Systems analysis of high-throughput data

Integrated data driven analysis identifies potential candidate genes associated with PCOS

Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ∼2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.

Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives

BACKGROUND

The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical.

SCOPE OF REVIEW

This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered.

MAJOR CONCLUSIONS

Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

Stable Iterative Variable Selection

Motivation

The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space.

Results

Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE.

Availability and implementation

The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package=sivs.

Supplementary information

Supplementary data are available at Bioinformatics online.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

Genomic and Genetic Approaches to Deciphering Acute Respiratory Distress Syndrome Risk and Mortality

Significance: Acute respiratory distress syndrome (ARDS) is a severe, highly heterogeneous critical illness with staggering mortality that is influenced by environmental factors, such as mechanical ventilation, and genetic factors. Significant unmet needs in ARDS are addressing the paucity of validated predictive biomarkers for ARDS risk and susceptibility that hamper the conduct of successful clinical trials in ARDS and the complete absence of novel disease-modifying therapeutic strategies. Recent Advances: The current ARDS definition relies on clinical characteristics that fail to capture the diversity of disease pathology, severity, and mortality risk. We undertook a comprehensive survey of the available ARDS literature to identify genes and genetic variants (candidate gene and limited genome-wide association study approaches) implicated in susceptibility to developing ARDS in hopes of uncovering novel biomarkers for ARDS risk and mortality and potentially novel therapeutic targets in ARDS. We further attempted to address the well-known health disparities that exist in susceptibility to and mortality from ARDS. Critical Issues: Bioinformatic analyses identified 201 ARDS candidate genes with pathway analysis indicating a strong predominance in key evolutionarily conserved inflammatory pathways, including reactive oxygen species, innate immunity-related inflammation, and endothelial vascular signaling pathways. Future Directions: Future studies employing a system biology approach that combines clinical characteristics, genomics, transcriptomics, and proteomics may allow for a better definition of biologically relevant pathways and genotype-phenotype connections and result in improved strategies for the sub-phenotyping of diverse ARDS patients via molecular signatures. These efforts should facilitate the potential for successful clinical trials in ARDS and yield a better fundamental understanding of ARDS pathobiology.

Internal driving factors leading to extrahepatic manifestation of the hepatitis C virus infection

The hepatitis C virus (HCV) infection is associated with various extrahepatic manifestations, which are correlated with poor outcomes, and thus increase the morbidity and mortality of chronic hepatitis C (CHC). Therefore, understanding the internal linkages between systemic manifestations and HCV infection is helpful for treatment of CHC. Yet, the mechanism by which the virus evokes the systemic diseases remains to be elucidated. In the present study, using gene set enrichment analysis (GSEA) and signaling pathway impact analysis (SPIA), a comprehensive analysis of microarray data of mRNAs was conducted in HCV-infected and -uninfected Huh7.5 cells, and signaling pathways (which are significantly activated or inhibited) and certain molecules (which are commonly important in those signaling pathways) were selected. Forty signaling pathways were selected using GSEA, and eight signaling pathways were selected with SPIA. These pathways are associated with cancer, metabolism, environmental information processing and organismal systems, which provide important information for further clarifying the intrinsic associations between syndromes of HCV infection, of which seven pathways were not previously reported, including basal transcription factors, pathogenic Escherichia coli infection, shigellosis, gastric acid secretion, dorso-ventral axis formation, amoebiasis and cholinergic synapse. Ten genes, SOS1, RAF1, IFNA2, IFNG, MTHFR, IGF1, CALM3, UBE2B, TP53 and BMP7 whose expression may be the key internal driving molecules, were selected using the online tool Anni 2.1. Furthermore, the present study demonstrated the internal linkages between systemic manifestations and HCV infection, and presented the potential molecules that are key to those linkages.

Reorganization of gene network for degradation of polycyclic aromatic hydrocarbons (PAHs) in Pseudomonas aeruginosa PAO1 under several conditions

Although polycyclic aromatic hydrocarbons (PAHs) are harmful to human health, their elimination from the environment is not easy. Biodegradation of PAHs is promising since many bacteria have the ability to use hydrocarbons as their sole carbon and energy sources for growth. Of various microorganisms that can degrade PAHs, Pseudomonas aeruginosa is particularly important, not only because it causes a series of diseases including infection in cystic fibrosis patients, but also because it is a model bacterium in various studies. The genes that are responsible for degrading PAHs have been identified in P. aeruginosa, however, no gene acts alone as various stresses often initiate different metabolic pathways, quorum sensing, biofilm formation, antibiotic tolerance, etc. Therefore, it is important to study how PAH degradation genes behave under different conditions. In this study, we apply network analysis to investigating how 46 PAH degradation genes reorganized among 5549 genes in P. aeruginosa PAO1 under nine different conditions using publicly available gene coexpression data from GEO. The results provide six aspects of novelties: (i) comparing the number of gene clusters before and after stresses, (ii) comparing the membership in each gene cluster before and after stresses, (iii) defining which gene changed its membership together with PAH degradation genes before and after stresses, (iv) classifying membership-changed-genes in terms of category in Pseudomonas Genome Database, (v) postulating unknown gene’s function, and (vi) proposing new mechanisms for genes of interests. This study can shed light on understanding of cooperative mechanisms of PAH degradation from the level of entire genes in an organism, and paves the way to conduct the similar studies on other genes.

Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review

High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

SO2 Emissions in China - Their Network and Hierarchical Structures

SO₂ emissions lead to various harmful effects on environment and human health. The SO₂ emission in China has significant contribution to the global SO₂ emission, so it is necessary to employ various methods to study SO₂ emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO₂ emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO₂ emissions from power generation sector were highly individualized as small-sized clusters, the SO₂ emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO₂ emissions from residential sector was not impacted by time, and the SO₂ emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO₂ emissions from all four sectors and is potentially useful to find out similar patterns of SO₂ emissions, which can provide information on understanding the mechanisms of SO₂ pollution and on designing different environmental measure to combat SO₂ emissions.

Viral Phylogenomics Using an Alignment-Free Method: A Three-Step Approach to Determine Optimal Length of k-mer

The development of rapid, economical genome sequencing has shed new light on the classification of viruses. As of October 2016, the National Center for Biotechnology Information (NCBI) database contained >2 million viral genome sequences and a reference set of ~4000 viral genome sequences that cover a wide range of known viral families. Whole-genome sequences can be used to improve viral classification and provide insight into the viral "tree of life". However, due to the lack of evolutionary conservation amongst diverse viruses, it is not feasible to build a viral tree of life using traditional phylogenetic methods based on conserved proteins. In this study, we used an alignment-free method that uses k-mers as genomic features for a large-scale comparison of complete viral genomes available in RefSeq. To determine the optimal feature length, k (an essential step in constructing a meaningful dendrogram), we designed a comprehensive strategy that combines three approaches: (1) cumulative relative entropy, (2) average number of common features among genomes, and (3) the Shannon diversity index. This strategy was used to determine k for all 3,905 complete viral genomes in RefSeq. The resulting dendrogram shows consistency with the viral taxonomy of the ICTV and the Baltimore classification of viruses.

Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China

Specification of PM_2.5 spatial and temporal characteristics is important for understanding PM_2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM_2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM_2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM_2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM_2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM_2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.

Animal-Free Chemical Safety Assessment

The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.

Screening of differentially expressed genes in chronic hepatitis B patients and prediction of related biological pathways

AIM: To study the molecular mechanism of pathogenesis of chronic hepatitis B.

METHODS: Based on microarray experiment, GeneSpring software was used to screen differentially expressed genes in chronic hepatitis B patients, and GeneTrail software was used to perform enrichment analysis of related biological pathways.

RESULTS: A total of 417 differentially expressed genes were identified, of which 205 were upregulated and 212 downregulated. Significant pathways to which downregulated genes belong include ErbB, non-small cell lung cancer, mTOR, RNA degradation, T cell receptor, chronic myeloid leukemia, and renal cell carcinoma pathways. Significant pathways to which upregulated genes belong include chemokine, lysosomes, Vibrio cholerae infection, and IgG Fc receptor-mediated phagocytosis pathways.

CONCLUSION: PI3K/AKT downregulation is likely a major molecular mechanism of persistent hepatitis B.

Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value ("0" for normal states and "1" for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis.