Omics Integration Research Articles

Multiple Omics Integration Reveals Key Circular RNAs in Hepatocellular Carcinoma.

BackgroundHCC is one of the most common malignancies with an increasing incidence worldwide, especially in Asian countries. However, even though targeted cancer therapy drugs such as sorafenib and regorafenib are available, the overall outcome of HCC remains unsatisfactory. Thus, it is urgent to investigate the molecular mechanisms of HCC progression, so as to provide accurate diagnostic criteria and therapeutic targets.MethodsRNA-seq data was used to identify and quantify circular RNAs (circRNAs). DESeq2 was used to identify the differentially expressed circRNAs. miRNA binding sites within circRNAs were identified by miRanda. Gene set enrichment analysis (GSEA) was conducted to predict the biological function of circRNAs.ResultsThe differential expression analysis identified 107 upregulated and 95 downregulated circRNAs in HCC tissues. We observed that a differentially expressed circRNA (DE-circRNA), hsa_circ_0141900 was highly negatively correlated with its parental gene RAB1A (PCC < -0.6), which was also closely associated with mTOR signaling pathway. Moreover, we also constructed competing endogenous RNA (ceRNA) network to identify key circRNAs involved in HCC. Notably, hsa_circ_0002130 and hsa_circ_0008774 were highly correlated with the genes involved in gluconeogenesis and HNF3A pathway via the target genes, GOT2 and AR, suggesting that the two circRNAs might regulate these pathways, respectively. Survival analysis revealed that GOT2 was associated with favorable prognosis. Furthermore, high expression of hsa_circ_0002130 was found to inhibit tumor cell growth and promotes GOT2 expression.ConclusionIn summary, the circRNAs highlighted by the integrative analysis greatly improved our understanding of the underlying mechanism of circRNAs in HCC.

PaIntDB: network-based omics integration and visualization using protein-protein interactions in Pseudomonas aeruginosa.

The Pseudomonas aeruginosa Interaction Database, PaIntDB, is an intuitive web-based tool for network-based systems biology analyses using protein-protein interactions (PPI) in this medically important pathogen. It enables the integration and visualization of omics analyses including RNA-Seq and Tn-Seq. High-throughput datasets can be mapped onto PPI networks, which can be explored visually and filtered to uncover novel putative molecular pathways related to the conditions of study. PaIntDB contains the most comprehensive P.aeruginosa interactome to date, collected from a variety of resources, including interactions predicted computationally to further expand analysis capabilities. The web server implementation makes it easily extendable to other bacterial species. PaIntDB is freely available at https://www.paintdb.ca, the source code and database file are available at https://github.com/yavyx/PaIntDB. Supplementary data are available at Bioinformatics online.

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer

BackgroundSurvival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analysis (NCA), a supervised feature selection algorithm selected relevant features from multi-omics datasets retrieved from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. The drug response framework used regression and unsupervised clustering (K-means) to segregate samples into responders and non-responders based on their predicted IC50 values (Z-score).ResultsThe survival prediction framework was highly effective in categorizing patients into risk subtypes with an accuracy of 94%. Compared to single-omics and early integration approaches, our drug response prediction models performed significantly better and were able to predict IC50 values (Z-score) with a mean square error (MSE) of 1.154 and an overall regression value of 0.92, showing a linear relationship between predicted and actual IC50 values.ConclusionThe proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology.

The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., "exposomic sensors"). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research. Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration. Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.

A Novel Prognostic Model of Early-Stage Lung Adenocarcinoma Integrating Methylation and Immune Biomarkers

Lung adenocarcinoma (LUAD) is caused by multiple biological factors. Therefore, it will be more meaningful to study the prognosis from the perspective of omics integration. Given the significance of epigenetic modification and immunity in tumorigenesis and development, we tried to combine aberrant methylation and tumor infiltration CD8 T cell-related genes to build a prognostic model, to explore the key biomarkers of early-stage LUAD. On the basis of RNA-seq and methylation microarray data downloaded from The Cancer Genome Atlas (TCGA), differentially expressed genes and aberrant methylated genes were calculated with “DEseq2” and “ChAMP” packages, respectively. A Chi-square test was performed to obtain methylation driver genes. Weighted correlation network analysis (WGCNA) was utilized to mine cancer biomarkers related to CD8 T cells. With the consequences of univariate Cox proportional hazards analysis and least absolute shrinkage and selection operator (LASSO) COX regression analysis, the prognostic index based on 17 methylation driver genes (ZNF677, FAM83A, TRIM58, CLDN6, NKD1, NFE2L3, FKBP5, ITGA5, ASCL2, SLC24A4, WNT3A, TMEM171, PTPRH, ITPKB, ITGA2, SLC6A17, and CCDC81) and four CD8 T cell-related genes (SPDL1, E2F7, TK1, and TYMS) was successfully established, which could make valuable predictions for the survival risk of patients with early-stage LUAD.

The coloring mechanism of a novel golden variety in &lt;i&gt;Populus deltoides&lt;/i&gt; based on the RGB color mode

Compared to flower color and red leaf mutants, the mechanism of yellow leaf formation in woody plants is more complicated and less understood. Poplars are common and widely distributed perennial trees. Here, we obtained a golden leaf mutant poplar variety (JHY) and compared it with its original green leaf poplar (<i>Populus sp</i>. Linn. ‘2025’) in terms of phenotype, pigment content, the transcriptomes and metabolomes. Through transcriptome sequencing, we found that the chlorophyll degradation genes, and the genes in anthocyanin metabolism in JHY were up-regulated compared to L2025 and the carotenoid synthesis genes were down-regulated. Further based on HPLC-ESI-MS/MS technology, 16 differentially expressed anthocyanins were identified, 14 reddish anthocyanins of which were significantly up-regulated. According to these results, we proposed a coloring mechanism of JHY based on the RGB color mode. The yellow color of carotenoids and the red color of anthocyanins overlay each other, combined with a reduction in chlorophyll, turning the leaves golden. This study systematic analyzes the causes of golden leaf formation through the omics integration approach, which will provide reference for the breeding of golden leaf trees.

Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease.

System biology analysis reveals the role of voltage-dependent anion channel in mitochondrial dysfunction during non-alcoholic fatty liver disease progression into hepatocellular carcinoma.

Non‐alcoholic fatty liver disease (NAFLD) is one of the most common causes of hepatocellular carcinoma (HCC), but the underlying mechanisms behind the correlation of NAFLD with HCC are unclear. We aimed to uncover the genes and potential mechanisms that drive this progression. This study uncovered the genes and potential mechanisms through a multiple ’omics integration approach. Quantitative proteomics combined with phenotype‐association analysis was performed. To investigate the potential mechanisms, a comprehensive transcriptome/lipidome/phenome‐wide association analysis was performed in genetic reference panel BXD mice strains. The quantitative proteomics combined with phenotype‐association results showed that VDAC1 was significantly increased in tumor tissues and correlated with NAFLD‐related traits. Gene co‐expression network analysis indicated that VDAC1 is involved in mitochondria dysfunction in the tumorigenic/tumor progression. The association between VDAC1 and mitochondria dysfunction can be explained by the fact that VDAC1 was associated with mitochondria membrane lipids cardiolipin (CL) composition shift. VDAC1 was correlated with the suppression of mature specie CL(LLLL) and elevation level of nascent CL species. Such profiling shift was supported by the significant positive correlation between VDAC1 and PTPMT1, as well as negative correlation with CL remodeling enzyme Tafazzin (TAZ). This study confirmed that the expression of VADC1 was dysregulated in NAFLD‐driven HCC and associated with NAFLD progression. The VDAC1‐driven mitochondria dysfunction is associated with cardiolipin composition shift, which causes alteration of mitochondria membrane properties.

The Pan-Omics Landscape of Renal Cell Carcinoma and Its Implication on Future Clinical Practice.

Renal cell carcinoma has traditionally been classified based on histological features. Contemporary studies have identified genomic, transcriptomic, epigenomic, and metabolomic signatures that correspond to or even transcend histological subtypes. Much remains to be learned about improving the algorithm of pan-omics integration for precision oncology, which will not only advance our understanding of RCC pathobiology and treatment response but also result in novel therapeutic opportunities. Accordingly, this review focuses on recent RCC multi-omics literature. Encouragingly, a few reports on omics integration into routinely employed prognostic risk models have shown early promise that could lay the foundation for future development of precision kidney cancer therapies. Hence, this article serves as a primer on what we have learned and how we might better realize the clinical potential of the burgeoning pan-omics data.

The human T‐cell receptor repertoire in health and disease and potential for omics integration

The adaptive immune system arose 600 million years ago in a cold-blooded fish. Over countless generations, our antecedents tuned the function of the T-cell receptor (TCR). The TCR system is arguably the most complex known to science. The TCR evolved hypervariability to fight the hypervariability of pathogens and cancers that look to consume our resources. This review describes the genetics and architecture of the human TCR and highlights surprising new discoveries over the past years that have disproved very old dogmas. The standardization of TCR sequencing data is discussed in preparation for big data bioinformatics and predictive analysis. We next catalogue new signatures and phenomenon discovered by TCR next generation sequencing (NGS) in health and disease and work that remain to be done in this space. Finally, we discuss how TCR NGS can add to immunodiagnostics and integrate with other omics platforms for both a deeper understanding of TCR biology and its use in the clinical setting.

Alternative splicing and its regulatory role in woody plants.

Alternative splicing (AS) is an important post-transcriptional process to enhance proteome diversity in eukaryotic organisms. In plants, numerous reports have primarily focused on AS analysis in model plant species or herbaceous plants, leading to a notable lack of research on AS in woody plants. More importantly, emerging evidence indicates that many important traits, including wood formation and stress resistance, in woody plants are controlled by AS. In this review article, we summarize the current progress of all kinds of AS studies in different tree species at various stages of development and in response to various stresses, revealing the significant role played by AS in woody plants, as well as the similar properties and differential regulation within their herbaceous counterparts. Furthermore, we propose several potential strategies to facilitate the functional characterization of splicing factors in woody plants and evaluate a general pipeline for the systematic characterization of splicing isoforms in a complex AS regulatory network. The utilization of genetic studies and high-throughput omics integration approaches to analyze AS genes and splicing factors is likely to further advance our understanding of AS modulation in woody plants.

Abstract 6588: Omics integration provides insight into esophageal cancer inhibitory mechanisms of a cranberry extract

Abstract Our laboratory studies chemoprevention of esophageal adenocarcinoma (EAC) through utilization of the rat esophagogastroduodenal anastomosis (EGDA) surgical model of reflux-induced EAC. Specifically, we evaluated mechanisms by which cranberry proanthocyanidins (C-PAC) inhibit reflux-induced EAC by utilizing multi-omics integrative approaches employing multiple program platforms. Herein, we investigated whether pathway-based integration could be used to examine cross-talk between genes, metabolites and the microbial profiles. For this analysis, we utilized transcriptomic and untargeted metabolomic data from rat esophagi of animals that received water or C-PAC in the drinking water alone or combined with reflux inducing EGDA surgery. Additionally, we isolated DNA from fecal pellets and performed 16S rRNA sequencing to assess gut microbiome composition and functionality. Each omics dataset was analyzed for significant pathway enrichment and network generation using Metacore, DAVID and Metabolync. PICRUSt was utilized to predict microbiota functionality. Analysis of transcriptomic and metabolomic data suggest that EGDA upregulates inflammatory, NF-kB, DNA damage, cell cycle and immune function pathways as well as metabolic pathways related to eicosanoids, amino acids and primary and secondary bile acids, while C-PAC mitigates these alterations. Preliminary microbiome data analysis also suggests that bacteria related to inflammation, metabolite transport and DNA damage are increased in abundance in EGDA, with a decrease in abundance following C-PAC treatment. Analysis of omics datasets can provide insightful relationships into the cross-talk between different processes regulated by transcription, metabolism and the microbiome. Single dataset and integrative analysis showed similarities in altered pathways for vehicle and C-PAC treated animals in the context of reflux. Integration is limited by the infancy of multi-omics analysis programs, as well as the limited amount of research involving prediction of bacterial function and integration of microbiome data with other omics datasets. Further research and development of these omics programs is needed to determine accuracy of predicted results as well as continued investigation of identified pathways. Citation Format: Katherine M. Weh, Connor L. Howard, Bridget A. Tripp, Jennifer L. Clarke, Amy B. Howell, Laura A. Kresty. Omics integration provides insight into esophageal cancer inhibitory mechanisms of a cranberry extract [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6588.

Systems Biology Approaches to Understand the Host–Microbiome Interactions in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology.

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.

Abstract 33: Drug sensitivity prediction modeling from genomics, transcriptomics and inferred protein activity

Abstract Background: Machine learning models that rely on single omics data for drug sensitivity prediction are challenging and frequently fail within precision medicine scenarios. Proteomics, for example, reflects the system biology and the regulatory network better than genomics. However, it is less likely available for many preclinical and in vivo data, mainly due to the cost of data generation. Currently, there are massive amounts of genomics data available, such as CNV, mutation, and RNA-seq signatures. However, these data do not characterize the post-translational modifications in proteins, limiting their utility for biomarker discovery. Objective: The main objective of this study is to overcome the lack of proteomics data gap. Herein, we propose a novel modeling approach to improve the prediction accuracy of drug sensitivity, that is, combining genomics and proteomics signatures. In addition to genomics and transcriptomics data, the model infers proteomic activity from gene expression using VIPER from in vitro data, and this is extendable to in vivo settings. Material and Methods: Using PharmacoGX package developed in our lab, we downloaded, curated, and annotated the genomic and pharmacologic data of the Cancer Cell line Encyclopedia (CCLE), as well as the Cancer Therapeutics Response Portal (CTRPV2) dataset that is a continuation of the CTRP project and the largest pharmacologic screen conducted to date, containing several hundreds of thousands of drug dose-response curves. In this study, we extracted from CCLE dataset the following signatures: RPPA, RNASeq, Mutation, and CNV, then inferred the VIPER protein using RNASeq. Then, we built a model that a) checks the different omics combinations, b) applies random forest with ten-fold cross-validation for sensitivity prediction using CTRPV2, and c) evaluates the significance of each model using the concordance index (CI) package developed in our lab. Results: The proposed model was tested in vitro using CCLE and CTRPv2 common cell lines. We tested the model with drugs having known biomarkers in pharmacogenomics literature. ERBB2 biomarker for lapatinib showed the best CI=0.95 by combining CNV and VIPER models while CI equals 0.77 and 0.9 for each of them, respectively. Moreover, MET biomarker for crizotinib showed best CI=0.89 by integrating RNASeq, Mutation, RPPA, and VIPER, while each model obtained CI=(0.45, 0.5, 0.85, 0.5), respectively. Conclusion: In conclusion, omics integration boosted the drug sensitivity prediction compared to single models. The application of the proposed model in vivo will improve drug development and increase the prediction quality of precision medicine. Citation Format: Hassan Mahmoud, Benjamin Haibe-Kains. Drug sensitivity prediction modeling from genomics, transcriptomics and inferred protein activity [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 33.

A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism

BackgroundBirthweight reflects in utero exposures and later health evolution. Despite existing studies employing high-dimensional molecular measurements, the understanding of underlying mechanisms of birthweight remains limited. MethodsTo investigate the systems biology of birthweight, we cross-sectionally integrated the methylome, the transcriptome, the metabolome and a set of inflammatory proteins measured in cord blood samples, collected from four birth-cohorts (n = 489). We focused on two sets of 68 metabolites and 903 CpGs previously related to birthweight and investigated the correlation structures existing between these two sets and all other omic features via bipartite Pearson correlations. ResultsThis dataset revealed that the set of metabolome and methylome signatures of birthweight have seven signals in common, including three metabolites [PC(34:2), plasmalogen PC(36:4)/PC(O-36:5), and a compound with m/z of 781.0545], two CpGs (on the DHCR24 and SC4MOL gene), and two proteins (periostin and CCL22). CCL22, a macrophage-derived chemokine has not been previously identified in relation to birthweight. Since the results of the omics integration indicated the central role of cholesterol metabolism, we explored the association of cholesterol levels in cord blood with birthweight in the ENVIRONAGE cohort (n = 1097), finding that higher birthweight was associated with increased high-density lipoprotein cholesterol and that high-density lipoprotein cholesterol was lower in small versus large for gestational age newborns. ConclusionsOur data suggests that an integration of different omic-layers in addition to single omics studies is a useful approach to generate new hypotheses regarding biological mechanisms. CCL22 and cholesterol metabolism in cord blood play a mechanistic role in birthweight.

Omics Integration Analyses Reveal the Early Evolution of Malignancy in Breast Cancer.

The majority of cancer evolution studies involve individual-based approaches that neglect the population dynamics necessary to build a global picture of cancer evolution for each cancer type. Here, we conducted a population-based study in breast cancer to understand the timing of malignancy evolution and its correlation to the genetic evolution of pathological stages. In an omics integrative approach, we integrated gene expression and genomic aberration data for pre-invasive (ductal carcinoma in situ; DCIS, early-stage) and post-invasive (invasive ductal carcinoma; IDC, late-stage) samples and investigated the evolutionary role of further genetic changes in later stages compared to the early ones. We found that single gene alterations (SGAs) and copy-number alterations (CNAs) work together in forward and backward evolution manners to fine-tune the signaling pathways operating in tumors. Analyses of the integrated point mutation and gene expression data showed that (i) our proposed fine-tuning concept is also applicable to metastasis, and (ii) metastases sometimes diverge from the primary tumor at the DCIS stage. Our results indicated that the malignant potency of breast tumors is constant over the pre- and post-invasive pathological stages. Indeed, further genetic alterations in later stages do not establish de novo malignancy routes; however, they serve to fine-tune antecedent signaling pathways.

Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.

Omics Integration for Mitochondria Systems Biology.

Significance: Elucidation of the central importance of mitophagy in homeostasis of cells and organisms emphasizes that mitochondrial functions extend far beyond short-term needs for energy production. In mitochondria systems biology, the mitochondrial genome, proteome, and metabolome operate as a functional network in coordination of cell activities. Organization occurs through subnetworks that are interconnected by membrane potential, transport activities, allosteric and cooperative interactions, redox signaling mechanisms, rheostatic control by post-translational modifications, and metal ion homeostasis. These subnetworks enable use of varied energy precursors, defense against environmental stressors, and macromolecular rewiring to titrate energy production, biosynthesis, and detoxification according to cell-specific needs. Rewiring mechanisms, termed mitochondrial reprogramming, enhance fitness to respond to metabolic resources and challenges from the environment. Maladaptive responses can cause cell death. Maladaptive rewiring can cause disease. In cancer, adaptive rewiring can interfere with effective treatment. Recent Advances: Many recent advances have been facilitated by the development of new omics tools, which create opportunities to use data-driven analysis of omics data to address these complex adaptive and maladaptive mechanisms of mitochondrial reprogramming in human disease. Critical Issues: Application of omics integration to model systems reveals a critical role for metal ion homeostasis broadly impacting mitochondrial reprogramming. Importantly, data show that trans-omics associations are more robust and biologically relevant than single omics associations. Future Directions: Application of omics integration to mitophagy research creates new opportunities to link the complex, interactive functions of mitochondrial form and function in mitochondria systems biology.

A review of omics approaches to study preeclampsia

Preeclampsia is a medical condition affecting 5–10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.