Multi-Omic Meta-Analysis of Transcriptomes and the Bibliome Uncovers Novel Hypoxia-Inducible Genes

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

Identification of drug responsible glycogene signature in liver carcinoma from meta-analysis using RNA-seq data

Glycans have attracted much attention in cancer therapeutic strategies, and cell surface proteins and lipids with glycans are known to be altered during the carcinogenic process. However, our understanding of how the glycogenes profile responds to drug stimulation remains incomplete. In this study, we search public databases for Sequence Read Archive data on drug-treated liver cancer cells, with the aim to comprehensively analyze the drug responses of glycogenes via bioinformatic meta-analysis. The study comprised 86 datasets, encompassing eight distinct liver cancer cell lines and 13 different drugs. Differentially expressed genes were quantified, and 399 glycogenes were identified. The glycogenes signature was then analyzed using bioinformatics methodologies. In the Protein-protein interaction network analysis, we identified drug-responsive glycogenes such as Beta-1,4-Galactosyltransferase 1, GDP-Mannose 4,6-Dehydratase, UDP-Glucose Ceramide Glucosyltransferase, and Solute Carrier Family 2 Member 4 as key glycan biomarkers. In the enrichment analysis using the pathway list of glycogenes, the results also demonstrated that drug stimulation resulted in alterations to glycopathway-related genes involved in several processes, namely O-Mannosylation, POMGNT2 Type, Capping, Heparan Sulfate Sulfation, and Glucuronidation pathways. These genes and pathways commonly exhibit variable expression across multiple liver cancer cells in response to the same drug, making them potential targets for new cancer therapies. In addition to their primary roles, drugs may also participate in the regulation of glycans. The insights from this study could pave the way for the development of liver cancer therapies that target the regulation of gene profiles involved in the biosynthesis of glycans.

Meta-analysis of public RNA sequencing data of abscisic acid-related abiotic stresses in Arabidopsis thaliana

Abiotic stresses such as drought, salinity, and cold negatively affect plant growth and crop productivity. Understanding the molecular mechanisms underlying plant responses to these stressors is essential for stress tolerance in crops. The plant hormone abscisic acid (ABA) is significantly increased upon abiotic stressors, inducing physiological responses to adapt to stress and regulate gene expression. Although many studies have examined the components of established stress signaling pathways, few have explored other unknown elements. This study aimed to identify novel stress-responsive genes in plants by performing a meta-analysis of public RNA sequencing (RNA-Seq) data in Arabidopsis thaliana, focusing on five ABA-related stress conditions (ABA, Salt, Dehydration, Osmotic, and Cold). The meta-analysis of 216 paired datasets from five stress conditions was conducted, and differentially expressed genes were identified by introducing a new metric, called TN [stress-treated (T) and non-treated (N)] score. We revealed that 14 genes were commonly upregulated and 8 genes were commonly downregulated across all five treatments, including some that were not previously associated with these stress responses. On the other hand, some genes regulated by salt, dehydration, and osmotic treatments were not regulated by exogenous ABA or cold stress, suggesting that they may be involved in the plant response to dehydration independent of ABA. Our meta-analysis revealed a list of candidate genes with unknown molecular mechanisms in ABA-dependent and ABA-independent stress responses. These genes could be valuable resources for selecting genome editing targets and potentially contribute to the discovery of novel stress tolerance mechanisms and pathways in plants.

Meta-Analysis of the Public RNA-Seq Data of the Western Honeybee Apis mellifera to Construct Reference Transcriptome Data

The Western honeybee (Apis mellifera) is valuable in biological research and agriculture. Its genome sequence was published before those for other insect species. RNA-Seq data for A. mellifera have been applied in several recently published studies. Nevertheless, these data have not been prepared for use in subsequent meta-analyses. To promote A. mellifera transcriptome analysis, we constructed reference transcriptome data using the reference genome sequence and RNA-Seq data curated from about 1,000 runs of public databases. The new reference transcriptome data construct comprised 149,685 transcripts, and 194,174 protein sequences were predicted. Approximately 50-60% of the predicted protein sequences were functionally annotated using the protein sequence data for several model and insect species. Novel candidate immune-related transcripts were searched by meta-analysis using immune-response-related RNA-Seq and reference transcriptome data. Three to twenty candidate transcripts including autophagy-related protein 3 were upregulated or downregulated in response to both viral and bacterial infections. The constructed reference transcriptome data may facilitate future transcriptome analyses of A. mellifera.

Exploratory meta-analysis of hypoxic transcriptomes using a precise transcript reference sequence set

Gene expression studies are intrinsically biased, with many studies influenced by concomitant information such as gene-disease associations. This limitation can be overcome using a data-driven analysis approach without relying on ancillary information. The FANTOM CAGE-Associated Transcriptome project provides a comprehensive meta-assembly of the human transcriptome using coding and noncoding genes. Hypoxia strongly influences gene expression; in addition, noncoding RNA (ncRNA) metabolism is down-regulated in response to hypoxic stimuli. We evaluated the differential response of various transcripts to hypoxia by determining their hypoxia responsiveness scores. Enrichment analysis revealed that several genes associated with ncRNA metabolism, particularly those involved in ribosomal RNA processing, were down-regulated in response to hypoxia. Previously published information from the FANTOM CAGE-Associated Transcriptome project was suitable for meta-analysis of the transcriptome sequencing data from both coding and ncRNAs and to evaluate the hypoxia responsiveness of target transcripts and relationship between sense-antisense transcripts from the same locus. Our results may facilitate functional annotation of various transcripts including ncRNAs, allowing for both sense and antisense and coding and noncoding evaluations.

Meta-Analysis of Transcriptomes in Insects Showing Density-Dependent Polyphenism

With increasing public data, a statistical analysis approach called meta-analysis, which combines transcriptome results obtained from multiple studies, has succeeded in providing novel insights into targeted biological processes. Locusts and aphids are representative of insect groups that exhibit density-dependent plasticity. Although the physiological mechanisms underlying density-dependent polyphenism have been identified in aphids and locusts, the underlying molecular mechanisms remain largely unknown. In this study, we performed a meta-analysis of public transcriptomes to gain additional insights into the molecular underpinning of density-dependent plasticity. We collected RNA sequencing data of aphids and locusts from public databases and detected differentially expressed genes (DEGs) between crowded and isolated conditions. Gene set enrichment analysis was performed to reveal the characteristics of the DEGs. DNA replication (GO:0006260), DNA metabolic processes (GO:0006259), and mitotic cell cycle (GO:0000278) were enriched in response to crowded conditions. To date, these processes have scarcely been the focus of research. The importance of the oxidative stress response and neurological system modifications under isolated conditions has been highlighted. These biological processes, clarified by meta-analysis, are thought to play key roles in the regulation of density-dependent plasticity.

Meta-Analysis of RNA Sequencing Data of Arabidopsis and Rice under Hypoxia

Hypoxia is an abiotic stress in plants. Flooding resulting from climate change is a major crop threat that increases the risk of hypoxic stress. The molecular mechanisms underlying hypoxia in plants were elucidated in recent years, but new genes related to this stress remain to be discovered. Thus, we aimed to perform a meta-analysis of the RNA sequencing (RNA-Seq) data of Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) under hypoxia. We collected 29 (Arabidopsis) and 26 (rice) pairs of RNA-Seq data involving hypoxic (including submergence) and normoxic (control) treatments and extracted the genes that were commonly upregulated or downregulated in the majority of the experiments. The meta-analysis revealed 40 and 19 commonly upregulated and downregulated genes, respectively, in the two species. Several WRKY transcription factors and cinnamate-4-hydroxylase were commonly upregulated, but their involvement in hypoxia remains unclear. Our meta-analysis identified candidate genes for novel molecular mechanisms in plants under hypoxia.

Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma

Background

Patients with lung adenocarcinoma (LUAD) exhibit significant heterogeneity in therapeutic responses and overall survival (OS). In recent years, accumulating research has uncovered the critical roles of hypoxia in a variety of solid tumors, but its role in LUAD is not currently fully elucidated. This study aims to discover novel insights into the mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers by exploring the potential association between hypoxia and LUAD.

Methods

Four machine learning approaches were implemented to screen out potential hypoxia-related genes for the prognosis of LUAD based on gene expression profile of LUAD samples obtained from The Cancer Genome Atlas (TCGA), then validated by six cohorts of validation datasets. The risk score derived from the hypoxia-related genes was proven to be an independent factor by using the univariate and multivariate Cox regression analyses and Kaplan-Meier survival analyses. Hypoxia-related mechanisms based on tumor mutational burden (TMB), the immune activity, and therapeutic value were also performed to adequately dig deeper into the clinical value of hypoxia-related genes. Finally, the expression level of hypoxia genes was validated at protein level and clinical samples from LUAD patients at transcript levels.

Results

All patients in TCGA and GEO-LUAD group were distinctly stratified into low- and high-risk groups based on the risk score. Survival analyses demonstrated that our risk score could serve as a powerful and independent risk factor for OS, and the nomogram also exhibited high accuracy. LUAD patients in high-risk group presented worse OS, lower TMB, and lower immune activity. We found that the model is highly sensitive to immune features. Moreover, we revealed that the hypoxia-related genes had potential therapeutic value for LUAD patients based on the drug sensitivity and chemotherapeutic response prediction. The protein and gene expression levels of 10 selected hypoxia gene also showed significant difference between LUAD tumors tissues and normal tissues. The validation experiment showed that the gene transcript levels of most of their genes were consistent with the levels of their translated proteins.

Conclusions

Our study might contribute to the optimization of risk stratification for survival and personalized management of LUAD patients by using the hypoxia genes, which will provide a valuable resource that will guide both mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers.

Systems approaches to understand oxygen sensing: how multi-omics has driven advances in understanding oxygen-based signalling

Hypoxia is a common denominator in the pathophysiology of a variety of human disease states. Insight into how cells detect, and respond to low oxygen is crucial to understanding the role of hypoxia in disease. Central to the hypoxic response is rapid changes in the expression of genes essential to carry out a wide range of functions to adapt the cell/tissue to decreased oxygen availability. These changes in gene expression are co-ordinated by specialised transcription factors, changes to chromatin architecture and intricate balances between protein synthesis and destruction that together establish changes to the cellular proteome. In this article, we will discuss the advances of our understanding of the cellular oxygen sensing machinery achieved through the application of 'omics-based experimental approaches.

Comparison of Oxidative and Hypoxic Stress Responsive Genes from Meta-Analysis of Public Transcriptomes

Analysis of RNA-sequencing (RNA-seq) data is an effective means to analyze the gene expression levels under specific conditions and discover new biological knowledge. More than 74,000 experimental series with RNA-seq have been stored in public databases as of 20 October 2021. Since this huge amount of expression data accumulated from past studies is a promising source of new biological insights, we focused on a meta-analysis of 1783 runs of RNA-seq data under the conditions of two types of stressors: oxidative stress (OS) and hypoxia. The collected RNA-seq data of OS were organized as the OS dataset to retrieve and analyze differentially expressed genes (DEGs). The OS-induced DEGs were compared with the hypoxia-induced DEGs retrieved from a previous study. The results from the meta-analysis of OS transcriptomes revealed two genes, CRIP1 and CRIP3, which were particularly downregulated, suggesting a relationship between OS and zinc homeostasis. The comparison between meta-analysis of OS and hypoxia showed that several genes were differentially expressed under both stress conditions, and it was inferred that the downregulation of cell cycle-related genes is a mutual biological process in both OS and hypoxia.