Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

Neuroendocrine pathways at risk? Simvastatin induces inter and transgenerational disruption in the keystone amphipod Gammarus locusta

The primary focus of environmental toxicological studies is to address the direct effects of chemicals on exposed organisms (parental generation - F0), mostly overlooking effects on subsequent non-exposed generations (F1 and F2 - intergenerational and F3 transgenerational, respectively). Here, we addressed the effects of simvastatin (SIM), one of the most widely prescribed human pharmaceuticals for the primary treatment of hypercholesterolemia, using the keystone crustacean Gammarus locusta. We demonstrate that SIM, at environmentally relevant concentrations, has significant inter and transgenerational (F1 and F3) effects in key signaling pathways involved in crustaceans' neuroendocrine regulation (Ecdysteroids, Catecholamines, NO/cGMP/PKG, GABAergic and Cholinergic signaling pathways), concomitantly with changes in apical endpoints, such as depressed reproduction and growth. These findings are an essential step to improve hazard and risk assessment of biological active compounds, such as SIM, and highlight the importance of studying the transgenerational effects of environmental chemicals in animals' neuroendocrine regulation.

In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology

Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.

GENAVOS: A New Tool for Modelling and Analyzing Cancer Gene Regulatory Networks Using Delayed Nonlinear Variable Order Fractional System

Gene regulatory networks (GRN) are one of the etiologies associated with cancer. Their dysregulation can be associated with cancer formation and asymmetric cellular functions in cancer stem cells, leading to disease persistence and resistance to treatment. Systems that model the complex dynamics of these networks along with adapting to partially known real omics data are closer to reality and may be useful to understand the mechanisms underlying neoplastic phenomena. In this paper, for the first time, modelling of GRNs is performed using delayed nonlinear variable order fractional (VOF) systems in the state space by a new tool called GENAVOS. Although the tool uses gene expression time series data to identify and optimize system parameters, it also models possible epigenetic signals, and the results show that the nonlinear VOF systems have very good flexibility in adapting to real data. We found that GRNs in cancer cells actually have a larger delay parameter than in normal cells. It is also possible to create weak chaotic, periodic, and quasi-periodic oscillations by changing the parameters. Chaos can be associated with the onset of cancer. Our findings indicate a profound effect of time-varying orders on these networks, which may be related to a type of cellular epigenetic memory. By changing the delay parameter and the variable order functions (possible epigenetics signals) for a normal cell system, its behaviour becomes quite similar to the behaviour of a cancer cell. This work confirms the effective role of the miR-17-92 cluster as an epigenetic factor in the cancer cell cycle.

miR-1184 regulates the proliferation and apoptosis of colon cancer cells via targeting CSNK2A1

MicroRNA (miRNA) exerts an important part in colon cancer cell proliferation and apoptosis. Meanwhile, the dysregulation of some miRNAs is detected in colon cancer cells. However, it remains unclear about the underlying mechanism of their effects on tumor pathogenesis. The current work aimed to examine the miR-1184 effect on colon cancer cells. The differentially expressed miRNAs (DEMs), including miR-9-3p, miR-1184, miR-492, miR-92a-1-5p and miR-20a-3p, were obtained from the GSE115108 and GSE132619 data sets using the 'GEO2R' online tool. Based on the findings, miR-1184 was significantly down-regulated within colon cancer cells and tissues. Moreover, the experimental results of CCK8, flow cytometry, colony formation and Western blotting assays showed that, miR-1184 over-expression suppressed colon cancer cell proliferation through inhibiting Ki67 expression and promoted their apoptosis through up-regulating cleaved caspase-3 and down-regulating Bcl-2 expression. By contrast, miR-1184 inhibition exerted the opposite effects. A total of 110 target genes of miR-1184 were predicted using the TargetScan and miRTarBase databases, which were then used to construct the protein-protein interaction (PPI) network based on the DAVID and STRING websites and to perform GO and KEGG pathway enrichment analyses. The MCODE plug-in of cytoscape was utilized to verify that CSNK2A1 was the target gene and key gene in significant modules. MiR-1184 directly targets CSNK2A1 via using RNA immunoprecipitation assay and luciferase reporter gene assay. According to the results, CSNK2A1 over-expression reversed the functions of miR-1184 over-expression in suppressing colon cancer cell proliferation and enhancing their apoptosis. In conclusion, over-expression of miR-1184 inhibits colon cancer cell proliferation but promotes their apoptosis through down-regulating CSNK2A1 expression.

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.

New signatures of poor CD4 cell recovery after suppressive antiretroviral therapy in HIV-1-infected individuals: involvement of miR-192, IL-6, sCD14 and miR-144

Up to 40% of newly diagnosed cases of HIV-1 infection are late diagnoses, with a profound decrease in CD4 cell counts in many cases. One-third of these individuals do not achieve optimal CD4 cell recovery (OR) after suppressive antiretroviral treatment (ART). This retrospective/longitudinal study of poor recovery (PR) included 79 HIV-1-infected individuals with CD4 count <200 cells/mm³ (25 PR and 54 OR) before ART. After suppressive ART, 21 PR and 24 OR individuals were further analysed, including paired samples. Selected miRs and plasma inflammatory markers were determined to investigate their potential predictive/diagnostic value for poor recovery. miR-192, IL-6 and sCD14 were independently associated with CD4 recovery before ART (p = 0.031, p = 0.007, and p = 0.008, respectively). The combination of these three factors returned a good discrimination (predictive value for PR) value of 0.841 (AUC, p < 0.001). After suppressive ART, miR-144 was independently associated with CD4 recovery (p = 0.017), showing a moderate discrimination value of 0.730 (AUC, p = 0.008) for PR. Our study provides new evidence on the relationship between miRs and HIV-1 infection that could help improve the management of individuals at HIV-1 diagnosis. These miRs and cytokines signature sets provide novel tools to predict CD4 cell recovery and its progression after ART.

Investigating HIV-Human Interaction Networks to Unravel Pathogenic Mechanism for Drug Discovery: A Systems Biology Approach

BACKGROUND

Two big issues in the study of pathogens are determining how pathogens infect hosts and how the host defends itself against infection. Therefore, investigating host-pathogen interactions is important for understanding pathogenicity and host defensive mechanisms and treating infections.

METHODS

In this study, we used omics data, including time-course data from high-throughput sequencing, real-time polymerase chain reaction, and human microRNA (miRNA) and protein-protein interaction to construct an interspecies protein-protein and miRNA interaction (PPMI) network of human CD4+ T cells during HIV-1 infection through system modeling and identification.

RESULTS

By applying a functional annotation tool to the identified PPMI network at each stage of HIV infection, we found that repressions of three miRNAs, miR-140-5p, miR-320a, and miR-941, are involved in the development of autoimmune disorders, tumor proliferation, and the pathogenesis of T cells at the reverse transcription stage. Repressions of miR-331-3p and miR-320a are involved in HIV-1 replication, replicative spread, anti-apoptosis, cell proliferation, and dysregulation of cell cycle control at the integration/replication stage. Repression of miR-341-5p is involved in carcinogenesis at the late stage of HIV-1 infection.

CONCLUSION

By investigating the common core proteins and changes in specific proteins in the PPMI network between the stages of HIV-1 infection, we obtained pathogenic insights into the functional core modules and identified potential drug combinations for treating patients with HIV-1 infection, including thalidomide, oxaprozin, and metformin, at the reverse transcription stage; quercetin, nifedipine, and fenbendazole, at the integration/replication stage; and staurosporine, quercetin, prednisolone, and flufenamic acid, at the late stage.

DNA Methylation Clocks in Aging: Categories, Causes, and Consequences

Age-associated changes to the mammalian DNA methylome are well documented and thought to promote diseases of aging, such as cancer. Recent studies have identified collections of individual methylation sites whose aggregate methylation status measures chronological age, referred to as the DNA methylation clock. DNA methylation may also have value as a biomarker of healthy versus unhealthy aging and disease risk; in other words, a biological clock. Here we consider the relationship between the chronological and biological clocks, their underlying mechanisms, potential consequences, and their utility as biomarkers and as targets for intervention to promote healthy aging and longevity.

Impact of viral presence in tumor on gene expression in non-small cell lung cancer

Background

In our recent study, most non-small-lung cancer (NSCLC) tumor specimens harbored viral DNA but it was absent in non-neoplastic lung. However, their targets and roles in the tumor cells remain poorly understood. We analyzed gene expression microarrays to identify genes and pathways differentially altered between virus-infected and uninfected NSCLC tumors.

Methods

Gene expression microarrays of 30 primary and 9 metastatic NSCLC patients were preprocessed through a series of quality control analyses. Linear Models for Microarray Analysis and Gene Set Enrichment Analysis were used to assess differential expression.

Results

Various genes and gene sets had significantly altered expressions between virus-infected and uninfected NSCLC tumors. Notably, 22 genes on the viral carcinogenesis pathway were significantly overexpressed in virus-infected primary tumors, along with three oncogenic gene sets. A total of 12 genes, as well as seven oncogenic and 133 immunologic gene sets, were differentially altered in squamous cell carcinomas, depending on the virus. In adenocarcinoma, 14 differentially expressed genes (DEGs) were identified, but no oncogenic and immunogenic gene sets were significantly altered. In bronchioloalveolar carcinoma, several genes were highly overexpressed in virus-infected specimens, but not statistically significant. Only five of 69 DEGs (7.2%) from metastatic tumor analysis overlapped with 1527 DEGs from the primary tumor analysis, indicating differences in host cellular targets and the viral impact between primary and metastatic NSCLC.

Conclusions

The differentially expressed genes and gene sets were distinctive among infected viral types, histological subtypes, and metastatic disease status of NSCLC. These results support the hypothesis that tumor viruses play a role in NSCLC by regulating host genes in tumor cells during NSCLC differentiation and progression.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4748-0) contains supplementary material, which is available to authorized users.

Nature versus nurture in the spectrum of rheumatic diseases: Classification of spondyloarthritis as autoimmune or autoinflammatory

Spondyloarthritides (SpA) include inflammatory joint diseases with various clinical phenotypes that may also include the axial skeleton and/or entheses. SpA include psoriatic arthritis, reactive arthritis, enteropathic arthritis and ankylosing spondylitis; the latter is frequently associated with extra-articular manifestations, such as uveitis, psoriasis, and inflammatory bowel disease. SpA are associated with the HLA-B27 allele and recognize T cells as key pathogenetic players. In contrast to other rheumatic diseases, SpA affect women and men equally and are not associated with detectable serum autoantibodies. In addition, but opposite to rheumatoid arthritis, SpA are responsive to treatment regimens including IL-23 or IL-17-targeting biologics, yet are virtually unresponsive to steroid treatment. Based on these differences with prototypical autoimmune diseases, such as rheumatoid arthritis or connective tissue diseases, SpA may be better classified among autoinflammatory diseases, with a predominant innate immunity involvement. This would rank SpA closer to gouty arthritis and periodic fevers in the spectrum of rheumatic diseases, as opposed to autoimmune-predominant diseases. We herein provide available literature on risk factors associated with SpA in support of this hypothesis with a specific focus on genetic and environmental factors.

Investigating the mechanism of hepatocellular carcinoma progression by constructing genetic and epigenetic networks using NGS data identification and big database mining method

The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2, MYC, and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3, PDGFB, and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3, IL1B, and NFKB1 markers influencing HCC progression from stage III to stage IV.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.