ArrayExpress update - from bulk to single-cell expression data

Suppression of GATA3 promotes epithelial-mesenchymal transition and simultaneous cellular senescence in human extravillous trophoblasts

The regulatory mechanism of the transcription factor GATA3 in the differentiation and maturation process of extravillous trophoblasts (EVT) in early pregnancy placenta, as well as its relevance to the occurrence of pregnancy disorders, remains poorly understood. This study leveraged single-cell RNA sequencing data from placental organoid models and placental tissue to explore the dynamic changes in GATA3 expression during EVT maturation. The expression pattern exhibited an initial upregulation followed by subsequent downregulation, with aberrant GATA3 localization observed in cases of recurrent miscarriage (RM). By identifying global targets regulated by GATA3 in primary placental EVT cells, JEG3, and HTR8/SVneo cell lines, this study offered insights into its regulatory mechanisms across different EVT cell models. Shared regulatory targets among these cell types and activation of trophoblast cell marker genes emphasized the importance of GATA3 in EVT differentiation and maturation. Knockdown of GATA3 in JEG3 cells led to repression of GATA3-induced epithelial-mesenchymal transition (EMT), as evidenced by changes in marker gene expression levels and enhanced migration ability. Additionally, interference with GATA3 accelerated cellular senescence, as indicated by reduced proliferation rates and increased activity levels for senescence-associated β-galactosidase enzyme, along with elevated expression levels for senescence-associated genes. This study provides comprehensive insights into the dual role of GATA3 in regulating EMT and cellular senescence during EVT differentiation, shedding light on the dynamic changes in GATA3 expression in normal and pathological placental conditions.

Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment

The EU-ToxRisk project (2016-2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape. Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021-2026)).

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

Neuromedin U receptor 1 deletion leads to impaired immunotherapy response and high malignancy in colorectal cancer

Colorectal cancer (CRC) exhibits significant heterogeneity, impacting immunotherapy efficacy, particularly in immune desert subtypes. Neuromedin U receptor 1 (NMUR1) has been reported to perform a vital function in immunity and inflammation. Through comprehensive multi-omics analyses, we have systematically characterized NMUR1 across various tumors, assessing expression patterns, genetic alterations, prognostic significance, immune infiltration, and pathway associations at both the bulk sequencing and single-cell scales. Our findings demonstrate a positive correlation between NMUR1 and CD8+ T cell infiltration, with elevated NMUR1 levels in CD8+ T cells linked to improved immunotherapy outcomes in patients with CRC. Further, we have validated the NMUR1 expression signature in CRC cell lines and patient-derived tissues, revealing its interaction with key immune checkpoints, including lymphocyte activation gene 3 and cytotoxic T-lymphocyte-associated protein 4. Additionally, NMUR1 suppression enhances CRC cell proliferation and invasiveness. Our integrated analyses and experiments open new avenues for personalized immunotherapy strategies in CRC treatment.

Transcriptional Signatures of Domestication Revealed through Meta-Analysis of Pig, Chicken, Wild Boar, and Red Junglefowl Gene Expression Data

Domesticated animals have undergone significant changes in their behavior, morphology, and physiological functions during domestication. To identify the changes in gene expression associated with domestication, we collected the RNA-seq data of pigs, chickens, wild boars, and red junglefowl from public databases and performed a meta-analysis. Gene expression was quantified, and the expression ratio between domesticated animals and their wild ancestors (DW-ratio) was calculated. Genes were classified as "upregulated", "downregulated", or "unchanged" based on their DW-ratio, and the DW-score was calculated for each gene. Gene set enrichment analysis revealed that genes upregulated in pigs were related to defense from viral infection, whereas those upregulated in chickens were associated with aminoglycan and carbohydrate derivative catabolic processes. Genes commonly upregulated in pigs and chickens are involved in the immune response, olfactory learning, epigenetic regulation, cell division, and extracellular matrix. In contrast, genes upregulated in wild boar and red junglefowl are related to stress response, cell proliferation, cardiovascular function, neural regulation, and energy metabolism. These findings provide valuable insights into the genetic basis of the domestication process and highlight potential candidate genes for breeding applications.

Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion

PURPOSE

The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions.

METHODS

Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology.

RESULTS

CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects.

CONCLUSION

A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Assessing the potential relevance of CEACAM6 as a blood transcriptional biomarker

Background

Changes in blood transcript abundance levels have been associated with pathogenesis in a wide range of diseases. While next generation sequencing technology can measure transcript abundance on a genome-wide scale, downstream clinical applications often require small sets of genes to be selected for inclusion in targeted panels. Here we set out to gather information from the literature and transcriptome datasets that would help researchers determine whether to include the gene CEACAM6 in such panels.

Methods

We employed a workflow to systematically retrieve, structure, and aggregate information derived from both the literature and public transcriptome datasets. It consisted of profiling the CEACAM6 literature to identify major diseases associated with this candidate gene and establish its relevance as a biomarker. Accessing blood transcriptome datasets identified additional instances where CEACAM6 transcript levels differ in cases vs controls. Finally, the information retrieved throughout this process was captured in a structured format and aggregated in interactive circle packing plots.

Results

Although it is not routinely used clinically, the relevance of CEACAM6 as a biomarker has already been well established in the cancer field, where it has invariably been found to be associated with poor prognosis. Focusing on the blood transcriptome literature, we found studies reporting elevated levels of CEACAM6 abundance across a wide range of pathologies, especially diseases where inflammation plays a dominant role, such as asthma, psoriasis, or Parkinson's disease. The screening of public blood transcriptome datasets completed this picture, showing higher abundance levels in patients with infectious diseases caused by viral and bacterial pathogens.

Conclusions

Targeted assays measuring CEACAM6 transcript abundance in blood may be of potential utility for the management of patients with diseases presenting with systemic inflammation and for the management of patients with cancer, where the assay could potentially be run both on blood and tumor tissues.

Modulating autophagy to treat diseases: A revisited review on in silico methods

BACKGROUND

Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated.

AIM OF REVIEW

Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.

Transcriptomic changes behind Sparus aurata hepatic response to different aquaculture challenges: An RNA-seq study and multiomics integration

Gilthead seabream (Sparus aurata) is an important species in Mediterranean aquaculture. Rapid intensification of its production and sub-optimal husbandry practices can cause stress, impairing overall fish performance and raising issues related to sustainability, animal welfare, and food safety. The advent of next-generation sequencing technologies has greatly revolutionized the study of fish stress biology, allowing a deeper understanding of the molecular stress responses. Here, we characterized for the first time, using RNA-seq, the different hepatic transcriptome responses of gilthead seabream to common aquaculture challenges, namely overcrowding, net handling, and hypoxia, further integrating them with the liver proteome and metabolome responses. After reference-guided transcriptome assembly, annotation, and differential gene expression analysis, 7, 343, and 654 genes were differentially expressed (adjusted p-value < 0.01, log2|fold-change| >1) in the fish from the overcrowding, net handling, and hypoxia challenged groups, respectively. Gene set enrichment analysis (FDR < 0.05) suggested a scenario of challenge-specific responses, that is, net handling induced ribosomal assembly stress, whereas hypoxia induced DNA replication stress in gilthead seabream hepatocytes, consistent with proteomics and metabolomics' results. However, both responses converged upon the downregulation of insulin growth factor signalling and induction of endoplasmic reticulum stress. These results demonstrate the high phenotypic plasticity of this species and its differential responses to distinct challenging environments at the transcriptomic level. Furthermore, it provides significant resources for characterizing and identifying potentially novel genes that are important for gilthead seabream resilience and aquaculture production efficiency with regard to fish welfare.

Network visualization of genes involved in skeletal muscle myogenesis in livestock animals

BACKGROUND

Muscle growth post-birth relies on muscle fiber number and size. Myofibre number, metabolic and contractile capacities are established pre-birth during prenatal myogenesis. The aim of this study was to identify genes involved in skeletal muscle development in cattle, sheep, and pigs - livestock.

RESULTS

The cattle analysis showed significant differences in 5043 genes during the 135-280 dpc period. In sheep, 444 genes differed significantly during the 70-120 dpc period. Pigs had 905 significantly different genes for the 63-91 dpc period.The biological processes and KEGG pathway enrichment results in each species individually indicated that DEGs in cattle were significantly enriched in regulation of cell proliferation, cell division, focal adhesion, ECM-receptor interaction, and signaling pathways (PI3K-Akt, PPAR, MAPK, AMPK, Ras, Rap1); in sheep - positive regulation of fibroblast proliferation, negative regulation of endothelial cell proliferation, focal adhesion, ECM-receptor interaction, insulin resistance, and signaling pathways (PI3K-Akt, HIF-1, prolactin, Rap1, PPAR); in pigs - regulation of striated muscle tissue development, collagen fibril organization, positive regulation of insulin secretion, focal adhesion, ECM-receptor interaction, and signaling pathways (PPAR, FoxO, HIF-1, AMPK). Among the DEGs common for studied animal species, 45 common genes were identified. Based on these, a protein-protein interaction network was created and three significant modules critical for skeletal muscle myogenesis were found, with the most significant module A containing four recognized hub genes - EGFR, VEGFA, CDH1, and CAV1. Using the miRWALK and TF2DNA databases, miRNAs (bta-miR-2374 and bta-miR-744) and transcription factors (CEBPB, KLF15, RELA, ZNF143, ZBTB48, and REST) associated with hub genes were detected. Analysis of GO term and KEGG pathways showed that such processes are related to myogenesis and associated with module A: positive regulation of MAP kinase activity, vascular endothelial growth factor receptor, insulin-like growth factor binding, focal adhesion, and signaling pathways (PI3K-Akt, HIF-1, Rap1, Ras, MAPK).

CONCLUSIONS

The identified genes, common to the prenatal developmental period of skeletal muscle in livestock, are critical for later muscle development, including its growth by hypertrophy. They regulate valuable economic characteristics. Enhancing and breeding animals according to the recognized genes seems essential for breeders to achieve superior gains in high-quality muscle mass.

Integrated transcriptomic landscape of the effect of anti-steatotic treatments in high-fat diet mouse models of non-alcoholic fatty liver disease

High-fat diet (HFD) mouse models are widely used in research to develop medications to treat non-alcoholic fatty liver disease (NAFLD), as they mimic the steatosis, inflammation, and hepatic fibrosis typically found in this complex human disease. The aims of this study were to identify a complete transcriptomic signature of these mouse models and to characterize the transcriptional impact exerted by different experimental anti-steatotic treatments. For this reason, we conducted a systematic review and meta-analysis of liver transcriptomic studies performed in HFD-fed C57BL/6J mice, comparing them with control mice and HFD-fed mice receiving potential anti-steatotic treatments. Analyzing 21 studies broaching 24 different treatments, we obtained a robust HFD transcriptomic signature that included 2,670 differentially expressed genes and 2,567 modified gene ontology biological processes. Treated HFD mice generally showed a reversion of this HFD signature, although the extent varied depending on the treatment. The biological processes most frequently reversed were those related to lipid metabolism, response to stress, and immune system, whereas processes related to nitrogen compound metabolism were generally not reversed. When comparing this HFD signature with a signature of human NAFLD progression, we identified 62 genes that were common to both; 10 belonged to the group that were reversed by treatments. Altered expression of most of these 10 genes was confirmed in vitro in hepatocytes and hepatic stellate cells exposed to a lipotoxic or a profibrogenic stimulus, respectively. In conclusion, this study provides a vast amount of information about transcriptomic changes induced during the progression and regression of NAFLD and identifies some relevant targets. Our results may help in the assessment of treatment efficacy, the discovery of unmet therapeutic targets, and the search for novel biomarkers. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

In-Silico Identification of Novel Pharmacological Synergisms: The Trabectedin Case

The in-silico strategy of identifying novel uses for already existing drugs, known as drug repositioning, has enhanced drug discovery. Previous studies have shown a positive correlation between expression changes induced by the anticancer agent trabectedin and those caused by irinotecan, a topoisomerase I inhibitor. Leveraging the availability of transcriptional datasets, we developed a general in-silico drug-repositioning approach that we applied to investigate novel trabectedin synergisms. We set a workflow allowing the identification of genes selectively modulated by a drug and possible novel drug interactions. To show its effectiveness, we selected trabectedin as a case-study drug. We retrieved eight transcriptional cancer datasets including controls and samples treated with trabectedin or its analog lurbinectedin. We compared gene signature associated with each dataset to the 476,251 signatures from the Connectivity Map database. The most significant connections referred to mitomycin-c, topoisomerase II inhibitors, a PKC inhibitor, a Chk1 inhibitor, an antifungal agent, and an antagonist of the glutamate receptor. Genes coherently modulated by the drugs were involved in cell cycle, PPARalpha, and Rho GTPases pathways. Our in-silico approach for drug synergism identification showed that trabectedin modulates specific pathways that are shared with other drugs, suggesting possible synergisms.

A model of human neural networks reveals NPTX2 pathology in ALS and FTLD

Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1, which involve human-specific mechanisms2-5 that cannot be directly studied in animal models. Here, to explore the emergence and consequences of TDP-43 pathologies, we generated induced pluripotent stem cell-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural precursors6. Single-cell transcriptomics and comparison to independent neural stem cells7 showed that iCoMoNSCs are uniquely homogenous and self-renewing. Differentiated iCoMoNSCs formed a self-organized multicellular system consisting of synaptically connected and electrophysiologically active neurons, which matured into long-lived functional networks (which we designate iNets). Neuronal and glial maturation in iNets was similar to that of cortical organoids8. Overexpression of wild-type TDP-43 in a minority of neurons within iNets led to progressive fragmentation and aggregation of the protein, resulting in a partial loss of function and neurotoxicity. Single-cell transcriptomics revealed a novel set of misregulated RNA targets in TDP-43-overexpressing neurons and in patients with TDP-43 proteinopathies exhibiting a loss of nuclear TDP-43. The strongest misregulated target encoded the synaptic protein NPTX2, the levels of which are controlled by TDP-43 binding on its 3' untranslated region. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-induced neurodegeneration. Notably, NPTX2 was consistently misaccumulated in neurons from patients with amyotrophic lateral sclerosis and frontotemporal lobar degeneration with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby revealing a TDP-43-dependent pathway of neurotoxicity.

Epidermal growth factor potentiates EGFR(Y992/1173)-mediated therapeutic response of triple negative breast cancer cells to cold atmospheric plasma-activated medium

Cold atmospheric plasma (CAP) holds promise as a cancer-specific treatment that selectively kills various types of malignant cells. We used CAP-activated media (PAM) to utilize a range of the generated short- and long-lived reactive species. Specific antibodies, small molecule inhibitors and CRISPR/Cas9 gene-editing approaches showed an essential role for receptor tyrosine kinases, especially epidermal growth factor (EGF) receptor, in mediating triple negative breast cancer (TNBC) cell responses to PAM. EGF also dramatically enhanced the sensitivity and specificity of PAM against TNBC cells. Site-specific phospho-EGFR analysis, signal transduction inhibitors and reconstitution of EGFR-depleted cells with EGFR-mutants confirmed the role of phospho-tyrosines 992/1173 and phospholipase C gamma signaling in up-regulating levels of reactive oxygen species above the apoptotic threshold. EGF-triggered EGFR activation enhanced the sensitivity and selectivity of PAM effects on TNBC cells. The proposed approach based on the synergy of CAP and EGFR-targeted therapy may provide new opportunities to improve the clinical management of TNBC.

The role of microRNAs in understanding sex-based differences in Alzheimer's disease

BACKGROUND

The incidence of Alzheimer's disease (AD)-the most frequent cause of dementia-is expected to increase as life expectancies rise across the globe. While sex-based differences in AD have previously been described, there remain uncertainties regarding any association between sex and disease-associated molecular mechanisms. Studying sex-specific expression profiles of regulatory factors such as microRNAs (miRNAs) could contribute to more accurate disease diagnosis and treatment.

METHODS

A systematic review identified six studies of microRNA expression in AD patients that incorporated information regarding the biological sex of samples in the Gene Expression Omnibus repository. A differential microRNA expression analysis was performed, considering disease status and patient sex. Subsequently, results were integrated within a meta-analysis methodology, with a functional enrichment of meta-analysis results establishing an association between altered miRNA expression and relevant Gene Ontology terms.

RESULTS

Meta-analyses of miRNA expression profiles in blood samples revealed the alteration of sixteen miRNAs in female and 22 miRNAs in male AD patients. We discovered nine miRNAs commonly overexpressed in both sexes, suggesting a shared miRNA dysregulation profile. Functional enrichment results based on miRNA profiles revealed sex-based differences in biological processes; most affected processes related to ubiquitination, regulation of different kinase activities, and apoptotic processes in males, but RNA splicing and translation in females. Meta-analyses of miRNA expression profiles in brain samples revealed the alteration of six miRNAs in female and four miRNAs in male AD patients. We observed a single underexpressed miRNA in female and male AD patients (hsa-miR-767-5p); however, the functional enrichment analysis for brain samples did not reveal any specifically affected biological process.

CONCLUSIONS

Sex-specific meta-analyses supported the detection of differentially expressed miRNAs in female and male AD patients, highlighting the relevance of sex-based information in biomedical data. Further studies on miRNA regulation in AD patients should meet the criteria for comparability and standardization of information.

GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.

Genomic resources for a historical collection of cultivated two-row European spring barley genotypes

Barley genomic resources are increasing rapidly, with the publication of a barley pangenome as one of the latest developments. Two-row spring barley cultivars are intensely studied as they are the source of high-quality grain for malting and distilling. Here we provide data from a European two-row spring barley population containing 209 different genotypes registered for the UK market between 1830 to 2014. The dataset encompasses RNA-sequencing data from six different tissues across a range of barley developmental stages, phenotypic datasets from two consecutive years of field-grown trials in the United Kingdom, Germany and the USA; and whole genome shotgun sequencing from all cultivars, which was used to complement the RNA-sequencing data for variant calling. The outcomes are a filtered SNP marker file, a phenotypic database and a large gene expression dataset providing a comprehensive resource which allows for downstream analyses like genome wide association studies or expression associations.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

DNA Damage Atlas: an atlas of DNA damage and repair

DNA damage and its improper repair are the major source of genomic alterations responsible for many human diseases, particularly cancer. To aid researchers in understanding the underlying mechanisms of genome instability, a number of genome-wide profiling approaches have been developed to monitor DNA damage and repair events. The rapid accumulation of published datasets underscores the critical necessity of a comprehensive database to curate sequencing data on DNA damage and repair intermediates. Here, we present DNA Damage Atlas (DDA, http://www.bioinformaticspa.com/DDA/), the first large-scale repository of DNA damage and repair information. Currently, DDA comprises 6,030 samples from 262 datasets by 59 technologies, covering 16 species, 10 types of damage and 135 treatments. Data collected in DDA was processed through a standardized workflow, including quality checks, hotspots identification and a series of feature characterization for the hotspots. Notably, DDA encompasses analyses of highly repetitive regions, ribosomal DNA and telomere. DDA offers a user-friendly interface that facilitates browsing, searching, genome browser visualization, hotspots comparison and data downloading, enabling convenient and thorough exploration for datasets of interest. In summary, DDA will stand as a valuable resource for research in genome instability and its association with diseases.

BloodChIP Xtra: an expanded database of comparative genome-wide transcription factor binding and gene-expression profiles in healthy human stem/progenitor subsets and leukemic cells

The BloodChIP Xtra database (http://bloodchipXtra.vafaeelab.com/) facilitates genome-wide exploration and visualization of transcription factor (TF) occupancy and chromatin configuration in rare primary human hematopoietic stem (HSC-MPP) and progenitor (CMP, GMP, MEP) cells and acute myeloid leukemia (AML) cell lines (KG-1, ME-1, Kasumi1, TSU-1621-MT), along with chromatin accessibility and gene expression data from these and primary patient AMLs. BloodChIP Xtra features significantly more datasets than our earlier database BloodChIP (two primary cell types and two cell lines). Improved methodologies for determining TF occupancy and chromatin accessibility have led to increased availability of data for rare primary cell types across the spectrum of healthy and AML hematopoiesis. However, there is a continuing need for these data to be integrated in an easily accessible manner for gene-based queries and use in downstream applications. Here, we provide a user-friendly database based around genome-wide binding profiles of key hematopoietic TFs and histone marks in healthy stem/progenitor cell types. These are compared with binding profiles and chromatin accessibility derived from primary and cell line AML and integrated with expression data from corresponding cell types. All queries can be exported to construct TF-gene and protein-protein networks and evaluate the association of genes with specific cellular processes.

A Knockout of the IFITM3 Gene Increases the Sensitivity of WI-38 VA13 Cells to the Influenza A Virus

One of the ways to regulate the sensitivity of human cells to the influenza virus is to knock out genes of the innate immune response. Promising targets for the knockout are genes of the interferon-inducible transmembrane protein (IFITM) family, in particular the IFITM3 gene, whose product limits the entry of a virus into the cell by blocking the fusion of the viral and endosomal membranes. In this study, by means of genome-editing system CRISPR/Cas9, monoclonal cell lines with an IFITM3 knockout were obtained based on WI-38 VA13 cells (human origin). It was found that such cell lines are more sensitive to infection by influenza A viruses of various subtypes. Nevertheless, this feature is not accompanied by an increased titer of newly formed viral particles in a culture medium.

Unravelling heterogeneous effects of cancer‑associated fibroblasts on poor prognosis markers in breast cancer EM‑G3 cell line: In vitro‑targeted treatment (anti‑IL-6, anti‑VEGF-A, anti‑MFGE8) based on transcriptomic profiling

Breast cancer is the most frequently diagnosed cancer in women worldwide. Although dramatically increased survival rates of early diagnosed cases have been observed, late diagnosed patients and metastatic cancer may still be considered fatal. The present study's main focus was on cancer‑associated fibroblasts (CAFs) which is an active component of the tumor microenvironment (TME) regulating the breast cancer ecosystem. Transcriptomic profiling and analysis of CAFs isolated from breast cancer skin metastasis, cutaneous basal cell carcinoma, and squamous cell carcinoma unravelled major gene candidates such as IL6, VEGFA and MFGE8 that induced co‑expression of keratins‑8/‑14 in the EM‑G3 cell line derived from infiltrating ductal breast carcinoma. Western blot analysis of selected keratins (keratin‑8, ‑14, ‑18, ‑19) and epithelial‑mesenchymal transition‑associated markers (SLUG, SNAIL, ZEB1, E‑/N‑cadherin, vimentin) revealed specific responses pointing to certain heterogeneity of the studied CAF populations. Experimental in vitro treatment using neutralizing antibodies against IL-6, VEGF‑A and MFGE8 attenuated the modulatory effect of CAFs on EM‑G3 cells. The present study provided novel data in characterizing and understanding the interactions between CAFs and EM‑G3 cells in vitro. CAFs of different origins support the pro‑inflammatory microenvironment and influence the biology of breast cancer cells. This observation potentially holds significant interest for the development of novel, clinically relevant approaches targeting the TME in breast cancer. Furthermore, its implications extend beyond breast cancer and have the potential to impact a wide range of other cancer types.

Transcriptomic and proteomic fingerprints induced by the fungicides difenoconazole and metalaxyl in zebrafish embryos

In this study, we applied OMICs analysis to identify substance-specific biomarker candidates, which may act as early indicators for specific ecotoxic modes of actions (MoA). Zebrafish embryos were exposed to two sublethal concentrations of difenoconazole and metalaxyl according to a modified protocol of the OECD test guideline No. 236. At the end of exposure, total RNA and protein were extracted, followed by transcriptomics and proteomics analysis. The analysis of significantly differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) revealed a positive exposure-response correlation in all test concentrations for both fungicides. Similarly, also a positive correlation between the obtained transcriptome and proteome data was observed, highlighting the robustness of our approach. From the detected DEGs, candidate biomarkers specific for difenoconazole (apoa1b, gatm, mylpfb and acta1b) and metalaxyl (lgals2b, abat, fabp1b.1 and myh9a) were selected, and their biological functions were discussed to assess the predictive potential.

Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model

Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.

Intron Editing Reveals SNORD-Dependent Maturation of the Small Nucleolar RNA Host Gene GAS5 in Human Cells

The GAS5 gene encodes a long non-coding RNA (lncRNA) and intron-located small nucleolar RNAs (snoRNAs). Its structure, splice variants, and diverse functions in mammalian cells have been thoroughly investigated. However, there are still no data on a successful knockout of GAS5 in human cells, with most of the loss-of-function experiments utilizing standard techniques to produce knockdowns. By using CRISPR/Cas9 to introduce double-strand breaks in the terminal intronic box C/D snoRNA genes (SNORDs), we created monoclonal cell lines carrying continuous deletions in one of the GAS5 alleles. The levels of GAS5-encoded box C/D snoRNAs and lncRNA GAS5 were assessed, and the formation of the novel splice variants was analyzed. To comprehensively evaluate the influence of specific SNORD mutations, human cell lines with individual mutations in SNORD74 and SNORD81 were obtained. Specific mutations in SNORD74 led to the downregulation of all GAS5-encoded SNORDs and GAS5 lncRNA. Further analysis revealed that SNORD74 contains a specific regulatory element modulating the maturation of the GAS5 precursor transcript. The results demonstrate that the maturation of GAS5 occurs through the m6A-associated pathway in a SNORD-dependent manner, which is a quite intriguing epitranscriptomic mechanism.

Computational protocol for the identification of X-linked genes contributing to X chromosome upregulation from RNA-sequencing datasets

The X chromosome/autosome ratio has been widely used to profile XCU at the chromosomal level. However, this approach overlooks features of inside genes. Here, we present a computational protocol for the identification of X-linked genes contributing to X chromosome upregulation from RNA-sequencing datasets. We describe steps for selecting data, preparing software, processing data, and data analysis. This protocol quantifies the contribution value and contribution increment of each X-linked gene to XCU. For complete details on the use and execution of this protocol, please refer to Lyu et al. (2022).1.

An integrated analysis identifies six molecular subtypes of pancreatic ductal adenocarcinoma revealing cellular and molecular landscape

Pancreatic ductal adenocarcinoma (PDA) has been found to have a high mortality rate. Despite continuous efforts, current histopathological classification is insufficient to guide individualized therapies of PDA. We first define the molecular subtypes of PDA (MSOP) based on a meta-cohort of 845 samples from 11 PDA datasets. We then performed functional analyses involving immunity, fibrosis and metabolism. We recognized six molecular subtypes with different survival statistics and molecular composition. The squamous basal-like (SBL) subtype had a poor prognosis and high infiltration of ENO1+ (Enolase 1)/ADM+ (Adrenomedullin) cancer-associated fibroblasts (CAFs). The immune mesenchymal-like (IML) subtype and the normal mesenchymal-like (NML) subtype were characterized by genes associated with extracellular matrix (ECM) activities and immune responses, having favorable prognoses. IML was featured by elevated exhausted immune signaling and inflammatory CAFs infiltration, whereas NML was featured with myofibroblastic CAFs infiltration. The exocrine-like (EL) subtype was high in exocrine signals, while the pure classical-like (PCL) subtype lacked immunocytes infiltration. The quiescent-like (QL) subtype had diminished metabolic signaling and high infiltration of NK cells. SBL, IML and NML were enriched in innate anti-PD-1 resistance signatures. In sum, this MSOP depicts a vivid cell-to-molecular atlas of the tumor microenvironment of PDA and might facilitate to design a precise combination of therapies that target immunity, metabolism and stroma.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

The Management of Data for the Banking, Qualification, and Distribution of Induced Pluripotent Stem Cells: Lessons Learned from the European Bank for Induced Pluripotent Stem Cells

The European Bank for induced pluripotent Stem Cells (EBiSC) was established in 2014 as a non-profit project for the banking, quality control, and distribution of human iPSC lines for research around the world. EBiSC iPSCs are deposited from diverse laboratories internationally and, hence, a key activity for EBiSC is standardising not only the iPSC lines themselves but also the data associated with them. This includes enabling unique nomenclature for the cells, as well as applying uniformity to the data provided by the cell line generator versus quality control data generated by EBiSC, and providing mechanisms to share personal data in a secure and GDPR-compliant manner. A joint approach implemented by EBiSC and the human pluripotent stem cell registry (hPSCreg®) has provided a solution that enabled hPSCreg® to improve its registration platform for iPSCs and EBiSC to have a pipeline for the import, standardisation, storage, and management of data associated with EBiSC iPSCs. In this work, we describe the experience of cell line data management for iPSC banking throughout the course of EBiSC's development as a central European banking infrastructure and present a model for how this could be implemented by other iPSC repositories to increase the FAIRness of iPSC research globally.

Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights

The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.

Anti-tumor activity of all-trans retinoic acid in gastric-cancer: gene-networks and molecular mechanisms

BACKGROUND

Gastric-cancer is a heterogeneous type of neoplastic disease and it lacks appropriate therapeutic options. There is an urgent need for the development of innovative pharmacological strategies, particularly in consideration of the potential stratified/personalized treatment of this tumor. All-Trans Retinoic-acid (ATRA) is one of the active metabolites of vitamin-A. This natural compound is the first example of clinically approved cyto-differentiating agent, being used in the treatment of acute promyelocytic leukemia. ATRA may have significant therapeutic potential also in the context of solid tumors, including gastric-cancer. The present study provides pre-clinical evidence supporting the use of ATRA in the treatment of gastric-cancer using high-throughput approaches.

METHODS

We evaluated the anti-proliferative action of ATRA in 27 gastric-cancer cell-lines and tissue-slice cultures from 13 gastric-cancer patients. We performed RNA-sequencing studies in 13 cell-lines exposed to ATRA. We used these and the gastric-cancer RNA-sequencing data of the TCGA/CCLE datasets to conduct multiple computational analyses.

RESULTS

Profiling of our large panel of gastric-cancer cell-lines for their quantitative response to the anti-proliferative effects of ATRA indicate that approximately half of the cell-lines are characterized by sensitivity to the retinoid. The constitutive transcriptomic profiles of these cell-lines permitted the construction of a model consisting of 42 genes, whose expression correlates with ATRA-sensitivity.  The model predicts that 45% of the TCGA gastric-cancers are sensitive to ATRA. RNA-sequencing studies performed in retinoid-treated gastric-cancer cell-lines provide insights into the gene-networks underlying ATRA anti-tumor activity. In addition, our data demonstrate that ATRA exerts significant immune-modulatory effects, which seem to be largely controlled by IRF1 up-regulation. Finally, we provide evidence of a feed-back loop between IRF1 and DHRS3, another gene which is up-regulated by ATRA.

CONCLUSIONS

ATRA is endowed with significant therapeutic potential in the stratified/personalized treatment gastric-cancer. Our data represent the fundaments for the design of clinical trials focusing on the use of ATRA in the personalized treatment of this heterogeneous tumor. Our gene-expression model will permit the development of a predictive tool for the selection of ATRA-sensitive gastric-cancer patients. The immune-regulatory responses activated by ATRA suggest that the retinoid and immune-checkpoint inhibitors constitute rational combinations for the management of gastric-cancer.

A higher dysregulation burden of brain DNA methylation in female patients implicated in the sex bias of Schizophrenia

Sex differences are pervasive in schizophrenia (SCZ), but the extent and magnitude of DNA methylation (DNAm) changes underlying these differences remain uncharacterized. In this study, sex-stratified differential DNAm analysis was performed in postmortem brain samples from 117 SCZ and 137 controls, partitioned into discovery and replication datasets. Three differentially methylated positions (DMPs) were identified (adj.p < 0.05) in females and 29 DMPs in males without overlap between them. Over 81% of these sex-stratified DMPs were directionally consistent between sexes but with different effect sizes. Females experienced larger magnitude of DNAm changes and more DMPs (based on data of equal sample size) than males, contributing to a higher dysregulation burden of DNAm in females SCZ. Additionally, despite similar proportions of female-related DMPs (fDMPs, 8%) being under genetic control compared with males (10%), significant enrichment of DMP-related single nucleotide polymorphisms (SNPs) in signals of genome-wide association studies was identified only in fDMPs. One DMP in each sex connected the SNPs and gene expression of CALHM1 in females and CCDC149 in males. PPI subnetworks revealed that both female- and male-related differential DNAm interacted with synapse-related dysregulation. Immune-related pathways were unique for females and neuron-related pathways were associated with males. This study reveals remarkable quantitative differences in DNAm-related sexual dimorphism in SCZ and that females have a higher dysregulation burden of SCZ-associated DNAm than males.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Genetic screening for anticancer genes highlights FBLN5 as a synthetic lethal partner of MYC

BACKGROUND

When ectopically overexpressed, anticancer genes, such as TRAIL, PAR4 and ORCTL3, specifically destroy tumour cells without harming untransformed cells. Anticancer genes can not only serve as powerful tumour specific therapy tools but studying their mode of action can reveal mechanisms underlying the neoplastic transformation, sustenance and spread.

METHODS

Anticancer gene discovery is normally accidental. Here we describe a systematic, gain of function, forward genetic screen in mammalian cells to isolate novel anticancer genes of human origin. Continuing with over 30,000 transcripts from our previous study, 377 cell death inducing genes were subjected to screening. FBLN5 was chosen, as a proof of principle, for mechanistic gene expression profiling, comparison pathways analyses and functional studies.

RESULTS

Sixteen novel anticancer genes were isolated; these included non-coding RNAs, protein-coding genes and novel transcripts, such as ZNF436-AS1, SMLR1, TMEFF2, LINC01529, HYAL2, NEIL2, FBLN5, YPEL4 and PHKA2-processed transcript. FBLN5 selectively caused inhibition of MYC in COS-7 (transformed) cells but not in CV-1 (normal) cells. MYC was identified as synthetic lethality partner of FBLN5 where MYC transformed CV-1 cells experienced cell death upon FBLN5 transfection, whereas FBLN5 lost cell death induction in MCF-7 cells upon MYC knockdown.

CONCLUSIONS

Sixteen novel anticancer genes are present in human genome including FBLN5. MYC is a synthetic lethality partner of FBLN5. Video Abstract.

Multiomics-empowered Deep Phenotyping of Ulcerative Colitis Identifies Biomarker Signatures Reporting Functional Remission States

INTRODUCTION

Ulcerative colitis [UC] is a chronic disease with rising incidence and unclear aetiology. Deep molecular phenotyping by multiomics analyses may provide novel insights into disease processes and characteristic features of remission states.

METHODS

UC pathomechanisms were assessed by proteome profiling of human tissue specimens, obtained from five distinct colon locations for each of the 12 patients included in the study. Systemic disease-associated alterations were evaluated thanks to a cross-sectional setting of mass spectrometry-based multiomics analyses comprising proteins, metabolites, and eicosanoids of plasma obtained from UC patients during acute episodes and upon remission, in comparison with healthy controls.

RESULTS

Tissue proteome profiling indicated colitis-associated activation of neutrophils, macrophages, B and T cells, fibroblasts, endothelial cells and platelets, and hypoxic stress, and suggested a general downregulation of mitochondrial proteins accompanying the establishment of apparent wound healing-promoting activities including scar formation. Whereas pro-inflammatory proteins were apparently upregulated by immune cells, the colitis-associated epithelial cells, fibroblasts, endothelial cells, and platelets seemed to predominantly contribute anti-inflammatory and wound healing-promoting proteins. Blood plasma proteomics indicated chronic inflammation and platelet activation, whereas plasma metabolomics identified disease-associated deregulations of gut and gut microbiome-derived metabolites. Upon remission several, but not all, molecular candidate biomarker levels recovered back to normal.

CONCLUSION

The findings may indicate that microvascular damage and platelet deregulation hardly resolve upon remission, but apparently persist as disease-associated molecular signatures. This study presents local and systemic molecular alterations integrated in a model for UC pathomechanisms, potentially supporting the assessment of disease and remission states in UC patients.

Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature

Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.

ELIXIR and Toxicology: a community in development

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Characterization of Cancer/Testis Antigens as Prognostic Markers of Ovarian Cancer

The main goal of this study was to characterize cancer/testis antigens (CTAs) as potential molecular markers of ovarian cancer. First, we gathered and analyzed a significantly large dataset of 21 selected CTAs that are encoded by 32 genes; the dataset consisted of the mutation data, expression data, and survival data of patients with ovarian cancer (n = 15,665). The 19 functionally significant missense mutations were identified in 9 CTA genes: ACRBP, CCT4, KDM5B, MAGEA1, MAGEA4, PIWIL1, PIWIL2, PRAME, and SPA17. The analysis of the mRNA expression levels of 21 CTAs in healthy and tumor ovarian tissue showed an up-regulation in the expression level of AKAP3, MAGEA4, PIWIL1, and PRAME in tumor samples and a down-regulation in the expression level of CTAG1A, CTAG1B, MAGEC1, and PIWIL2. The CCT4 up-regulation and PRAME mutations were correlated with a good prognosis for ovarian cancer, while higher levels of GAGE2A and CT45A1 mRNAs were correlated with a poor prognosis for ovarian cancer patients. Thus, GAGE2, CT45, CCT4, and PRAME cancer/testis antigens can be considered as potential prognostic markers for ovarian tumors, and GAGE2, CCT4, and PRAME were revealed to be correlated with the prognosis for ovarian cancer patients for the first time.

Coordination of Single-cell and Bulk RNA Sequencing to Construct a Cuproptosis-related Gene Prognostic Index for Endometrial Cancer Prognosis, Immune Microenvironment Infiltration, and Immunotherapy Treatment Options

Background: This study aims to identify molecular subtypes and develop a cuproptosis-related gene prognostic index (CRGPI) for endometrial cancer (EC), in addition to outlining the immune features and the efficacy of immune checkpoint inhibitor (ICI) therapy in CRGPI-defined groups of EC. Methods: Between malignant and normal cells distinguished from single-cell RNA sequencing data GSE154763 dataset, the difference in KEGG pathways and cuproptosis-related gene (CRG) scores was intensively investigated. On the basis of TCGA dataset (n=492), CRGs were used to identify two distinct molecular subtypes. Using the Cox regression method, a CRGPI was constructed and externally validated with the IMvigor210 dataset (n=348) and GSE78220. Then, the molecular and immune characteristics and the efficacy of ICI therapy in subgroups defined by CRGPI were investigated. Results: Compared to normal cells, the expression of the TCA cycle and CRGs genes was significantly higher in malignant cells. The CRGPI was established on the premise of ATF5, FBXO46, P2RX4, SMARCD3, DAPK3, and C1orf53. Comprehensive results demonstrated a correlation between a low CRGPI score and better overall survival, younger age, early stage, immune cells and functions activation, high tumor mutation burden and high microsatellite instability, as well as better benefit from ICI therapy, and its significance for forecasting immunotherapeutic effects was verified as well (IMvigor210 cohort: HR, 1.358; 95% CI, 1.047, 1.761; p=0.02; GSE78220 cohort: HR, HR = 3.857, 95% CI, 1.009, 14.74; p=0.034). CRGPI anticipated the immunotherapy medication. Conclusions: CRGPI is a prospective biomarker to estimate the prognosis, immune and molecular characteristics, and treatment benefit of immunotherapy in EC.

Identifying New Contributors to Brain Metastasis in Lung Adenocarcinoma: A Transcriptomic Meta-Analysis

Lung tumors frequently metastasize to the brain. Brain metastasis (BM) is common in advanced cases, and a major cause of patient morbidity and mortality. The precise molecular mechanisms governing BM are still unclear, in part attributed to the rarity of BM specimens. In this work, we compile a unique transcriptomic dataset encompassing RNA-seq, microarray, and single-cell analyses from BM samples obtained from patients with lung adenocarcinoma (LUAD). By integrating this comprehensive dataset, we aimed to enhance understanding of the molecular landscape of BM, thereby facilitating the identification of novel and efficient treatment strategies. We identified 102 genes with significantly deregulated expression levels in BM tissues, and discovered transcriptional alterations affecting the key driver 'hub' genes CD69 (a type II C-lectin receptor) and GZMA (Granzyme A), indicating an important role of the immune system in the development of BM from primary LUAD. Our study demonstrated a BM-specific gene expression pattern and revealed the presence of dendritic cells and neutrophils in BM, suggesting an immunosuppressive tumor microenvironment. These findings highlight key drivers of LUAD-BM that may yield therapeutic targets to improve patient outcomes.

Activation of AMPK promotes cardiac differentiation by stimulating the autophagy pathway

Autophagy, a critical catabolic process for cell survival against different types of stress, has a role in the differentiation of various cells, such as cardiomyocytes. Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is an energy-sensing protein kinase involved in the regulation of autophagy. In addition to its direct role in regulating autophagy, AMPK can also influence other cellular processes by regulating mitochondrial function, posttranslational acetylation, cardiomyocyte metabolism, mitochondrial autophagy, endoplasmic reticulum stress, and apoptosis. As AMPK is involved in the control of various cellular processes, it can influence the health and survival of cardiomyocytes. This study investigated the effects of an AMPK inducer (Metformin) and an autophagy inhibitor (Hydroxychloroquine) on the differentiation of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs). The results showed that autophagy was upregulated during cardiac differentiation. Furthermore, AMPK activation increased the expression of CM-specific markers in hPSC-CMs. Additionally, autophagy inhibition impaired cardiomyocyte differentiation by targeting autophagosome-lysosome fusion. These results indicate the significance of autophagy in cardiomyocyte differentiation. In conclusion, AMPK might be a promising target for the regulation of cardiomyocyte generation by in vitro differentiation of pluripotent stem cells.

Global comparative transcriptomes uncover novel and population-specific gene expression in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to understand its molecular basis, the impact of population-specific differences on this disease remains unexplored.

AIMS

This study aimed to investigate the population-specific differences in gene expression patterns among ESCC samples obtained from six distinct global populations, identify differentially expressed genes (DEGs) and their associated pathways, and identify potential biomarkers for ESCC diagnosis and prognosis. In addition, this study deciphers population specific microbial and chemical risk factors in ESCC.

METHODS

We compared the gene expression patterns of ESCC samples from six different global populations by analyzing microarray datasets. To identify DEGs, we conducted stringent quality control and employed linear modeling. We cross-compared the resulting DEG lists of each populations along with ESCC ATLAS to identify known and novel DEGs. We performed a survival analysis using The Cancer Genome Atlas Program (TCGA) data to identify potential biomarkers for ESCC diagnosis and prognosis among the novel DEGs. Finally, we performed comparative functional enrichment and toxicogenomic analysis.

RESULTS

Here we report 19 genes with distinct expression patterns among populations, indicating population-specific variations in ESCC. Additionally, we discovered 166 novel DEGs, such as ENDOU, SLCO1B3, KCNS3, IFI35, among others. The survival analysis identified three novel genes (CHRM3, CREG2, H2AC6) critical for ESCC survival. Notably, our findings showed that ECM-related gene ontology terms and pathways were significantly enriched among the DEGs in ESCC. We also found population-specific variations in immune response and microbial infection-related pathways which included genes enriched for HPV, Ameobiosis, Leishmaniosis, and Human Cytomegaloviruses. Our toxicogenomic analysis identified tobacco smoking as the primary risk factor and cisplatin as the main drug chemical interacting with the maximum number of DEGs across populations.

CONCLUSION

This study provides new insights into population-specific differences in gene expression patterns and their associated pathways in ESCC. Our findings suggest that changes in extracellular matrix (ECM) organization may be crucial to the development and progression of this cancer, and that environmental and genetic factors play important roles in the disease. The novel DEGs identified may serve as potential biomarkers for diagnosis, prognosis and treatment.

A standard for sharing spatial transcriptomics data

Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.

Transcription factor expression repertoire basis for epigenetic and transcriptional subtypes of colorectal cancers

Colorectal cancers (CRCs) form a heterogenous group classified into epigenetic and transcriptional subtypes. The basis for the epigenetic subtypes, exemplified by varying degrees of promoter DNA hypermethylation, and its relation to the transcriptional subtypes is not well understood. We link cancer-specific transcription factor (TF) expression alterations to methylation alterations near TF-binding sites at promoter and enhancer regions in CRCs and their premalignant precursor lesions to provide mechanistic insights into the origins and evolution of the CRC molecular subtypes. A gradient of TF expression changes forms a basis for the subtypes of abnormal DNA methylation, termed CpG-island promoter DNA methylation phenotypes (CIMPs), in CRCs and other cancers. CIMP is tightly correlated with cancer-specific hypermethylation at enhancers, which we term CpG-enhancer methylation phenotype (CEMP). Coordinated promoter and enhancer methylation appears to be driven by downregulation of TFs with common binding sites at the hypermethylated enhancers and promoters. The altered expression of TFs related to hypermethylator subtypes occurs early during CRC development, detectable in premalignant adenomas. TF-based profiling further identifies patients with worse overall survival. Importantly, altered expression of these TFs discriminates the transcriptome-based consensus molecular subtypes (CMS), thus providing a common basis for CIMP and CMS subtypes.