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Critical review rubric

Length

minimum of 8 pages, maximum of 10 pages (excluding bibliography and supporting figures in page count)

Voice

tone and style appropriate to audience; paper displays control, variety and complexity of prose; written in a professional manner; uses clear transitions that connect sentences and paragraphs; each paragraph has a topic sentence (typically the first sentence of each paragraph); entirely written in past tense

Sections

Clear sections, including an introduction and Conclusion; subheadings that help reader follow organization (eg Introduction, Methods, Results, Discussion of the evaluated articles; Introduction, Analysis and Conclusion for your paper); set apart from other text through an increase in font and bolding

US English use

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Content

Introduction

Opening

Citation

cites each analyzed article completely and set off with bullet points and thereafter refers to each article by first author’s last name and publication year; at least one supporting reference used

Summary

summarizes the 3 analyzed articles: author’s purpose; major methods used to accomplish purpose; what evidence obtained in support of author’s objectives; interpretation of results

Body, Analysis

Overall

follows the structure of the journal articles; evaluates each section of the article; highlight strengths and weakness of each section; evaluates each section thoroughly according to the points below; compares and contrasts articles to one another

Introduction

title of article appropriate; abstract statement of purpose and introduction of paper match; objectives/hypotheses of studies given; is the information given logically so that it builds to the stated objectives/hypotheses; compares and contrasts articles to one another

Methods

methods valid; enough detail given that could be repeated; are there flaws (sample selection, experimental design); flow logical and details pertinent; compares and contrasts articles to one another

Results

titles/legends of tables and figures accurate; data organization easy to interpret; text complements but does not repeat table/figure information; discrepancies between text and figures/tables; results test objectives/hypotheses;; compares and contrasts articles to one another

Discussion

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Overview

discusses if the abstract accurately summarize article, structure of reviwed articles appropriate and divided logically; stylistic concerns; compares and contrasts articles to one another

Conclusion

Summary

sums up the strengths and weaknesses of each article; compares and contrasts articles to one another

Significance

establishes practical and theoretical significance of body of work; has your chosen article been cited by others; did your articles spark other researches hypotheses or questions; are there any practical applications; implication (social, political, technological, medical) to the research; cites at least one other supporting reference (unique from introduction)

LIterature cited

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one journal format chosen and used throughout in bibliography and in-text citations

Subject

Chosen articles were all on the same topic; topic was specific enough so that an analysis was possible

Citation

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quantity

minimum of 5, 1 unique to intro, 1 unique to discussion and 3 crtically reviewed

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

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 2 / 21

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

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 3 / 21

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.

https://doi.org/10.1371/journal.pone.0223014.g002

Mitochondrial fragmentation in degenerative diseases

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 4 / 21

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 ?X1
!
a1

b1

X2;

X1 ?X2
!
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

PLOS ONE | https://doi.org/10.1371/journal.pone.0223014 September 26, 2019 5 / 21

<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.

https://doi.org/10.1371/journal.pone.0223014.g003

Mitochondrial fragmentation in degenerative diseases

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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

? 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 disea

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