You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression}
Article:
Abstract:
The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA( mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins( mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted( ρ0) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ0 cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions.
Introduction:
Proteomics and expression profiling have been widely applied to understand the cellular processes[1]. Proteins with significant changes in expression have particular interest as markers for various diseases and cellular phenotypes[2]. While there are useful information encoded in the list of differentially expressed proteins, identifying the molecular mechanism of cellular processes from the long list of candidate proteins is challenging[3]. Data-integrative approaches have been successfully applied to address the challenge of interpreting lists of differentially expressed proteins by mapping them onto the protein functional network[4],[5]. A protein functional network describes the functional and physical associations among proteins and provides a framework for understanding how individual protein work together to perform critical cellular functions and how protein compositions respond to changes in cellular environments[6]. Proteins rarely act alone but rather interact with other proteins and comprise specific functional modules in the network[7]. Functional module is a group of proteins which are related by one or more cellular and genetic interactions such as co-regulation, co-expression, and the member of a biological pathway or a protein complex[8]. Such module is the building block of cellular organization and carries out unique biological process[9]. Therefore, understanding the modular structure of protein functional network should be useful for characterizing the dynamic organization of cellular systems. In eukaryotic cells, mitochondria are involved in many cellular processes including energy production, apoptosis, ion homeostasis, and the metabolism of glucose, lipids, and amino acids[10]. Although mitochondria possess their own DNA, it is estimated that at least 98% of the 1, 500–2, 000 mitochondrial proteins are encoded by nuclear genes and shuttled posttranslationally into the mitochondria[11],[12]. In addition, the majority of human mt disorders are known to be related with nuclear genome defects[13]. Thus, compiling a comprehensive list of mt proteins is essential to understand mitochondrial biogenesis and pathology. Large-scale approaches such as mass spectrometry( MS)-based proteomics[14], epitope tagging combined with microscopy[15], genome-wide predictions of protein subcellular localizations[16], and comparative genomics analyses[17],[18],[19] have revealed the localization of the mt protein inventory. The mt protein inventory is dynamically changed depending on the cellular state, tissue type, and species[11],[20]. For example, the compositions of mt proteins are different across various tissues and organs in mice[21],[22] and changed by fermentation states in yeast[18] or environmental stresses in plants[23]. Additionally, in humans, dynamic changes in the mt proteome affect the functional organizations of mt proteins and disease susceptibilities[24]. Genetic or biochemical abnormalities in mitochondria caused by complete or partial mtDNA depletion have been linked to a wide range of human diseases including metabolic syndrome, neurodegenerative disorders, mitochondrial myopathy, and cancer[12],[25]. We have previously reported that mtDNA depletion can lead to impairments of glucose metabolism[26], insulin signaling[27], and apoptosis[28]. However, the changes of the mt protein functional network in response to mt dysregulation remain to be revealed. Here, we investigated the systemic alterations of human mt protein functional network under normal and dysfunctional mt states through a data-integrative computational biology approach and quantitative proteomic analysis. Specifically, a systematic data-integrative analysis was devised to evaluate the reliability of mt proteomics data and cluster the identified proteins into the modules of mt protein functional network. Our results revealed that human mt proteins comprise specific network modules to control unique biological processes in cells exposed to dysfunctional mt states. Furthermore, network clustering suggests that cells respond to pathological conditions by modulating the coordinated expression and transport of mitochondrial proteins. We believe that our results may provide critical information to gain better understanding of mitochondria function in the cell.
Discussion:
Mitochondrial dysfunction caused by mtDNA damage is involved in many diseases and is likely to be a driving force behind aging and apoptosis[34]. Here, we investigated the systemic alterations of mt protein expressions by using a data-integrative network analysis. Respiratory-deficient ρ0 cells have been studied to characterize retrograde signaling, which is a controlling mechanism of information flow from the mitochondria to the nucleus and cytoplasm[35], but the systems property of dysfunctional mt state has not been studied extensively. Through network clustering analysis, we discovered that cells respond to pathological conditions by modulating the coordinated expression/transport of mt proteins. Using a network analysis of proteomics data, we were able to find modules reflecting differentially regulated functions between normal and dysfunctional mt states. We found that up-regulated and down-regulated proteins of dysfunctional mt states were organized into two predominant subnetworks that exhibited distinct biological processes. It has been suggested that proteins cooperate with other proteins as a part of functional module which tend to physically associated or share similar expressions to deliver a certain biological function[36],[37]. Network analyses of molecular pathways or complexes elucidated the collective behavior of differentially expressed proteins and provided complementary information to conventional single gene-based analysis which routinely performed in proteomic analyses[38],[39]. We discovered that not only the relevant proteins changed their expression under dysfunctional mt state, but also the subnetworks composed of multiple functional protein groups changed their expression cooperatively to regulate biological processes. These network-module alterations are particular importance for relating an altered phenotype with dysfunctional mt state at molecular level because phenotypic alterations are more closely related with pathway remodeling than individual gene expression changes[38]. We found that functional modules controlling mt protein translation, folding, proteolysis, and mtDNA repair were up-regulated in the dysfunctional ρ0 state( Figure 3). To properly respond to changes in cellular states, these processes may require retrograde regulation[35]. Retrograde signaling regulates many cellular activities under pathophysiological conditions by changing the protein inventories of subcellular organelles. It may be possible that up-regulated mt proteins shuttle between the mitochondria and other organelles for intracellular communication[40]. Indeed, we found that the proteins of up-regulated functional modules tended to have multiple annotations of subcellular localization and to be involved in processes occurring inside and outside of mitochondria( Table S1). For example, AP-endonuclease 1( APEX1) of the DNA repair module is known to shuttle from mitochondria to the nucleus in response to oxidative stress[41]. Heterogeneous nuclear ribonucleoprotein K( HNRPK), a member of the ribonucleoprotein complex associated with mtDNA transcription, has been detected in the nucleus, cytoplasm, and mitochondria[42]. These observations suggest that up-regulated functional modules act as cross-talk components connecting mitochondria with other organelles to sense and propagate the retrograde signal upon mtDNA depletion stress to facilitate mitochondrial survival. We observed that up-regulated and down-regulated functional modules contain a small fraction of proteins with opposite protein expression patterns( Figure 3). On average, levels of 5. 5% of the proteins in down-regulated functional modules were increased, while levels of 4. 7% of the proteins in up-regulated functional modules were decreased under the dysfunctional ρ0 state. Proteins with opposite expression patterns in the functional module could act as negative regulators of the module[43]. For example, in the TCA cycle module, acetolactate synthase( AHAS) was up-regulated, while other proteins were down-regulated. It has been reported that upregulation of AHAS decreases the activity of the TCA cycle by reducing pyruvate flow into the cycle[44]. In addition, ATP6V1B2 and ATP6V1G1 in the ATP synthesis module were up-regulated, while other proteins were down-regulated. Upregulation of these proteins is known to participate in ATP hydrolysis, leading to the downregulation of ATP synthesis[45]. Moreover, two heat shock proteins, HSP90AA1 and HSP90AB1, involved in protein folding on the mt surface were down-regulated, while other proteins were up-regulated. The heat shock proteins regulate chaperone activity in response to ATP concentrations in the cell[46]. Under the dysfunctional ρ0 state, ATP reduction could induce the disruption of HSP90 chaperone activity. Thus, we suspect that oppositely expressed proteins may compensate for the function of other proteins in the same modules. The expression of up- and down-regulated proteins was controlled in several different levels. From the comparisons between protein and mRNA abundance under the dysfunctional ρ0 state, we found that most up- and down-regulated mt proteins had increased mRNA expression levels( Figure 4). This indicated that up-regulated proteins were successfully recruited into ρ0 mitochondria. It has been proposed that there is a compensatory mt protein import pathway independent from the dissipated membrane potential[47]. This pathway is known to facilitate the translocations of proteins involved in mt protein folding and mtDNA repair to the mitochondria[48]. It implies that “emergency” proteins are increasingly imported to dysfunctional ρ0 mitochondria to repair pathophysiological mt conditions. The cross-talk properties of up-regulated functional modules support this idea. One might expect that protein abundances in cytoplasm and mitochondria to be correlated with mRNA expression levels. However, we found that the functional modules of down-regulated proteins have up-regulated mRNA levels, suggesting that down-regulated proteins might experience difficulties in protein import into ρ0 mitochondria. To understand the differences of mRNA and protein expression of those proteins, we examined the levels of protein expression in ρ0 cytoplasm and mitochondria by using western blot analyses. The tested proteins, GLUD1, GTPBP3, ATP5A1, TFAM, and NDUFS6, were selected from each down-regulated functional module. We found that the cytoplasmic protein levels of GLUD1( amino acid metabolism), GTPBP3( tRNA metabolism), and ATP5A1( ATP synthesis) were increased, whereas the mitochondrial levels of those proteins were found to be decreased( Figure S3), suggesting that those proteins remained in the cytoplasm and did not properly transport into mitochondria. Meanwhile, both cytoplasmic and mitochondrial protein levels of TFAM( mtDNA replication) and NDUFS6( electron transport) were decreased( Figure S3). It might be possible that these proteins degraded more rapidly than synthesized in ρ0 cell, consequently showed down-regulated protein expression levels. Mitochondrial protein compositions change to remodel the organization of mt protein functional networks in response to changes in cellular states. Analysis of mt protein functional networks elucidated the biological implications of mt regulatory mechanisms under dysfunctional mt states. Our efforts of systematic data-integrative analysis to evaluate the reliability of proteomics data and to identify important functional modules of mitochondrial proteins can be valuable to computational biology community working on gene expression and proteomics analysis. First, we applied a data-integrative approach to select protein list by using various databases, proteomics datasets, and protein functional network. Mitochondrial proteins were organized into functional modules to identify significantly altered biological processes under different cellular states. The framework of our systematic data-integrative analysis may be useful to reliable proteome analyses for other cellular systems. Second, organizing the thousand mitochondrial proteins into groups of up- or down-regulated ones and identifying functional modules are necessary steps in getting an in-depth understanding of the complex molecular mechanism of mitochondria. Third, mapping both mitochondrial proteins and mRNA expression information together is critical to understand the cooperative expression regulation of mitochondrial functional modules. Such multi-dimensional data analysis can be a valuable asset to develop novel system level models and methods. Also, experimental biologists can utilize our dataset as a resource for target selection to elucidate regulatory mechanisms of mt proteins under dysfunctional mt states.
Mitochondria are dynamic organelles that are essential for energy production and cellular processes in eukaryotic cells , and their functional failure is a major cause of age-associated degenerative diseases . To meet the specific needs of different cellular states , mitochondrial protein repertoires are adjusted . It is critical to characterize the systemic alterations of mitochondria to different cellular states to understand the dynamic organization of mitochondrial systems . In this study , we modularized the quantified proteomics data into protein functional networks to characterize gene expression changes under dysfunctional mitochondrial conditions . Our results demonstrate that mitochondrial protein repertoires changed to compensate for dysfunctional cellular states by reorganizing mitochondrial protein functional network . Through network clustering analysis , we discovered that cells respond to pathological conditions by modulating the coordinated expression/transport of mitochondrial proteins . Network analysis of mt proteins can advance our understanding of dysfunctional mitochondrial systems and elucidate the candidate mt proteins involved in human mitochondrial diseases .