Integrated multi-omic data analysis and validation with yeast model show oxidative phosphorylation modulates protein aggregation in amyotrophic lateral sclerosis

Abstract Amyotrophic Lateral Sclerosis is a progressive, incurable amyloid aggregating neurodegenerative disease involving the motor neurons. Identifying potential biomarkers and therapeutic targets can assist in the better management of the disease. We used an integrative approach encompassing analysis of transcriptomic datasets of human and mice from the GEO database. Our analysis of ALS patient datasets showed deregulation in Non-alcoholic fatty acid liver disease and oxidative phosphorylation. Transgenic mice datasets of SOD1, FUS and TDP-43 showed deregulation in oxidative phosphorylation and ribosome-associated pathways. Commonality analysis between the human and mice datasets showed oxidative phosphorylation as a major deregulated pathway. Further, protein-protein and protein-drug interaction network analysis of mitochondrial electron transport chain showed enrichment of proteins and inhibitors of mitochondrial Complex III and IV. The results were further validated using the yeast model system. Inhibitor studies using metformin (Complex-I inhibitor) and malonate (Complex-II inhibitor) did not show any effect in mitigating the amyloids, while antimycin (Complex-III inhibitor) and azide (Complex-IV inhibitor) reduced amyloidogenesis. Knock-out of QCR8 (Complex-III) or COX8 (Complex–IV) cleared the amyloids. Taken together, our results show a critical role for mitochondrial oxidative phosphorylation in amyloidogenesis and as a potential therapeutic target in ALS. Communicated by Ramaswamy H. Sarma


Introduction
Amyotrophic Lateral Sclerosis (ALS), often referred to as motor neuron disease is a neuro-muscular disease associated with neurodegeneration and amyloid aggregation (Blokhuis et al., 2013). Diagnosis of ALS remains complicated and heavily relies upon clinical and electrophysiological examinations (Ishpekova & Milanov, 2000). Further, radiological methods are used to negate other neurological conditions (Brooks, 1994). Riluzole is the only drug available worldwide (Clark & Kendall, 1996). The average age of onset of the disease is predicted to be 55-65 years (Nalini et al., 2008). Currently, there are no effective therapies available in reversing the disease progression (Dash et al., 2018).
There are around 126 genes involved in ALS, among which 118 are well curated in literature (Abel et al., 2012). The majority of them are sporadic, while some exhibit familial onset . Mutations in Superoxide dismutase-1 (SOD-1), Tar DNA binding protein (TDP-43), Fused in sarcoma (FUS), and C9orf72 are known to occur in the majority of the familial forms of ALS . Mutations in C9orf72 are observed in almost 34% of ALS cases . Mutation in C9orf72 arises due to hexanucleotide expansion of GGGCC repeats in chromosome-9 (Walker et al., 2017). Mutations in SOD1 are associated with around 20% of familial ALS cases (Niemann et al., 2004). Some commonly observed SOD1 mutations are D90A, A4V, and G93A (Kaur et al., 2016). Mutations like A4V are more lethal while few others provide a much longer life expectancy (Tiwari & Hayward, 2005). FUS (Fused in Sarcoma) accounts for about 7.5% of familial ALS cases (Sun et al., 2011). FUS gene is located on chromosome 16 and encodes a 526 amino acid protein that is involved in DNA repair, RNA splicing, transcriptional regulation, and transport of mRNA from the nucleus to the cytoplasm (Colombrita et al., 2012,Belly et al., 2005. FUS protein is extensively found in the nucleus and mutations lead to cytoplasmic aggregation (Sun et al., 2011). The mechanism by which FUS aggregates is less understood to date (Strong & Volkening, 2011). FUS shows similarity in structure and function with TDP-43 (Strong & Volkening, 2011). TDP-43 is located in chromosome 18 and is expressed ubiquitously in the nucleus and the cytoplasm (Sreedharan et al., 2008). Two commonly observed mutations in patients are A382T and M337V (Chen-Plotkin et al., 2010), while the Q331K represents an aggressive form (Prasad et al., 2019).
'Omic' technologies have helped to understand molecular signatures associated with several neurological disorders (Morello, 2020). Transcriptomics of ALS post-mortem brain has shown severe deregulation of apoptosis, signal transduction, energy metabolism and immune response in the motor cortex region of ALS patients (Aronica et al., 2015). Several proteomic studies have shown dysfunction in metabolic pathways, intracellular transport, cellular stress response and protein clearance mechanism, which might modulate amyloid formation in the affected motor neurons (Hedl et al., 2019). Mutations in FUS and TDP-43 are known to cause severe amyloid aggregates (Blokhuis et al., 2013). ALS phenotype is associated with hyper metabolism (Ferri & Coccurello, 2017). Hyper-metabolism is also known to increase the degeneration of lower motor neurons and the severity of the disease (Ferri & Coccurello, 2017). Metabolomic studies have shown significantly different metabolic profiles across different familial forms of the disease (Lanznaster et al., 2020). However, there is a need to understand the disease from a system biology perspective to elucidate the mechanisms, potential biomarkers and therapeutic targets associated with the disease. Previous studies using multi-omic datasets and cell culture model systems have been used to understand diseases like Glaucoma, Rheumatoid Arthritis and Avascular necrosis (Pulukool et al., 2021;Naik et al., 2020;Krishna et al., 2021).
Herein, we carry out a comparative analysis of different transcriptomic data sets obtained from the GEO (Gene Expression Omnibus) database pertaining to different regions of ALS brain from both patients and mice models of disease. Transcriptomic analysis of muscle from ALS patients was also carried out. Furthermore, the cell type analysis of the gene expression data sets from the human cortex and cerebellum post-mortem sections was carried out. Pathway enrichment analyses pertaining to the frontal cortex, motor cortex, and cerebellum were carried out for the post-mortem sections. Our results show the deregulation of similar pathways across different datasets and study settings. The results obtained from patient data were further compared with transcriptomic data sets of mice model of ALS transgenic for SOD1, FUS and TDP-43. The comparative analysis shows deregulation of similar pathways in patients and model systems. Finally, the yeast model has been used to validate the results using inhibitor studies, knock-out studies and SOD1 expression levels from the transcriptomic data set. The study results show a potential role for oxidative phosphorylation and reactive oxygen species (ROS) in the progression of ALS. Modulating oxidative phosphorylation and ROS might help to reduce amyloid load which in turn might help in better management of ALS.

Materials and methods
Transcriptomic analysis of GEO data sets pertaining to post-mortem sections of cortex, cerebellum and muscle biopsy sections: Gene expression data pertaining to post mortem cortex samples were obtained from GEO dataset GSE124439 (Tam et al., 2019) containing gene expression data of ALS patients and control samples (146 samples þ 16 controls). GSE124439 was analyzed using Agilent Genespring and Network Analyst (www.networkanalyst.ca) Xia et al., 2015;Zhou et al., 2019;. Differential gene expression for GSE67196 (Prudencio et al., 2017, Prudencio et al., 2015(10 samples þ 8 controls) was carried using GREIN (www.ilincs.org/apps/grein) (Al Mahi et al., 2019). Gene set enrichment analysis was carried out using the Kegg database and Network analyst. GEO dataset (GSE41414 (Bernardini et al., 2013)) of muscle biopsies from ALS patients was used for the study. Analysis was carried out using the GEO2R tool that utilizes bio-conductor packages GEO Query and Limma. The volcano plot was made using Origin software. Volcano plots for the differentially expressed genes are presented in Figures 1A and 2A, while heat maps representing significant genes are provided in Supplementary 3. Commonality analysis was carried out to identify common pathways. Common pathways between different datasets were identified using Venny (bioinfogp.cnb.csic.es/tools/venny) (Venny, 2007) and draw custom Venn (bioinformatics.psb.ugent.be/webtools/ Venn). Tissue-specific protein interaction networks and drug interaction networks were constructed for the common pathways using Network Analyst, DifferentialNet Database and Drug Bank. Finally, deconvolution analysis (Cell type analysis) was carried out using Enrichr and Azimuth cell type database.

Pathway enrichment analysis of proteomics datasets:
Proteomic datasets pertaining to the cortex were obtained from the literature (Hedl et al., 2019,Oeckl et al., 2020Chen et al., 2016;Kametani, 2016). Enrichment analysis was carried out using Enrichr (Kuleshov et al., 2016;Xie et al., 2021;Chen et al., 2013) (maayanlab. cloud/Enrichr) and KEGG database. Enrichr carries out analysis using an adjusted P-value(p) of 0.05 and ranking score. The top enriched pathways are represented using a bar diagram.  (Lerman et al., 2012), GSE112629 (Chaprov et al., 2021)). GSEA was performed using Network Analyst. The gene expression counts table for transgenic mice datasets with sample details (meta-data table) were downloaded from the GREIN server. The table obtained was uploaded to Network analyst, and an analysis was carried out. Differential gene expression was carried out using the Deseq2 bio-conductor package. GSEA was further carried out using the same portal. Volcano plots for the differentially expressed genes are presented in Figures 4 and 5, while heat maps representing significant genes are provided in Supplementary 3. The results obtained from transcriptomics of ALS patient cortex sections were also compared with transcriptomic datasets of other common neurodegerative diseases like Multiple Sclerosis brain (GSE84113 (Predeus, 2017)), Parkinson's brain (GSE7621 (Lesnick et al., 2007)), Alzheimer's brain (GSE5281 (Liang et al., 2008)) and Huntington's brain(GSE79666 (Lin et al., 2016), GSE26927 (Durrenberger et al., 2012;Durrenberger et al., 2015)). The details of the datasets, sample size, enrichment tools and common pathways is provided in Supplementary 4.

Commonality analysis:
Common pathways enriched between different familial forms of ALS datasets were obtained by plotting a Venn Diagram using an online tool Venny and Draw custom Venn Diagram (bioinfogp.cnb.csic.es/tools/venny).

Transformation and induction of Saccharomyces cerevisiae with FUS and TDP-43 plasmids:
Wild type and mutant plasmids of FUS (426 Gal-FUS-YFP, 426 Gal-FUS-1-373aa-YFP, 426 Gal-FUS-1-413aa-YFP, 426 Gal-FUS-RRM Mutant-YFP) and TDP-43 (pRS416Gal TDP43 WT YFP and pRS416 Gal Q331K YFP) were obtained from Adgene. Saccharomyces cerevisiae (strain BY4741) was transformed with the above plasmids using standard electroporation protocols (1.5KV/25uF/200X) with the help of a Bio-Rad electroporator. Transformed cells were grown in Himedia URA-YNB-Dextrose-Agar media at 30 C for 3-4 days. After the growth of the transformed colonies, a single colony from each plate was inoculated in Himedia URA-YNB-Dextrose liquid broth. The yeast cells were allowed to grow for 12 hours. After 12 hours, 200 ml of the culture was inoculated in a raffinose medium containing Himedia URA-YNB-raffinose for 12 hours. After 12 hours, the cells were pelleted and washed with Phosphate Buffer Saline (pH 7) and were transferred to Himedia URA-YNB-Galactose media for induction. Cells were allowed to remain in galactose media for 7 hours and were harvested at an OD 600 of 0.8. The induced cells were used for further studies.

Fluorescence imaging and quantification:
Induced Cells were observed under Laben fluorescence microscope at 100X magnification. FUS and TDP-43 protein were tagged with E-YFP, which has an excitation wavelength of 510 nm and emission at 535 nm. The yeast cells were illuminated with a fluorescence LASER, and the images were captured. Fluorescence quantification was carried out using Icy software (icy.bioimageanalysis.org) and Microsoft Excel 2019.
RNA sequencing and measurement of super oxide dismutase levels: mixture was vortexed vigorously and stored at -80 C. RNA isolation was carried out using standard protocols. Library preparation was carried out using the NEB library preparation kit (E7490) for Illumina. Briefly, mRNA was purified using oligo-dT beads from the total RNA. Purified mRNA was subjected to fragmentation at high temperatures and then converted to cDNA. The cDNA fragments are ligated with the adapters and purified to obtain final libraries. Paired-end sequencing was performed using an Illumina Hiseq 2500. The samples had more than 10 million reads. After the quality check using the Fastqc tool, reads were aligned to the reference Saccharomyces cerevisiae S288C strain (Downloaded from The University of California, Santa Cruz (UCSC) genome browser) using bowtie2 (Langmead & Salzberg, 2012) with default parameters. The output from bowtie was converted to a binary file using Samtools (Ramirez-Gonzalez et al., 2012). This binary alignment map file (BAM) was used to generate read counts using bedtools (Quinlan & Hall, 2010). The annotation file used was specific to the Saccharomyces cerevisiae S288C strain downloaded from UCSC. DESeq package from Bioconductor software, R (Love et al., 2016) was used to obtain differentially expressed genes. Significant genes were identified with special reference to the expression levels of SOD1 (Super Oxide Dismutase1) and SOD2 (Super Oxide Dismutase2). The transcriptomic dataset of yeast model of ALS has been deposited in the GEO database (Bio Project ID: PRJNA817798).
Inhibitor and knock-out studies using fluorescence imaging: Inhibitor treatment studies were carried out on Saccharomyces cerevisiae transformed with FUS, TDP-43, and its mutants. Transformed cells were treated with a non-toxic concentration of inhibitors during galactose induction. Fluorescence imaging was carried out after 7 hours of induction along with non-treated controls. Fluorescence quantification was performed using Icy software (icy.bioimageanalysis.org) and Microsoft Excel 2019. Yeast knock-out library was procured from Dharmacon (Product No. YSC1054). Yeast knock-outs pertaining to mitochondrial complex-III (DQCR8) and complex-IV (DCOX8) were used for the study. Knock-outs were transformed, and fluorescence study was carried out as stated above.

Results
Gene set enrichment and pathway analysis of post-mortem brain sections show enrichment of oxidative phosphorylation and NAFLD (non-alcoholic fatty liver disease) Three different transcriptomic data sets of ALS patients pertaining to cortex, cerebellum, and muscles were identified from the GEO database (Supplementary 1). GSEA was carried out on different gene expression datasets pertaining to different postmortem sections. Enrichment of cortex sections showed oxidative phosphorylation, Alzheimer's disease, Parkinson's disease, NAFLD, etc. ( Figure 1B). Enrichment analysis of the cerebellum showed enrichment of NAFLD, glutamatergic synapse, oxidative phosphorylation, Parkinson's disease, etc. ( Figure 2B). Pathway enrichment analysis for proteomic data obtained from the literature pertaining to the cortex was also carried out ( Figure 3A). The results showed enrichment of nicotinate-nicotinamide metabolism, riboflavin metabolism, alanine-aspartate-glutamate metabolism, etc. Finally, commonality analysis for pathways was carried out between gene expression datasets of the cortex, cerebellum and proteomic dataset of the cortex to identify common pathways that are deregulated. Oxidative phosphorylation and NAFLD were found to be common between different cortex sections ( Figure 3B). Gene set enrichment of analysis and pathway enrichment analysis of familial mice brain, spinal cord and motor neuron datasets show enrichment of oxidative phosphorylation, vitamin metabolism, signalling pathways and axonal conduction Mice datasets pertaining to common familial forms of the disease (SOD1, FUS, and TDP-43) were also identified from the GEO database (Supplementary 1). Pathway enrichment analysis and GSEA analysis of SOD1, FUS, and TDP-43 brain, spinal cord and motor neurons were carried out. The results of the individual analysis are summarized in Figures 4 and 5. Commonality analysis was performed to identify common pathways enriched between SOD1, FUS, and TDP-43 brain datasets. Oxidative phosphorylation and ribosome pathways were found to be common between different mice brain and spinal cord datasets ( Figure 6) Oxidative phosphorylation was found to be common between different brain datasets Different brain datasets, including human and mice brains, showed enrichment of oxidative phosphorylation. Protein interaction network analysis was carried out with significant genes clustered into oxidative phosphorylation. Further, protein interaction networks were integrated with Protein drug networks to identify drugs that can act as therapeutic targets. Drugs that have direct activity on mitochondrial complex-III and IV were found to be significantly enriched in the post-mortem section datasets ( Figure 7A and B). Further, cell type analysis was carried out to identify the tissues associated with deregulated pathways. Astrocytes and glutamatergic neurons were found to be significantly affected cell types in the transcriptomic data sets of post-mortem human datasets ( Figure 8A and B). Astrocytes are the cell types which nourish the neurons through the astrocyte-neuron Lactate shuttle (Sun et al., 2020). Dysfunction of the electron transport chain in astrocytes can lead to elevated lactate levels (Newington et al., 2013).

Pathway and cell type analysis of ALS muscle shows deregulation of oxidative phosphorylation in myofibroblast
ALS is a neuro-muscular disease. Hence, the understanding metabolic state of muscle of ALS patients is essential in understanding the disease condition. Pathway enrichment of ALS muscle biopsies showed enrichment of Parkinson's disease, Alzheimer's disease, oxidative phosphorylation, thermogenesis etc. (Figure 9B). Further cell type analysis showed that major metabolic pathways are deregulated in the fibroblast of ALS muscle ( Figure 9C). Based on our earlier observations, it is evident that the mitochondrial oxidative phosphorylation pathway and Complexes-I and Complex-IV, in particular, might be involved in ALS disease parse. Studies from the literature have shown the role of deregulated mitochondrial complexes in generating reactive oxygen species associated with FUS and TDP-43. Hence, we validated our findings from ALS patients and transgenic mice model of ALS using FUS or TDP43 expressing yeast model of ALS. Saccharomyces cerevisiae has been prominently used for studying disease mechanisms, protein misfolding, aggregation, and other neurodegenerative phenotypes (Miller-Fleming et al., 2008). Further, it has been considered as the best model system for studying amyloid diseases (Miller-Fleming et al., 2008). Hence, we used yeast model to validate the role of oxidative phosphorylation pathway on amyloidogenesis.
Inhibitors of mitochondrial electron transport chain and knock out of genes in complex-III and IV confirm their role in modulating aggregation of FUS and TDP-43 Yeast is a very simple and bio-chemically characterized model organism (Tenreiro & Outeiro, 2010).
It has been prominently used for studying disease mechanisms, protein misfolding, amyloid aggregation and other neurodegenerative phenotypes (Pereira et al., 2012). It has been considered as the best model system for studying amyloid diseases like Huntington, Parkinson, ALS etc (Kaminska & Zoladek, 2021). The basic advantage of yeast is its high level of similarity in basic metabolic pathways with human beings and its easy handling. Yeast amyloids show bio-chemical properties similar to that seen in human beings (Miller-Fleming et al., 2008). The main limitation of yeast is its unicellular nature. Hence, it cannot be used for studying complex interactions (Chernova et al., 2019). Absence of a nervous system as such, is a major drawback of yeast model system (Rzepnikowska et al., 2020). The current study focuses on amyloid and associated pathways. FUS and TDP-43 are known to cause aggressive forms of ALS. Hence, yeast was used as a model system to study FUS and TDP-43 induced ALS. Saccharomyces cerevisiae transformed with plasmid DNAs of FUS, TDP-43 and their mutants were used for the study. Transformed cells were treated with inhibitors of mitochondrial complexes ( Figure 10A and B). Complex-I and II inhibitors did not show anti-amyloid activity. However, inhibition of Complex-III and Complex-IV reduced amyloidogenesis. We also treated cells with Co-enzyme Q10, a modulator of complex-III that inhibits ROS production. Treatment with Coenzyme Q10 reduced amyloid aggregation in the majority of mutants. Fluorescence quantification was carried out to quantitate the levels of amyloidogenesis and amyloid clearance (Supplementary 2). The results obtained through quantification had a strong correlation with the imaging data. The results were further validated using knock-out studies. Critical knock-outs of mitochondrial complex III and IV showed reduced amyloidogenesis in most mutants. Complex-IV is also known to be associated with ROS production. SOD1 and SOD2 levels are known to be directly correlated with ROS levels. Hence, SOD1 and SOD2 levels in FUS and TDP-43 transformed yeast cells were measured using the RNA sequencing technique. Our results showed that SOD1 levels were much higher in FUS mutants while they were slightly lesser in TDP-43 mutants ( Figure 10C). Higher ROS levels indicate greater mitochondrial dysfunction, thereby expediting neurodegeneration.

Discussion
Amyotrophic Lateral Sclerosis is an amyloid hyper-metabolic disease involving the brain, spinal cord, and muscles (Tefera & Borges, 2016). However, the metabolic micro-environment depends on numerous extraneous variables such as tissue specificity, region specificity, and patients' life-style. The severity of the disease is also completely dependent on the metabolic homeostasis maintained by the affected regions (Ioannides et al., 2016). We performed transcriptomic analysis of GEO datasets to understand the disease biology by identifying pathways that were deregulated in the post-mortem section of ALS patients and mice model of disease. Oxidative phosphorylation and NAFLD were common between postmortem human brain and spinal cord datasets.
Further cell type analysis shows astrocytes and Glutamatergic neurons as being involved in the disease. Previous studies have shown that the astrocyte-neuron lactate shuttle is affected in many neurodegenerative diseases (Newington et al., 2013). The astrocytes also exhibit an impaired ability to take up glutamate and convert it into glutamine, which is further released back to be taken up by the neurons (Jonnalagadda et al., 2021). This impaired ability to take up glutamate by astrocytes is often ascribed to glutamate-mediated excitotoxicity in many neurodegenerative diseases (Staats & Van Den Bosch, 2014). The proteomic data sets from the brain also showed enrichment of oxidative phosphorylation. Cell type analysis of ALS muscle shows enrichment of myofibroblasts. Myofibroblasts are known to contribute to the disproportionate deposition of connective matrix proteins, which delay tissue repair and contribute to muscle scarring and fibrosis (D'Ambrosi & Apolloni, 2020).
The pathways mitophagy, riboflavin metabolism, nicotinate nicotinamide metabolism, alanine, aspartate and glutamate metabolism, etc., were also enriched in different data sets. Previous studies have shown impaired electron transport chain function and oxidative phosphorylation in ALS (Ladd et al., 2014).
The function of mitochondrial Complex I is known to be compromised in ALS (Ghiasi et al., 2012). Similarly, compromised complex III and complex VI function has been reported in ALS (Xu et al., 2001,Levy, 2006. The compromised ETC lead to elevated ROS, which in turn results in damage to other mitochondria and causes cell death (Smith et al., 2019). In addition, mitophagy was found to be compromised in ALS (Evans & Holzbaur, 2019). Mitophagy is a process that helps to maintain mitochondrial quality by selectively damaged mitochondria. In ALS, the compromised mitophagy leads to the accumulation of damaged mitochondria resulting in elevated ROS (Evans & Holzbaur, 2019).
Further, our analysis also shows a compromised riboflavin pathway. Studies have shown a role for riboflavin in FMN biosynthesis (Evans & Holzbaur, 2019). FMN addition was shown to rescue the yeast model of Alzheimer's Disease (Evans & Holzbaur, 2019). Overall, the results show that improved mitochondrial quality control might mitigate amyloidogenesis in ALS.
Experimental mice datasets of different familial forms of ALS also showed similar results. Having observed the common and conserved oxidative phosphorylation pathway in different data sets, we looked for the deregulated mitochondrial complexes in ALS. Protein interaction networks and disease gene interaction networks showed enrichment of drugs that inhibit Complex-III and Complex-IV (Trumpower & Haggerty, 1980;Kr€ ahenb€ uhl et al., 1994;Pathways, 2020). The drug-protein interaction network also shows cholic acid as a potential drug targeting Complex IV. Complex IV has ten sites for binding ADP of which seven could be competitively replaced by ATP (Ramzan et al., 2021). ATP reduces the activity of complex IV. Cholic acid could substitute for ADP or ATP and could reduce complex IV activity. Under normal conditions, complex IV activity depends on ATP-ADP ratio (Ramzan et al., 2021). The results show a potential role for Complex III and IV hyperactivity in disease progression. Hence, yeast model system was used to evaluate the effect of inhibitors of complex-III and complex-IV on amyloid formation.
Mitochondrial complexes in ETC contribute to ROS generation (Emerit et al., 2004). In addition, the enzymes of TCA cycle aconitase, a-ketoglutarate dehydrogenase, pyruvate dehydrogenase, glycerol-3-phosphate dehydrogenase, dihydroorotate dehydrogenase, the monoamine oxidase A and B, and cytochrome B5 reductase also contribute to ROS (Angelova & Abramov, 2018). Studies have shown that compromised function of complex I lead to elevated levels of ROS (Gandhi & Abramov, 2012). Complex I related disease mutations lead to increased ROS production and oxidative stress (Abramov et al., 2010). Supplementation of NAD þ or intermediates in its biosynthesis was shown to mitigate symptoms and was found to be beneficial in mitochondrial and neurodegenerative diseases (Pfister et al., 2008). Increased complex II activity can lead to reverse electron transfer from complex II, resulting in increased ROS production (Ranganayaki, 2021). The primary production of ROS in mitochondria takes place in Complex III, followed by complex I and II, and to a lesser extent in complex IV (Li et al., 2013). The site production in complex I is the Q reduction site, while in the case of complex III, it is the QH2 -cytochrome c reductase site (Turrens, 2003).
Further, complex IV is also capable of producing ROS, and complex IV dysfunction is associated with many diseases (Das et al., 2021). To reiterate the importance of mitochondrial ETC in amyloidogenesis, we employed inhibitors and modulators of ETC and gene knock-out studies. Our results showed that azide, an inhibitor of complex IV, attenuated amyloidogenesis in both FUS and TDP 43 yeast models of ALS. The results were validated by inhibitor and knock-out studies that were carried out using our model systems.
Yeast knock-out of COX-8 (Knock-out of cytochrome C oxidase) which is a part of complex IV, was used for the study. COX-8 knock-out showed a reduction in amyloids. Dysregulation of complex IV is observed in several neurodegenerative diseases. Dysfunction of complex IV genes lead to the production of excessive quantities of ROS, which can lead to the progression of neurodegeneration in ALS patients. QCR8 is involved in the electron transfer from ubiquinol to cytochrome c. The ubiquinol binding site in complex III is a major ROS-producing site (Cardol et al., 2009). Knockout of QCR8 leads to attenuation of ETC function (Lee et al., 2001). QCR8 knock-out cells also have increased ubiquinone. The increased ubiquinone could be reduced by complex I and II (He et al., 2017). Knock-out of COX8 in yeast was shown to reduce the activity of complex IV. The reduced activity of COX4 is shown to reduce ROS production with potentially favourable consequences (Patterson & Poyton, 1986). Knock-out of COX5A or 5B is also known to drop oxygen consumption significantly (Gandhi & Abramov, 2012). Previous studies have shown that TDP-43 induced cell death through apoptosis or necrosis in the yeast model of ALS is dependent on ETC function (Braun et al., 2011). Co-enzyme Q10 is known to mitigate disease severity and increase life span in the mice model of ALS (Beal, 2004). Consistent with this, our study also shows coenzyme Q10 reduced amyloid load in yeast model. However, Coenzyme Q10 was not found to be effective in clinical trials (Wang et al., 2019). Our Multiomic analysis shows a potential role for Co-enzyme Q 10 , ROS scavengers like glutathione, and metabolic pathways associated with the disease.
Oxidative phosphorylation is also known to be affected in several other neurodegenerative diseases (Singh et al., 2019). Our transcriptomic analysis across different datasets of Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Huntington's Disease (HD), Alzheimer's Disease (AD) and Parkinson's Disease (PD) conjured oxidative phosphorylation as a common pathway (Supplementary 4). Though oxidative phosphorylation is conjured as a common pathway, different mitochondrial complexes are affected across different neurodegenerative diseases. Mitochondrial complex I is known to be affected in Parkinson's disease (PD) (Greenamyre et al., 2001). Mitochondrial complex II and III deficiency is observed in Huntington disease (HD) (Schapira, 1998). Mitochondrial complex IV and V are known to be altered in Alzheimer's disease (AD) (Kawamata & Manfredi, 2017). Detailed comparison between mitochondrial complexes that are involved ALS and other neurodegenerative diseases is provided in (Supplementary 4). Further, ROS generation and reduced ROS scavengers are key players for the progression of several neurodegenerative diseases. Taken together, mitochondrial dysfunction is one of the common features of neurodegenerative diseases albeit differences in genes or functional modules.
ALS is associated with oxidative stress (Singh et al., 2019). The increased ROS production leads to the activation of pathways to neutralize it. This in turn, leads to the activation of glutathione and other pathways involved in ROS scavenging (Dwivedi et al., 2020). Consistent with this observation, analysis of SOD1 expression levels in transcriptomic datasets of the majority of disease mutant yeast model of ALS was significantly higher. SOD1 levels are often correlated with ROS levels in literature. Therefore, scavengers of ROS can be potential therapeutic agents in ALS disease biology. Oxidative phosphorylation was also enriched in fibroblasts of ALS muscle. Our studies conclusively pinpoint the role of oxidative phosphorylation and ROS in ALS brain and muscle (Figure 11). The above finding can have implications for understanding mechanisms associated with disease, potential biomarkers associated progression and prognosis as well as potential therapeutic targets in ALS.

Conclusion
Our results of transcriptomic data sets from ALS patients and transgenic mice models of ALS (SOD1, TDP43 and FUS) show deregulation of oxidative phosphorylation. Gene expression analysis of GEO datasets shows the deregulation of genes belonging to mitochondrial complex III and IV. FUS and TDP-43 expressing yeast show changes in oxidative stress marker expression levels (SOD1). Mitochondrial complex I inhibitor (metformin) did not have any effect on protein aggregation, while inhibitor of complex II (malonate) did not have any impact in mitigating amyloids. Inhibitors of complex III (Antimycin) and complex IV (Azide) significantly reduced amyloid levels. Knock out of QCR8 and COX8, which are subunits of complex III and IV respectively, significantly reduced amyloid levels. Overall, our results show a critical role for oxidative phosphorylation involving complex III and IV in protein aggregation.
Further, reactive oxygen levels can be a major contributor to the progression of the disease condition. Our study suggests Complex III and IV as potential therapeutic targets and the use of mitochondrial Complex III and IV inhibitors, ROS scavengers and Co-enzyme Q10 as potential therapeutic agents, which might help to reduce the severity of the disease. Mitochondria might emerge as a potential therapeutic target in ALS. Further, reactive oxygen levels can be a significant contributor to the progression of the disease condition.

Author's contribution
The study was equally contributed by S.S.R and A.P.S. S.S.P and B.P helped in standardizing and processing several bio-informatic pipelines used for the study. B.P also contributed by providing "Agilent Genespring software "used for the study. R.R.K and M.M performed the transcriptomic analysis and assisted in analysis of the results. B.C helped in analysis and interpretation of transcriptomic yeast datasets. S.V conceptualized the entire idea, interpreted the results and played a major role in the preparation of manuscript.