Network clustering revealed the systemic alterations of mitochondrial protein expression

Transcriptome profiling for developmental stages Protaetia brevitarsis seulensis with focus on wing development and metamorphosis

A white-spotted flower chafer Protaetia brevitarsis seulensis widely distributed in Asian countries is traditionally used in oriental medicine. This study explored gene expression abundance with respect to wing development and metamorphosis in P. b. seulensis based on the large-scale RNA-seq data. The transcriptome assembly consists of 23,551 high-quality transcripts which are approximately 96.7% covered. We found 265 wing development genes, 19 metamorphosis genes, and 1,314 candidates. Of the 1,598 genes, 1,594 are included exclusively in cluster 4 with similar gene co-expression patterns. The network centrality analyses showed that wing development- and metamorphosis-related genes have a high degree of betweenness centrality and are expressed most highly in eggs, moderately in pupa and adults, and lowest in larva. This study provides some meaningful clues for elucidating the genetic modulation mechanism of wing development and metamorphosis in P. b. seulensis.

Network-based machine learning approach to predict immunotherapy response in cancer patients

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

Cell-penetrating artificial mitochondria-targeting peptide-conjugated metallothionein 1A alleviates mitochondrial damage in Parkinson's disease models

An excess of reactive oxygen species (ROS) relative to the antioxidant capacity causes oxidative stress, which plays a role in the development of Parkinson’s disease (PD). Because mitochondria are both sites of ROS generation and targets of ROS damage, the delivery of antioxidants to mitochondria might prevent or alleviate PD. To transduce the antioxidant protein human metallothionein 1A (hMT1A) into mitochondria, we computationally designed a cell-penetrating artificial mitochondria-targeting peptide (CAMP). The recombinant CAMP-conjugated hMT1A fusion protein (CAMP-hMT1A) successfully localized to the mitochondria. Treating a cell culture model of PD with CAMP-hMT1A restored tyrosine hydroxylase expression and mitochondrial activity and reduced ROS production. Furthermore, injection of CAMP-hMT1A into the brain of a mouse model of PD rescued movement impairment and dopaminergic neuronal degeneration. CAMP-hMT1A delivery into mitochondria might be therapeutic against PD by alleviating mitochondrial damage, and we predict that CAMP could be used to deliver other cargo proteins to the mitochondria.

A peptide targeting mitochondria can help deliver an antioxidant protein to mitigate the effects of Parkinson’s disease in cellular and mouse models. Youngmi Pak from Kyung Hee University, Seoul, South Korea, and co-workers engineered bacteria to express the human version of an antioxidant protein called metallothionein 1A fused to a short peptide sequence so that it localizes to mitochondria, the cellular power plants. Once inside the mitochondria, the peptide is removed, leaving the mature antioxidant protein to mop up damaging free radicals, a common problem seen in the cells of patients with Parkinson’s disease, and restore mitochondria to a healthier state. The protein improved mitochondrial function in both a human cell line and in the neurons of mice with a Parkinson’s-like disease, suggesting it might also help patients with this devastating neurological condition.

Transcriptome profiling of whisker follicles in methamphetamine self-administered rats

Methamphetamine (MA) is a highly addictive psychostimulant that disturbs the central nervous system; therefore, diagnosis of MA addiction is important in clinical and forensic toxicology. In this study, a MA self-administration rat model was used to illustrate the gene expression profiling of the rewarding effect caused by MA. RNA-sequencing was performed to examine changes in gene expression in rat whisker follicles collected before self-administration, after MA self-administration, and after withdrawal sessions. We identified six distinct groups of genes, with statistically significant expression patterns. By constructing the functional association network of these genes and performing the subsequent topological analysis, we identified 43 genes, which have the potential to regulate MA reward and addiction. The gene pathways were then analysed using the Reactome and Knowledgebase for Addiction-Related Gene database, and it was found that genes and pathways associated with Alzheimer's disease and the heparan sulfate biosynthesis were enriched in MA self-administration rats. The findings suggest that changes of the genes identified in rat whisker follicles may be useful indicators of the rewarding effect of MA. Further studies are needed to provide a comprehensive understanding of MA addiction.

miR-24-mediated knockdown of H2AX damages mitochondria and the insulin signaling pathway

Mitochondrial deficits or altered expressions of microRNAs are associated with the pathogenesis of various diseases, and microRNA-operated control of mitochondrial activity has been reported. Using a retrovirus-mediated short-hairpin RNA (shRNA) system, we observed that miR-24-mediated H2AX knockdown (H2AX-KD) impaired both mitochondria and the insulin signaling pathway. The overexpression of miR-24 decreased mitochondrial H2AX and disrupted mitochondrial function, as indicated by the ATP content, membrane potential and oxygen consumption. Similar mitochondrial damage was observed in shH2AX-mediated specific H2AX-KD cells. The H2AX-KD reduced the expression levels of mitochondrial transcription factor A (TFAM) and mitochondrial DNA-dependent transcripts. H2AX-KD mitochondria were swollen, and their cristae were destroyed. H2AX-KD also blocked the import of precursor proteins into mitochondria and the insulin-stimulated phosphorylation of IRS-1 (Y632) and Akt (S473 and T308). The rescue of H2AX, but not the nuclear form of ΔC24-H2AX, restored all features of miR-24- or shH2AX-mediated impairment of mitochondria. Hepatic miR-24 levels were significantly increased in db/db and ob/ob mice. A strong feedback loop may be present among miR-24, H2AX, mitochondria and the insulin signaling pathway. Our findings suggest that H2AX-targeting miR-24 may be a novel negative regulator of mitochondrial function and is implicated in the pathogenesis of insulin resistance.

Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks

Module identification is a frequently used approach for mining local structures with more significance in global networks. Recently, a wide variety of bilayer networks are emerging to characterize the more complex biological processes. In the light of special topological properties of bilayer networks and the accompanying challenges, there is yet no effective method aiming at bilayer module identification to probe the modular organizations from the more inspiring bilayer networks. To this end, we proposed the pseudo-3D clustering algorithm, which starts from extracting initial non-hierarchically organized modules and then iteratively deciphers the hierarchical organization of modules according to a bottom-up strategy. Specifically, a modularity function for bilayer modules was proposed to facilitate the algorithm reporting the optimal partition that gives the most accurate characterization of the bilayer network. Simulation studies demonstrated its robustness and outperformance against alternative competing methods. Specific applications to both the soybean and human miRNA-gene bilayer networks demonstrated that the pseudo-3D clustering algorithm successfully identified the overlapping, hierarchically organized and highly cohesive bilayer modules. The analyses on topology, functional and human disease enrichment and the bilayer subnetwork involved in soybean fat biosynthesis provided both the theoretical and biological evidence supporting the effectiveness and robustness of pseudo-3D clustering algorithm.

Spatiotemporal positioning of multipotent modules in diverse biological networks

A biological network exhibits a modular organization. The modular structure dependent on functional module is of great significance in understanding the organization and dynamics of network functions. A huge variety of module identification methods as well as approaches to analyze modularity and dynamics of the inter- and intra-module interactions have emerged recently, but they are facing unexpected challenges in further practical applications. Here, we discuss recent progress in understanding how such a modular network can be deconstructed spatiotemporally. We focus particularly on elucidating how various deciphering mechanisms operate to ensure precise module identification and assembly. In this case, a system-level understanding of the entire mechanism of module construction is within reach, with important implications for reasonable perspectives in both constructing a modular analysis framework and deconstructing different modular hierarchical structures.

Mitochondrial proteome remodeling in ischemic heart failure

AIMS

Mitochondrial dysfunction is an important part of the decline in cardiac function in heart failure. We hypothesized for hypothesized that there would be specific abnormalities in mitochondrial function and proteome with the progression of ischemic heart failure (HF).

MAIN METHODS

We used a high left anterior descending artery (LAD) ligation in 3-4month old male rats to generate HF. Rats were studied 9weeks post-ligation.

KEY FINDINGS

Electron microscopy of left ventricle samples showed mitochondrial changes including decreased size, increased number, abnormal distribution, and cristae loss. Mitochondria in ischemic HF exhibited decreased total ATP, impaired mitochondrial respiration, as well as reduced complex I activity. Analysis of LV mitochondrial proteins by mass spectrometry was performed, and 31 differentially expressed proteins (p<0.05) of more than 500 total proteins were identified. Of these proteins, 15 were up-regulated and 16 were down-regulated in the failing heart. A set of complex I proteins was significantly decreased, consistent with the impairment of complex I activity. There were distinct changes in mitochondrial function and proteome in ischemic HF. Although there were similarities, the distinction between the reported proteomic changed with TAC pressure overload induced HF and ischemic HF in the current study suggested different pathological mechanisms.

SIGNIFICANCE

Specific changes in mitochondrial protein expression, which correlate with changes in mitochondrial function, have been identified in ischemic HF for the first time.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Glucocorticoids and their receptors: insights into specific roles in mitochondria

Glucocorticoids (GCs) affect most physiological systems and are the most frequently used drugs for multiple disorders and organ transplantation. GC functions depend on a balance between circulating GC and cytoplasmic glucocorticoid receptor II (GR). Mitochondria individually enclose circular, double-stranded DNA that is expressed and replicated in response to nuclear-encoded factors imported from the cytoplasm. Fine-tuning and response to cellular demands should be coordinately regulated by the nucleus and mitochondria; thus mitochondrial-nuclear interaction is vital to optimal mitochondrial function. Elucidation of the direct and indirect effects of steroids, including GCs, on mitochondria is an important and emerging field of research. Mitochondria may also be under GC control because GRs are present in mitochondria, and glucocorticoid response elements (GREs) reside in the mitochondrial genome. Therefore, mitochondrial gene expression can be regulated by GCs via at least two different mechanisms: direct action on mitochondrial DNA and oxidative phosphorylation (OXPHOS) genes, or by an indirect effect through interaction with nuclear genes. In this review, we outline possible mechanisms of regulation of mitochondrial genes in response to GCs in view of translocation of the GR into mitochondria and the possible regulation of OXPHOS genes by GREs in the mitochondrial genome.