoNEET knockout mouse embryonic fibroblasts with diabetes pathology and

their normal counterparts.

(TIFF)

S3 Fig. A larger decrease in C1 leads to a significant decrease in the number of loops and

branches. Here we compare mitochondrial network fragmentation in OPA (mouse embryonic

fibroblasts with the pathogenic mutation R149W in human YME1L1) versus control [66] with

C1n/C1d = 1.18 (top row) and PD (human primary skin fibroblasts obtained from sporadic

late-onset PD patients) versus those from healthy age-matched control subjects [81] with C1n/

C1d = 1.22 (bottom row). Distributions of (a1) loop sizes, (a2) branch lengths, and (a3) cluster

sizes (cumulative probability) for NL (red) and diseased cells (blue) from experimental images.

(a4) Fraction of X1 (NL: green, diseased: red), X2 (NL: magenta, diseased: blue) and X3 (NL:

black, diseased: cyan) species from the model as functions of the number of iterations using

C1 and C2 values for OPA experiments. The model results show average of 100 runs. (b1-b4)

shows the same mitochondrial network features as (a1-a4) for human primary skin fibroblasts

with PD pathology and their normal counterparts. Note that the curves for X3 species in dis-

eased and normal cells overlap (a4, b4).

(TIFF)

S1 Table. Experimental micrographs processed in this study. Column 1 provides the disease,

column 3 reports the cell/animal model, column 4 lists the condition for the experiment (nor-

mal versus diseased), and column 5 provides references where the images were originally pub-

lished.

Abbreviations: WT-Wild type, KO-Knockout, NL-Normal, MEF-Mouse embryonic fibro-

blasts, HSF-Human skin fibroblasts, MEMN Mouse embryonic motor neurons, TM-YAC128

Transgenic mice Yeast Artificial Chromosome 128, HSF Human Skin Fibroblasts, RPTCs rat

proximal tubular cells, RCA rat cortical astrocytes, CHCHD10—Coiled-coil-helix-coiled-coil-

helix domain-containing protein 10, YME1L1—Yeast mitochondrial escape 1-like 1 gene.

(DOCX)

S2 Table. Comparison of microscopic parameters of mitochondrial network obtained

from simulations and experiments for additional cell/animal models or experimental con-

ditions on AD, PD, and ALS diseases. Column 1 lists the condition for which images of nor-

mal (NL) and diseased cells were analyzed (column 2). Columns 3 & 4 and 5 & 6 compare

<k> and Ng/N respectively from experiment and theory. Columns 7 & 8 are the C1 (tip-to-tip

fusion/fission) and C2 (tip-to-side fusion/fission) values obtained by fitting the model to the

data and used in simulations.

(DOCX)

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 14 / 21

Author Contributions

Conceptualization: Ghanim Ullah.

Data curation: Syed I. Shah, Johanna G. Paine, Carlos Perez, Ghanim Ullah.

Formal analysis: Syed I. Shah, Johanna G. Paine, Carlos Perez, Ghanim Ullah.

Funding acquisition: Ghanim Ullah.

Investigation: Syed I. Shah, Johanna G. Paine, Ghanim Ullah.

Methodology: Syed I. Shah, Johanna G. Paine, Ghanim Ullah.

Project administration: Ghanim Ullah.

Resources: Ghanim Ullah.

Software: Syed I. Shah, Johanna G. Paine, Carlos Perez, Ghanim Ullah.

Supervision: Ghanim Ullah.

Validation: Syed I. Shah, Johanna G. Paine, Carlos Perez, Ghanim Ullah.

Visualization: Syed I. Shah, Johanna G. Paine, Ghanim Ullah.

Writing – original draft: Syed I. Shah, Johanna G. Paine.

Writing – review & editing: Syed I. Shah, Ghanim Ullah.

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http://dx.doi.org/10.14336/AD.2021.0404

*Correspondence should be addressed to: Dr. Jeremy M. Van Raamsdonk, McGill University, MeDiC and BRaIN Programs, McGill

University Health Centre, Quebec, Canada. Email:[email protected]. #these authors equally contributed this work.

Copyright: © 2021 Machiela E et al. This is an open-access article distributed under the terms of the Creative Commons Attribution

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ISSN: 2152-5250 1753

Original Article

Targeting Mitochondrial Network Disorganization is

Protective in C. elegans Models of Huntington’s Disease

Emily Machiela1,#, Paige D. Rudich2,3,#, Annika Traa2,3,#, Ulrich Anglas2,3, Sonja K. Soo2,3,

Megan M. Senchuk1, Jeremy M. Van Raamsdonk1,2,3,4,5,*

1 Laboratory of Aging and Neurodegenerative Disease, Center for Neurodegenerative Science, Van Andel

Research Institute, Grand Rapids MI 49503, USA 2 Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, H4A 3J1, Canada

3 Metabolic Disorders and Complications Program, and Brain Repair and Integrative Neuroscience Program,

Research Institute of the McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada 4 Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada

5 Department of Genetics, Harvard Medical School, Boston MA 02115, USA

[Received January 2, 2021; Revised April 3, 2021; Accepted April 3, 2021]

ABSTRACT: Huntington’s disease (HD) is an adult-onset neurodegenerative disease caused by a trinucleotide

CAG repeat expansion in the HTT gene. While the pathogenesis of HD is incompletely understood, mitochondrial

dysfunction is thought to be a key contributor. In this work, we used C. elegans models to elucidate the role of

mitochondrial dynamics in HD. We found that expression of a disease-length polyglutamine tract in body wall

muscle, either with or without exon 1 of huntingtin, results in mitochondrial fragmentation and mitochondrial

network disorganization. While mitochondria in young HD worms form elongated tubular networks as in wild-

type worms, mitochondrial fragmentation occurs with age as expanded polyglutamine protein forms aggregates.

To correct the deficit in mitochondrial morphology, we reduced levels of DRP-1, the GTPase responsible for

mitochondrial fission. Surprisingly, we found that disrupting drp-1 can have detrimental effects, which are

dependent on how much expression is decreased. To avoid potential negative side effects of disrupting drp-1, we

examined whether decreasing mitochondrial fragmentation by targeting other genes could be beneficial. Through

this approach, we identified multiple genetic targets that rescue movement deficits in worm models of HD. Three

of these genetic targets, pgp-3, F25B5.6 and alh-12, increased movement in the HD worm model and restored

mitochondrial morphology to wild-type morphology. This work demonstrates that disrupting the mitochondrial

fission gene drp-1 can be detrimental in animal models of HD, but that decreasing mitochondrial fragmentation

by targeting other genes can be protective. Overall, this study identifies novel therapeutic targets for HD aimed

at improving mitochondrial health.

Key words: Huntington’s disease, mitochondria, mitochondrial dynamics, C. elegans, neuroprotection, genetics,

neuroprotection, neurodegeneration, aggregation, DRP1, animal model

Huntington’s disease (HD) is an autosomal dominant

neurodegenerative disease caused by an expansion of the

polyglutamine tract in the N-terminal of the huntingtin

(Htt) protein. The expression of the expanded

polyglutamine tract is both necessary and sufficient for

cellular toxicity [1], although loss of wild-type huntingtin

function might also contribute to disease pathogenesis [2].

HD is characterized by progressive cognitive decline,

neuropsychiatric abnormalities, and motor impairment

[3]. In unaffected individuals, the polyglutamine tract of

the Htt protein is polymorphic, containing from 9-34

glutamines. However, mutations in the HD gene leading

Volume 12, Number 7; 1753-1772, October 2021

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1754

to 35 or more glutamines have been shown to cause HD.

Within the disease range of 35 glutamines and above, age

of onset negatively correlates with the number of

glutamines present [4, 5]. Although Htt is expressed in

every cell of the body and pathology has been observed in

multiple tissues, cellular dysfunction and atrophy are most

severe in the GABAergic medium spiny neurons of the

striatum. The reasons for this selective vulnerability are

still unknown.

While the cause of cellular dysfunction in HD is still

incompletely understood, mitochondrial dysfunction is

thought to play a central role in disease pathogenesis [6,

7]. There is significant evidence for mitochondrial

dysfunction in HD patients and animal models including

decreased activity of complexes in the electron transport

chain [8], increased lactate production in the brain [9],

decreased levels of ATP production [10], decreased

mitochondrial membrane potential [11], and impaired

trafficking of mitochondria within the cell [12]. The

importance of mitochondrial dysfunction to HD

pathogenesis is also suggested by the fact that systemic

administration of 3-nitropropionic acid, a neurotoxin that

inhibits mitochondrial function, can reproduce symptoms

and neuropathological deficits that occur in HD [13, 14].

In addition, a genome-wide association study

investigating genetic modifiers for age of onset of HD

found pathways involving mitochondrial fission to

significantly modify age of onset of the disease [15].

Recent work has demonstrated that mitochondrial

dynamics are disrupted in HD. Mitochondria continually

change their shape in response to the needs of the cell, and

these changes impact both the function and distribution of

the mitochondria. Mitochondrial morphology is

determined by two opposing processes: fission and fusion.

Mitochondrial fission results in an increase in

mitochondrial fragmentation as new mitochondria are

pinched off of existing mitochondria or mitochondrial

networks. The fission process is mediated by dynamin-

related protein 1 (DRP-1/DRP1) with the help of

mitochondrial fission proteins (FIS-1/FIS-2/FIS1) and

mitochondrial fission factors (MFF-1/MFF-2/MFF1).

Conversely, mitochondrial fusion leads to decreased

mitochondrial fragmentation by joining individual

mitochondria together with other mitochondria to form

interconnected mitochondrial networks. The fusion

process requires merging of the inner mitochondrial

membrane by optic atrophy protein 1 (EAT-3/OPA1) and

the merging of the outer mitochondrial membrane by

mitofusin (FZO-1/MFN).

In HD cell lines [16-21], the 3-nitropropionic acid

neurotoxin model of HD [22], cells from HD mouse

models [23] and cells derived from HD patients [19, 23],

it has been found that mitochondria are more fragmented

than in unaffected controls. In addition, examination of

mitochondria by electron microscopy in brain sections

from R6/2 mice [20] and YAC128 mice [17] revealed the

presence of smaller mitochondria in HD mice compared

to controls, suggesting that increased mitochondrial

fragmentation also occurs in vivo. In these studies, it has

been shown that the expression of exon 1 fragments of

mutant Htt is sufficient to cause mitochondrial

fragmentation [16-18]. The increase in mitochondrial

fragmentation in HD could result from excess

mitochondrial fission, decreased mitochondrial fusion or

both. While the precise mechanism by which mutant Htt

causes mitochondrial fragmentation is still unclear,

contributing factors may include: alterations in expression

levels of fission and fusion proteins [20, 24, 25], an

increase in DRP-1 enzymatic activity resulting from

increased interaction with mutant Htt [17, 26], increased

S-nitrosylation of DRP-1 leading to increased fission

activity [18], increased levels of reactive oxygen species

[22, 27], decreased Nrf2 signaling [20], and increased

calcineurin activity [23].

Importantly, reducing mitochondrial fragmentation

has been shown to be beneficial in models of HD.

Decreasing the activity or expression of the mitochondrial

fission protein DRP-1 increases survival in cell models of

HD [16, 17, 19]. In addition, treating a worm model of

HD expressing exon 1 fragment of mutant Htt with 74

CAG repeats in body wall muscle with RNAi against drp-

1 improved the movement deficit present in these worms,

although the effect of this treatment on mitochondrial

morphology in these worms was not assessed [16].

Furthermore, treatment of the R6/2 mouse model of HD

with a DRP-1 inhibitor (P110-Tat) improved behavior,

survival and neuropathology in these mice, and resulted

in a significant increase in cristae area in electron

micrographs [19]. The P110-Tat DRP-1 inhibitor was also

able to ameliorate mitochondrial structural deficits in the

hearts of R6/2 mice, indicating that this inhibitor can also

be effective in muscle tissue [21]. Combined, these

results suggest that developing interventions that inhibit

DRP-1 may be beneficial in the treatment of HD.

In this work, we explore the role of mitochondrial

fragmentation in the pathogenesis of HD, and whether

targeting this deficit may be an effective strategy to treat

HD. To do this, we use C. elegans models, which permit

the visualization of mitochondrial morphology in a live

organism that exhibits quantifiable, disease-relevant

phenotypic deficits. We find that C. elegans models of HD

exhibit mitochondrial fragmentation, which is temporally

correlated with polyglutamine aggregation. We find that

decreasing levels of drp-1 fails to correct the deficit in

mitochondrial morphology and can be detrimental,

depending on the level of disruption. In contrast, treating

worms with other RNAi clones that decrease

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1755

mitochondrial fragmentation improved movement in

worm models of HD.

MATERIALS AND METHODS

Strains

The following strains were used in this study:

N2 (WT)

JVR240 syIs243[Pmyo-3::TOM20:RFP] referred to as

mitoRFP

MQ1699 Punc-54::Htt28Q:GFP referred to as BW-

Htt28Q

MQ1698 Punc-54::Htt74Q:GFP referred to as BW-

Htt74Q

AM138 rmIs120[Punc-54::24Q:YFP] referred to as

BW-24Q

AM141 rmIs133[Punc-54::40Q:YFP] referred to as

BW-40Q

MQ1753 drp-1 (tm1108)

JVR248 Punc-54::Htt28Q:GFP;syIs243[Pmyo-

3::TOM20:RFP]

JVR250 Punc-54::Htt74Q:GFP;syIs243[Pmyo-

3::TOM20:RFP]

JVR251 drp-1(tm1108);Punc-54::Htt74Q:GFP

JVR255 drp-1(tm1108);syIs243[Pmyo-3::TOM20:RFP]

JVR259 drp-1(tm1108);Punc-

54::Htt74Q:GFP;syIs243[Pmyo-3::TOM20:RFP]

JVR473 syIs243[Pmyo-3::TOM20:RFP];rol-6(su1006)

JVR474 rmIs120[Punc-54::24Q:YFP];syIs243[Pmyo-

3::TOM20:RFP];rol-6(su1006)

JVR475 rmIs133[Punc-54::40Q:YFP];syIs243[Pmyo-

3::TOM20:RFP];rol-6(su1006)

JVR463 Punc-54::Htt74Q:GFP;syIs243[Pmyo-

3::TOM20:RFP];rol-6(su1006)

JVR520 Punc-54::Htt28Q:GFP;syIs243[Pmyo-

3::TOM20:RFP]; rol-6(su1006)

JVR521 drp-1(tm1108);Punc-

54::Htt74Q:GFP;syIs243[Pmyo-3::TOM20:RFP]; rol-

6(su1006)

All strains were maintained at 20°C on NGM plates

seeded with OP50 bacteria. All crosses were confirmed by

genotyping using PCR and, where applicable, confirmed

by fluorescent microscopy.

Confocal imaging and quantification

Mitochondrial morphology was imaged and quantified

using worms that express mitochondrially-targeted RFP

specifically in body wall muscle (syIs243[Pmyo-

3::TOM20:RFP]). In order to facilitate imaging, these

worms were crossed into a rol-6 background. The rol-6

mutation results in animals moving in a twisting motion,

thus displaying a helix of muscle cells upon imaging

(Supplementary Fig. 1). This is beneficial when imaging

the mitochondria of body wall muscle cells in the

nematode as it ensures that several portions of both the

ventral and dorsal quadrants of muscle cells are within the

plane of view (facing the objective lens). This facilitates

mitochondrial imaging as the tubular mitochondrial

organization can be seen within much of the muscle.

Without the rol-6 mutation, the lateral side of the

nematode may face the objective lens with only the

longitudinal edges of the muscle being visible, thus

making it difficult to observe mitochondrial organization.

To image the mitochondria, approximately 20 young

adult worms were mounted on 2% agar pads and

immobilized using 10 µM levamisole. Worms were

imaged under a 63x objective lens on a Zeiss LSM 780 or

Nikon A1R Ti confocal microscope. All conditions were

kept the same for all images. For representative images, a

z-stack of images spaced 0.125-0.40 µm apart were

collected and a z-stack projection was created using either

Nikon Elements or ImageJ to compress stacks into a

single image.

For quantification, a single plane image taken in the

same body region for each worm was used to avoid the

complication of mitochondria being present in two planes.

This slice was made binary using the Nikon Elements

thresholding tool. A background subtraction of a constant

50 was applied. Next, a pixel picker was applied to

several control mitoRFP images to define the low and

high threshold levels. Once optimum threshold numbers

were defined for control images, these limits were applied

to all images to be quantified with the separate function

on. Size and circularity were not used to define thresholds.

Prior to creating the binary mask, images were manually

inspected for proper threshold parameters. In the event

that threshold parameters mislabeled mitochondria,

objects were manually included or excluded prior to

masking. Mitochondrial circularity, number, and area

were measured using the measure objects tool in Nikon

Elements AR after the threshold mask was applied. For

mitochondrial circularity, raw numbers were exported to

Microsoft Excel and averages were calculated prior to

statistical analysis in Graphpad Prism. All other

calculations were exported directly to Prism for analysis.

Oxygen consumption

Basal oxygen consumption rate was measured using a

Seahorse XFe96 analyzer (Seahorse bioscience Inc., North

Billerica, MA, USA)[28]. Synchronized worms at day 1

of adulthood were cleaned in M9 buffer (22 mM KH2PO4,

34 mM NA2HPO4, 86 mM NaCl, 1 mM MgSO4). Cleaned

nematodes were pipetted in calibrant (~50 worms per

well) into a Seahorse 96-well plate. Oxygen consumption

was measured six times and rates of respiration were

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1756

normalized to the number of worms in each individual

well. The plate readings were begun within 20 minutes of

introduction of the worms into the well. Reading from

each well were normalized relative to the number of

animals per well. Well probes were hydrated in a 175 µL

Seahorse calibrant overnight before this assay was begun.

We found it is important to turn off the heating incubator

to allow the Seahorse machine to reach room temperature

before placing nematodes inside the machine. For these

experiments, we chose to measure oxygen consumption

per worm so that we could compare the rate of oxidative

phosphorylation for the whole organism to the whole

organism phenotypes that we were measuring (e.g.

movement and lifespan). Also, we chose to use a Seahorse

extracellular flux analyzer to measure oxygen

consumption so that all of the strains being compared

could be measured at the same time so that the conditions

would be identical. With the low number of worms that

are used in each well, it would be difficult to accurately

measure protein content, especially given that worms will

often stick to pipet tips or the side of the dish during

transfer.

ATP production

ATP levels were measured using a luminescence-based

ATP kit [29]. Approximately 200 worms were age-

synchronized by a limited lay. Worms were collected in

de-ionized water, washed, and freeze-thawed three times.

The resulting pellet was sonicated in a Bioruptor

(Diagenode) with 30 cycles of 30 seconds on, 30 seconds

off. The pellet was boiled for 15 minutes to release ATP,

then spun at 4°C at 11,000  g for 10 minutes. The

supernatant was collected and measured using a

Molecular Probes ATP determination Kit (Life

Technologies). Luminescence was normalized to protein

content, which was measured with a Pierce BCA protein

determination kit (Thermo Scientific).

Rate of movement

For measuring the effects of drp-1, the rate of movement

was assessed by measuring thrashing rate in liquid using

video-tracking and computer analysis [30].

Approximately 50 pre-fertile day 1 young adult worms

were placed in M9 buffer on a clean NGM plate. Videos

were taken with an Allied Vision Tech Stingray F-145 B

Firewire Camera (Allied Vision, Exton, PA, USA) at

1024×768 resolution, 8-bit using the MATLAB image

acquisition toolbox. Analysis was performed using

wrMTrck plugin for ImageJ (publicly available at

www.phage.dk/plugins).

For screening the mitochondrial fragmentation genes,

the rate of movement was assessed by measuring absolute

crawling speed and thrashing rate using WormLab

2019.1.2 (MBF BioSciences). Experiments were done on

day 1 adults, and the animals were isolated as L4’s 24 hrs

before. Animals were exposed to RNAi using the parental

paradigm, or if the RNAi clone inhibited development, the

animals were grown on empty vector RNAi and placed on

the respective RNAi clone as L4’s (E04A4.4, C33A12.1,

abhd-11.1, iars-1, his-12 and acs-1). For the experiment,

worms were removed from their plates with M9 buffer,

washed twice with M9 buffer, and placed on clean 3-cm

NGM plates. A Kimwipe was used to remove excess

liquid and the worms were allowed to acclimate for 5

minutes. The plates were placed under a monochrome

digital camera (Basler acA2440 camera with an AF Micro

Nikkor 60 mm f/2.8 D lens) and tapped to stimulate

movement. 20-30 animals were normally in frame. The

worms were recorded using the WormLab software

(Version 2019.1.2), in 45 s long videos with a resolution

of 2456×2052 at a scale of 9.9 m/pixel. Crawling was

recorded at a frame rate of 7.5 frames/s. After recording

crawling, M9 buffer was added to the plate. Worms were

allowed to acclimate for 5 minutes and then recorded

while swimming at a frame rate of 14 frames/s. Worms

that were tracked for less than half of the video were

excluded from the analysis because the worms could have

left the field-of-view and then returned, causing double

counting. Crawling speed and thrashing rates were

analyzed using the Absolute Peristaltic Speed results and

Wave Initiation Rate results, respectfully, exported from

WormLab and processed using Microsoft Excel 2016

(Microsoft, Redmond, WA, USA). 3 replicates of ~20

worms were performed for each RNAi clone.

Imaging and quantification of aggregation

Experimental animals were day 1 adults and were isolated

at the L4 stage 24 hours before the experiments. Animals

were exposed to RNAi using the parental paradigm, or if

the RNAi clone inhibited development, the animals were

grown on empty vector RNAi and placed on the respective

RNAi clone as L4’s (E04A4.4, C33A12.1, abhd-11.1,

iars-1, his-12 and acs-1). 10 animals were mounted on 3%

agarose pads using 10mM levamisole for anesthesia and

imaged within 45 minutes of levamisole exposure. Images

were taken on a Nikon Eclipse Ti microscope with a

Nikon Plan Apo 20x/0.75 NA objective and a Zyla Andor

sCM05 camera. z-stacks of the animals were recorded

with a 2m step size. Analysis was performed in ImageJ

(Version 2.1.0/1.53c) by merging the z-stack using

Temporal-Color Code to color code the different z planes,

and then the aggregates were manually counted using the

Cell Counter plugin. At least 10 animals per clone were

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1757

quantified. Clones which caused a measurable decrease

were repeated for confirmation.

Lifespan

Lifespan was determined on nematode growth media

(NGM) agar plates with 25 μM 5-fluoro-2′-deoxyuridine

(FUdR) in order to reduce the development of progeny.

Plates with 25 μM FUdR do not completely prevent the

development of progeny to adulthood in the first

generation so animals were transferred to fresh agar plates

after 4 days [31]. After the initial transfer, worms were

moved to fresh plates weekly. Animal survival was

observed every 2 days by gentle prodding. Three

replicates of 30 animals each were completed.

Brood size

Brood size was determined by placing individual young

adult staged animals onto agar plates with daily transfers

to new plates until progeny production ceased. The

resulting progeny were allowed to develop to adulthood

before quantification. Three replicates of 5 animals each

were completed.

Post-embryonic development

Post-embryonic development (PED) was assessed by

moving eggs to agar plates. After 3 hours, newly hatched

L1 worms were transferred to a new plate. The hours from

hatching to the young adult transition was measured as the

PED time. Three replicates of 20 animals each were

completed.

Quantitative reverse-transcription PCR (qPCR)

mRNA was collected from pre-fertile young adult worms

using Trizol as previously described [32]. We collected

three biological replicates each for WT, BW-40Q and

BW-Htt74Q worms. The mRNA was converted to cDNA

using a High-Capacity cDNA Reverse Transcription kit

(Life Technologies/Invitrogen) according to the

manufacturer’s directions. qPCR was performed using a

FastStart Universal SYBR Green kit (Roche) in an AP

Biosystems real-time PCR machine [33, 34]. Primer

sequences utilized:

drp-1 L-GGTTTTCACAGACTTCGATGC

R-TA GGCTCCGAAGTAGCGAAA

fis-1 L-AGAAATTCTGGCGGCTCGT

R-GCG TGTGCAAGAGCAAGATA

fis-2 L-GGGAATCGTGTGTCTTGAGAAG

R-GG CCATCATGAGTCATTGC

mff-1 L-CCGCTCAATAGATTGATGAACA

R-T TGGGGACTTCCATTCTGAG

mff-2 L-TGGATAAACTTCCAACGGAAA

R-CC GGGCTGTGTCTAGCTCT

eat-3 L-GCGGCTAGAACGTGGTATGA

R-CGG GCTCTTTTACTGGAACA

fzo-1 L-GCTTTCTGCAGGTTGAAGGT

R-CGA CACCAGGGCTATCAAGT

gfp L-GACGACGGCAACTACAAGAC

R-TCC TTGAAGTCGATGCCCTT

Quantification of mitochondrial DNA

Mitochondrial:nuclear DNA ratios were calculated as

previously described with minor modifications [35].

Wild-type, BW-40Q and BW-Htt74Q worms were grown

on OP50 at 20°C. Animals were isolated at the L4

developmental stage and collected the following day as

day 1 adults, where three replicates of six worms for each

strain were isolated in 90 µl of worm lysis buffer and

lysed. The lysed worm samples were analyzed by

quantitative RT-PCR as previously described for C.

elegans [35], using established mitochondrial-DNA

specific nd-1 primers and nuclear-DNA specific cox-4

primers [35, 36]. Samples were collected three times, each

time with three biological replicates. We performed three

technical replicates on each of these samples. The primer

sequences used are as follows:

nd-1 L-AGCGTCATTTATTGGGAAGAAGAC

R-AAGCTTGTGCTAATCCCATAAATGT

cox-4 L-GCCGACTGGAAGAACTTGTC

R-GCG GAGATCACCTTCCAGTA

RNA interference (RNAi)

All RNAi clones were sequence verified. To knockdown

expression of genes, the RNAi clones were grown

approximately 12 hours in LB with 50 μg/ml carbenicillin.

Cultures were concentrated (5x) and seeded onto NGM

plates containing 5 mM IPTG and 50 μg/ml carbenicillin.

Plates were incubated to induce RNAi for 2 days at room

temperature. RNAi was performed at 20°C. For

experiments examining RNAi knockdown beginning in

the parental generation (L4 parental paradigm), L4 worms

were plated on RNAi plates, transferred to a new plate the

following day as gravid adults, and then removed after 24

hours. The progeny from these worms were used for

analysis.

Quantification of knockdown of drp-1 mRNA by drp-1

RNAi

Wild-type and BW-Htt74Q worms were grown on empty

vector (EV) and drp-1 RNAi following the RNAi parental

paradigm described in the RNAi methods. Levels of drp-

1 and act-3 were quantified through quantitative RT-PCR.

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1758

Primers were specifically designed to exclude the drp-1

RNA used to induce RNAi (drp-1: L-GAGATGTC

GCTATTATCGAACG, R-CTTTCGGCACACTATCC

TG). Three biological replicates each with three technical

replicates were performed.

Effect of RNAi clones on mitochondrial morphology

In order to examine the effect of RNAi clones on

mitochondrial morphology in mitoRFP;rol-6 and BW-

Htt74Q;mitoRFP;rol-6 worms, 30 L4 worms were picked

to plates seeded with EV, pgp-3, F25B5.6, and alh-12

RNAi-expressing bacteria. After 24 hours gravid adults

were picked to new RNAi plates. After another 24 hours

gravid adults were removed from the RNAi plates.

Progeny were allowed to develop to the young adult stage

before imaging.

Experimental design and statistical analysis

Experiments were performed such that the experimenter

was blinded to the genotype of the worms. Experimental

worms were randomly selected from maintenance plates.

For all experiments, we completed a minimum of three

biological replicates (independent population of worms

tested on a different day). Where possible (e.g.,

measurement of movement) assays were completed using

automated approaches with computer analysis to

eliminate any potential experimental bias. We did not

perform power calculations to determine the N required

for experiments as the Ns that are used in C. elegans

experiments are typically much greater than required to

identify a statistically significant difference. For

measurements of mitochondrial morphology, we used 8

biological replicates. For oxygen consumption we

performed at least 8 replicates with ~50 worms per

replicate. For ATP measurements, we performed 3

biological replicates with ~200 worms per replicate. For

mRNA measurements, we used 3 biological replicates of

a full 60 mm plate of worms. For the thrashing assays, we

quantified movement in at least 40 worms over 3

biological replicates. For lifespan assays, we completed

three biological replicates with at least 30 worms per

replicate. Brood size was measured in 6 worms

individually. Post-embryonic development time was

measured in 3 replicates of 25 worms per replicate.

Statistical significance of differences between groups was

determined by one-way, two-way or repeated measures

ANOVA using Graphpad Prism. Lifespan data were

graphed using a Kaplan-Meier survival plot and the

significance of differences between two plots was

determined using the Log-rank test. Error bars indicate

standard error of the mean. This study was not pre-

registered. No sample size calculations were performed.

This study did not include a pre-specified primary

endpoint.

RESULTS

Mitochondrial networks are disrupted in C. elegans

models of Huntington’s disease

In order to study the relationship between mitochondrial

fragmentation and disease pathogenesis, we first sought to

determine if mitochondrial dynamics are disrupted in

worm models of HD. To visualize the morphology of the

mitochondria, we crossed two different worm models of

HD to mitoRFP worms, which express the mitochondrial

targeting sequence of TOM-20 linked to RFP under the

body wall muscle myo-3 promoter (Pmyo-3::TOM-

20:RFP). The first is a worm model of HD that expresses

an exon 1 fragment of human huntingtin (Htt) connected

to either an unaffected-length 28Q or disease-length 74Q

repeats tagged with GFP in body wall muscle, which will

be referred to as BW-Htt28Q and BW-Htt74Q,

respectively. Both of these lines have been characterized

previously [16, 37].

To image these worms, we used confocal microscopy

in live, immobilized worms, as we and others have done

previously [28, 38-40]. While wild-type worms exhibit

parallel tracts of elongated mitochondria in their body

wall muscle cells, BW-Htt74Q worms, which express a

disease-length polyglutamine tract, exhibit mitochondrial

fragmentation (Fig. 1A) and disorganized mitochondrial

networks (Supplementary Fig. 2) at day 1 of adulthood. In

contrast, there was no change in mitochondrial structure

in BW-Htt28Q worms, which express an unaffected-

length polyglutamine tract (Fig. 1A; Supplementary Fig.

2). Quantification of mitochondrial morphology revealed

that BW-Htt74Q worms have a significantly increased

number of mitochondria (Fig. 1B) and decreased

mitochondrial area (Fig. 1C) compared to wild-type

mitochondria, but mitochondrial shape was unaffected

(Fig. 1D).

An increase in mitochondrial number could result

from mitochondrial biogenesis or mitochondrial

fragmentation. To distinguish between these two

possibilities, we measured mitochondrial DNA (mtDNA)

content. We found that mtDNA content in BW-Htt74Q

worms is the same as wild-type worms (Supplementary

Fig. 3). This suggests that the increase in mitochondrial

number results from fragmentation of existing

mitochondria, not increased mitochondrial biogenesis.

To determine how the disruption of mitochondrial

networks in BW-Htt74Q worms affects the function of the

mitochondria, we measured basal oxygen consumption

and ATP levels at day 1 of adulthood. Although

mitochondrial morphology is clearly disrupted in BW-

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1759

Htt74Q worms, both oxygen consumption (Fig. 1E) and

ATP levels (Fig. 1F) were equivalent to wild-type levels

in these worms. However, this result should be interpreted

cautiously as these are whole worm measurements in a

model in which the mutant Htt exon 1 fragment protein is

only expressed in the 95 body wall muscle cells out of 959

total cells in the worm.

Figure 1. Mitochondrial networks are disrupted in C. elegans models of Huntington’s disease. Worms expressing an

expanded, disease-length polyglutamine tract of 74Q in body wall muscle (BW-Htt74Q worms) exhibit mitochondrial

fragmentation and mitochondrial network disorganization (see Supplementary Fig. 2). In contrast, worms expressing a

shorter, unaffected-length polyglutamine tract of 28Q (BW-Htt28Q worms) have tubular mitochondria, similar to control

worms (mitoRFP worms) (A). Mitochondria are labelled with RFP (red), while Htt is labelled with GFP (green). mitoRFP

strain is syIs243[Pmyo-3::TOM20:RFP]. BW-Htt28Q and BW-Htt74Q worms also express syIs243[Pmyo-

3::TOM20:RFP] transgene. The images shown are from a single focal plane collected on a confocal microscope. Scale bars

indicate 15 µM. Quantification of mitochondrial morphology at day 1 of adulthood reveals that BW-Htt74Q worms have

an increased number of mitochondria (B) and decreased average mitochondrial area (C), both of which are consistent with

increased mitochondrial fragmentation. Mitochondrial shape is not significantly changed in BW-Htt74Q worms compared

to BW-Htt28Q and mitoRFP control worms (D). Despite the disruption of mitochondrial morphology, whole worm oxygen

consumption (E) and ATP levels (F) are unchanged in BW-Htt74Q worms. A minimum of three biological replicates were

performed. Bars indicate the mean value. One-way ANOVA was used to assess significance. Error bars indicate SEM. ROI

– region of interest. *p<0.05, **p<0.01, ***p<0.001.

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1760

Figure 2. Mitochondrial networks are disrupted in BW-40Q worm model of Huntington’s disease. Worms expressing

an expanded, disease-length polyglutamine tract of 40Q in body wall muscle (BW-40Q worms) exhibit mitochondrial

fragmentation and disorganized mitochondrial networks (see Supplementary Fig. 4). In contrast, worms expressing a shorter,

unaffected-length polyglutamine tract of 24Q (BW-24Q worms) have tubular mitochondria, similar to control worms

(mitoRFP worms) (A). Mitochondria are labelled with RFP (red), while polyglutamine protein is labelled with YFP

(green/yellow). BW-24Q and BW-40Q worms express syIs243[Pmyo-3::TOM20:RFP] transgene. The images shown are

from a single focal plane collected on a confocal microscope. Scale bars indicate 15 µM. Quantification of mitochondrial

morphology at day 1 of adulthood reveals that BW-40Q worms have an increased number of mitochondria (B) and a trend

towards decreased average mitochondrial area (C). Mitochondrial circularity is significantly increased in BW-40Q worms

compared with wild-type worms (D). A minimum of three biological replicates were performed. Bars indicate the mean

value. One-way ANOVA was used to assess significance. Error bars indicate SEM. ROI – region of interest. *p<0.05.

To determine the extent to which the disruption of the

mitochondrial network is dependent on the presence of

Htt exon 1 fragment, we also examined mitochondrial

morphology in a model of HD that expresses a pure

polyglutamine tract. We examined worms that express

either an unaffected (24 glutamines) or disease-length (40

glutamines) polyglutamine tract tagged with YFP in body

wall muscle under the unc-54 promoter. These worms will

be referred to as BW-Q24 and BW-Q40 worms,

respectively. Both lines are integrated and previously

characterized [41].

As with BW-Htt74Q worms, we found that BW-40Q

worms (containing a disease-length polyglutamine tract)

exhibit mitochondrial fragmentation (Fig. 2A) and have

disrupted mitochondrial networks (Supplementary Fig.

4), while BW-24Q worms (containing an unaffected-

length polyglutamine tract) have elongated, tubular

mitochondria, similar to wild-type worms (Fig. 2A;

Supplementary Fig. 4). Like BW-Htt74Q worms, BW-

40Q worms have increased mitochondrial number (Fig.

2B) and decreased mitochondrial area (Fig. 2C) compared

to wild-type worms. The levels of mtDNA in BW-40Q

worms are equivalent to wild-type levels (Supplementary

Fig. 3), suggesting that the increase in mitochondrial

number results from mitochondrial fragmentation. In

addition, mitochondria in BW-40Q worms exhibit

increased circularity compared to those in wild-type

worms (Fig. 2D). This indicates that the expression of a

disease-length polyglutamine tract is sufficient to cause

mitochondrial fragmentation in body wall muscle

independent of any Htt protein sequence.

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Aging and Disease • Volume 12, Number 7, October 2021 1761

Figure 3. Disruption of mitochondrial

network is associated with polyglutamine

aggregation. Images show neighboring body

wall muscle cells from worms expressing a

disease-length polyglutamine tract in body wall

muscles (BW-40Q worms) at the L4 stage of

development. One cell has diffuse expression of

the polyglutamine (PolyQ) protein and normal,

tubular mitochondrial networks, while the other

cell has a polyglutamine aggregate and a

disrupted mitochondrial network (A). Initially,

body wall muscle cells have tubular

mitochondria and diffuse polyglutamine protein.

Over time, an increasing number of body wall

muscle cells have fragmented mitochondria and

aggregated polyglutamine. Very few cells

exhibit mitochondrial fragmentation and diffuse

polyglutamine protein expression, or tubular

mitochondria with aggregated polyglutamine

protein (B). In BW-Htt-74Q worms,

mitochondrial morphology is similar to that in

wild-type worms during early development (C).

Polyglutamine protein aggregation increases

throughout development in BW-Htt74Q worms

(D). The images in panels A and C are

compressed z-stacks collected on a confocal

microscope. Scale bars indicate 25 µM. A

minimum of three biological replicates were

performed. Bars indicate the mean value. Error

bars indicate SEM.

Mitochondrial fragmentation is associated with

polyglutamine aggregation

In imaging mitochondrial morphology in worm models of

HD, we observed that, during development, neighboring

muscle cells could exhibit different mitochondrial

morphologies. While some cells exhibited parallel tracts

of elongated mitochondria in combination with diffuse

expression of polyglutamine protein, adjacent cells had

fragmented or disorganized mitochondrial networks and

aggregated polyglutamine protein (Fig. 3A). We rarely

observed the co-occurrence of disrupted mitochondrial

networks and diffuse polyglutamine localization.

To further explore this relationship, we performed a

time course examining mitochondrial morphology and

polyglutamine protein aggregation throughout

development in BW-40Q worms (Fig. 3B). Initially most

cells had tubular, elongated mitochondria and diffuse

polyglutamine protein. Over time, the number of cells

exhibiting this phenotype declined, while an increasing

number of cells had fragmented mitochondria and

aggregated polyglutamine protein. Throughout the time

course we observed few cells with fragmented

mitochondria and diffuse polyglutamine protein, or

tubular mitochondria and aggregated polyglutamine

protein. This suggests that polyglutamine aggregation and

mitochondrial fragmentation are temporally-linked

events.

To extend these findings to another model, we

examined aggregation and mitochondrial morphology

throughout development in BW-Htt74Q worms. We

found that during early development (developmental

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1762

stages from L1 to L4), mitochondria in BW-Htt74Q

worms are elongated and exist in parallel tracts (Fig. 3C),

similar to mitochondria in wild-type worms, but become

fragmented by the time worms reached adulthood (see

Fig. 1A). At the same time, BW-Htt74Q worms initially

have diffuse expression of the Htt exon 1 fragment protein

but show increased aggregation with age (Fig. 3D). Thus,

as in BW-40Q worms, both aggregation and

mitochondrial fragmentation increase with age, and both

tend to occur together in the same cell. Combined, this

indicates that mitochondrial fragmentation is strongly

associated with polyglutamine aggregation.

Figure 4. Inhibition of mitochondrial fission has detrimental effects in C. elegans models of Huntington’s disease

expressing expanded polyglutamine tracts in body wall muscle. To examine the effect of disrupting mitochondrial

fission in worm models of HD, a body wall muscle (BW-Htt74Q worms) model of HD was crossed to a drp-1 deletion

mutant. The drp-1 mutation significantly decreased movement (A) and lifespan (B) in BW-Htt74Q worms but had no

effect on wild-type worms (A, C). Loss of drp-1 resulted in decreased fertility (D) and slower post-embryonic development

(E) in both BW-Htt74Q and wild-type worms. While the drp-1 deletion did not affect oxygen consumption (F) in either

genotype, it resulted in a decreased levels of ATP (G). Deletion of drp-1 did not decrease the mitochondrial fragmentation

that is present in BW-Htt74Q worms (H), as indicated by quantification of mitochondrial number (I), mitochondrial area

(J) and mitochondrial shape (K). Note that we used wild-type worms as a control instead of BW-Htt28Q worms because

we observed the formation of aggregates in BW-Htt28Q worms, which could complicate the interpretation of the results.

The images in panel H are compressed z-stacks collected on a confocal microscope. Scale bars indicate 10 µM. A minimum

of three biological replicates were performed. Bars indicate the mean value. One-way ANOVA was used to assess

significance in H, I and J. Two-way ANOVA was used to assess significance in A, C, E, and F. Log-rank test was used to

assess significance in B. Repeated measures ANOVA was used to assess significance in D. Error bars indicate SEM. ROI

– region of interest. *p<0.05, **p<0.01, ***p<0.001.

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C. elegans models of Huntington’s disease have

increased expression of mitochondrial fission and

fusion genes

In order to explore the mechanism underlying the

disrupted mitochondrial networks observed in the HD

worm models, we used quantitative reverse transcription

PCR (qPCR) to measure expression of fission and fusion

genes in day 1 adult animals. We found that both HD

models in which the disease-length polyglutamine protein

is expressed in body wall muscle (BW-Htt74Q, BW-40Q)

showed significant changes in the expression of both

mitochondrial fission and fusion genes (Supplementary

Fig. 5). In both models, fis-1 and eat-3 mRNA levels were

elevated. It is unclear whether these changes in gene

expression contribute to the disruption of mitochondrial

networks observed in these strains, or whether these genes

are activated in an attempt to restore wild-type

mitochondrial morphology.

Disruption of mitochondrial fission can be detrimental

in a body wall muscle model of Huntington’s disease

Having shown that worm models of HD exhibit increased

mitochondrial fragmentation, we next sought to determine

if decreasing mitochondrial fission would help to restore

mitochondria morphology, and whether this would

ameliorate phenotypic deficits present in these worms. To

decrease mitochondrial fission, we crossed BW-Htt74Q

worms to a drp-1 deletion mutant (tm1108) [42]. In

worms, drp-1 is expressed highly in body wall muscle and

neurons [43], making it a good genetic target for the worm

models expressing an expanded polyglutamine tract in

body wall muscle. In these experiments, we used wild-

type worms as a control instead of BW-Htt28Q worms

because we observed the formation of polyglutamine

aggregates in BW-Htt28Q worms. Since this length of

polyglutamine tract does not aggregate in most HD

models, and is within the unaffected range in humans, the

aggregation in BW-Htt28Q worms could complicate the

interpretation of the results.

Surprisingly, we found that the drp-1 deletion

resulted in decreased movement, as measured by the rate

of thrashing in liquid of day 1 young adult animals (Fig.

4A), and decreased lifespan (Fig. 4B) in BW-Htt74Q

worms, but had no significant effect on these phenotypes

in wild-type worms (Fig. 4A, C). Although we and others

find that drp-1 mutants have a wild-type lifespan [44, 45],

it should be noted that one report has indicated that drp-1

mutants are short-lived compared to wild-type worms

[46]. The drp-1 deletion also decreased fertility, as

measured by self-brood size (Fig. 4D) and slowed

development (Fig. 4E) in both BW-Htt74Q worms and

wild-type worms. Examining the effect of the drp-1

deletion on mitochondrial function revealed no effect

on the rate of oxidative phosphorylation, as measured by

oxygen consumption (Fig. 4F), but caused a small

decrease in ATP levels (Fig. 4G). Finally, we found that

disruption of drp-1 did not decrease the mitochondrial

fragmentation present in BW-Htt74Q worms (Fig. 4H-K).

Taken together, these results suggest that inhibition of

mitochondrial fission can be detrimental in a body wall

muscle model of HD.

drp-1 deletion increases expression of polyglutamine

transgene

It was previously reported that RNAi against drp-1

increases expression of the polyglutamine transgene [16].

As increasing the levels of the polyglutamine protein

would be expected to increase toxicity, we sought to

determine whether drp-1 deletion also resulted in

increased transgene expression. Accordingly, we used

qPCR to measure the levels of polyglutamine transgene

expression in BW-Htt74Q worms compared to BW-

Htt74Q;drp-1 worms. We found that BW-Htt74Q;drp-1

worms showed a 92% increase in Htt74Q:GFP mRNA

compared to BW-Htt74Q worms (Supplementary Fig. 6).

This increase in polyglutamine expression could

contribute to the detrimental effects of drp-1 deletion in

this strain.

Decreasing mitochondrial fission through drp-1 RNAi

can be beneficial in a body wall muscle model of

Huntington’s disease

In contrast to our results obtained with a drp-1 deletion,

another group previously observed that RNAi against drp-

1 improved movement in BW-Htt74Q worms [16]. Since

a complete loss of DRP-1 can lead to a wide range of

abnormalities [47], we wondered if the drp-1 deletion has

detrimental effects that are independent of, or that

synergize with polyglutamine toxicity, which masked a

potential beneficial effect of decreasing drp-1 levels.

Accordingly, we investigated whether decreasing levels

of drp-1 by RNAi would be more beneficial in the HD

model. To do this, we fed BW-Htt74Q worms with RNAi

bacteria that directly targets drp-1. The RNAi was

administered using an L4 parental protocol, in which

RNAi knockdown is begun at the L4 stage of the parental

generation prior to testing their progeny (the experimental

generation). Using this paradigm, drp-1 RNAi-treated

worms exhibited drp-1 mRNA levels of approximately

30% of empty vector (EV) controls, and there was no

difference in the knockdown efficiency between wild-

type and BW-Htt74Q worms (Supplementary Fig. 7).

Unlike the drp-1 mutation, drp-1 RNAi did not

decrease the rate of movement in BW-Htt74Q worms

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Aging and Disease • Volume 12, Number 7, October 2021 1764

(Fig. 5A) and resulted in a small but significant increase

in lifespan in these worms (Fig. 5B, C). This suggests that

the detrimental effect of the drp-1 mutation on movement

in BW-Htt-74Q worms requires drp-1 levels to be reduced

beyond a specific threshold or be completely absent. Like

the drp-1 mutation, both wild-type and BW-Htt74Q

worms treated with drp-1 RNAi have markedly decreased

brood size compared to worms grown on EV RNAi (Fig.

5D).

To determine whether the mild improvement in

lifespan was associated with changes in mitochondrial

function, we quantified the effect of drp-1 RNAi on

mitochondrial form and function. While there was no

effect of drp-1 RNAi on oxygen consumption in wild-type

or BW-Htt74Q worms (Fig. 5E), it did result in decreased

ATP levels (Fig. 5F). As with the drp-1 mutation, the

disrupted mitochondrial networks present in BW-Htt74Q

worms were not rescued by drp-1 RNAi (Fig. 5G).

Mitochondrial number, area, and circularity were all

unchanged in BW-Htt74Q worms treated with drp-1

RNAi compared to EV (Fig. 5H-J).

Figure 5. Decreasing expression of mitochondrial fission protein DRP-1 through RNAi increases lifespan in

body wall muscle model of Huntington’s disease. The rate of movement in BW-Htt74Q worms is unchanged with

drp-1 RNAi (A). Knocking down drp-1 results in a small increase in lifespan in BW-Htt74Q worms (B), but has no

effect in wild-type worms (C). drp-1 RNAi decreases fertility in wild-type and BW-Htt74Q worms (D). While drp-

1 RNAi did not affect oxygen consumption in wild-type or BW-Htt74Q worms (E), knockdown of drp-1 reduced

ATP levels in both strains (F). Knocking down drp-1 expression using RNAi does not affect mitochondrial

morphology in control mitoRFP worms and does not restore tubular mitochondrial networks in BW-Htt74Q worms

(G). Quantification of mitochondrial morphology reveals no significant changes in mitochondrial number (H),

mitochondrial size (I) or mitochondrial shape (J) after treatment with drp-1 RNAi. The images in panel G are

compressed z-stacks collected on a confocal microscope. Scale bars indicate 10 µM. A minimum of three biological

replicates were performed. Bars indicate the mean value. Significance was assessed using two-way ANOVA

(A,C,D,E,G,H,I) or log-rank test (B,C). Error bars indicate SEM. ROI – region of interest. *p<0.05,

**p<0.01,***p<0.001.

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Figure 6. RNAi clones that decrease mitochondrial fragmentation improve movement in body wall

muscle model of Huntington’s disease. BW-Htt74Q and BW-Htt28Q control worms were treated with RNAi

against genes that were previously shown to decrease mitochondrial fragmentation when knocked down by

RNAi. Movement was then assessed by crawling and thrashing assays using an unbiased video-tracking

automated system. Ten of the 25 RNAi clones that decrease mitochondrial fragmentation were found to

increase the crawling rate in BW-Htt74Q worms (A). Of these 10 RNAi clones, two RNAi clones also

increased crawling speed in BW-Htt28Q control worms, indicating that 8 RNAi clones specifically improve

movement in the disease model (B). Five of the 25 RNAi clones that decrease mitochondrial fragmentation

increased the rate of movement in liquid (thrashing rate) of BW-Htt74Q worms (C). Only one of the RNAi

clones increased the thrashing rate of BW-Htt28Q worms (D). Three RNAi clones, F25B5.6, alh-12 and pgp-

3 increased both crawling speed and thrashing rate in BW-Htt74Q worms. Blue bars show BW-Htt28Q worms

treated with empty vector (EV). Grey bars show BW-Htt74Q worms treated with empty vector. Green bars

show RNAi clones that significantly increased movement. Red bars show RNAi clones that significantly

decreased movement. Blue dotted line shows rate of movement for BW-Htt28Q treated with EV. Grey dotted

line shows rate of movement for BW-Htt74Q treated with EV. Bars indicate the mean value. Significance was

assessed using one-way ANOVA. Error bars indicate SEM. *p<0.05, **p<0.01, ***p<0.001.

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1766

Table 1. RNAi clones that improved movement in C. elegans models of Huntington’s disease.

Target

gene

Drosophila

homolog

Mammalian

homolog

Effect on

crawling

BW-Htt74Q

Effect of

crawling

BW-Htt28Q

Effect on

thrashing

BW-Htt74Q

Effect of

thrashing

BW-Htt28Q

Effect on

aggregation

drp-1 Drp1 DNM1L No effect No effect No effect No effect Decreased

sdha-2 SdhA SdhA Increased Increased No effect Decreased No effect

C34B2.8 ND-B16.6 NDUFA13 Increased No effect Decreased No effect No effect

gpd-4 Gapdh2 GAPDH Increased No effect No effect Decreased No effect

F25B5.6 Fpgs FPGS Increased No effect Increased No effect No effect

oatr-1 Oat OAT Increased Increased No effect No effect No effect

alh-12 Aldh ALDH9A1 Increased No effect Increased Decreased No effect

R10H10.6 CG2846 RFK Increased No effect No effect No effect No effect

dlat-2 muc DLAT Increased Decreased No effect Decreased No effect

pgp-3 Mdr49 ABCB4 Increased No effect Increased Decreased No effect

wht-1 w ABCG1 Increased No effect No effect No effect No effect

gpx-1 PHGPx GPX4 No effect No effect Increased No effect No effect

immt-2 Mitofilin IMMT No effect No effect Increased No effect No effect

Decreasing mitochondrial fragmentation through

multiple genetic targets rescues movement deficits in a

body wall muscle model of Huntington’s disease

Given that DRP-1 is the main protein required for

mitochondrial fission and we observed detrimental effects

of disrupting drp-1 in both wild-type worms and worm

models of HD, inhibiting DRP-1 might not be an ideal

therapeutic target for HD. Accordingly, we explored the

therapeutic potential of other genetic targets that decrease

mitochondrial fragmentation. A previous study performed

a targeted RNAi screen to examine the effect of knocking

down mitochondria-associated genes on mitochondrial

morphology [48]. In this study, they examined 719 genes

predicted to encode mitochondrial proteins and identified

25 RNAi clones that decrease mitochondrial

fragmentation in body wall muscle. We performed a

targeted RNAi screen to examine the effect of these 25

RNAi clones in BW-Htt74Q worms. Treatment with

RNAi was begun at the L4 stage of the parental generation

and the rate of movement was assessed in the progeny

(experimental generation). The rate of movement was

assessed by unbiased video-tracking of movement on

solid plates (crawling) and movement in liquid

(thrashing).

Figure 7. Most RNAi clones that decrease mitochondrial fragmentation do not cause a decrease in polyglutamine

aggregation. BW-40Q worms were treated with RNAi against genes which decrease mitochondrial fragmentation in

C. elegans when knocked down via RNAi. Animals were imaged as day 1 adults. Of the 26 RNAi clones tested, four

RNAi clones resulted in a small but significant decrease in total aggregates per worm: drp-1, abhd-11.1, E04A4.4, and

C33A12.1. Grey bars show BW-40Q worms treated with empty vector (EV). Green bars show RNAi clones that

significantly decreased aggregate number. None of the RNAi clones resulted in increased aggregation. Bars indicate

the mean value. Significance was assessed using one-way ANOVA. Error bars indicate SEM. *p<0.05, **p<0.01,

***p<0.001.

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We found that 10 of the 25 RNAi clones that decrease

mitochondrial fragmentation increase crawling speed in

BW-Htt74Q worms (Fig. 6A). Of these 10 RNAi clones,

two clones also increased movement in BW-Htt28Q

worms suggesting a non-specific beneficial effect on

movement (Fig. 6B). Thus, eight RNAi clones

specifically rescued movement deficits in worm models

of HD (Table 1). In examining the effect of these same 25

RNAi clones on movement in liquid, we found that five

of these clones significantly increased the thrashing rate

in BW-Htt74Q worms (Fig. 6C), while a separate clone

improved movement in BW-Htt28Q control worms (Fig.

6D). Of the five clones that improved thrashing, three of

them, F25B5.6, alh-12 and pgp-3, also improved crawling

speed (Table 1).

Decreasing mitochondrial fragmentation does not affect

polyglutamine aggregation

To better understand how the RNAi clones that suppress

mitochondrial fragmentation caused a decrease in motility

defects, we examined the effect of these clones on

polyglutamine aggregation. We found that drp-1 RNAi

caused a small but statistically significant decrease in the

number of aggregates, as did three of the other RNAi

clones (E04A4.4, C33A12.1, abhd-11.1). However, all of

these clones were also found to disrupt development and

may be suppressing aggregation by slowing development.

As none of these RNAi clones rescued motility deficits in

the HD worm model, this indicates that decreasing

aggregation is not sufficient to improve movement.

Importantly, we found that none of the RNAi clones

which did suppress motility defects caused a decrease in

aggregation levels (Fig. 7, Table 1). Thus, the ability of

these clones to rescue movement deficits is not through a

decrease in aggregation.

Figure 8. RNAi clones that improve movement correct mitochondrial fragmentation in worm model of

Huntington’s disease. BW-Htt74Q worms or mitoRFP control worms were treated with RNAi clones that were

shown to improve crawling speed on solid plates and thrashing rate. In every case, the RNAi clones decreased

mitochondrial fragmentation such that mitochondrial morphology in the treated worms was equivalent to mitoRFP

controls (A). Treatment with RNAi against pgp-3, F25B5.6 or alh-12 significantly decreased the number of

mitochondria in the region of interest (ROI) (B), significantly increased mitochondrial area (C), and significantly

decreased mitochondrial circularity (D). The images shown in panel A are a compressed z-stack collected on a

confocal microscope. Scale bar indicates 25 µM. Bar graphs indicate the mean value. Significance shown is the

difference from the respective EV RNAi control, and was assessed using one-way ANOVA. Segmentation of

mitochondria for quantification was performed using ImageJ’s segmentation and quantification of subcellular shapes

(SQUASSH) tool. Average number of mitochondria, average mitochondrial area and average mitochondrial

circularity were then measured using the analyze particles tool on ImageJ. Error bars indicate SEM. ***p<0.001.

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Amelioration of movement deficits is associated with

restoration of wild-type mitochondrial morphology

To further characterize the three RNAi clones that rescued

the movement deficits in BW-Htt74Q worms, we

examined the effect of these genes on mitochondrial

morphology and lifespan. We found that the mitochondria

of BW-Htt74Q worms treated with pgp-3, F25B5.6 or

alh-12 RNAi was indistinguishable from the

mitochondria from mitoRFP control worms (Fig. 8A).

The RNAi-treated BW-Htt74Q worms exhibited

elongated, tubular mitochondria with no signs of

mitochondrial fragmentation. In quantifying the effect of

these three RNAi clones on mitochondrial morphology in

BW-Htt74Q worms, we found that pgp-3, F25B5.6 or alh-

12 RNAi significantly decreased mitochondrial number

(Fig. 8B), increased average mitochondrial area (Fig. 8C),

and decreased mitochondrial circularity (Fig. 8D).

Finally, we examined the effect of pgp-3, F25B5.6 or

alh-12 RNAi on the lifespan of BW-Htt74Q worms. We

found that treatment with these three RNAi clones was

unable to restore BW-Htt74Q lifespan to wild-type length

(Supplementary Fig. 8). Combined, this suggests that

decreasing mitochondrial fragmentation in BW-Htt74Q

worms through treatment with RNAi against pgp-3,

F25B5.6 or alh-12 increases healthspan, as measured by

movement in liquid and on solid plates, but not overall

lifespan.

DISCUSSION

Expression of a disease-length polyglutamine tract in C.

elegans causes mitochondrial fragmentation

While the genetic cause of HD was identified in 1993,

there are still no treatments available for patients with this

devastating disorder that can alter disease progression

[49]. Based on several observations linking HD and

mitochondrial dysfunction, correcting mitochondrial

deficits has been the focus of much research on disease-

modifying therapies for HD [50]. Accumulating evidence

indicates that mitochondrial dynamics are disrupted in

HD [16-23]. We have extended these findings to show

that mitochondrial networks are also disrupted in two

different C. elegans models of HD in which a disease-

length polyglutamine tract is expressed in body wall

muscle. In both BW-Htt74Q and BW-40Q worms we

observed mitochondrial fragmentation and mitochondrial

network disorganization leading to an increased number

of smaller, more rounded mitochondria, with no increase

in mtDNA content. The increase in mitochondrial

fragmentation may be a precursor to mitophagy in order

to replace mitochondria that are damaged by

polyglutamine toxicity or could be a direct effect of the

expanded polyglutamine protein. The fact that we observe

increased expression of mitochondrial fission genes

suggests that the increase in mitochondrial fragmentation

might be at least partially due to an active process to

increase mitochondrial fission.

Importantly, our results indicate that polyglutamine

toxicity can cause mitochondrial fragmentation

independently of the huntingtin protein, as a worm model

expressing a pure polyglutamine tract linked to YFP also

exhibited disrupted mitochondrial networks (BW-40Q

worms). Consistent with this idea, mitochondrial

fragmentation has been observed in models of other

polyglutamine toxicity disorders including

Spinocerebellar ataxia 3 (SCA3), Spinocerebellar ataxia 7

(SCA7) and Spinal and bulbar muscular atrophy (SBMA)

[51-53]. Combined, this suggests that expression of an

expanded polyglutamine tract (or the presence of a CAG

repeat expansion in the DNA/RNA) may be sufficient to

cause mitochondrial fragmentation independently of the

surrounding protein context.

DRP1 may not be an ideal therapeutic target for

Huntington’s disease

Based on the observation of mitochondrial fragmentation

in HD patients and models, multiple groups have explored

the effect of decreasing the levels or activity of DRP-1 in

various models of HD [16, 17, 19, 21]. In each case,

decreasing the levels or activity of DRP-1 showed a

beneficial effect in HD models. However, in thinking

about developing a treatment for HD, DRP1 may not be

an ideal target. DRP1 is the main GTPase responsible for

mitochondrial fission, which is crucial for proper cellular

function. Consistent with this, a number of studies have

indicated that loss of DRP1 function can be detrimental

[46, 47, 54-56]. For example, loss-of-function mutations

in the gene that encodes DRP1 causes a wide range of

abnormalities in humans, including epilepsy and

encephalopathy [57]. Our results indicate that deletion of

drp-1 can be detrimental in wild-type worms (slow

development, decreased fertility) and exacerbate

phenotypic deficits in a body wall muscle model of HD

(decrease movement, shorten lifespan). Because of the

potential negative side effects of directly targeting DRP1,

it may be important to explore other approaches to

decrease mitochondrial fragmentation as potential

therapeutic strategies for HD.

Alternatively, it may be necessary to precisely control

the level of drp-1 disruption. While deletion of drp-1 had

detrimental effects in BW-Htt74Q worms, we found that

decreasing drp-1 levels through RNAi resulted in a small

but beneficial effect on lifespan. Similarly, a previous

study found that drp-1 RNAi can increase movement in

the same BW-Htt74Q worms [16]. This suggests that the

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1769

precise level of drp-1 depletion may need to be controlled

to observe a beneficial effect. The difference between drp-

1 deletion and drp-1 RNAi could also have resulted from

the fact that neurons in C. elegans have decreased

sensitivity to RNAi. In addition to identifying the optimal

level of drp-1 knockdown, it may be possible to minimize

detrimental side effects of disrupting drp-1 by knocking it

down in specific tissues, which could be tested using

tissue-specific RNAi strains, or by knocking it down

during specific periods of development or adulthood,

which could be done by administering the RNAi bacteria

targeting drp-1 during specific periods of time.

Novel therapeutic targets for Huntington’s disease

aimed at reducing mitochondrial fragmentation

Because of the potential detrimental effects of targeting

DRP-1, we examined 25 other genes which also suppress

mitochondrial fragmentation when knocked down using

RNAi. Eight of the RNAi clones improved crawling only

in the Htt-74Q strain, while five clones improved

thrashing only in the Htt-74Q strain. Combined, we

identified three RNAi clones (pgp-3, F25B5.6, and alh-

12) that improved motility in both assays. These three

RNAi clones were also able to completely restore

mitochondria morphology in BW-Htt74Q worms to wild-

type morphology. These genes represent novel

therapeutic targets for HD, which will be important to

validate in other models of HD.

The main trait shared between pgp-3, F25B5.6 and

alh-12 are that they cause elongated mitochondria when

knocked down by RNAi [48]. pgp-3 is a p-glycoprotein

and performs ATP-dependent export of toxins and

xenobiotics out of the cytoplasm. It is required for drug

resistance to colchicine and chloroquine and upregulated

when animals are exposed to heavy metals [58, 59]. pgp-

3 is conserved and has a human orthologue, ABCB4 (ATP

binding cassette subfamily B member 4), which is

upregulated in an R6/2 mouse model of HD [60].

F25B5.6 also has ATP-binding activity and is

predicted to be a folylpolyglutamate synthase. The human

homologue, FPGS, is a mitochondrial enzyme, which

maintains folylpolyglutamate concentrations in the

cytoplasm and mitochondria. It has not previously been

associated with HD or neurodegeneration.

alh-12 is an aldehyde dehydrogenase, which is

upregulated in long-lived C. elegans mutants [60]. The

human homologue, ALDH9A1, has not been

experimentally linked with HD, however a meta-analysis

of pathways affected in HD predicted that ALDH9A1 is

important for HD as it is involved in multiple metabolic

pathways that are affected in HD [61].

As all three of these genes have homologs in mice and

humans, a key next step will be to examine the effect of

these genes on mitochondrial morphology and

polyglutamine toxicity in mammalian models. These

validation steps could be performed in mammalian cells

or mouse models, which we and others have shown to

recapitulate many features of the human disease [62-65].

Polyglutamine aggregation is associated with

mitochondrial fragmentation, but can be experimentally

dissociated

While polyglutamine aggregation is associated with

toxicity, whether or not aggregation causes toxicity,

reduces toxicity or is an epiphenomenon is still debated.

Both polyglutamine aggregation and toxicity increase

with both age and the length of the glutamine repeat [41];

however, decreases in polyglutamine aggregation do not

always cause a decrease in polyglutamine toxicity [66],

and the formation of aggregates has been associated with

a decreased probability of death [67]. In our study, we

show that polyglutamine aggregation is associated with

mitochondrial fragmentation. In both the BW-Htt74Q

model and BW-40Q model, mitochondria are tubular at

hatching and polyglutamine proteins exhibit diffuse

localization. By the L4 stage of development,

mitochondria start to become fragmented, and this event

is temporally correlated with the formation of aggregates.

Once worms have reached young adulthood, essentially

all muscle cells have fragmented mitochondria and

aggregated polyglutamine protein.

Despite the tight correlation between aggregation and

mitochondrial fragmentation, our data show that these

phenotypes can be experimentally dissociated. Knocking

down the expression of pgp-3, F25B5.6 or alh-12

prevented mitochondrial fragmentation in BW-Htt74Q

worms (Fig. 8) but had no effect on aggregation. This

indicates that mitochondrial fragmentation is not required

for aggregation.

Similarly, while BW-Htt74Q worms and BW-40Q

worms have both movement deficits and polyglutamine

aggregation, our data indicates that these phenotypes can

be separated. None of the RNAi clones which improved

either crawling or thrashing defects in BW-Htt74Q worms

caused a decrease in the number of polyglutamine

aggregates, while RNAi clones that did decrease

aggregation in these worms did not have a beneficial

effect on movement. Combined, our results suggest that

polyglutamine aggregation is not responsible for the

movement deficits in this worm model of HD.

Conclusions

In this work, we show that C. elegans models of HD

exhibit mitochondrial fragmentation and disorganized

mitochondrial networks, which are associated with

Machiela E., et al Mitochondrial dynamics and HD

Aging and Disease • Volume 12, Number 7, October 2021 1770

polyglutamine aggregation. Our observation that

decreasing DRP-1 levels can have detrimental effects in a

body wall muscle model of HD, suggests that DRP1 may

not be an ideal therapeutic target for HD, or that great care

must be taken to ensure that DRP1 levels are only

decreased by a certain amount. As an alternative to

targeting DRP1, we identified three novel genetic targets

(pgp-3, F25B5.6, and alh-12) that improved both crawling

and swimming in a C. elegans model of HD. These

genetic targets also corrected deficits in mitochondrial

morphology, thereby demonstrating that mitochondrial

fragmentation can be prevented without disrupting the

mitochondrial fission machinery. These results suggest

that strategies aimed at correcting mitochondrial

fragmentation may be beneficial in the treatment of HD.

Acknowledgments

We would like to thank Rick Morimoto, Mervyn Monteiro

and Paul Sternberg for generating strains used in this

research. Some strains were provided by the CGC, which

is funded by NIH Office of Research Infrastructure

Programs (P40 OD010440). We would also like to

acknowledge the C. elegans knockout consortium and the

National Bioresource Project of Japan for providing

strains used in this research. This work was supported by

the Canadian Institutes of Health Research (CIHR; PI:

Van Raamsdonk); the Natural Sciences and Engineering

Research Council of Canada (NSERC; PI: Van

Raamsdonk); the National Institutes of General Medical

Sciences (Grant number R01GM121756; PI: Van

Raamsdonk); and the Van Andel Research Institute

(VARI). JVR received a salary award from Fonds de

Recherche du Quebec Santé (FRQS). AT received

scholarships from NSERC and FRQS. SKS received a

scholarship from FRQS. The funders had no role in study

design, data collection and analysis, decision to publish,

or preparation of the manuscript.

Conflict of Interest

The authors declare no financial or competing interests.

Supplementary Materials

The Supplemenantry data can be found online at:

www.aginganddisease.org/EN/10.14336/AD.2021.0404.

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International Journal of

Molecular Sciences

Article

Identification of Novel Therapeutic Targets for Polyglutamine Diseases That Target Mitochondrial Fragmentation

Annika Traa 1,2,3, †, Emily Machiela 4, †, Paige D. Rudich 1,2,3, Sonja K. Soo 1,2,3, Megan M. Senchuk 4

and Jeremy M. Van Raamsdonk 1,2,3,4,5,6,*

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Citation: Traa, A.; Machiela, E.;

Rudich, P.D.; Soo, S.K.; Senchuk,

M.M.; Van Raamsdonk, J.M.

Identification of Novel Therapeutic

Targets for Polyglutamine Diseases

That Target Mitochondrial

Fragmentation. Int. J. Mol. Sci. 2021,

22, 13447. https://doi.org/10.3390/

ijms222413447

Academic Editors: Luis M. Valor and

Antonio Campos-Caro

Received: 27 October 2021

Accepted: 9 December 2021

Published: 14 December 2021

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

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iations.

Copyright: © 2021 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

1 Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada; [email protected] (A.T.); [email protected] (P.D.R.); [email protected] (S.K.S.)

2 Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada

3 Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada

4 Laboratory of Aging and Neurodegenerative Disease, Center for Neurodegenerative Science, Van Andel Research Institute, Grand Rapids, MI 49503, USA; [email protected] (E.M.); [email protected] (M.M.S.)

5 Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC H4A 3J1, Canada

6 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA * Correspondence: [email protected] † There authors contributed equally to this work.

Abstract: Huntington’s disease (HD) is one of at least nine polyglutamine diseases caused by a trinucleotide CAG repeat expansion, all of which lead to age-onset neurodegeneration. Mitochon- drial dynamics and function are disrupted in HD and other polyglutamine diseases. While multiple studies have found beneficial effects from decreasing mitochondrial fragmentation in HD models by disrupting the mitochondrial fission protein DRP1, disrupting DRP1 can also have detrimental consequences in wild-type animals and HD models. In this work, we examine the effect of decreasing mitochondrial fragmentation in a neuronal C. elegans model of polyglutamine toxicity called Neur- 67Q. We find that Neur-67Q worms exhibit mitochondrial fragmentation in GABAergic neurons and decreased mitochondrial function. Disruption of drp-1 eliminates differences in mitochondrial morphology and rescues deficits in both movement and longevity in Neur-67Q worms. In testing twenty-four RNA interference (RNAi) clones that decrease mitochondrial fragmentation, we identi- fied eleven clones—each targeting a different gene—that increase movement and extend lifespan in Neur-67Q worms. Overall, we show that decreasing mitochondrial fragmentation may be an effective approach to treating polyglutamine diseases and we identify multiple novel genetic targets that circumvent the potential negative side effects of disrupting the primary mitochondrial fission gene drp-1.

Keywords: Huntington’s disease; mitochondria; mitochondrial dynamics; polyglutamine diseases; C. elegans; genetics; DRP1

1. Introduction

Huntington’s disease (HD) is an adult-onset neurodegenerative disease caused by a trinucleotide CAG repeat expansion in the first exon of the HTT gene. The resulting expansion of the polyglutamine tract in the huntingtin protein causes a toxic gain of function that contributes to disease pathogenesis. HD is the most common of at least nine polyglutamine (polyQ) diseases, including spinal and bulbar muscular atrophy (SBMA), dentatorubral–pallidoluysian atrophy (DRPLA), and spinocerebellar ataxia types 1, 2, 3, 6, 7, and 17 (SCA1, SCA2, SCA3, SCA6, SCA7, and SCA17) [1,2]. Each disease occurs due to an expansion of a CAG repeat above a specific threshold number of repeats. The minimum

Int. J. Mol. Sci. 2021, 22, 13447. https://doi.org/10.3390/ijms222413447 https://www.mdpi.com/journal/ijms

Int. J. Mol. Sci. 2021, 22, 13447 2 of 19

number of disease-causing CAG repeats range from 21 CAG repeats (SCA6) to 55 CAG repeats (SCA3). These disorders are all unique neurodegenerative diseases that typically present in mid-life but can present earlier in life with larger CAG repeat expansions [3,4]. The genes responsible for these disorders appear to be unrelated, except for the presence of the CAG repeat sequence, indicating that CAG repeat expansion, independent of the genetic context, is likely sufficient to cause disease.

Multiple lines of evidence suggest a role for mitochondrial dysfunction in the patho- genesis of polyQ diseases [5–9]. Both HD patients and animal models of the disease display several signs of mitochondrial dysfunction, including decreased activity in the complexes of the mitochondrial electron transport chain [10], increased lactate production in the brain [11], decreased levels of ATP production [12], lowered mitochondrial membrane potentials [13], and impaired mitochondrial trafficking [14]. While less well studied than HD, other polyQ diseases also have evidence of mitochondrial deficits [15–18].

Mitochondrial fragmentation is a consistent feature of HD as it occurs in HD cell lines, HD worm models, HD mouse models, and cells derived from HD patients [19–27]. Mitochondrial fragmentation has also been observed in models of other polyQ diseases, including SCA3, SCA7, and SBMA [28–30]. This suggests that CAG repeat expansion may be sufficient to cause mitochondrial fragmentation.

In order to decrease HD-associated mitochondrial fragmentation, multiple groups have targeted the mitochondrial fission protein DRP1 in models of HD. While genetic or pharmacologic treatments that either directly or indirectly inhibit DRP1 activity typically exhibit beneficial effects in HD models [19,20,22,31–33], the disruption of DRP1 has also been found to exacerbate disease phenotypes [26]. The difference in effect may be due to the level of DRP1 disruption, as deletion of drp-1 was detrimental in an HD model, while RNAi knockdown of drp-1 in the same model had mixed effects [26]. Decreasing DRP1 levels can also be detrimental in a wild-type background [26,34–39]. Thus, reducing mitochondrial fragmentation through other genetic targets may be a more ideal therapeutic strategy for HD and other polyQ diseases than disrupting DRP1.

In this work, we show that CAG repeat expansion is sufficient to disrupt mitochondrial morphology and function in a neuronal model of polyQ toxicity. The neuronal model of polyQ toxicity also displays deficits in movement and lifespan, which are ameliorated by the deletion of drp-1. Using this model, we performed a targeted RNAi screen and identified eleven novel genetic targets that improve movement and increase lifespan. Overall, this work demonstrates that decreasing mitochondrial fragmentation may be an effective therapeutic strategy for polyQ diseases and identifies multiple potential genetic therapeutic targets for these disorders.

2. Results 2.1. Mitochondrial Morphology Is Disrupted in a Neuronal Model of Polyglutamine Toxicity

In order to study the effect of polyQ toxicity on mitochondrial dynamics in neurons, we utilized a model that expresses a polyQ protein containing 67 glutamines tagged with YFP under the pan-neuronal rgef-1 promoter [40]. Henceforth, these worms will be referred to as Neur-67Q worms. While this model has been studied previously, mitochondrial morphology and function in these worms have not been characterized. To visualize mitochondrial morphology in GABAergic neurons, we generated a new strain expressing mScarlet fused with the N-terminus of TOMM-20 (translocase of outer mitochondrial membrane 20), thus targeting the red fluorescent protein mScarlet to the mitochondria. In the rest of the paper, these worms (rab-3p::tomm-20::mScarlet) are referred to as mito- mScarlet worms. After crossing Neur-67Q worms to mito-mScarlet worms, we examined mitochondrial morphology in the dorsal nerve cord in day 1 adult worms.

We found that day 1 adult Neur-67Q worms exhibit mitochondrial fragmentation (Figure 1A). Compared to mito-mScarlet control worms, Neur-67Q; mito-mScarlet worms have a decreased number of mitochondria (Figure 1B). Although the mitochondrial area is not significantly affected by CAG repeat expansion at day 1 of adulthood (Figure 1C), Neur-

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67Q worms exhibit a decreased axonal mitochondrial load (Figure 1D), which is calculated as mitochondria area per length of the axon. In addition, the shape of the mitochondria is affected as Neur-67Q; mito-mScarlet worms have more circular mitochondria (Figure 1E) and a decreased maximum Feret’s diameter of the mitochondria (Figure 1F), which is the maximum distance between two parallel tangents to the mitochondria.

Figure 1. CAG repeat expansion disrupts mitochondrial morphology in neurons. Representative images of mitochondria (red) in the dorsal nerve cord in control and Neur-67Q worms at day 1 and day 7 of adulthood demonstrate that Neur- 67Q worms exhibit mitochondrial fragmentation and decreased numbers of mitochondria in neurons (A). Mitochondrial morphology was visualized by fusing the red fluorescent protein mScarlet to the mitochondrially-targeted protein TOMM-20 in order to target mScarlet to the mitochondria. Yellow fluorescence is bleed-through from the 67Q::YFP protein. Scale bar indicates 10 µM. Quantification of mitochondrial morphology at day 1 of adulthood reveals that Neur-67Q worms have a decreased number of mitochondria compared to control worms (B). While mitochondrial area is not significantly affected in Neur-67Q worms at day 1 of adulthood (C), these worms have a significant decrease in axonal mitochondrial load (D) compared to control worms. The mitochondria of day 1 adult Neur-67Q worms have increased circularity (E) and a decreased Feret’s diameter (F) compared to the mitochondria of control worms. Similarly, Neur-67Q worms at day 7 of adulthood have a significantly decreased number of mitochondria compared to control worms (G). Day 7 adult Neur-67Q worms also exhibit decreased mitochondrial area (H), decreased axonal mitochondrial load (I), increased mitochondrial circularity (J), and a decreased mitochondrial Feret’s diameter (K) compared to control worms. Control worms are rab-3p::tomm-20::mScarlet. For panels (B–K), Neur-67Q refers to Neur-67Q worms expressing mitochondrially targeted mScarlet (Neur-67Q; rab-3p::tomm-20::mScarlet worms). Three biological replicates were performed. Statistical significance was assessed using a t-test. Error bars indicate SEM. * p < 0.05, *** p < 0.001.

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2.2. Differences in Mitochondrial Morphology in Neuronal Model of Polyglutamine Toxicity Are Exacerbated with Increasing Age

To determine the effect of age on mitochondrial dynamics in Neur-67Q worms, we imaged and quantified mitochondrial morphology in worms at day 7 of adulthood. As in young adult worms, adult day 7 Neur-67Q worms exhibit mitochondrial fragmentation and a decrease in axonal mitochondria, which is much greater than observed in day 1 adult worms (Figure 1A). Quantification of mitochondrial morphology revealed that day 7 Neur- 67Q worms have a decreased mitochondrial number (Figure 1G), decreased mitochondrial area (Figure 1H), decreased axonal mitochondrial load (Figure 1I), increased mitochondrial circularity (Figure 1J), and decreased Feret’s diameter of the mitochondria (Figure 1K). These results indicate that aged Neur-67Q worms have a highly disconnected mitochondrial network morphology. Furthermore, the percentage decreases in mitochondrial number (−27% day 1 versus −64% day 7), mitochondrial area (−10% day 1 versus −22% day 7), and axonal mitochondrial load (−35% day 1 versus −70% day 7) are all much greater at day 7 than at day 1, indicating that the deficits in mitochondrial morphology in Neur-67Q worms worsen with age (Figure S1, see Supplementary Materials).

2.3. Neuronal Model of Polyglutamine Toxicity Exhibits Altered Mitochondrial Function

To determine if the differences in mitochondrial morphology that we observed af- fect mitochondrial function, we measured the rate of oxidative phosphorylation (oxygen consumption) and energy production (ATP levels) in day 1 adult worms. We found that Neur-67Q worms have increased oxygen consumption (Figure 2A) but decreased levels of ATP (Figure 2B). This suggests that the mitochondria in Neur-67Q are less efficient than in wild-type worms, possibly due to mitochondrial uncoupling. Combined, these results show that the presence of a disease-length CAG repeat expansion is sufficient to disrupt mitochondrial morphology and function.

Figure 2. CAG repeat expansion in neurons disrupts mitochondrial function. Mitochondrial function in Neur-67Q worms was assessed by quantifying oxygen consumption and ATP levels in whole worms at day 1 of adulthood. Day 1 adult Neur-67Q worms have increased oxygen consumption (A) and decreased ATP levels (B) compared to wild-type worms. A minimum of three biological replicates were performed. Statistical significance was assessed using a one-way ANOVA with Bonferroni’s multiple comparison test. Error bars indicate SEM. * p < 0.05, ** p < 0.01, *** p < 0.001.

2.4. Disruption of Mitochondrial Fission Is Beneficial in a Neuronal Model of Polyglutamine Toxicity

As disruption of drp-1 has been shown to ameliorate phenotypic deficits in various models of HD, we examined whether disruption of drp-1 would be beneficial in Neur-67Q worms. We found that deletion of drp-1 significantly improved mobility (Figure 3A) and

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increased lifespan (Figure 3B) in Neur-67Q worms. While the drp-1 deletion decreased fertility (Figure 3C) and slowed development (Figure 3D) in wild-type worms, it did not affect either of these phenotypes in Neur-67Q worms. This may be because mitochondrial morphology is already different than wild-type in Neur-67Q worms, while disruption of drp-1 in wild-type worms results in hyperfused mitochondria.

Figure 3. Inhibition of mitochondrial fission is beneficial in a neuronal model of polyglutamine toxicity. To examine the effect of disrupting mitochondrial fission in a neuronal model of polyglutamine toxicity, Neur-67Q worms were crossed to drp-1 deletion mutants. Deletion of drp-1 partially ameliorated phenotypic deficits in Neur-67Q worms. Neur-67Q;drp-1 worms showed significantly increased movement (A) and lifespan (B) compared to Neur-67Q worms. Unlike wild-type worms, deletion of drp-1 did not decrease fertility (C) or development time (D) in Neur-67Q worms. Combined, this indicates that inhibiting mitochondrial fission is beneficial in a neuronal model of polyglutamine toxicity. Neur-67Q worms have increased oxygen consumption compared to wild-type worms, and a mutation in drp-1 decreases oxygen consumption in these worms (E). Deletion of drp-1 causes a decrease in ATP levels in both wild-type and Neur-67Q worms (F). Control data for wild-type and drp-1 worms was previously published in Machiela et al., 2021, as experiments for both papers were performed simultaneously using the same controls. For panels (A,C,E,F), “Control” refers to worms in a wild-type background with a normal expression of drp-1 (either wild-type or Neur-67Q worms). A minimum of three biological replicates were performed. Statistical significance was assessed using a two-way ANOVA with Bonferroni posttest (panels (A,C,E,F)), the log-rank test (panel (B)), or a repeated measures ANOVA (panel (D)). Error bars indicate SEM. * p < 0.05, *** p < 0.001.

Finally, we examined the effect of drp-1 deletion on mitochondrial function in Neur- 67Q worms. We found that the increased oxygen consumption observed in Neur-67Q worms is significantly decreased by disruption of drp-1 (Figure 3E). However, the drp-1 deletion was unable to increase the low ATP levels in Neur-67Q worms and decreased ATP levels in wild-type worms (Figure 3F). Although the effects of drp-1 deletion in Neur-67Q worms are primarily beneficial, the loss of drp-1 increased expression of the disease-length polyQ mRNA (Figure S2), as we and others have previously observed [19,26], which would be predicted to cause increased toxicity.

To ensure that the beneficial effects of the drp-1 deletion in Neur-67Q worms are caused by the disruption of drp-1, we examined the effect of drp-1 RNAi in Neur-67Q worms. Because most C. elegans neurons are resistant to RNAi knockdown [41], we first crossed Neur-67Q worms to a worm strain that exhibits enhanced RNAi knockdown

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specifically in the neurons but is resistant to RNAi in other tissues [42]. In the resulting strain (Neur-67Q;sid-1;unc-119p::sid-1), RNAi is only active in the nervous system.

As with the drp-1 deletion, knocking down drp-1 expression throughout life increased the rate of movement (Figure S3A) and increased lifespan (Figure S3B) in Neur-67Q worms, while having no effect on fertility in Neur-67Q worms (Figure S3C). As with the drp-1 deletion, drp-1 RNAi decreased both oxygen consumption and ATP levels in Neur-67Q worms (Figure S3D,E).

2.5. Disruption of Mitochondrial Fission Decreases Mitochondrial Fragmentation in Neurons

Having shown that drp-1 deletion ameliorates phenotypic deficits in Neur-67Q worms, we wondered whether the alterations in mitochondrial morphology were also corrected. Accordingly, we imaged and quantified mitochondrial morphology in Neur-67Q; drp-1 worms at day 1 (Figure S4) and day 7 (Figure 4) of adulthood. On day 1 of adulthood, disruption of drp-1 markedly elongated the neuronal mitochondria, leading to decreased mitochondrial fragmentation in both Neur-67Q worms and wild-type worms (Figure S4A). Quantification of these differences revealed that deletion of drp-1 results in significantly decreased numbers of mitochondria (Figure S4B), significantly increased mitochondrial area (Figure S4C), significantly increased axonal mitochondrial load (Figure S4D), signifi- cantly decreased mitochondrial circularity (Figure S4E), and significantly increased Feret’s diameter (Figure S4F).

The beneficial effects of drp-1 disruption on mitochondrial morphology in Neur-67Q worms are also observed at day 7 of adulthood (Figure 4A). In day 7 adult Neur-67Q worms, disruption of drp-1 increases mitochondrial number (Figure 4B), mitochondrial area (Figure 4C), and axonal mitochondrial load (Figure 4D), while decreasing mitochondrial circularity (Figure 4E) and increasing the Feret’s diameter of the mitochondria (Figure 4F). Similar changes are observed in wild-type worms with the exception of mitochondrial number, which is significantly decreased by drp-1 disruption (Figure 4B).

Combined, these results indicate that drp-1 has a beneficial effect on mitochondrial morphology in Neur-67Q worms. Interestingly, CAG repeat expansion in Neur-67Q worms has no effect on mitochondrial morphology in the drp-1 mutant background (Figure S5).

2.6. Targeting Genes That Affect Mitochondrial Fragmentation Improves Thrashing Rate and Lifespan in a Neuronal Model of Polyglutamine Toxicity

While our results show that decreasing levels of drp-1 are beneficial in a neuronal worm model of polyQ toxicity, this treatment had a detrimental effect in a C. elegans model of HD in which exon 1 of mutant huntingtin is expressed in the body wall muscle [26]. Moreover, a number of studies have found that disruption of DRP1 can be detrimental in organisms ranging from worms to humans [34–39].

To circumvent potential detrimental effects of disrupting drp-1, we targeted other genes that have been previously found to decrease mitochondrial fragmentation [43]. In the previous study, a targeted RNAi screen identified 24 mitochondria-related RNAi clones that decrease mitochondrial fragmentation in the body wall muscle of C. elegans. We examined the effect of these 24 RNAi clones in neuron-specific RNAi Neur-67Q worms (Neur-67Q; sid-1; unc-119p::sid-1). Treatment with RNAi was begun at the L4 stage of the parental gen- eration, and the rate of movement was assessed in the progeny (experimental generation).

We found that 16 of the 24 RNAi clones that decrease mitochondrial fragmenta- tion significantly increased the rate of movement in the neuron-specific RNAi Neur-67Q model (Figure 5A). To ensure that the improved movement in Neur-67Q worms did not result from a general effect of these RNAi clones on the rate of movement, we treated sid-1;unc-119p::sid-1 control worms with the same panel of 24 RNAi clones and examined movement. Unlike the Neur-67Q worms, we found that only four of the RNAi clones improved movement in the control neuron-specific RNAi strain (Figure 5B). This indicates that, for the majority of the RNAi clones that show a benefit, the improvement in movement is specific to the neuronal model of polyQ toxicity.

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We next examined whether the genes that improved motility in neuron-specific RNAi Neur-67Q worms also improved longevity. We found that 11 of the 16 RNAi clones that increased the rate of movement also increased lifespan in neuron-specific RNAi Neur- 67Q worms (Figure 6). In contrast, only three of these RNAi clones increased lifespan in the neuron-specific RNAi control strain (Figure S6). Overall, RNAi clones that decrease mitochondrial fragmentation in body wall muscle are beneficial in a neuronal model of polyQ toxicity.

Figure 4. Disruption of drp-1 rescues deficits in mitochondrial morphology caused by CAG repeat expansion. Deletion of drp-1 decreased mitochondrial fragmentation in Neur-67Q and control worms at day 7 of adulthood. Representative images of Neur-67Q worms and control worms in wild-type and drp-1 deletion background (A). Scale bar indicates 10 µM. Disruption of drp-1 in Neur-67Q worms increased mitochondrial number (B), increased mitochondrial area (C), increased axonal mitochondrial load (D), decreased mitochondrial circularity (E), and increased the Feret’s diameter of the mitochondria (F). Control worms are rab-3p::tomm-20::mScarlet. For panels (B–F), “Control” refers to worms in a wild-type background with normal expression of drp-1 (either wild-type or Neur-67Q worms). Control data is from Figure 1 and is shown to facilitate a direct comparison of the effects of drp-1 deletion. Three biological replicates were performed. Statistical significance was assessed using a two-way ANOVA with Bonferroni post-test. Error bars indicate SEM. *** p < 0.001.

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Figure 5. Decreasing mitochondrial fragmentation improves rate of movement in a neuronal model of polyglutamine toxicity. Neur-67Q worms in which RNAi is only effective in neurons (Neur-67Q;sid-1;unc-119p::sid-1 worms) and a neuron-specific RNAi control strain (sid-1;unc-119p::sid-1 worms) were treated with RNAi clones that decrease mitochondrial fragmentation in body wall muscle. RNAi against 16 of the 24 genes tested improved the rate of movement in Neur-67Q worms (A). RNAi against four of these genes also increased movement in the neuron specific RNAi strain (B). Green indicates a significant increase in movement, while red indicates a significant decrease in movement. The positive control drp-1 is indicated with blue. “Neur-67Q” refers to Neur-67Q;sid-1;unc-119p::sid-1 worms (panel (A)), while “Control” refers to sid-1;unc-119p::sid-1 worms (panel (B)). Three biological replicates were performed. Statistical significance was assessed using a one-way ANOVA with Dunnett’s multiple comparison test. Error bars indicate SEM. * p < 0.05, ** p < 0.01, *** p < 0.001.

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Figure 6. RNAi clones previously shown to decrease mitochondrial fragmentation in body wall muscle rescue shortened lifespan in neuronal model of polyglutamine toxicity. Neur-67Q worms in which RNAi is only effective in neurons (Neur- 67Q;sid-1;unc-119p::sid-1 worms) were treated with RNAi clones that decrease mitochondrial fragmentation in body wall muscle and that we found to increase movement in Neur-67Q worms (Figure 5). Eleven of the sixteen RNAi clones that improved movement in Neur-67Q worms also resulted in increased lifespan. Each of these RNAi clones targets a different gene. Three biological replicates were performed. Statistical significance was assessed using the log-rank test.

3. Discussion

Since the discovery of the genes responsible for HD and other polyQ diseases [44,45], multiple animal models of these disorders have been generated to gain insight into disease pathogenesis [46,47]. This includes C. elegans models of HD and polyQ toxicity [40,48–50]. C. elegans offers a number of advantages for studying neurodegenerative diseases, in- cluding being able to perform large-scale screens for disease modifiers rapidly and cost- effectively [51,52]. In addition, the interconnections of all of the neurons in C. elegans have been mapped. In terms of studying mitochondrial dynamics, the transparent nature of C. elegans facilitates imaging mitochondrial morphology in a live organism, which can then be correlated with whole-organism phenotypes.

3.1. CAG Repeat Expansion Disrupts Mitochondrial Morphology and Function in Neurons

HD and other polyQ diseases are neurodegenerative diseases in which the most severe pathology occurs in neurons. We previously examined mitochondrial fragmentation in a muscle model of HD as it is more experimentally accessible [26]. However, to gain greater physiological relevance, in this study, we generated novel strains to examine mitochondrial morphology in neurons. We found that CAG repeat expansion in Neur-67Q worms is sufficient to cause mitochondrial fragmentation neurons, as well as a progressive decrease in the abundance of mitochondria in the axons of the dorsal nerve cord. The differences in mitochondrial number, axonal load, size, circularity, and length (Feret’s diameter) in the neuronal model of polyQ toxicity are quantifiable and highly significant.

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In these experiments, we used wild-type worms as a control rather than worms expressing a CAG repeat tract within the unaffected range. As a result, we can’t exclude the possibility that mitochondrial fragmentation might also be caused by the expression of short CAG repeat sequences. However, we think that this is unlikely as we have previously found that expression of CAG repeat expansions of 24–28 does not lead to mitochondrial fragmentation in the body wall muscle [26]. Similarly, others have only observed mitochondrial fragmentation with disease length CAG repeat sequences [22].

Importantly, Neur-67Q worms also exhibited changes in mitochondrial function, including a significant increase in oxygen consumption and a significant decrease in ATP levels. These differences are particularly striking given that oxygen consumption and ATP levels were measured in whole worms while the expanded polyQ transgene is only expressed in neurons, which make up 302 of the worm’s 959 cells. Given the magnitude of the differences observed, it is possible that changes occurring in the neurons are having cell-non-autonomous effects on mitochondrial function in other tissues.

Although the yield of ATP from oxidative phosphorylation is variable [53], oxygen consumption and ATP production normally correlate under basal conditions due to the high dependence of ATP production on the electron transport chain in C. elegans [54,55]. The opposing changes in ATP and oxygen consumption suggest that the mitochondria in Neur- 67Q worms are inefficient or damaged, leading to a marked decrease in the ATP produced per amount of oxygen consumed. We observed a similar pattern in a mitophagy-defective worm model of Parkinson’s disease in which there is a deletion of pdr-1/PRKN [56].

It is interesting to note that drp-1 deletion was still able to improve movement in Neur-67Q worms despite further decreasing the levels of ATP. Similarly, drp-1 deletion decreased ATP levels in wild-type worms but did not decrease their movement. These findings suggest that the movement deficit in Neur-67Q worms is not simply a result of decreased levels of ATP but more likely due to a disruption of neuronal function.

3.2. Tissue-Specific Effects of Disrupting Mitochondrial Fission

One of the most surprising findings of our current study is that deletion of drp-1 has different effects in neuronal and body wall muscle models of polyQ toxicity (see Table S1 for comparison). In the neuronal model, deletion of drp-1 increases movement and lifespan and has no detrimental effect on development or fertility. In contrast, disruption of drp-1 in the body wall muscle model decreases movement, lifespan, fertility, and the rate of development [26]. The opposing effects of reducing drp-1 on polyQ toxicity in neurons compared to body wall muscle suggest that the optimal balance between mitochondrial fission and fusion may differ between tissues. Alternatively, it is possible that the loss of mitochondrial fission is better tolerated in neurons than in body wall muscle, even though both tissues are post-mitotic in C. elegans. Finally, it could be that decreasing drp-1 levels is beneficial in neurons because it is more effective at correcting disruptions in mitochondrial networks in that tissue (Figure 4) than in body wall muscle, where drp-1 deletion had little or no effect on mitochondrial morphology [26].

It should be noted that the neuronal model of polyQ toxicity used in this study and the HD muscle model that we utilized previously cannot be directly compared due to differences between these strains beyond the tissue of expression (Table S1). Notably, BW- Htt-74Q worms have a small fragment of the huntingtin protein linked to the expanded polyQ tract, while Neur-67Q only have the expanded polyQ tract. The size of the polyQ tract is different between these two strains, and BW-Htt-74Q worms have the polyQ tagged with GFP, while the polyQ is tagged with YFP in Neur-67Q worms. Thus, while our results do not rule out other factors contributing to the differences between the neuronal strain and the muscle strain, they clearly show that decreasing drp-1 levels can be beneficial in worms expressing an expanded polyQ tract in neurons, and that decreasing drp-1 levels can be detrimental in worms expressing an expanded polyQ tract in muscle cells.

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3.3. Decreasing Mitochondrial Fragmentation as a Therapeutic Strategy for Polyglutamine Diseases

Due to the many roles drp-1 plays in promoting proper cellular function through control of the mitochondria and the previously observed detrimental effects of decreasing drp-1 in a body wall muscle model [26], decreasing levels or activity of DRP-1 may be a non-ideal therapeutic target for HD or other polyQ diseases. Accordingly, we explored other possible genetic targets that decrease mitochondrial fragmentation. We performed a targeted RNAi screen using 24 RNAi clones previously found to decrease mitochondrial fragmentation in the body wall muscle [43]. A high percentage of these RNAi clones increased movement (16 of 24 RNAi clones that decrease fragmentation) and lifespan (11 of 16 RNAi clones that improve movement) in Neur-67Q worms.

As we obtained numerous positive hits, we did not confirm knockdown by qPCR or confirm a decrease in mitochondrial fragmentation. Thus, we can’t exclude the possibility that the remaining eight genes that failed to show a beneficial effect may have had either insufficient genetic knockdown or did not exhibit the predicted effect on mitochondrial morphology. Nonetheless, a high proportion of RNAi clones previously found to decrease mitochondrial fragmentation increased movement in the neuronal HD model indicating that multiple genetic approaches to decreasing mitochondrial fragmentation are beneficial in worm models of polyQ toxicity.

In order to prioritize therapeutic targets for further characterization and validation, we analyzed the results from the current study with our previous study of these RNAi clones in a body wall muscle model of HD [26] (Table 1). The genes were ranked by giving one point for improving either: thrashing rate in Neur-67Q worms; lifespan in Neur-67Q worms; the crawling rate in BW-Htt74Q worms; or thrashing rate in BW-Htt74Q worms. Of the 24 RNAi clones tested in both models, 21 clones exhibited a beneficial effect on at least one phenotype. This indicates that multiple approaches to decreasing mitochondrial fragmentation can ameliorate deficits caused by CAG repeat expansion. The top-ranked therapeutic targets were alh-12 and pgp-3, which resulted in improvement of all four assessments, and gpd-4, immt-2, sdha-2 and wht-1, which resulted in improvement in three of the assessments (Table 1). As the RNAi clones targeting alh-12 and pgp-3 were the top-ranked hits, we confirmed that these RNAi clones successfully knocked down the expression of alh-12 and pgp-3, respectively (Figure 7). Interestingly, knockdown of alh-12 or pgp-3 has detrimental effects on lifespan or movement in control strains, indicating that their beneficial effect is specific to animals with disease-length CAG repeats.

Table 1. Effect of RNAi clones that decrease mitochondrial fragmentation in neuronal and body wall muscle models of polyglutamine toxicity. “ND” indicates not done. “=” indicates no change. Data from BW-Htt74Q worms and BW-Htt28Q worms is from Machiela et al. 2021, Aging and Disease, PMID: 34631219).

Target Gene

Drosophila Homolog

Mammalian Homolog

Effect on Thrashing in

Neur-67Q Worms

Effect on Lifespan in Neur-67Q

Worms

Effect on Crawling in BW-Htt74Q

Worms

Effect on Thrashing in BW-Htt74Q

Worms

Effect on Thrashing in

Neuron Specific

RNAi Strain

Effect on Lifespan in

Neuron Specific

RNAi Strain

Effect of Crawling in BW-Htt28Q

Worms

Effect of Thrashing

in BW-Htt28Q

Worms alh-12 Aldh ALDH9A1 Increased Increased Increased Increased No effect Decreased No effect Decreased pgp-3 Mdr49 ABCB4 Increased Increased Increased Increased Decreased Increased No effect Decreased gpd-4 Gapdh2 GAPDH Increased Increased Increased No effect Increased No effect No effect Decreased

immt-2 Mitofilin IMMT Increased Increased No effect Increased Decreased Increased No effect No effect sdha-2 SdhA SdhA Increased Increased Increased No effect No effect Increased Increased Decreased wht-1 w ABCG1 Increased Increased Increased No effect Decreased Decreased No effect No effect

C34B2.8 ND-B16.6 NDUFA13 Increased Increased Increased Decreased Increased No effect No effect No effect drp-1 Drp1 DNM1L Increased Increased No effect No effect No effect No effect No effect No effect

F25B5.6 Fpgs FPGS No effect ND Increased Increased Decreased ND No effect No effect his-12 His2A HIS2H2AB Increased Increased No effect No effect = Decreased Decreased Decreased

sfxn-1.4 Sfxn1-3 SFXN1/3 Increased Increased No effect No effect = Decreased No effect No effect abhd-11.1 CG2059 ABHD11 Increased = No effect No effect Decreased = No effect No effect

acs-1 Acsf2 ACSF2 Increased = No effect No effect Decreased = Decreased No effect crls-1 CLS CRLS1 Increased = No effect No effect Increased = No effect No effect

cyp-35A1 Cyp18a1 CYP2C8 Increased Increased Decreased No effect Decreased = No effect No effect

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Table 1. Cont.

Target Gene DrosophilaHomolog Mammalian

Homolog

Effect on Thrashing in

Neur-67Q Worms

Effect on Lifespan in Neur-67Q

Worms

Effect on Crawling in BW-Htt74Q

Worms

Effect on Thrashing in BW-Htt74Q

Worms

Effect on Thrashing in

Neuron Specific

RNAi Strain

Effect on Lifespan in

Neuron Specific

RNAi Strain

Effect of Crawling in BW-Htt28Q

Worms

Effect of Thrashing

in BW- Htt28Q Worms

D2023.6 Adck1 ADCK1 Increased Increased Decreased No effect = = Decreased No effect dlat-2 muc DLAT No effect ND Increased No effect No effect ND Decreased Decreased gpx-1 PHGPx GPX4 No effect ND No effect Increased No effect ND No effect No effect

timm-17B.1 Tim17b TIMM17A/B Increased = No effect No effect Increased = No effect Decreased oatr-1 Oat OAT No effect ND Increased No effect No effect ND Increased No effect

R10H10.6 CG2846 RFK No effect ND Increased No effect Decreased ND No effect No effect alh-12 iso B Aldh ALDH9A1 = ND Decreased No effect = ND No effect No effect C33A12.1 ND-13B NDUFA5 = ND No effect No effect = ND No effect No effect K02F3.2 aralar1 SLC25A12 Increased = Decreased No effect = = No effect Decreased T10F2.2 CG1628 SLC25A15 = ND No effect No effect = ND No effect No effect

Figure 7. Decreasing the levels of alh-12 and pgp-3 mRNA using RNA interference. Wild-type and Neur-67Q worms were treated with RNAi bacteria targeting alh-12 (A) or pgp-3 (B) beginning at the L4 stage of the parental generation. mRNA levels were measured when the progeny reached the young adult stage using quantitative RT-PCR. In both wild-type and Neur-67Q worms, there was a significant decrease in alh-12 and pgp-3 mRNA levels when treated with RNAi bacteria targeting alh-12 or pgp-3, respectively. The level of knockdown was highly significant for both genes, but the magnitude of knockdown was greater for alh-12. Bars indicate the mean value of three biological replicates. Statistical significance was assessed using a two-way ANOVA with Bonferroni post-test. Error bars indicate SEM. *** p < 0.001.

The alh-12 gene encodes a cytoplasmic aldehyde dehydrogenase that is expressed in the intestine, body wall muscle, and specific neurons. It is involved in multiple metabolic pathways, including arginine metabolism, glycerolipid metabolism, glycol- ysis/gluconeogenesis, and tryptophan degradation. As very little is known about the functions of ALH-12, it is hard to speculate how disrupting alh-12 may be acting to improve movement and lifespan in the worm models of polyQ toxicity. The human homolog of ALH- 12, ALDH9A1 can be inhibited by diethylaminobenzaldehyde [57], which can potentially be used to validate the neuroprotective effects of ALH-12 inhibition in mammalian models.

The pgp-3 gene encodes a p-glycoprotein related protein. It is a transmembrane protein that transports molecules out of the cytoplasm. PGP-3 is primarily expressed in the intestine [58], but has also been reported in other tissues. Disruption of pgp-3 sensitizes worms to P. aeruginosa in a toxin-based fast kill assay [59], as well as exposure to colchicine and chloroquinone [60], presumably by disrupting the active removal of the toxic compounds from cells. While it seems counterintuitive that loss of a protective function against toxins and xenobiotics is protective against polyQ toxicity, knockdown of pgp-3 may be acting through hormesis, the process by which exposure to mild stress activates protective pathways that can increase resistance to subsequent stresses and extend longevity. Exposing worms to mild stress (e.g., heat stress) increases both stress resistance and lifespan [61]. Thus, it is possible that preventing the transport of specific molecules out of the cytoplasm through disruption of pgp-3 induces mild stress, which leads to

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activation of protective stress response pathways. Alternatively, it could be that retention of specific molecules in the cytoplasm somehow protects against the toxic effects of the CAG repeat expansion.

As relatively little is known about alh-12 and pgp-3, it will be important to further characterize their biological functions in order to gain insight into mechanisms of neuro- protection. As both genes have homologs in mice and humans, it will also be important to validate these genes in mammalian models to determine if their ability to protect against polyQ disease is conserved across species. Demonstrating a protective effect in mammalian models would provide strong support for these genes as potential therapeutic targets in polyQ diseases.

4. Materials and Methods 4.1. Strains

N2 (WT) AM102 rmIs111[rgef-1p::40Q:YFP] referred to as Neur-40Q AM717 rmIs284[rgef-1p::67Q:YFP] referred to as Neur-67Q JVR258 drp-1(tm1108);rmIs284[rgef-1p::67Q:YFP] JVRV438 rmIs284[rgef-1p::67Q:YFP]; sid-1(pk3321); uIs69 [pCFJ90 (myo-2p::mCherry) + unc-119p::sid-1] JVR443 rmIs284[rgef-1p::67Q:YFP]; uIs69 [pCFJ90 (myo-2p::mCherry) + unc-119p::sid-1] PHX3820 sybIs3820[rab-3p::tomm-20::mScarlet] referred to as mito-mScarlet JVR611 rmIs284[rgef-1p::67Q:YFP];drp-1(tm1108); sybIs3820[rab-3p::tomm-20::mScarlet] referred to as Neur-67Q;drp-1;mito-mScarlet JVR612 rmIs284[rgef-1p::67Q:YFP]; sybIs3820[rab-3p::tomm-20::mScarlet] referred to as Neur-67Q;mito-mScarlet JVRV613 drp-1(tm1108); sybIs3820[rab-3p::tomm-20::mScarlet] referred to as drp-1;mito- mScarlet MQ17V53 drp-1 (tm1108) TU3401 sid-1(pk3321); uIs69 [pCFJ90 (myo-2p::mCherry) + unc-119p::sid-1]

Strains were maintained at 20 ◦C on NGM plates seeded with OP50 bacteria. The Neur- 67Q model of HD is an integrated line that has been well characterized previously [40]. All crosses were confirmed by genotyping using PCR for deletion mutations, sequencing for point mutations, and confirmation of fluorescence for fluorescent transgenes.

4.2. Generation of Strains to Monitor Mitochondrial Morphology in GABA Neurons

The rab-3p::tomm-20::mScarlet strain was generated by SunyBiotech Co., Ltd., Fujian, China, The 1208 bp rab-3 promoter sequence (Addgene Plasmid #110880) was inserted directly upstream of the N-terminal TOMM-20 coding region. The first 47 amino acids of TOMM-20 were connected through a flexible linker (3xGGGGS) to the N-terminal of wrm- Scarlet [62]. The strain was generated through microinjection of rab-3p::tomm-20::mScarlet in the pS1190 plasmid (20 ng/µL) into wild-type N2 worms. The transgenic strain was integrated by γ-irradiation and the outcrossed 5X to remove background mutations.

4.3. Confocal Imaging and Quantification

Mitochondrial morphology was imaged and quantified using worms that express mitochondrially-targeted mScarlet specifically in neurons (rab-3p::tomm-20::mScarlet). Worms at day 1 or day 7 of adulthood were mounted on 2% agar pads and immobilized using 10 µM levamisole. Worms were imaged under a 63× objective lens on a Zeiss LSM 780 confocal microscope. All conditions were kept the same for all images. Single plane images were collected for a total of twenty-five young adult worms over three biological replicates for each strain. Quantification of mitochondrial morphology was performed using ImageJ. Segmentation analysis was carried out using the SQUASSH (segmentation and quantifi- cation of subcellular shapes) plugin. Particle analysis was then used to quantify number of mitochondria, mitochondrial area, axonal mitochondrial load, mitochondrial circularity,

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and maximum Feret’s diameter (an indicator of particle length). Axonal load was calculated as the total mitochondrial area (µm2) in a region of interest (ROI), per length (µm) of axon in the ROI. For representative images, mScarlet and YFP channels were merged. We ob- served some bleed-through of YFP into the red channel for strains expressing the 67Q-YFP transgene. Particles that showed up in the mScarlet images as a result of YFP bleed-through were manually excluded from morphology quantification based on the numbered particle mask output from the ImageJ particle analyzer.

4.4. Oxygen Consumption

To measure basal oxygen consumption, a Seahorse XFe96 analyzer (Seahorse bio- science Inc., North Billerica, MA, USA) [57] was used. Adult day 1 worms were washed in M9 buffer (22 mM KH2PO4, 34 mM NA2HPO4, 86 mM NaCl, 1 mM MgSO4) and pipetted in calibrant (~50 worms per well) into a Seahorse 96-well plate. Oxygen consumption rate was measured six times. One day before the assay, well probes were hydrated in 175 µL of Seahorse calibrant solution overnight. The heating incubator was turned off to allow the Seahorse machine to reach room temperature before placing worms inside. Rates of respiration were normalized to the number of worms per well. Plate readings were within 20 min of introducing the worms into the well and normalized relative to the number of worms per well.

4.5. ATP Production

To measure ATP production, a luminescence-based ATP kit was used [63]. Approx- imately 200 age-synchronized worms were collected in deionized water before being washed and freeze-thawed three times. A Bioruptor (Diagenode) was used to sonicate the worm pellet for 30 cycles of alternating 30 s on and 30 s off. The pellet was boiled for 15 min to release ATP and then centrifuged at 11,000× g for 10 min at 4 ◦C before the resulting supernatant was collected. A Molecular Probes ATP determination Kit (Life Technologies) was used to measure ATP. Luminescence was normalized to protein levels determined by a Pierce BCA protein determination kit (Thermo Scientific, Waltham, MA, USA).

4.6. Rate of Movement

To measure rate of movement, thrashing rate in liquid was assessed using video- tracking and computer analysis [64]. Approximately 50 day 1 adult worms were placed in M9 buffer on an unseeded NGM plate. An Allied Vision Tech Stingray F-145 B Firewire Camera (Allied Vision, Exton, PA, USA) was used to capture videos at 1024 × 768 resolution and 8-bit using the MATLAB image acquisition toolbox. The wrMTrck plugin for ImageJ (http://www.phage.dk/plugins) was used to analyze rate of movement.

4.7. Lifespan

To measure lifespan, worms were placed on nematode growth media (NGM) agar plates containing 25 µM 5-fluoro-2′-deoxyuridine (FUdR). FUdR was used to reduce progeny development. At a 25 µM concentration of FUdR, progeny development into adulthood is not completely prevented in the first generation so animals were transferred to fresh plates after 4 days [65]. Worms were moved to fresh plates weekly and survival was observed by gentle prodding every 2 days. Lifespan experiments were conducted with three replicates of 30 worms each.

4.8. Brood Size

To determine brood size, individual young adult worms were placed onto agar plates and transferred every day to new plates until progeny production stopped. Plates of result- ing progeny were quantified when adulthood was reached. Experiments were conducted with three replicates of five worms each.

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4.9. Post-Embryonic Development

To measure post-embryonic development (PED), eggs were moved to agar plates and left to hatch for 3 h. L1 worms that were newly hatched were transferred to a new plate. The PED time was considered the total time from hatching to the young adult stage. Experiments were conducted with three replicates of 20 animals each.

4.10. Quantitative Reverse-Transcription PCR (qPCR)

To quantify mRNA levels, pre-fertile young adult worms were harvested in Trizol as previously described [66]. Three biological replicates for N2, BW-40Q, and BW-Htt74Q worms were collected to quantify gene expression. A High-Capacity cDNA Reverse Transcription kit (Life Technologies/Invitrogen) was used to convert mRNA to cDNA. A FastStart Universal SYBR Green kit (Roche) in an AP Biosystems real-time PCR machine was used to perform qPCR [67,68]. Primer sequences used:

yfp (L-GACGACGGCAACTACAAGAC, R-TCCTTGAAGTCGATGCCCTT); pgp-3 (L-CTGTCTGGTGGACAGAAGCA, R-AAGAGCTGACGTGGCTTCAT); alh-12 (L-GCCTTCAAGCTGGAACTGTTT, R-TTGCCTTTGTCTGAGTATGAGC).

4.11. RNAi

To knockdown gene expression, sequence-verified RNAi clones from the Ahringer RNAi library were grown approximately 12 h in LB with 50 µg/mL carbenicillin. Bacteria cultures were 5× concentrated and seeded onto NGM plates containing 5 mM IPTG and 50 µg/mL carbenicillin. Plates were incubated at room temperature for 2 days to induce RNAi. For the L4 parental paradigm, in which RNAi knockdown began in the parental generation, L4 worms were plated on RNAi plates for one day and then transferred to a new plate as gravid adults. After 24 h, the worms were removed from the plates. The resulting progeny from these worms were analyzed. RNAi experiments were conducted at 20 ◦C.

4.12. Experimental Design and Statistical Analysis

All experiments were performed with experimenters blinded to the genotype of the worms. Worms used for experiments were randomly selected from maintenance plates. A minimum of three biological replicates, in which independent populations of worms tested on different days, were performed for each experiment. Automated computer analysis was performed in assays where possible to eliminate potential bias. Power calculations were not used to determine sample size for experiments, since the sample size used in C. elegans studies are typically larger than required for observing a difference that is statistically significant. Three biological replicates were used for measurements of mitochondrial morphology. At least six replicates of ~50 worms each were used for measurements of oxygen consumption. Three biological replicates of ~200 worms each were used for ATP measurements. Three biological replicates of a 60 mm plate of worms were used for mRNA measurements. At least three biological replicates of ~40 worms each were used for the thrashing assays. Three biological replicates of thirty worms each were used for lifespan assays. Six individual worms were used for measuring brood size. Three biological replicates of twenty-five worms each were used to measure post-embryonic development time. GraphPad Prism was used to perform statistical analysis. One-way, two-way, or repeated-measures ANOVA were used to determine statistically significant differences between groups with Dunnett’s or Bonferroni’s multiple comparisons test. For analysis of lifespan, Kaplan-Meier survival plots were graphed, and the Log-rank test was used to determine significant differences between the two groups. This study has no pre-specified primary endpoint. Sample size calculations were not performed.

5. Conclusions

In this study, we showed that a C. elegans neuronal model of polyQ toxicity exhibits deficits in mitochondrial morphology and function, which are associated with decreased

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movement and lifespan. Decreasing the levels of the mitochondrial fission gene drp-1 through genetic deletion or RNAi increases both movement and lifespan in Neur-67Q worms. Similarly, treatment of Neur-67Q worms with RNAi clones that decrease mito- chondrial fragmentation results in increased movement and lifespan. Overall, this work suggests that decreasing mitochondrial fragmentation may be beneficial in treating HD and other polyQ diseases and identifies alternative genetic targets that circumvent the negative effects of disrupting DRP-1. Future studies will be needed to further investigate the mechanisms by which the genes we identified are beneficial and to validate these targets in other models of polyQ diseases.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/ijms222413447/s1.

Author Contributions: Conceptualization, A.T., E.M., P.D.R. and J.M.V.R.; methodology, A.T., E.M., P.D.R., S.K.S. and J.M.V.R.; validation, A.T., E.M., P.D.R., S.K.S. and J.M.V.R.; formal analysis, A.T., E.M., P.D.R., S.K.S. and J.M.V.R.; investigation, A.T., E.M., P.D.R., S.K.S. and J.M.V.R.; writing— original draft preparation, E.M. and J.M.V.R.; writing—review and editing, A.T., E.M., P.D.R., S.K.S., M.M.S. and J.M.V.R.; visualization, A.T., E.M., P.D.R., S.K.S. and J.M.V.R.; supervision, M.M.S. and J.M.V.R. All authors have read and agreed to the published version of the manuscript.

Funding: This work was supported by the Canadian Institutes of Health Research (CIHR; http://www.cihr- irsc.gc.ca/; (JVR), the Natural Sciences and Engineering Research Council of Canada (NSERC; https://www.nserc- crsng.gc.ca/index_eng.asp; (JVR), the National Institute of General Medical Sciences (NIGMS; https://www.nigms.nih.gov/; (JVR) by grant number R01 GM121756 and the Van Andel Research Institute (VARI). JVR is the recipient of a Senior Research Scholar career award from the Fonds de Recherche du Québec Santé (FRQS) and Parkinson Quebec. AT received scholarships from NSERC and FRQS. SKS received a scholarship from FRQS. PDR received a fellowship award from FRQS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: All data is available upon request.

Acknowledgments: Some strains were provided by the Caenorhabditis Genetics Center (CGC), which is funded by the National Institutes of Health (NIH) Office of Research Infrastructure Programs (P40 OD010440). We would also like to acknowledge the C. elegans knockout consortium and the National Bioresource Project of Japan for providing strains used in this research.

Conflicts of Interest: The authors declare no conflict of interest.

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