INMEX--a web-based tool for integrative meta-analysis of expression data

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

Analysis of gene expression profiles to elucidate racial differences in African American and White patients with Triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the second most diagnosed subtype of breast cancer. It is known to be the most aggressive one that lacks known targetable receptors. One of the concerns in TNBC is the disparities in its prevalence and tumor pathogenesis among women with non-Hispanic African American backgrounds. Despite extensive research, the genetic underpinnings that lead to these disparities remain elusive. The current study aims to provide initiative for further clinical research in the development of targeted therapy for TNBC. Gene expression profiles from African American (AA) and European American (EA) patients with TNBC were collected from Gene Expression Omnibus and performed differential gene expression (DEG)analysis. Candidate genes for a significant correlation between expression and survival rates for breast invasive carcinoma were analyzed using UALCAN. The DAVID annotation tool, Enrichr web server, KEGG database, and Gene Ontology (GO) database were used for functional enrichment analysis of target genes. The Network Analyst server was used to identify ligands with strong affinities, SeamDock server for molecular docking between the biomarkers/associated ligands and examined protein-protein interactions (PPI) from the STRING server. Data from public breast cancer cohorts was utilized to identify expression patterns associated with poor survival outcomes of AA patients with TNBC. Our results showed three genes of interest ( CCT3 , LSM2 , and MRPS16 ) and potential ligands for molecular docking. Molecular docking was performed for the ICG001 ligand to CCT3 (binding affinities of -9.3 kcal/mol and -8.9 kcal/mol) and other interacting proteins ( CDC20 and PPP2CA ) with high degrees of connectivity. The results determined molecular docking of ICG001 to the CDC20 protein resulted in the highest binding affinity. Our results demonstrated that CCT3 and its interacting partners could serve as potential biomarkers due to their association with the survival outcome of AA patients with TNBC and ICG 001 could be the therapeutic lead for these biomarkers.

Novel lncRNA regulatory elements in milk somatic cells of Holstein dairy cows associated with mastitis

Despite regulatory elements such as long non - coding RNAs representing most of the transcriptome, the functional understanding of long non - coding RNAs in relation to major health conditions including bovine mastitis is limited. This study examined the milk somatic cell transcriptome from udder quarters of 6 Holstein dairy cows to identify differentially expressed long non - coding RNAs using RNA - Sequencing. Ninety - four differentially expressed long non - coding RNAs are identified, 5 of which are previously annotated for gene name and length, 11 are annotated for gene name and 78 are novel, having no gene name or length previously annotated. Significant inflammatory response and regulation of immune response pathways (false discovery rate < 0.05) are associated with the differentially expressed long non - coding RNAs. QTL annotation analysis revealed 31 QTL previously annotated in the genomic regions of the 94 differentially expressed long non - coding RNAs, and the majority are associated with milk traits. This research provides a better understanding of long non - coding RNAs regulatory elements in milk somatic cells, which may enhance current breeding strategies for more adaptable or high mastitis resistant cattle.

Unraveling Epigenetic Interplay between Inflammation, Thrombosis, and Immune-Related Disorders through a Network Meta-analysis

Inflammation and thrombosis are two distinct yet interdependent physiological processes. The inflammation results in the activation of the coagulation system that directs the immune system and its activation, resulting in the initiation of the pathophysiology of thrombosis, a process termed immune-thrombosis. Still, the shared underlying molecular mechanism related to the immune system and coagulation has not yet been explored extensively. Inspired to answer this, we carried out a comprehensive gene expression meta-analysis using publicly available datasets of four diseases, including venous thrombosis, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. A total of 609 differentially expressed genes (DEGs) shared by all four datasets were identified based on the combined effect size approach. The pathway enrichment analysis of the DEGs showed enrichment of various epigenetic pathways such as histone-modifying enzymes, posttranslational protein modification, chromatin organization, chromatin-modifying enzymes, HATs acetylate proteins. Network-based protein-protein interaction analysis showed epigenetic enzyme coding genes dominating among the top hub genes. The miRNA-interacting partner of the top 10 hub genes was determined. The predomination of epitranscriptomics regulation opens a layout for the meta-analysis of miRNA datasets of the same four diseases. We identified 30 DEmiRs shared by these diseases. There were 9 common DEmiRs selected from the list of miRNA-interacting partners of top 10 hub genes and shared significant DEmiRs from microRNAs dataset acquisition. These common DEmiRs were found to regulate genes involved in epigenetic modulation and indicate a promising epigenetic aspect that needs to be explored for future molecular studies in the context of immunothrombosis and inflammatory disease.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

A meta-analysis of differentially expressed and regulatory genes with their functional enrichment analysis for brain transcriptome data in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interactions and repetitive behavioral patterns. It is a significant problem emerging worldwide, as one in 100 children is affected by this disorder globally. In this study, a meta-analysis was performed for the identification of differentially expressed genes (DEGs) along with the expression analysis of regulatory genes. Functional enrichment analysis was an integral part of current findings to notify the significant pathways of this complex disorder. The study was conducted with two RNA-Seq datasets, viz., GSE64018 and GSE62098, for ASD patients and control samples from the GEO database. The identification of up-regulatory and down-regulatory genes was performed by the interaction analysis of transcription factors (TF) and DEGs. As an outcome of the meta-analysis, 2543 DEGs were identified as common across both of the datasets in which 1402 DEGs exhibited upregulation and 1130 genes have shown downregulation. In network analysis, upregulatory genes have shown strong interaction while downregulatory genes exhibit weak or null interaction. Further, in the enrichment analysis of screened upregulatory DEGs, three major significant pathways were identified namely the ATP synthesis pathway, FAS signaling pathway, and the Huntington's disease pathway. The common expression of CYC 1 gene in all the identified pathways has indicated that it is an important key regulator gene for the majorly associated pathways. The study concludes that all the potential DEGs were found to show their related high expression in neurobiological regulations specifically with ASD.Communicated by Ramaswamy S. Sarma.

The Novel Link between Gene Expression Profiles of Adult T-Cell Leukemia/Lymphoma Patients' Peripheral Blood Lymphocytes and Ferroptosis Susceptibility

Ferroptosis, a regulated cell death dependent on iron, has garnered attention as a potential broad-spectrum anticancer approach in leukemia research. However, there has been limited ferroptosis research on ATL, an aggressive T-cell malignancy caused by HTLV-1 infection. Our study employs bioinformatic analysis, utilizing dataset GSE33615, to identify 46 ferroptosis-related DEGs and 26 autophagy-related DEGs in ATL cells. These DEGs are associated with various cellular responses, chemical stress, and iron-related pathways. Autophagy-related DEGs are linked to autophagy, apoptosis, NOD-like receptor signaling, TNF signaling, and the insulin resistance pathway. PPI network analysis revealed 10 hub genes and related biomolecules. Moreover, we predicted crucial miRNAs, transcription factors, and potential pharmacological compounds. We also screened the top 20 medications based on upregulated DEGs. In summary, our study establishes an innovative link between ATL treatment and ferroptosis, offering promising avenues for novel therapeutic strategies in ATL.

Clonostachys rosea 'omics profiling: identification of putative metabolite-gene associations mediating its in vitro antagonism against Fusarium graminearum

BACKGROUND

Clonostachys rosea is an established biocontrol agent. Selected strains have either mycoparasitic activity against known pathogens (e.g. Fusarium species) and/or plant growth promoting activity on various crops. Here we report outcomes from a comparative 'omics analysis leveraging a temporal variation in the in vitro antagonistic activities of C. rosea strains ACM941 and 88-710, toward understanding the molecular mechanisms underpinning mycoparasitism.

RESULTS

Transcriptomic data highlighted specialized metabolism and membrane transport related genes as being significantly upregulated in ACM941 compared to 88-710 at a time point when the ACM941 strain had higher in vitro antagonistic activity than 88-710. In addition, high molecular weight specialized metabolites were differentially secreted by ACM941, with accumulation patterns of some metabolites matching the growth inhibition differences displayed by the exometabolites of the two strains. In an attempt to identify statistically relevant relationships between upregulated genes and differentially secreted metabolites, transcript and metabolomic abundance data were associated using IntLIM (Integration through Linear Modeling). Of several testable candidate associations, a putative C. rosea epidithiodiketopiperazine (ETP) gene cluster was identified as a prime candidate based on both co-regulation analysis and transcriptomic-metabolomic data association.

CONCLUSIONS

Although remaining to be validated functionally, these results suggest that a data integration approach may be useful for identification of potential biomarkers underlying functional divergence in C. rosea strains.

Using system biology and bioinformatics to identify the influences of COVID-19 co-infection with influenza virus on COPD

Coronavirus disease 2019 (COVID-19) has speedily increased mortality globally. Although they are risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), less is known about the common molecular mechanisms behind COVID-19, influenza virus A (IAV), and chronic obstructive pulmonary disease (COPD). This research used bioinformatics and systems biology to find possible medications for treating COVID-19, IAV, and COPD via identifying differentially expressed genes (DEGs) from gene expression datasets (GSE171110, GSE76925, GSE106986, and GSE185576). A total of 78 DEGs were subjected to functional enrichment, pathway analysis, protein-protein interaction (PPI) network construct, hub gene extraction, and other potentially relevant disorders. Then, DEGs were discovered in networks including transcription factor (TF)-gene connections, protein-drug interactions, and DEG-microRNA (miRNA) coregulatory networks by using NetworkAnalyst. The top 12 hub genes were MPO, MMP9, CD8A, HP, ELANE, CD5, CR2, PLA2G7, PIK3R1, SLAMF1, PEX3, and TNFRSF17. We found that 44 TFs-genes, as well as 118 miRNAs, are directly linked to hub genes. Additionally, we searched the Drug Signatures Database (DSigDB) and identified 10 drugs that could potentially treat COVID-19, IAV, and COPD. Therefore, we evaluated the top 12 hub genes that could be promising DEGs for targeted therapy for SARS-CoV-2 and identified several prospective medications that may benefit COPD patients with COVID-19 and IAV co-infection.

SMAD genes are up-regulated in brain and blood samples of individuals with schizophrenia

Schizophrenia is a severe psychiatric disorder, with heritability around 80%, but a not fully understood pathophysiology. Signal transduction through the mothers against decapentaplegic (SMADs) are eight different proteins involved in the regulation of inflammatory processes, cell cycle, and tissue patterning. The literature is not consistent regarding the differential expression of SMAD genes among subjects with schizophrenia. In this article, we performed a systematic meta-analysis of the expression of SMAD genes in 423 brain samples (211 schizophrenia vs. 212 healthy controls), integrating 10 datasets from two public repositories, following the PRISMA guidelines. We found a statistically significant up-regulation of SMAD1, SMAD4, SMAD5, and SMAD7, and a tendency for up-regulation of SMAD3 and SMAD9 in brain samples of patients with schizophrenia. Overall, six of the eight genes showed a tendency for up-regulation, and none of them was found to have a tendency for down-regulation. SMAD1 and SMAD4 were up-regulated also in blood samples of 13 individuals with schizophrenia versus eight healthy controls, suggesting the SMAD genes' potential role as biomarkers of schizophrenia. Furthermore, SMAD genes' expression levels were significantly correlated with those of Sphingosine-1-phosphate receptor-1 (S1PR1), which is known to regulate inflammatory processes. Our meta-analysis supports the involvement of SMAD genes in the pathophysiology of schizophrenia through their role in inflammatory processes, as well as demonstrates the importance of gene expression meta-analysis for improving our understanding of psychiatric diseases.

Pathogenomic Signature and Aberrant Neurogenic Events in Experimental Cerebral Ischemic Stroke: A Neurotranscriptomic-Based Implication for Dementia

BACKGROUND

Cerebral ischemic stroke is caused due to neurovascular damage or thrombosis, leading to neuronal dysfunction, neuroinflammation, neurodegeneration, and regenerative failure responsible for neurological deficits and dementia. The valid therapeutic targets against cerebral stroke remain obscure. Thus, insight into neuropathomechanisms resulting from the aberrant expression of genes appears to be crucial.

OBJECTIVE

In this study, we have elucidated how neurogenesis-related genes are altered in experimental stroke brains from the available transcriptome profiles in correlation with transcriptome profiles of human postmortem stroke brain tissues.

METHODS

The transcriptome datasets available on the middle cerebral artery occlusion (MCAo) rat brains were obtained from the Gene Expression Omnibus, National Center for Biotechnology Information. Of the available datasets, 97 samples were subjected to the meta-analysis using the network analyst tool followed by Cytoscape-based enrichment mapping analysis. The key differentially expressed genes (DEGs) were validated and compared with transcriptome profiling of human stroke brains.

RESULTS

Results revealed 939 genes are differently expressed in the brains of the MCAo rat model of stroke, in which 30 genes are key markers of neural stem cells, and regulators of neurogenic processes. Its convergence with DEGs from human stroke brains has revealed common targets.

CONCLUSION

This study has established a panel of highly important DEGs to signify the potential therapeutic targets for neuroregenerative strategy against pathogenic events associated with cerebral stroke. The outcome of the findings can be translated to mitigate neuroregeneration failure seen in various neurological and metabolic disease manifestations with neurocognitive impairments.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Central gene transcriptional regulatory networks shaping monocyte development in bone marrow

The development of monocytes in bone marrow is a complex process with multiple steps. We used RNA-seq data to analyze the transcriptome profiles in developing stages of monocytes, including hematopoietic stem cells (HSCs), common myeloid progenitors (CMPs), granulocyte-monocyte progenitors (GMPs), and monocytes. We found that genes related to potassium and other cation transmembrane activities and ion binding were upregulated during the differentiation of HSCs into CMPs. Protein transport and membrane surface functional molecules were significantly upregulated in the GMP stage. The CD42RAC and proteasome pathways are significantly upregulated during the development of HSCs into monocytes. Transcription factors Ank1, Runx2, Hmga2, Klf1, Nfia, and Bmyc were upregulated during the differentiation of HSCs into CMPs; Gfi1 and Hmgn2 were highly expressed during the differentiation of CMPs into GMPs; Seventeen transcription factors including Foxo1, Cdkn2d, Foxo3, Ep300, Pias1, Nfkb1, Creb1, Bcl6, Ppp3cb, Stat5b, Nfatc4, Mef2a, Stat6, Ifnar2, Irf7, Irf5, and Cebpb were identified as potentially involved in the development of GMPs into monocytes in mice and humans. In metabolism pathway regulation, HSCs have high glucose, lipid, and nucleic acid metabolism activities; CMPs mainly up regulate the TCA cycle related genes; and GMPs have extremely active metabolisms, with significantly elevated pentose phosphate pathway, TCA cycle, histidine metabolism, and purine metabolism. In the monocyte phase, the tricarboxylic acid (TCA) cycle is reduced, and the anaerobic glycolysis process becomes dominated. Overall, our studies offer the kinetics and maps of gene transcriptional expressions and cell metabolisms during monocyte development in bone marrow.

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD.

Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from “ImmPort” database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein–protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman’s rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed.

Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells.

Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.

GBP2 as a potential prognostic predictor with immune-related characteristics in glioma

Guanylate binding protein 2 (GBP2) is a member of the guanine binding protein family, and its relationship with prognostic outcomes and tumor immune microenvironments in glioma remains elusive. We found GBP2 were increased in glioma tissues at both mRNA and protein levels. Kaplan-Meier curves revealed that high GBP2 expression was linked with worse survival of glioma patients, and multivariate Cox regression analysis indicated that high GBP2 expression was an independent prognostic factor for glioma. Combined analysis in immune database revealed that the expression of GBP2 was significantly related to the level of immune infiltration and immunomodulators. Single-cell analysis illustrated the high expression of GBP2 in malignant glioma cells showed the high antigen presentation capability, which were confirmed by real-time polymerase chain reaction (qRT-PCR) data. Additionally, the hsa-mir-26b-5p and hsa-mir-335-5p were predicted as GBP2 regulators and were validated in U87 and U251 cells. Our results first decipher immune-related characteristics and noncoding regulators of GBP2 in glioma, which may provide insights into associated immunotherapies and prognostic predictor.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

Integrative study of gene expression datasets in retinal samples of Diabetic Retinopathy

Diabetic Retinopathy is prevalent among patients with uncontrolled hyperglycemia resulting in vision loss. Despite numerous challenges to create a link among these conditions, the characterization of pathological neovascularization causing retinal damage due to the prognosis of early non-proliferative diabetic retinopathy to late proliferative diabetic retinopathy needs deep understanding. In this study, meta-analysis-based integration of gene expression datasets for the fibrovascular membrane of PDR and neural retina of NPDR were compared, to investigate the differentially expressed genes involved in retinal angiogenesis. Human samples with gene expression profiling of the same experiment type and platform with sufficient information for analysis were included in the study. The studies from cell lines and non-human studies, human samples that include serum, cornea, lens, and/or other ocular tissues or fluids, and studies that lack basic information for analysis were excluded. The microarray datasets available in the Gene Expression Omnibus database of the early and late stages in DR were screened to find common gene expression profiles. Using the INMEX bioinformatics tool, significantly upregulated and downregulated genes in the neural retina of Non-Proliferative Diabetic Retinopathy and fibrovascular membrane of Proliferative Diabetic Retinopathy were compared and studied by the combine effect size method. Using the STRING database PPI network, 50 upregulated and 50 downregulated genes were used to find the key candidate genes involved in retinal disease/degeneration in eye/retinal tissues. In the extensive gene expression meta-analysis performed using INMEX bioinformatics tool, overall, 7935 differentially expressed genes were identified and the respective heatmap was created by using the visualization tools of INVEX. STRING database PPI network identified Retinol Binding Protein 3, Neural Retina Leucine Zipper, S-Antigen Visual Arrestin, Peripherin 2, and Aryl Hydrocarbon Receptor Interacting Protein Like-1 to be the most highly ranked hub genes. The newly discovered potential genes related to retinal angiogenesis causing FVM formation in DR may provide insight into the cellular pathogenesis of NPDR to PDR.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Integrated multi-omic data analysis and validation with yeast model show oxidative phosphorylation modulates protein aggregation in amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis is a progressive, incurable amyloid aggregating neurodegenerative disease involving the motor neurons. Identifying potential biomarkers and therapeutic targets can assist in the better management of the disease. We used an integrative approach encompassing analysis of transcriptomic datasets of human and mice from the GEO database. Our analysis of ALS patient datasets showed deregulation in Non-alcoholic fatty acid liver disease and oxidative phosphorylation. Transgenic mice datasets of SOD1, FUS and TDP-43 showed deregulation in oxidative phosphorylation and ribosome-associated pathways. Commonality analysis between the human and mice datasets showed oxidative phosphorylation as a major deregulated pathway. Further, protein-protein and protein-drug interaction network analysis of mitochondrial electron transport chain showed enrichment of proteins and inhibitors of mitochondrial Complex III and IV. The results were further validated using the yeast model system. Inhibitor studies using metformin (Complex-I inhibitor) and malonate (Complex-II inhibitor) did not show any effect in mitigating the amyloids, while antimycin (Complex-III inhibitor) and azide (Complex-IV inhibitor) reduced amyloidogenesis. Knock-out of QCR8 (Complex-III) or COX8 (Complex-IV) cleared the amyloids. Taken together, our results show a critical role for mitochondrial oxidative phosphorylation in amyloidogenesis and as a potential therapeutic target in ALS.Communicated by Ramaswamy H. Sarma.

Aberrant methylmalonylation underlies methylmalonic acidemia and is attenuated by an engineered sirtuin

Organic acidemias such as methylmalonic acidemia (MMA) are a group of inborn errors of metabolism that typically arise from defects in the catabolism of amino and fatty acids. Accretion of acyl-CoA species is postulated to underlie disease pathophysiology, but the mechanism(s) remain unknown. Here, we surveyed hepatic explants from patients with MMA and unaffected donors, in parallel with samples from various mouse models of methylmalonyl-CoA mutase deficiency. We found a widespread posttranslational modification, methylmalonylation, that inhibited enzymes in the urea cycle and glycine cleavage pathway in MMA. Biochemical studies and mouse genetics established that sirtuin 5 (SIRT5) controlled the metabolism of MMA-related posttranslational modifications. SIRT5 was engineered to resist acylation-driven inhibition via lysine to arginine mutagenesis. The modified SIRT5 was used to create an adeno-associated viral 8 (AAV8) vector and systemically delivered to mutant and control mice. Gene therapy ameliorated hyperammonemia and reduced global methylmalonylation in the MMA mice.

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.

Autophagy Induced by BCL2-Related ceRNA Network Participates in the Occurrence of COPD

Purpose

Chronic obstructive pulmonary disease (COPD) is a predominant cause of mortality worldwide. Autophagy, which depends on a lysosomal degradation pathway, plays an essential role in the occurrence of COPD. The aim of our study was to identify the potential function of autophagy and construct a BCL2-related competing endogenous RNA (ceRNA) network that induces autophagy in COPD.

Methods

Blood sample data from GSE31568, GSE24709, and GSE61741 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs in COPD and controls were identified via GEO2R. Transcription factors were obtained from FunRich. DIANA, miRDB, miRTarBase, and TargetScan were used to predict target genes of miRNAs. Autophagy genes were collected from the Human Autophagy Database (HADb). The GSE151052 dataset was used to identify autophagy-related differentially expressed genes in tissues. Functional enrichment and protein–protein interaction (PPI) network analyses were conducted via Metascape and the STRING network. Spearman correlation analysis was used to analyze the relationship between autophagy-related differentially expressed genes and lung function. The BCL2-related ceRNA network was modeled by Cytoscape.

Results

We obtained 41 differentially expressed miRNAs and 10 significantly different transcription factors. We identified 19 autophagy-related differentially expressed genes that were significantly different (P<0.05) in tissue samples. The most significant enrichment in Metascape was an autophagy item, which further confirmed autophagy participation in the occurrence of COPD. PPI network analysis found four genes (BCL2, BECN1, MAPK8, and ITPR1), among which BCL2 was correlated with both FEV1/FVC and FEV1 prediction. Finally, the BCL2-related ceRNA network was constructed to clarify the interaction of RNAs and occurrence of autophagy, including 18 miRNAs and 65 lncRNAs.

Conclusion

We identified 19 autophagy-related differentially expressed genes that participated in COPD; among them, BCL2 was correlated with lung function, and a BCL2-related ceRNA network was constructed, which further revealed the potential mechanism of autophagy involvement in COPD.

Reduction of Metastasis via Epigenetic Modulation in a Murine Model of Metastatic Triple Negative Breast Cancer (TNBC)

This study investigates the effects of a dual selective Class I histone deacetylase (HDAC)/lysine-specific histone demethylase 1A (LSD1) inhibitor known as 4SC-202 (Domatinostat) on tumor growth and metastasis in a highly metastatic murine model of Triple Negative Breast Cancer (TNBC). 4SC-202 is cytotoxic and cytostatic to the TNBC murine cell line 4T1 and the human TNBC cell line MDA-MB-231; the drug does not kill the normal breast epithelial cell line MCF10A. Furthermore, 4SC-202 reduces cancer cell migration. In vivo studies conducted in the syngeneic 4T1 model, which closely mimics human TNBC in terms of sites of metastasis, reveal reduced tumor burden and lung metastasis. The mechanism of action of 4SC-202 may involve effects on cancer stem cells (CSC) which can self-renew and form metastatic lesions. Approximately 5% of the total 4T1 cell population grown in three-dimensional scaffolds had a distinct CD44high/CD24low CSC profile which decreased after treatment. Bulk transcriptome (RNA) sequencing analyses of 4T1 tumors reveal changes in metastasis-related pathways in 4SC-202-treated tumors, including changes to expression levels of genes implicated in cell migration and cell motility. In summary, 4SC-202 treatment of tumors from a highly metastatic murine model of TNBC reduces metastasis and warrants further preclinical studies.

Identification of novel alternative splicing associated with mastitis disease in Holstein dairy cows using large gap read mapping

Background

Mastitis is a very common disease in the dairy industry that producers encounter daily. Transcriptomics, using RNA-Sequencing (RNA - Seq) technology, can be used to study the functional aspect of mastitis resistance to identify animals that have a better immune response to mastitis. When the cow has mastitis, not only genes but also specific mRNA isoforms generated via alternative splicing (AS) could be differentially expressed (DE), leading to the phenotypic variation observed. Therefore, the objective of this study was to use large gap read mapping to identify mRNA isoforms DE between healthy and mastitic milk somatic cell samples (N = 12). These mRNA isoforms were then categorized based on being 1) annotated mRNA isoforms for gene name and length, 2) annotated mRNA isoforms with different transcript length and 3) novel mRNA isoforms of non - annotated genes.

Results

Analysis identified 333 DE transcripts (with at least 2 mRNA isoforms annotated, with at least one being DE) between healthy and mastitic samples corresponding to 303 unique genes. Of these 333 DE transcripts between healthy and mastitic samples, 68 mRNA isoforms are annotated in the bovine genome reference (ARS.UCD.1.2), 249 mRNA isoforms had novel transcript lengths of known genes and 16 were novel transcript lengths of non - annotated genes in the bovine genome reference (ARS.UCD.1.2). Functional analysis including gene ontology, gene network and metabolic pathway analysis was performed on the list of 288 annotated and unique DE mRNA isoforms. In total, 67 significant metabolic pathways were identified including positive regulation of cytokine secretion and immune response. Additionally, numerous DE novel mRNA isoforms showed potential involvement with the immune system or mastitis. Lastly, QTL annotation analysis was performed on coding regions of the DE mRNA isoforms, identifying overlapping QTLs associated with clinical mastitis and somatic cell score.

Conclusion

This study identified novel mRNA isoforms generated via AS that could lead to differences in the immune response of Holstein dairy cows and be potentially implemented in future breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08430-x.

Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

A transcriptomic analysis based on aberrant methylation levels revealed potential novel therapeutic targets for nasopharyngeal carcinoma

Background

This study aimed to identify potential novel therapeutic targets for nasopharyngeal carcinoma (NPC) by identifying aberrantly methylated-differentially expressed genes (DEGs) and pathways based on a comprehensive bioinformatics analysis.

Methods

Eight gene expression data sets and 2 methylation microarray data sets that included NPC and control groups from the Gene Expression Omnibus were identified. Meta-analyses of the DEGs were performed using the online analysis database “NetworkAnalyst”. Aberrantly methylated gene loci were obtained from the GEO2R. Aberrantly methylated DEGs were obtained from Venn diagrams. The enrichment analysis was carried out on the “Metascape” website, and the protein-protein interaction (PPI) network construction, network analysis, and visualization of the analysis results were carried out on the “String” website using “Cytoscape” software.

Results

In total, 544 hypomethylation high-expression genes and 164 hypermethylation low-expression genes were obtained. The enrichment and PPI network analyses suggested that several pathways and hub genes with abnormal gene expression accompanied by methylation change, including inositol-trisphosphate 3-kinase B (ITPKB), G protein subunit beta 5 (GNB5), FYN proto-oncogene, Src family tyrosine kinase (FYN), LCK proto-oncogene, Src family tyrosine kinase (LCK), nuclear factor of activated T cells 1 (NFATC1), GNAS complex locus (GNAS), protein kinase C beta (PRKCB), zeta chain of T cell receptor associated protein kinase 70 (ZAP70), lysophosphatidic acid receptor 1 (LPAR1), protein kinase C epsilon (PRKCE), tumor protein p53 (TP53), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), fibronectin 1 (FN1), cyclin D1 (CCND1), vascular endothelial growth factor A (VEGFA), HRas proto-oncogene, GTPase (HRAS), signal transducer and activator of transcription 3 (STAT3), fibroblast growth factor 2 (FGF2), amyloid beta precursor protein (APP), and matrix metallopeptidase 2 (MMP2), may be related to the occurrence of nasopharyngeal carcinoma .

Conclusions

The identification of novel and important pathways and hub genes and their roles in the occurrence and development of NPC will guide clinical research and the development of pharmaceutical targets.

Identification of the GTPase IMAP family as an immune-related prognostic biomarker in the breast cancer tumor microenvironment

INTRODUCTION

Breast cancer is the most common malignancy threatening women's health worldwide. The GTPase IMAP family genes are proteins belonging to the immune-associated nucleotide subfamily of the GTP-binding superfamily and nucleotide-binding proteins. However, little is known about the role of different GTPase IMAP family genes in breast cancer.

METHODS

We obtained differential genes from the GEPIA and UALCAN databases and then used the Kaplan-Meier plotter, The Human Protein Atlas, NetworkAnalyst, STRING, and TIMER to analyze the prognostic value, protein expression, and immune cell infiltration of the GTPase IMAP family in patients with breast cancer.

RESULTS

Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly low in breast tumor tissues. In the overall population, patients with high expression of all genes of the GIMAP family had a significantly higher overall survival (OS), with the most significant increase correlated with the GIMAP2 gene (hazard ratio [HR] = 0.45, 95% confidence interval [CI], 0.34-0.59, P = 3.1e-09). However, patients with high expression of the GIMAP family genes in triple-negative breast cancer compared to those with low expression had a significant OS benefit, with the most pronounced benefit correlated with the GIMAP2 gene (HR = 0.37, 95% CI, 0.23-0.59, P = 1.4e-05). GIMAP7 and GIMAP8 were significantly upregulated in breast tumor tissues. The expression of genes in different GIMAP families was positively correlated with the infiltration and expression of six immune cell types (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells).

CONCLUSION

This study may provide novel insights into the selection of GIMAP family prognostic biomarkers for breast cancer.

Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Peripheral Mononuclear Cells in Patients With Ankylosing Spondylitis

Objective

Genetic studies on ankylosing spondylitis (AS) have identified more than 100 pathogenic genes. Building a bridge between these genes and biologically targeted therapies is the current research hotspot.

Methods

We integrated single-cell assaying transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) to explore the key genes and related mechanisms associated with AS pathogenesis.

Results

We identified 18 cell types in peripheral mononuclear cells from patients with AS and normal controls and summarized the cell-type-specific abnormal genes by scRNA-seq. Interestingly, we found that the pathogenic gene NFKB involved in AS progression originated from CD8+ T cells. Moreover, we observed an abnormal tumor TNF pathway mediated by abnormal expression of TNF, NFKB, FOS, JUN, and JUNB, and scATAC-seq results confirmed the abnormal accessible binding sites of transcriptional factors FOS, JUN, and JUNB. The final magnetic bead sorting and quantitative real-time PCR(RT-qPCR) confirmed that NFKB, FOS, JUN, and JUNB in CD8+ T cells differed in the AS group.

Conclusions

Our results revealed a possible mechanism by which NFKB abnormally regulates FOS, JUN, and JUNB and drives AS progression, providing a novel perspective from a single cell point of view in AS.

RHBDF2 gene functions are correlated to facilitated renal clear cell carcinoma progression

Background

The rhomboids are a family of multi-transmembrane proteins, many of which have been implicated in facilitating tumor progression. Little is yet known, however, about rhomboid-associated biomarkers in cancers. An analysis of such biomarkers could yield important insights into the role of the rhomboids in cancer pathology.

Methods

In this study, we carried out the univariate Cox regression analysis and compared gene expression patterns of several rhomboid genes in 30 types of cancers by using The Cancer Genome Atlas (TCGA) database and the methods delineated in Gene Expression Profiling Interactive Analysis (GEPIA). We then used datasets GSE47032, GSE126964, GSE68417 and 75 paired pathological specimens to verify the influences of the rhomboid genes in cancer progression. Moreover, we carried out Weighted Gene Correlation Network Analysis (WGCNA) to investigate gene-related functions and we exploited potential correlations between rhomboid genes expression and immune cell infiltration in cancer tissues. Furthermore, we constructed gene-knockdown cancer cell lines to investigate rhomboid gene functions.

Results

We find that kidney renal clear cell carcinoma (KIRC) disease progression is affected by fluctuations in the expression of a number of the rhomboid family of genes and, more specifically, high levels of RHBDF2 gene expression are a good indicator of poor prognosis of the disease, as patients with high RHBDF2 expression levels exhibit less favorable survival rates compared to those with low RHBDF2 levels. Silencing of the RHBDF2 gene in KIRC cell lines leads to significantly diminished cell proliferation and migration; this is in good agreement with the identification of an enhanced presence of a number of cell growth and migration promoting signaling molecules in KIRC tumors. We found that, although high level of RHBDF2 correlated with increased infiltration of lymphocytes in cancer tissues, artificially overexpressed RHBDF2 led to an inhibition of the activity of the infiltrated immune cells through sustaining PD-L1 protein level. Furthermore, we show that RHBDF2 related cell migration and PD-L1 regulation were potentially mediated by EGFR signaling pathway.

Conclusions

RHBDF2 gene functions are correlated to facilitated renal clear cell carcinoma progression and may serve as a critical prognostic biomarker for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02277-0.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Fasting drives the metabolic, molecular and geroprotective effects of a calorie-restricted diet in mice

Calorie restriction (CR) promotes healthy ageing in diverse species. Recently, it has been shown that fasting for a portion of each day has metabolic benefits and promotes lifespan. These findings complicate the interpretation of rodent CR studies, in which animals typically eat only once per day and rapidly consume their food, which collaterally imposes fasting. Here we show that a prolonged fast is necessary for key metabolic, molecular and geroprotective effects of a CR diet. Using a series of feeding regimens, we dissect the effects of calories and fasting, and proceed to demonstrate that fasting alone recapitulates many of the physiological and molecular effects of CR. Our results shed new light on how both when and how much we eat regulate metabolic health and longevity, and demonstrate that daily prolonged fasting, and not solely reduced caloric intake, is likely responsible for the metabolic and geroprotective benefits of a CR diet.

CPA: a web-based platform for consensus pathway analysis and interactive visualization

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Mortalin/glucose-regulated protein 75 promotes the cisplatin-resistance of gastric cancer via regulating anti-oxidation/apoptosis and metabolic reprogramming

Platinum drug treatment is one of the most predominant chemotherapeutic strategies for patients with gastric cancer (GC). However, the therapeutic effect is less than satisfactory, largely due to the acquired resistance to platinum drugs. Therefore, a better understanding of the underlying mechanisms can greatly improve the therapeutic efficacy of GC. In this study, we aimed to investigate the chemo-resistance related functions/mechanisms and clinical significance of glucose-regulated protein 75 (GRP75) in GC. Here, our data showed that compared with SGC7901 cells, the expression of GRP75 was markedly higher in cisplatin-resistance cells (SGC7901^CR). Knockdown of GRP75 abolished the maintenance of mitochondrial membrane potential (MMP) and inhibited the nuclear factor erythroid-2-related factor 2 (NRF2), phosphatidylinositol 3 kinase/protein kinase B (PI3K/AKT), hypoxia-inducible factor 1α (HIF-1α), and c-myc, which resulted in blocking the activation of their downstream targets. These processes attenuated the anti-oxidation/apoptosis abilities and altered the metabolic reprogramming in SGC7901^CR cells, leading to re-sensitizing these cells to cisplatin. However, overexpression of GRP75 in SGC7901 cells caused the opposite effects. A xenografts model confirmed the abovementioned results. In GC patients receiving platinum chemotherapy and a meta-analysis, a high level of GRP75 was positively associated with aggressive characteristics and poor prognosis including but not limited to gastrointestinal cancers, and was an independent predictor for overall survival. Collectively, our study indicated that GRP75 was involved in the cisplatin-resistance of GC and that GRP75 could be a potential therapeutic target for restoring the drug response in platinum-resistance cells and a useful additive prognostic tool in guiding clinical management of GC patients.

Comprehensive transcriptome mining identified the gene expression signature and differentially regulated pathways of the late-onset preeclampsia

Preeclampsia (PE) is categorized as a pregnancy-related hypertensive disorder and is a serious concern in pregnancies. Several factors, including genetic factors (placenta gene expression, and imprinting), oxidative stress, the inaccurate immune response of the mother, and the environmental factors are responsible for PE development, but still, the exact mechanism of the pathogenesis has remained unknown. The main aim of the present study is to identify the gene expression signature in placenta tissue, to unveil disease etiology mechanisms. The GEO, PubMed, and ArrayExpress databases have selected to identify gene expression datasets on placenta samples of both preeclampsia and the normotensive controls. A comprehensive gene expression meta-analysis of fourteen publicly available microarray data of preeclampsia disease has performed to identify gene expression signature and responsible biological pathways and processes. Using two different meta-analysis pipeline (in-house and INMEX) we have identified a total of 1234 differentially expressed genes (DEGs) with in-house method, including 713 overexpressed and 356 under-expressed genes whereas 272 DEGs (131 over and 141 under-expressed) have identified with INMEX, across PEs and healthy controls. Comprehensive functional enrichment and pathway analysis was performed by EnrichR library, whic revealed "Asparagine N-linked glycosylation Homo sapiens", "Nef and signal transduction", "Hemostasis", and "immune system" among the most enriched terms. The present study sets out to explain a novel database of candidate genetic markers and biological pathways that play a critical role in PE development, which might aid in the identification of diagnostic, prognostic, and therapeutic informative molecules.

MicroRNA-29c Acting on FOS Plays a Significant Role in Nonalcoholic Steatohepatitis Through the Interleukin-17 Signaling Pathway

Nonalcoholic fatty liver disease is the most common hepatic disease in western countries and is even more ubiquitous in Asian countries. Our study determined that TH17/Treg cells were imbalanced in animal models. Based on our interest in the mechanism underlying TH17/Treg cell imbalance in nonalcoholic fatty liver mice, we conducted a joint bioinformatics analysis to further investigate this process. Common gene sequencing analysis was based on one trial from one sequencing platform, where gene expression analysis and enrichment analysis were the only analyses performed. We compared different sequencing results from different trials performed using different sequencing platforms, and we utilized the intersection of these analytical results to perform joint analysis. We used a bioinformatics analysis method to perform enrichment analysis and map interaction network analysis and predict potential microRNA sites. Animal experiments were also designed to validate the results of the data analysis based on quantitative polymerase chain reaction (qPCR) and western blotting. Our results revealed 8 coexisting differentially expressed genes (DEGs) and 7 hinge genes. The identified DEGs may influence nonalcoholic steatosis hepatitis through the interleukin-17 pathway. We found that microRNA-29c interacts with FOS and IGFBP1. Polymerase chain reaction analyses revealed both FOS and microRNA-29c expression in NASH mice, and western blot analyses indicated the same trend with regard to FOS protein levels. Based on these results, we suggest that microRNA-29c acts on FOS via the interleukin-17 signaling pathway to regulate TH17/Treg cells in NASH patients.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). The online STRING tool was used to construct a protein-protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.

Identification of Prognostic Stromal-Immune Score-Based Genes in Hepatocellular Carcinoma Microenvironment

A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score–based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein–protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. MMP9 (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment–associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.

GPRC5A: An emerging prognostic biomarker for predicting malignancy of Pancreatic Cancer based on bioinformatics analysis

Background: Pancreatic cancer (PaCa) is a highly lethal malignancy. The treatment options for PaCa lack efficacy. The study aimed to explore the molecular biomarkers for predicting survival of PaCa and identify the potential carcinogenic mechanisms of the selected gene. Methods: Based on public databases of PaCa, differentially expressed genes (DEGs) were identified using Networkanalyst. Survival analyses were exerted on GEPIA. Oncomine and The Human Protein Atlas were used for verifying the expression on mRNA and protein levels. Enrichment analyses were generated on Metascape and gene set enrichment analysis (GSEA). Univariate analyses were performed to determine the clinical factors associated with the expression of GPRC5A. Results: GPRC5A was identified as the most valuable gene in predicting survival of PaCa patients. Patients with high expression of GPRC5A showed larger tumor size, higher TNM stages, higher tumor grade, and more positive resection margin. In mutant KRAS, TP53, CDKN2A and SMAD4 group, the expression of GPRC5A was higher than non-mutant group. Mechanistically, GPRC5A may promote metastasis of PaCa mainly via regulating epithelial-mesenchymal transition (EMT) and neuroactive ligand-receptor interaction. Conclusion: GPRC5A may act as an oncogene in the progression of PaCa and could be a prognostic biomarker in predicting survival of PaCa.

Interdependence of metals and its binding proteins in Parkinson's disease for diagnosis

Metalloproteins utilizes cellular metals which plays a crucial function in brain that linked with neurodegenerative disorders. Parkinson's disease (PD) is a neurodegenerative disorder that affects geriatric population world-wide. Twenty-four metal-binding protein networks were investigated to identify key regulating protein hubs in PD blood and brain. Amongst, aluminum, calcium, copper, iron, and magnesium protein hubs are the key regulators showing the ability to classify PD from control based on thirty-four classification algorithms. Analysis of these five metal proteins hubs showed involvement in environmental information processing, immune, neuronal, endocrine, aging, and signal transduction pathways. Furthermore, gene expression of functional protein in each hub showed significant upregulation of EFEMP2, MMP9, B2M, MEAF2A, and TARDBP in PD. Dysregulating hub proteins imprint the metal availability in a biological system. Hence, metal concentration in serum and cerebrospinal fluid were tested, which were altered and showed significant contribution towards gene expression of metal hub proteins along with the previously reported PD markers. In conclusion, analyzing the levels of serum metals along with the gene expression in PD opens up an ideal and feasible diagnostic intervention for PD. Hence, this will be a cost effective and rapid method for the detection of Parkinson's disease.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

Host-Viral Interactions Revealed among Shared Transcriptomics Signatures of ARDS and Thrombosis: A Clue into COVID-19 Pathogenesis

Severe novel corona virus disease 2019 (COVID-19) infection is associated with a considerable activation of coagulation pathways, endothelial damage, and subsequent thrombotic microvascular injuries. These consistent observations may have serious implications for the treatment and management of this highly pathogenic disease. As a consequence, the anticoagulant therapeutic strategies, such as low molecular weight heparin, have shown some encouraging results. Cytokine burst leading to sepsis which is one of the primary reasons for acute respiratory distress syndrome (ARDS) drive that could be worsened with the accumulation of coagulation factors in the lungs of COVID-19 patients. However, the obscurity of this syndrome remains a hurdle in making decisive treatment choices. Therefore, an attempt to characterize shared biological mechanisms between ARDS and thrombosis using comprehensive transcriptomics meta-analysis is made. We conducted an integrated gene expression meta-analysis of two independently publicly available datasets of ARDS and venous thromboembolism (VTE). Datasets GSE76293 and GSE19151 derived from National Centre for Biotechnology Information–Gene Expression Omnibus (NCBI-GEO) database were used for ARDS and VTE, respectively. Integrative meta-analysis of expression data (INMEX) tool preprocessed the datasets and effect size combination with random effect modeling was used for obtaining differentially expressed genes (DEGs). Network construction was done for hub genes and pathway enrichment analysis. Our meta-analysis identified a total of 1,878 significant DEGs among the datasets, which when subjected to enrichment analysis suggested inflammation–coagulation–hypoxemia convolutions in COVID-19 pathogenesis. The top hub genes of our study such as tumor protein 53 (TP53), lysine acetyltransferase 2B (KAT2B), DExH-box helicase 9 (DHX9), REL-associated protein (RELA), RING-box protein 1 (RBX1), and proteasome 20S subunit beta 2 (PSMB2) gave insights into the genes known to be participating in the host–virus interactions that could pave the way to understand the various strategies deployed by the virus to improve its replication and spreading.

Transcriptome profiling of different developmental stages of corpus luteum during the estrous cycle in pigs

To better understand the molecular basis of corpus luteum (CL) development and function RNA-Seq was utilized to identify differentially expressed genes (DEGs) in porcine CL during different physiological stages of the estrous cycle viz. early (EL), mid (ML), late (LL) and regressed (R) luteal. Stage wise comparisons obtained 717 (EL vs. ML), 568 (EL vs. LL), 527 (EL vs. R), 786 (ML vs. LL), 474 (ML vs. R) and 534 (LL vs. R) DEGs with log₂(FC) ≥1 and p < 0.05. The process of angiogenesis, steroidogenesis, signal transduction, translation, cell proliferation and tissue remodelling were significantly (p < 0.05) enriched in EL, ML and LL stages, where as apoptosis was most active in regressed stage. Pathway analysis revealed that most annotated genes were associated with lipid metabolism, translation, immune and endocrine system pathways depicting intra-luteal control of diverse CL function. The network analysis identified genes AR, FOS, CDKN1A, which were likely the novel hub genes regulating CL physiology.

Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes

Objective

Fulminant type 1 diabetes (FT1D) is a type of type 1 diabetes, which is characterized by rapid onset of disease and severe metabolic disorders. We intend to screen for crucial genes and potential molecular mechanisms in FT1D in this study.

Method

We downloaded GSE44314, which includes six healthy controls and five patients with FT1D, from the GEO database. Identification of differentially expressed genes (DEGs) was performed by NetworkAnalyst. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were screened by an online tool-Database for Annotation, Visualization, and Integration Discovery (DAVID). Protein-protein interaction (PPI) network and hub genes among DEGs were analyzed by NetworkAnalyst. And we also use NetworkAnalyst to find out the microRNAs (miRNAs) and transcription factors (TFs) which regulate the expression of DEGs.

Result

We identified 130 DEGs (60 upregulated and 70 downregulated DEGs) between healthy controls and FT1D patients. GO analysis results revealed that DEGs were mostly enriched in generation of precursor metabolites and energy, neurohypophyseal hormone activity, and mitochondrial inner membrane. KEGG pathway analysis demonstrated that DEGs were mostly involved in nonalcoholic fatty liver disease. Results indicated that NCOA1, SRF, ERBB3, EST1, TOP1, UBE2S, INO80, COX7C, ITGAV, and COX6C were the top hub genes in the PPI network. Furthermore, we recognized that LDLR, POTEM, IFNAR2, BAZ2A, and SRF were the top hub genes in the miRNA-target gene network, and SRF, TSPAN4, CD59, ETS1, and SLC25A25 were the top hub genes in the TF-target gene network.

Conclusion

Our study pinpoints key genes and pathways associated with FT1D by a sequence of bioinformatics analysis on DEGs. These identified genes and pathways provide more detailed molecular mechanisms of FT1D and may provide novel therapeutic targets.

Transcriptional insights into the CD8+ T cell response in mono-HIV and HCV infection

Background

Disease progression in the absence of therapy varies significantly in mono-HIV and HCV infected individuals. Virus-specific CD8⁺ T cells play an important role in restricting lentiviral replication and determining the rate of disease progression during HIV and HCV mono- and co-infection. Thus, understanding the similarities in the characteristics of CD8⁺ T cells in mono-HIV and HCV infection at the transcriptomic level contributes to the development of antiviral therapy. In this study, a meta-analysis of CD8⁺ T cell gene expression profiles derived from mono-HIV and HCV infected individuals at different stages of disease progression, was conducted to understand the common changes experienced by CD8⁺ T cells.

Methods

Five microarray datasets, reporting CD8⁺ T cell mRNA expression of the mono-HIV and HCV infected patients, were retrieved from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified via integrative meta-analysis of expression data (INMEX) program. Network analysis methods were used to assess protein–protein interaction (PPI) networks, Gene Ontology (GO) terms and pathway enrichment for DEGs. MirDIP and miRDB online prediction tools were used to predict potential microRNAs (miRNAs) targeting hub genes.

Results

First, we identified 625 and 154 DEGs in the CD8⁺ T cells originating from mono-HIV and HCV chronic progressor patients, respectively, compared to healthy individuals. Among them, interferon-stimulated genes (ISGs) including ISG15, IFIT3, ILI44L, CXCL8, FPR1 and TLR2, were upregulated after mono-HIV and HCV infection. Pathway enrichment analysis of DEGs showed that the “cytokine–cytokine receptor interaction” and “NF-kappa B” signaling pathways were upregulated after mono-HIV and HCV infection. In addition, we identified 92 and 50 DEGs in the CD8⁺ T cells of HIV non-progressor and HCV resolver patients, respectively, compared with corresponding chronic progressors. We observed attenuated mitosis and reduced ISG expression in HIV non-progressors and HCV resolvers compared with the corresponding chronic progressors. Finally, we identified miRNA-143-3p, predicted to target both IFIT3 in HIV and STAT5A in HCV infection.

Conclusions

We identified DEGs and transcriptional patterns in mono-HIV and HCV infected individuals at different stages of disease progression and identified miRNA-143-3p with potential to intervene disease progression, which provides a new strategy for developing targeted therapies.

ID1 overexpression increases gefitinib sensitivity in non-small cell lung cancer by activating RIP3/MLKL-dependent necroptosis

ID1 is an oncogenic factor in cancer, but its role in relation to drug sensitivity is unclear. This study aimed to investigate the role of ID1 in drug sensitivity in non-small cell lung cancer (NSCLC). ID1 overexpression in NSCLC cells harboring either EGFR or KRAS mutation was performed and the sensitivity of NSCLC to gefitinib (ZD1839) was measured. A murine orthotopic lung carcinoma model with or without stable ID1 overexpression was developed and treated with gefitinib. Transcriptomic and bioinformatics analyses showed that ID1 overexpression promoted inflammation-related cell death but not apoptosis in gefitinib-treated NSCLC cells. ID1 induced necroptosis by triggering activation of RIP1/RIP3/MLKL pathways. Protein kinase array further suggested that ID1 overexpression maintains Akt activity in gefitinib-treated NSCLC cells, which in turn upregulated FLICE-like inhibitory protein to dissociate the caspase-8-RIP1 complex. The association of RIP1 and RIP3 further activated necroptotic cell death in gefitinib-treated NSCLC. In conclusion, ID1 overexpression in NSCLC induced cellular sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors, regardless of the mutational status of NSCLC. The results may provide scientific evidence for optimizing the treatment outcomes of gefitinib for NSCLC patients.

ImaGEO: integrative gene expression meta-analysis from GEO database

SUMMARY

The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies.

AVAILABILITY AND IMPLEMENTATION

ImaGEO is accessible at http://bioinfo.genyo.es/imageo/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.