The plant hormone abscisic acid (ABA) regulates essential processes in plant development and responsiveness to abiotic and biotic stresses. ABA perception triggers a post-translational signaling cascade that elicits the ABA gene regulatory network (GRN), encompassing hundreds of transcription factors (TFs) and thousands of transcribed genes. To further our knowledge of this GRN, we performed an RNA-seq time series experiment consisting of 14 time points in the 16 h following a one-time ABA treatment of 5-week-old Arabidopsis rosettes. During this time course, ABA rapidly changed transcription levels of 7151 genes, which were partitioned into 44 coexpressed modules that carry out diverse biological functions. We integrated our time-series data with publicly available TF-binding site data, motif data, and RNA-seq data of plants inhibited in translation, and predicted (i) which TFs regulate the different coexpression clusters, (ii) which TFs contribute the most to target gene amplitude, (iii) timing of engagement of different TFs in the ABA GRN, and (iv) hierarchical position of TFs and their targets in the multi-tiered ABA GRN. The ABA GRN was found to be highly interconnected and regulated at different amplitudes and timing by a wide variety of TFs, of which the bZIP family was most prominent, and upregulation of genes encompassed more TFs than downregulation. We validated our network models in silico with additional public TF-binding site data and transcription data of selected TF mutants. Finally, using a drought assay we found that the Trihelix TF GT3a is likely an ABA-induced positive regulator of drought tolerance.

The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF), and principal-component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.

UNASSIGNED: This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms.
UNASSIGNED: mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software \"limma\" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments.
UNASSIGNED: A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients.
UNASSIGNED: This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

The development of targeted drugs for the early prevention and management of chronic kidney disease (CKD) is of great importance. However, the success rates and cost-effectiveness of traditional drug development approaches are extremely low. Utilizing large sample genome-wide association study data for drug repurposing has shown promise in many diseases but has not yet been explored in CKD. Herein, we investigated actionable druggable targets to improve renal function using large-scale Mendelian randomization and colocalization analyses. We combined two population-scale independent genetic datasets and validated findings with cell-type-dependent eQTL data of kidney tubular and glomerular samples. We ultimately prioritized two drug targets, opioid receptor-like 1 and F12, with potential genetic support for restoring renal function and subsequent treatment of CKD. Our findings explore the potential pathological mechanisms of CKD, bridge the gap between the molecular mechanisms of pathogenesis and clinical intervention, and provide new strategies in future clinical trials of CKD.

As a research infrastructure with a mission to provide services for bioinformatics, ELIXIR aims to identify and inform its target audiences. Here, we present a survey on a community of researchers studying the environment with omics approaches in Greece, one of the youngest member countries of ELIXIR. Personal interviews followed by quantitative and qualitative analysis were employed to document interactions and practices of the community and to perform a gap analysis for the transition toward multiomics and systems biology. Environmental omics in Greece mostly concerns production of data, in large majority on microbes and non-model organisms. Our survey highlighted (1) the popularity and suitability of targeted hands-on training events; (2) data quality and management issues as important elements for the transition to multiomics, and (3) lack of knowledge and misconceptions regarding interoperability, metadata standards, and pre-registration. The publicly available collected answers represent a valuable resource in view of future strategic planning.

Multi-omics methods for analysing postgenomic data have become firmly established in the tools of molecular gerontology only in recent years, since previously there were no comprehensive integrative approaches adequate to the task of calculating biological age. This paper provides an overview of existing papers on multi-omics integrative approaches in calculating the biological age of a human. An analysis of the most common options for integrating methylomic, transcriptomic, proteomic, microbiomic and metabolomic datasets was carried out. We defined (1) concatenation (machine learning), in which models are developed using a concatenated data matrix, formed by combining multiple omics data sets; (2) fusion model approaches that create multiple intermediate submodels for different omics data to then build a final integrated model from the various intermediate submodels; and (3) transformation methods (via artificial intelligence) that first transform each of the single omics data sets into core plots or matrices, and then combine them all into one graph before building an integral complex model. It is unlikely that multi-omics approaches will find application in anti-aging personalized medicine, but they will undoubtedly deepen and expand the understanding of the fundamental processes standing behind the phenomenon of the biological aging clocks.
В работе дан обзор существующих исследований, использующих мультиомиксные интегративные подходы при подсчете биологического возраста человека. Проведен анализ наиболее распространенных вариантов интеграции метиломного, транскриптомного, протеомного, микробиомного и метаболомного блоков данных. Выделены: 1) конкатенация (машинное обучение), при которой разрабатываются модели с использованием объединенной матрицы данных, формируемые путем слияния нескольких наборов омиксных данных; 2) подходы на основе объединенных моделей, в рамках которых создается несколько промежуточных подмоделей для различных омиксных данных, чтобы затем построить окончательную интегральную модель; 3) методы преобразования (искусственным интеллектом), которые сначала трансформируют каждый из наборов единичных омиксных данных в сводные графики или матрицы, а затем объединяют их все в один график перед построением интегральной комплексной модели. Мультиомиксные подходы едва ли найдут применение в антивозрастной персонализированной медицине, но, вероятно, углубят и расширят понимание биологических часов старения.

OBJECTIVE: Uveal melanoma is an ocular malignancy whose prognosis severely worsens following metastasis. In order to improve the understanding of molecular physiology of metastatic uveal melanoma, we identified genes and pathways implicated in metastatic vs non-metastatic uveal melanoma.
METHODS: A previously published dataset from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes between metastatic and non-metastatic samples as well as to conduct pathway and perturbagen analyses using Gene Set Enrichment Analysis (GSEA), EnrichR, and iLINCS.
RESULTS: In male metastatic uveal melanoma samples, the gene LOC401052 is significantly down-regulated and FHDC1 is significantly up-regulated compared to non-metastatic male samples. In female samples, no significant differently expressed genes were found. Additionally, we identified many significant up-regulated immune response pathways in male metastatic uveal melanoma, including \"T cell activation in immune response\". In contrast, many top up-regulated female pathways involve iron metabolism, including \"heme biosynthetic process\". iLINCS perturbagen analysis identified that both male and female samples have similar discordant activity with growth factor receptors, but only female samples have discordant activity with progesterone receptor agonists.
CONCLUSIONS: Our results from analyzing genes, pathways, and perturbagens demonstrate differences in metastatic processes between sexes.

Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not well understood. We used integrated multi-omics analysis to explore the clinical and genetic roles of efferocytosis in SCM. We identified six module genes (ATP11C, CD36, CEBPB, MAPK3, MAPKAPK2, PECAM1) strongly associated with SCM, leading to an accurate predictive model. Subgroups defined by EFFscore exhibited distinct clinical features and immune infiltration levels. Survival analysis showed that the C1 subtype with a lower EFFscore had better survival outcomes. scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from sepsis patients identified four genes (CEBPB, CD36, PECAM1, MAPKAPK2) associated with high EFFscores, highlighting their role in SCM. Molecular docking confirmed interactions between diagnostic genes and tamibarotene. Experimental validation supported our computational results. In conclusion, our study identifies a novel efferocytosis-related SCM subtype and diagnostic biomarkers, offering new insights for clinical diagnosis and therapy.

The bioinformatic analysis of cannabinoid receptors (CBRs) CB1 and CB2 reveals a detailed picture of their structure, evolution, and physiological significance within the endocannabinoid system (ECS). The study highlights the evolutionary conservation of these receptors evidenced by sequence alignments across diverse species including humans, amphibians, and fish. Both CBRs share a structural hallmark of seven transmembrane (TM) helices, characteristic of class A G-protein-coupled receptors (GPCRs), which are critical for their signalling functions. The study reports a similarity of 44.58 % between both CBR sequences, which suggests that while their evolutionary paths and physiological roles may differ, there is considerable conservation in their structures. Pathway databases like KEGG, Reactome, and WikiPathways were employed to determine the involvement of the receptors in various signalling pathways. The pathway analyses integrated within this study offer a detailed view of the CBRs interactions within a complex network of cannabinoid-related signalling pathways. High-resolution crystal structures (PDB ID: 5U09 for CB1 and 5ZTY for CB2) provided accurate structural information, showing the binding pocket volume and surface area of the receptors, essential for ligand interaction. The comparison between these receptors\' natural sequences and their engineered pseudo-CBRs (p-CBRs) showed a high degree of sequence identity, confirming the validity of using p-CBRs in receptor-ligand interaction studies. This comprehensive analysis enhances the understanding of the structural and functional dynamics of cannabinoid receptors, highlighting their physiological roles and their potential as therapeutic targets within the ECS.

Drug resistance is currently the biggest challenge in cancer chemotherapy. Here, we present a protocol to develop a chemotherapy drug screening process by constructing a cancer prognostic model (PM) using public databases. We describe steps for downloading code and data, preparing the expression matrix and metadata for analysis, screening modeling genes, and constructing a PM. We then detail procedures for constructing predictive websites for cancer patients\' survival based on their age, tumor stage, gene expression levels, and risk scores. For complete details on the use and execution of this protocol, please refer to Bai et al.1.