Spatial and functional organization of mitochondrial protein network

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.

Identifying biomarkers for response to immunotherapy in cancer remains challenging. Here, the authors develop an approach based on network biology and machine learning -NetBio- to identify molecular biomarkers of response to immunotherapy across different cancer types and cohorts.

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.

Mitochondrial fragmentation and network architecture in degenerative diseases

Fragmentation of mitochondrial network has been implicated in many neurodegenerative, renal, and metabolic diseases. However, a quantitative measure of the microscopic parameters resulting in the impaired balance between fission and fusion of mitochondria and consequently 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 Alzheimer's disease (AD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), optic neuropathy (OPA), diabetes/cancer, acute kidney injury, Ca2+ overload, and Down Syndrome (DS) pathologies that indicates significant network fragmentation in all these conditions. Furthermore, we found key differences in the way the microscopic rates of fission and fusion are affected in different conditions. The observed fragmentation in cells with AD, HD, DS, kidney injury, Ca2+ overload, and diabetes/cancer pathologies results from the imbalance between the fission and fusion through lateral interactions, whereas that in OPA, PD, and ALS results from impaired balance between fission and fusion arising from longitudinal interactions of mitochondria. Such microscopic difference 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 different diseases.

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

Specific changes in mitochondrial lipidome alter mitochondrial proteome and increase the geroprotective efficiency of lithocholic acid in chronologically aging yeast

We have previously found that exogenously added lithocholic acid delays yeast chronological aging. We demonstrated that lithocholic acid enters the yeast cell, is sorted to mitochondria, resides in both mitochondrial membranes, changes the relative concentrations of different membrane phospholipids, triggers changes in the concentrations of many mitochondrial proteins, and alters some key aspects of mitochondrial functionality. We hypothesized that the lithocholic acid-driven changes in mitochondrial lipidome may have a causal role in the remodeling of mitochondrial proteome, which may in turn alter the functional state of mitochondria to create a mitochondrial pattern that delays yeast chronological aging. Here, we test this hypothesis by investigating how the ups1?, ups2? and psd1? mutations that eliminate enzymes involved in mitochondrial phospholipid metabolism influence the mitochondrial lipidome. We also assessed how these mutations affect the mitochondrial proteome, influence mitochondrial functionality and impinge on the efficiency of aging delay by lithocholic acid. Our findings provide evidence that 1) lithocholic acid initially creates a distinct pro-longevity pattern of mitochondrial lipidome by proportionally decreasing phosphatidylethanolamine and cardiolipin concentrations to maintain equimolar concentrations of these phospholipids, and by increasing phosphatidic acid concentration; 2) this pattern of mitochondrial lipidome allows to establish a specific, aging-delaying pattern of mitochondrial proteome; and 3) this pattern of mitochondrial proteome plays an essential role in creating a distinctive, geroprotective pattern of mitochondrial functionality.

Exploiting mitochondria as targets for the development of new antifungals

Mitochondria are essential for cell growth and survival of most fungal pathogens. Energy (ATP) produced during oxidation/reduction reactions of the electron transport chain (ETC) Complexes I, III and IV (CI, CIII, CIV) fuel cell synthesis. The mitochondria of fungal pathogens are understudied even though more recent published data suggest critical functional assignments to fungal-specific proteins. Proteins of mammalian mitochondria are grouped into 16 functional categories. In this review, we focus upon 11 proteins from 5 of these categories in fungal pathogens, OXPHOS, protein import, stress response, carbon source metabolism, and fission/fusion morphology. As these proteins also are fungal-specific, we hypothesize that they may be exploited as targets in antifungal drug discovery. We also discuss published transcriptional profiling data of mitochondrial CI subunit protein mutants, in which we advance a novel concept those CI subunit proteins have both shared as well as specific responsibilities for providing ATP to cell processes.

Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts

Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew’s correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.

Mechanisms by which different functional states of mitochondria define yeast longevity

Mitochondrial functionality is vital to organismal physiology. A body of evidence supports the notion that an age-related progressive decline in mitochondrial function is a hallmark of cellular and organismal aging in evolutionarily distant eukaryotes. Studies of the baker’s yeast Saccharomyces cerevisiae, a unicellular eukaryote, have led to discoveries of genes, signaling pathways and chemical compounds that modulate longevity-defining cellular processes in eukaryotic organisms across phyla. These studies have provided deep insights into mechanistic links that exist between different traits of mitochondrial functionality and cellular aging. The molecular mechanisms underlying the essential role of mitochondria as signaling organelles in yeast aging have begun to emerge. In this review, we discuss recent progress in understanding mechanisms by which different functional states of mitochondria define yeast longevity, outline the most important unanswered questions and suggest directions for future research.

Mitochondrial matrix proteases as novel therapeutic targets in malignancy

Although mitochondrial function is often altered in cancer, it remains essential for tumor viability. Tight control of protein homeostasis is required for the maintenance of mitochondrial function, and the mitochondrial matrix houses several coordinated protein quality control systems. These include three evolutionarily conserved proteases of the AAA+ superfamily-the Lon, ClpXP and m-AAA proteases. In humans, these proteases are proposed to degrade, process and chaperone the assembly of mitochondrial proteins in the matrix and inner membrane involved in oxidative phosphorylation, mitochondrial protein synthesis, mitochondrial network dynamics and nucleoid function. In addition, these proteases are upregulated by a variety of mitochondrial stressors, including oxidative stress, unfolded protein stress and imbalances in respiratory complex assembly. Given that tumor cells must survive and proliferate under dynamic cellular stress conditions, dysregulation of mitochondrial protein quality control systems may provide a selective advantage. The association of mitochondrial matrix AAA+ proteases with cancer and their potential for therapeutic modulation therefore warrant further consideration. Although our current knowledge of the endogenous human substrates of these proteases is limited, we highlight functional insights gained from cultured human cells, protease-deficient mouse models and other eukaryotic model organisms. We also review the consequences of disrupting mitochondrial matrix AAA+ proteases through genetic and pharmacological approaches, along with implications of these studies on the potential of these proteases as anticancer therapeutic targets.