ArrayExpress--a public database of microarray experiments and gene expression profiles

Pattern Recognition-Driven Detection of Circadian-Disruptive Compounds from Gene Expressions: High-Throughput Screening and Experimental Verification

Circadian rhythms regulate the timing of numerous biological functions in organisms. Besides well-known external stimuli like the light-dark cycle and temperature, circadian rhythms can also be modulated by environmental substances. However, this area remains largely underexplored. Here, we developed a robust Pattern Recognition-Driven Prediction Approach (PRD-PA) that enables the identification of circadian-disruptive compounds from large-scale zebrafish transcriptomic profiling. The approach utilizes a circadian gene panel consisting of over 270 Circadian-Indicating Genes (CIGs) with stable and robust periodicity and combines it with a predictive model, known as the Differential Gene Expression Values of an Individual Comparison Model (DGVICM), that can effectively predict internal circadian phases from transcriptomic samples. By analyzing 692 aggregated gene expression profiles across 40 environmental substances, several were identified as having significant circadian-disruptive potential. These include glucocorticoids (e.g., prednisone (PRE) and triamcinolone (TRI)), the antithyroid agent propylthiouracil (PTU), and the widely used UV filter benzophenone-3 (BP-3). Both glucocorticoids and PTU are well-documented disruptors of circadian rhythms, and BP-3's circadian-disrupting properties were validated through experimental exposures. Moreover, BP-3 analogs, including 2,4-dihydroxybenzophenone (BP-1) and 2,2'-dihydroxy-4-methoxybenzophenone (BP-8), were also found to exhibit similar circadian-disruptive effects. Overall, the present findings demonstrated the reliability of the PRD-PA approach for circadian disruption screening and highlighted the presence of diverse circadian-disruptive substances in our environment.

A novel framework to build saliva-based DNA methylation biomarkers: Quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

Sex-specific role of epigenetic modification of a leptin upstream enhancer in adipose tissue

OBJECTIVE

Maternal hormonal status can have long-term effects on offspring metabolic health and is likely regulated via epigenetic mechanisms. We elucidated the effects of maternal thyroid hormones on the epigenetic regulation of leptin (Lep) transcription in adipose tissue (AT) and subsequently investigated the role of DNA methylation at a Lep upstream enhancer (UE) in adipocyte biology.

RESULTS

Pregnant mice treated with triiodothyronine (T3) produced offspring with reduced body weight, total fat mass, and gonadal white adipose tissue (gWAT) mass at 6 months of age (treatment: N = 8; control: N = 12). Compared with control offspring, exclusively female offspring of T3-treated mothers presented lower Lep mRNA levels and higher Lep UE methylation in gWAT. In murine preadipocytes, targeted demethylation of the Lep UE via a dCas9-SunTag-TET1 system reduced methylation by ~ 20%, but this effect was insufficient to alter Lep expression or lipid accumulation after differentiation. In human omental visceral AT (OVAT) samples from the Leipzig Obesity BioBank (LOBB, N = 52), LEP UE methylation was associated with body fat percentage, and mediation analysis indicated that leptin serum levels partially mediate this association exclusively in females.

CONCLUSION

Findings from the animal model suggest that maternal thyroid hormones influence offspring gWAT Lep expression in a sex-specific manner, potentially through changes in Lep UE methylation. However, in vitro experiments indicate that Lep UE methylation alone is not sufficient to regulate Lep expression or adipocyte lipid accumulation. In humans with obesity, LEP UE methylation is associated with body fat percentage, with leptin serum levels potentially acting as a mediator exclusively in females.

Integrative mapping of human CD8+ T cells in inflammation and cancer

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

A dynamic co-expression approach reveals Gins2 as a potential upstream modulator of HNSCC metastasis

Head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer that is notably associated with a high risk of lymph node metastasis, a major cause of cancer mortality. Current therapeutic options remain limited to surgery supplemented by radio- or chemotherapy; however, these interventions often result in high-grade toxicities. Distant metastasis significantly contributed to the poor prognosis and decreased survival rates. However, the underlying molecular mechanisms remain poorly understood. Disease-related "omics" data provide a comprehensive overview of gene relationships, helping to decode the complex molecular mechanisms involved. Interactions between biological molecules are complex and highly dynamic across various cellular conditions, making traditional co-expression methods inadequate for understanding these intricate relationships. In the present study, a novel three-way interaction approach was employed to uncover dynamic co-expression relationships underlying the metastatic nature of HNSCC. Subsequently, the biologically relevant triples from statistically significant ones were defined through gene set enrichment analysis and reconstruction of the gene regulatory network. Finally, the validity of biologically relevant triplets was assessed at the protein level. The results highlighted the "PI3K/AKT/mTOR (PAM) signaling pathway" as a disrupted pathway involved in the metastatic nature of HNSCC. Notably, Gins2, identified as a switch gene, along with the gene pair {Akt2, Anxa2}, formed a statistically significant and biologically relevant triplet. It suggests that Gins2 could serve as a potential upstream modulator in the PAM signaling pathway, playing a crucial role in the distant metastasis of HNSCC. In addition, survival analysis of significant switch genes indicated that two genes, C19orf33 and Usp13, may be especially important for prognostic purposes in HNSCC.

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer

Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective. To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. Our classifier model accurately identified the precise phenotypic status, achieving an area under the curve (AUC) of 0.886 at the tile-level and an AUC of 0.910 at the slide-level. Furthermore, we evaluated the efficacy of the classifier in predicting endocrine response using data from an independent ER+ breast cancer patient cohort. Our classifier achieved a predicting accuracy of 81.25%, and 88.7% slides labeled as endocrine resistant were predicted as the mesenchymal-phenotype, while 75.6% slides labeled as sensitive were predicted as the epithelial-phenotype. Our work introduces an H&E-based framework capable of accurately predicting EMT phenotype and endocrine response for ER+ breast cancer, demonstrating its potential for clinical application and benefit.

PerturbAtlas: a comprehensive atlas of public genetic perturbation bulk RNA-seq datasets

Manipulating gene expression is crucial for understanding gene function, with high-throughput sequencing techniques such as RNA-seq elucidating the downstream mechanisms involved. However, the lack of a standardized metadata format for small-scale perturbation expression datasets in public repositories hinders their reuse. To address this issue, we developed PerturbAtlas, an add-value resource that re-analyzes publicly archived RNA-seq libraries to provide quantitative data on gene expression, transcript profiles, and alternative splicing events following genetic perturbation. PerturbAtlas assists users in identifying trends at the gene and isoform levels in perturbation assays by re-analyzing a curated set of 122 801 RNA-seq libraries across 13 species. This resource is freely available at https://perturbatlas.kratoss.site as both raw data tables and an interactive browser, allowing searches by species, tissue or genomic features. The results provide detailed information on alterations following perturbations, accessible through both forward and reverse approaches, thereby enabling the exploration of perturbation consequences and the identification of potential causal perturbations.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Alleviating batch effects in cell type deconvolution with SCCAF-D

Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.

Building a modular and multi-cellular virtual twin of the synovial joint in Rheumatoid Arthritis

Rheumatoid arthritis is a complex disease marked by joint pain, stiffness, swelling, and chronic synovitis, arising from the dysregulated interaction between synoviocytes and immune cells. Its unclear etiology makes finding a cure challenging. The concept of digital twins, used in engineering, can be applied to healthcare to improve diagnosis and treatment for complex diseases like rheumatoid arthritis. In this work, we pave the path towards a digital twin of the arthritic joint by building a large, modular biochemical reaction map of intra- and intercellular interactions. This network, featuring over 1000 biomolecules, is then converted to one of the largest executable Boolean models for biological systems to date. Validated through existing knowledge and gene expression data, our model is used to explore current treatments and identify new therapeutic targets for rheumatoid arthritis.

Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis

BACKGROUND

Precision oncology's implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking.

METHODS

The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.

RESULTS

Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells.

CONCLUSION

This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway.

From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.

Real-World Performance of Integrative Clinical Genomics in Pediatric Precision Oncology

Despite significant improvement in the survival of pediatric patients with cancer, treatment outcomes for high-risk, relapsed, and refractory cancers remain unsatisfactory. Moreover, prolonged survival is frequently associated with long-term adverse effects due to intensive multimodal treatments. Accelerating the progress of pediatric oncology requires both therapeutic advances and strategies to mitigate the long-term cytotoxic side effects, potentially through targeting specific molecular drivers of pediatric malignancies. In this report, we present the results of integrative genomic and transcriptomic profiling of 230 patients with malignant solid tumors (the "primary cohort") and 18 patients with recurrent or otherwise difficult-to-treat nonmalignant conditions (the "secondary cohort"). The integrative workflow for the primary cohort enabled the identification of clinically significant single nucleotide variants, small insertions/deletions, and fusion genes, which were found in 55% and 28% of patients, respectively. For 38% of patients, molecularly informed treatment recommendations were made. In the secondary cohort, known or potentially driving alteration was detected in 89% of cases, including a suspected novel causal gene for patients with inclusion body infantile digital fibromatosis. Furthermore, 47% of findings also brought therapeutic implications for subsequent management. Across both cohorts, changes or refinements to the original histopathological diagnoses were achieved in 4% of cases. Our study demonstrates the efficacy of integrating advanced genomic and transcriptomic analyses to identify therapeutic targets, refine diagnoses, and optimize treatment strategies for challenging pediatric and young adult malignancies and underscores the need for broad implementation of precision oncology in clinical settings.

Flu-CED: A comparative transcriptomics database of influenza virus-infected human and animal models

BACKGROUND

The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models.

METHODS

We collected a large amount of transcriptome research data related to influenza virus-infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database.

RESULTS

Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu-CED database (Influenza comparative expression database, https://flu.com-med.org.cn/). Using integrated and processed transcriptomic data, we established a user-friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu-CED can quickly query single and multi-gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs.

CONCLUSION

Flu-CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction

The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped.

Methods

To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources.

Results

Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments.

Discussion

We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

EGFR and MUC1 as dual-TAA drug targets for lung cancer and colorectal cancer

Background

Epidermal growth factor receptor (EGFR) is a key protein in cellular signaling that is overexpressed in many human cancers, making it a compelling therapeutic target. On-target severe skin toxicity has limited its clinical application. Dual-targeting therapy represents a novel approach to overcome the challenges of EGFR-targeted therapies.

Methods

A single-cell tumor-normal RNA transcriptomic meta-atlas of lung adenocarcinoma (LUAD) and normal lung tissues was constructed from published data. Tumor associated antigens (TAAs) were screened from the genes which were expressed on cell surface and could distinguish cancer cells from normal cells. Expression of MUC1 and EGFR in tumors and normal tissues was detected by immunohistochemistry (IHC), bulk transcriptomic and single-cell transcriptomic analyses. RNA cut-off values were calculated using paired analysis of RNA sequencing and IHC in patient-derived tumor xenograft samples. They were used to estimate the abundance of EGFR- and MUC-positive subjects in The Cancer Genome Atlas Program (TCGA) database. Survival analysis of EGFR and MUC1 expression was carried out using the transcription and clinical data from TCGA.

Results

A candidate TAA target, transmembrane glycoprotein mucin 1 (MUC1), showed strong expression in cancer cells and low expression in normal cells. Single-cell analysis suggested EGFR and MUC1 together had better tumor specificity than the combination of EGFR with other drug targets. IHC data confirmed that EGFR and MUC1 were highly expressed on LUAD and colorectal cancer (CRC) clinical samples but not on various normal tissues. Notably, co-expression of EGFR and MUC1 was observed in 98.4% (n=64) of patients with LUAD and in 91.6% (n=83) of patients with CRC. It was estimated that EGFR and MUC1 were expressed in 97.5% of LUAD samples in the TCGA dataset. Besides, high expression of EGFR and MUC1 was significantly associated with poor prognosis of LUAD and CRC patients.

Conclusions

Single-cell RNA, bulk RNA and IHC data demonstrated the high expression levels and co-expression patterns of EGFR and MUC1 in tumors but not normal tissues. Therefore, it is a promising TAA combination for therapeutic targeting which could enhance on-tumor efficacy while reducing off-tumor toxicity.

Reproductomics: Exploring the Applications and Advancements of Computational Tools

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

Unveiling novel prognostic biomarkers and therapeutic targets for HBV-associated hepatocellular carcinoma through integrated bioinformatic analysis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, with limited treatment options. The goal of this study was to use integrated bioinformatic analysis to find possible biomarkers for prognosis and therapeutic targets for hepatitis B (HBV)-associated HCC. Three microarray datasets (GSE84402, GSE121248, and E-GEOD-19665) from patients with HBV-associated HCC were combined and analyzed. We identified differentially expressed genes (DEGs) and performed pathway enrichment analysis. We constructed protein-protein interaction networks to identify hub genes. We identified a total of 374 DEGs, which included 90 up-regulated and 284 down-regulated genes. Pathway enrichment analysis revealed associations with cell cycle, oocyte meiosis, and the p53 signaling pathway for up-regulated DEGs. Twenty hub genes were identified, and 9 of them (ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1) were validated using the Cancer Genome Atlas data and Kaplan-Meier survival analysis. These genes were significantly associated with a poor prognosis in HCC patients. Our research shows that ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1 may be useful for predicting how HBV-associated HCC will progress and for finding new ways to treat it. In addition to these further studies are needed to elucidate the functions of the remaining 11 identified hub genes (RRM2, NUSAP1, PBK, CCNB1, CCNB2, BUB1B, NEK2, CENPF, ASPM, TOP2A, and BUB1) in HCC development and progression.

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study

Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.

Investigating the impact of the interstitial fluid flow and hypoxia interface on cancer transcriptomes using a spheroid-on-chip perfusion system

Solid tumours are complex and heterogeneous systems, which exist in a dynamic biophysical microenvironment. Conventional cancer research methods have long relied on two-dimensional (2D) static cultures which neglect the dynamic, three-dimensional (3D) nature of the biophysical tumour microenvironment (TME), especially the role and impact of interstitial fluid flow (IFF). To address this, we undertook a transcriptome-wide analysis of the impact of IFF-like perfusion flow using a spheroid-on-chip microfluidic platform, which allows 3D cancer spheroids to be integrated into extracellular matrices (ECM)-like hydrogels and exposed to continuous perfusion, to mimic IFF in the TME. Importantly, we have performed these studies both in experimental (normoxia) and pathophysiological (hypoxia) oxygen conditions. Our data indicated that gene expression was altered by flow when compared to static conditions, and for the first time showed that these gene expression patterns differed in different oxygen tensions, reflecting a differential role of spheroid perfusion in IFF-like flow in tumour-relevant hypoxic conditions in the biophysical TME. We were also able to identify factors primarily linked with IFF-like conditions which are linked with prognostic value in cancer patients and therefore could correspond to a potential novel biomarker of IFF in cancer. This study therefore highlights the need to consider relevant oxygen conditions when studying the impact of flow in cancer biology, as well as demonstrating the potential of microfluidic models of flow to identify IFF-relevant tumour biomarkers.

Characteristics of H3K27M-mutant diffuse gliomas with a non-midline location

PURPOSE

Diffuse midline gliomas (DMG) with H3K27 alterations (H3K27M-DMG) are a highly aggressive form of brain cancer. In rare cases, H3K27 mutations have been observed in diffuse non-midline gliomas (DNMG). It is currently unclear how these tumors should be classified. Herein, we analyze the characteristics of DNMG with H3K27M mutations.

METHODS

We reviewed the clinical, radiological and histological characteristics of all patients with an H3K27M mutated diffuse glioma diagnosed in our institution, between 2016 and 2023, to identify cases with a non-midline location. We then performed a molecular characterization (DNA methylation profiling, whole genome and transcriptome sequencing or targeted sequencing) of patients with an H3K27M-mutant DNMG and reviewed previously reported cases.

RESULTS

Among 51 patients (18 children and 33 adults) diagnosed with an H3K27M diffuse glioma, we identified two patients (4%) who had a non-midline location. Including our two patients, 39 patients were reported in the literature with an H3K27M-mutant DNMG. Tumors were most frequently located in the temporal lobe (48%), affected adolescents and adults, and were associated with a poor outcome (median overall survival was 10.3 months (0.1-84)). Median age at diagnosis was 19.1 years. Tumors frequently harbored TP53 mutations (74%), ATRX mutations (71%) and PDGFRA mutations or amplifications (44%). In DNA methylation analysis, H3K27M-mutant DNMG clustered within or close to the reference group of H3K27M-mutant DMG. Compared to their midline counterpart, non-midline gliomas with H3K27M mutations seemed more frequently associated with PDGFRA alterations.

CONCLUSION

DNMG with H3K27M mutations share many similarities with their midline counterpart, suggesting that they correspond to a rare anatomical presentation of these tumors. This is of paramount importance, as they may benefit from new therapeutic approaches such as ONC201.

A brain cell atlas integrating single-cell transcriptomes across human brain regions

While single-cell technologies have greatly advanced our comprehension of human brain cell types and functions, studies including large numbers of donors and multiple brain regions are needed to extend our understanding of brain cell heterogeneity. Integrating atlas-level single-cell data presents a chance to reveal rare cell types and cellular heterogeneity across brain regions. Here we present the Brain Cell Atlas, a comprehensive reference atlas of brain cells, by assembling single-cell data from 70 human and 103 mouse studies of the brain throughout major developmental stages across brain regions, covering over 26.3 million cells or nuclei from both healthy and diseased tissues. Using machine-learning based algorithms, the Brain Cell Atlas provides a consensus cell type annotation, and it showcases the identification of putative neural progenitor cells and a cell subpopulation of PCDH9high microglia in the human brain. We demonstrate the gene regulatory difference of PCDH9high microglia between hippocampus and prefrontal cortex and elucidate the cell-cell communication network. The Brain Cell Atlas presents an atlas-level integrative resource for comparing brain cells in different environments and conditions within the Human Cell Atlas.

AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

Systematic transcriptomic analysis and temporal modelling of human fibroblast senescence

Cellular senescence is a diverse phenotype characterised by permanent cell cycle arrest and an associated secretory phenotype (SASP) which includes inflammatory cytokines. Typically, senescent cells are removed by the immune system, but this process becomes dysregulated with age causing senescent cells to accumulate and induce chronic inflammatory signalling. Identifying senescent cells is challenging due to senescence phenotype heterogeneity, and senotherapy often requires a combinatorial approach. Here we systematically collected 119 transcriptomic datasets related to human fibroblasts, forming an online database describing the relevant variables for each study allowing users to filter for variables and genes of interest. Our own analysis of the database identified 28 genes significantly up- or downregulated across four senescence types (DNA damage induced senescence (DDIS), oncogene induced senescence (OIS), replicative senescence, and bystander induced senescence) compared to proliferating controls. We also found gene expression patterns of conventional senescence markers were highly specific and reliable for different senescence inducers, cell lines, and timepoints. Our comprehensive data supported several observations made in existing studies using single datasets, including stronger p53 signalling in DDIS compared to OIS. However, contrary to some early observations, both p16 and p21 mRNA levels rise quickly, depending on senescence type, and persist for at least 8-11 days. Additionally, little evidence was found to support an initial TGFβ-centric SASP. To support our transcriptomic analysis, we computationally modelled temporal protein changes of select core senescence proteins during DDIS and OIS, as well as perform knockdown interventions. We conclude that while universal biomarkers of senescence are difficult to identify, conventional senescence markers follow predictable profiles and construction of a framework for studying senescence could lead to more reproducible data and understanding of senescence heterogeneity.

Combined Analysis of Multi-Study miRNA and mRNA Expression Data Shows Overlap of Selected miRNAs Involved in West Nile Virus Infections

The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been analyzed by several studies under different conditions and with different outcomes. A systematic comparison is therefore necessary. Furthermore, additional information from mRNA target expression data has rarely been taken into account to understand miRNA expression profiles under WNV infections. We conducted a meta-analysis of publicly available miRNA expression data from multiple independent studies, and analyzed them in a harmonized way to increase comparability. In addition, we used gene-set tests on mRNA target expression data to further gain evidence about differentially expressed miRNAs. For this purpose, we also studied the use of target information from different databases. We detected a substantial number of miRNA that emerged as differentially expressed from several miRNA datasets, and from the mRNA target data analysis as well. When using mRNA target data, we found that the targetscan databases provided the most useful information. We demonstrated improved miRNA detection through research synthesis of multiple independent miRNA datasets coupled with mRNA target set testing, leading to the discovery of multiple miRNAs which should be taken into account for further research on the molecular mechanism of WNV infections.

Blood methylation pattern reflects epigenetic remodelling in adipose tissue after bariatric surgery

BACKGROUND

Studies on DNA methylation following bariatric surgery have primarily focused on blood cells, while it is unclear to which extend it may reflect DNA methylation profiles in specific metabolically relevant organs such as adipose tissue. Here, we investigated whether adipose tissue depots specific methylation changes after bariatric surgery are mirrored in blood.

METHODS

Using Illumina 850K EPIC technology, we analysed genome-wide DNA methylation in paired blood, subcutaneous and omental visceral AT (SAT/OVAT) samples from nine individuals (N = 6 female) with severe obesity pre- and post-surgery.

FINDINGS

The numbers and effect sizes of differentially methylated regions (DMRs) post-bariatric surgery were more pronounced in AT (SAT: 12,865 DMRs from -11.5 to 10.8%; OVAT: 14,632 DMRs from -13.7 to 12.8%) than in blood (9267 DMRs from -8.8 to 7.7%). Cross-tissue DMRs implicated immune-related genes. Among them, 49 regions could be validated with similar methylation changes in blood from independent individuals. Fourteen DMRs correlated with differentially expressed genes in AT post bariatric surgery, including downregulation of PIK3AP1 in both SAT and OVAT. DNA methylation age acceleration was significantly higher in AT compared to blood, but remained unaffected after surgery.

INTERPRETATION

Concurrent methylation pattern changes in blood and AT, particularly in immune-related genes, suggest blood DNA methylation mirrors AT's inflammatory state post-bariatric surgery.

FUNDING

The funding sources are listed in the Acknowledgments section.

Enterococcal quorum-controlled protease alters phage infection

Increased prevalence of multidrug-resistant bacterial infections has sparked interest in alternative antimicrobials, including bacteriophages (phages). Limited understanding of the phage infection process hampers our ability to utilize phages to their full therapeutic potential. To understand phage infection dynamics, we performed proteomics on Enterococcus faecalis infected with the phage VPE25. We discovered that numerous uncharacterized phage proteins are produced during phage infection of E. faecalis. Additionally, we identified hundreds of changes in bacterial protein abundances during infection. One such protein, enterococcal gelatinase (GelE), an fsr quorum-sensing-regulated protease involved in biofilm formation and virulence, was reduced during VPE25 infection. Plaque assays showed that mutation of either the quorum-sensing regulator fsrA or gelE resulted in plaques with a "halo" morphology and significantly larger diameters, suggesting decreased protection from phage infection. GelE-associated protection during phage infection is dependent on the putative murein hydrolase regulator LrgA and antiholin-like protein LrgB, whose expression have been shown to be regulated by GelE. Our work may be leveraged in the development of phage therapies that can modulate the production of GelE thereby altering biofilm formation and decreasing E. faecalis virulence.

Evaluating zoonotic metacestodes: gross and histopathological alterations of beef in north-west Ethiopia one health approach for meat inspection and animal management

Zoonotic metacestodes present a significant threat to both veterinary and public health. Specifically, the prevalence of metacestodes is often concentrated among consumers of raw meat and underdeveloped countries. The objective of this study was to estimate the prevalence of condemned red offal and examine the gross and histopathology features of zoonotic metacestodes. A cross-sectional study was conducted from November 2022 to July 2023 at the Bahir Dar municipal abattoir. A simple random sampling method employed in the abattoir survey to investigate pathological changes of offal and its rate of condemnation. Following a gross inspection of the red offal, representative tissue samples collected and preserved in 10% neutral buffered formalin. Subsequently, the size and number of cysts determined, and their viability and fertility evaluated. Hematoxylin and eosin staining utilized to analyze various lesions with microscope. A total of 340 cattle examined and 7.5% red offal condemned due to hydatid cysts 4.12% in the lungs, 3% in the liver, 0.6% in the kidneys, and 0.9% in other organs. Red offal condemned due to Cysticercus bovis 0.6% in the liver and 0.3% in the tongue. A statistically significance relationship was found between lung rejection due to hydatidosis (p < 0.05), body condition score, and origin of the animal. Among the detected calcified cysts, 83.34% of C. bovis and 47.62% of hydatid cysts. Histopathological examination revealed hydatid cysts and their oncospheres within the portal circulation, as well as necrotized, calcified daughter cysts observed on Bowman's capsule. The alveoli and bronchiole parenchyma compressed with pressure of protoscolices and it infiltrated by eosinophils. The cyst wall is attached to the thick hepatic capsule of the liver, with the hepatic parenchyma displaying islands of irregular hepatocytes. Cysticercus bovis detected in the deteriorated and necrotized muscle bundles, along with granulomatous lesions characterized by infiltration of mononuclear cells. Gross and histological examinations is invaluable tool for diagnosing hydatidosis and cysticercosis, providing well-organized baseline data to enhance our understanding the burden of zoonotic metacestodes.

A foundational large language model for edible plant genomes

Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b  for future research purposes.

Unveiling the peptidases of parasites from the office chair - The endothelin-converting enzyme case study

The emergence of high-throughput methodologies such as next-generation sequencing and proteomics has necessitated significant advancements in biological databases and bioinformatic tools, therefore reshaping the landscape of research into parasitic peptidases. In this review we outline the development of these resources along the -omics technologies and their transformative impact on the field. Apart from extensive summary of general and specific databases and tools, we provide a general pipeline on how to use these resources effectively to identify candidate peptidases from these large datasets and how to gain as much information about them as possible without leaving the office chair. This pipeline is then applied in an illustrative case study on the endothelin-converting enzyme 1 homologue from Schistosoma mansoni and attempts to highlight the contemporary capabilities of bioinformatics. The case study demonstrate how such approach can aid to hypothesize enzyme functions and interactions through computational analysis alone effectively and emphasizes how such virtual investigations can guide and optimize subsequent wet lab experiments therefore potentially saving precious time and resources. Finally, by showing what can be achieved without traditional wet laboratory methods, this review provides a compelling narrative on the use of bioinformatics to bridge the gap between big data and practical research applications, highlighting the key role of these technologies in furthering our understanding of parasitic diseases.

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.

Comprehensive molecular analyses and experimental validation of CDCAs with potential implications in kidney renal papillary cell carcinoma prognosis

Previous reports have revealed that the abnormal expression of the cell division cycle-associated gene family (CDCAs) is closely associated with some human cancers. However, the precise functional roles and mechanisms of CDCAs in kidney renal papillary cell carcinoma (KIRP) remain unclear. In this study, RNA sequencing data from the Cancer Genome Atlas database and Genotype-Tissue Expression databases were utilized to perform the expression, correlation, survival, mutation, functional enrichment analysis, and immunoinfiltration analyses of CDCAs in KIRP. We found that the expression levels of CDCA genes were significantly increased in KIRP across multiple databases, as confirmed by immunohistochemistry and quantitative reverse transcription PCR (RT-qPCR). Moreover, increased expression of CDCA genes is significantly associated with poor prognosis. Univariate and multivariate Cox regression analyses demonstrated that pathologic T and N staging, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 were independent prognostic factors for patients with KIRP. Utilizing these nine variables, we developed a nomogram prognostic model. Furthermore, the results of GO and KEGG functional enrichment analyses suggested that CDCA genes were associated with nuclear division, mitotic nuclear division, and chromosome segregation and were involved in the cell cycle, p53 signaling pathway, and cellular senescence. We found that the expression of NUF2, CDCA2, CDCA5, and CBX2 was closely associated with the expression of lymphocytes, immunostimulatory molecules, immunoinhibitory molecules, and chemokines. In summary, NUF2, CDCA2, CDCA3, CDCA5, CBX2, CDCA7, and CDCA8 are potential biomarkers for KIRP diagnosis and prognosis.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

Enterococcal quorum-controlled protease alters phage infection

Increased prevalence of multidrug resistant bacterial infections has sparked interest in alternative antimicrobials, including bacteriophages (phages). Limited understanding of the phage infection process hampers our ability to utilize phages to their full therapeutic potential. To understand phage infection dynamics we performed proteomics on Enterococcus faecalis infected with the phage VPE25. We discovered numerous uncharacterized phage proteins are produced during phage infection of Enterococcus faecalis. Additionally, we identified hundreds of changes in bacterial protein abundances during infection. One such protein, enterococcal gelatinase (GelE), an fsr quorum sensing regulated protease involved in biofilm formation and virulence, was reduced during VPE25 infection. Plaque assays showed that mutation of either the fsrA or gelE resulted in plaques with a "halo" morphology and significantly larger diameters, suggesting decreased protection from phage infection. GelE-associated protection during phage infection is dependent on the murein hydrolase regulator LrgA and antiholin-like protein LrgB, whose expression have been shown to be regulated by GelE. Our work may be leveraged in the development of phage therapies that can modulate the production of GelE thereby altering biofilm formation and decreasing E. faecalis virulence.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

scPerturb: harmonized single-cell perturbation data

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.

Phenotypic screening reveals a highly selective phthalimide-based compound with antileishmanial activity

Pharmacophores such as hydroxyethylamine (HEA) and phthalimide (PHT) have been identified as potential synthons for the development of compounds against various parasitic infections. In order to further advance our progress, we conducted an experiment utilising a collection of PHT and HEA derivatives through phenotypic screening against a diverse set of protist parasites. This approach led to the identification of a number of compounds that exhibited significant effects on the survival of Entamoeba histolytica, Trypanosoma brucei, and multiple life-cycle stages of Leishmania spp. The Leishmania hits were pursued due to the pressing necessity to expand our repertoire of reliable, cost-effective, and efficient medications for the treatment of leishmaniases. Antileishmanials must possess the essential capability to efficiently penetrate the host cells and their compartments in the disease context, to effectively eliminate the intracellular parasite. Hence, we performed a study to assess the effectiveness of eradicating L. infantum intracellular amastigotes in a model of macrophage infection. Among eleven L. infantum growth inhibitors with low-micromolar potency, PHT-39, which carries a trifluoromethyl substitution, demonstrated the highest efficacy in the intramacrophage assay, with an EC50 of 1.2 +/- 3.2 μM. Cytotoxicity testing of PHT-39 in HepG2 cells indicated a promising selectivity of over 90-fold. A chemogenomic profiling approach was conducted using an orthology-based method to elucidate the mode of action of PHT-39. This genome-wide RNA interference library of T. brucei identified sensitivity determinants for PHT-39, which included a P-type ATPase that is crucial for the uptake of miltefosine and amphotericin, strongly indicating a shared route for cellular entry. Notwithstanding the favourable properties and demonstrated efficacy in the Plasmodium berghei infection model, PHT-39 was unable to eradicate L. major infection in a murine infection model of cutaneous leishmaniasis. Currently, PHT-39 is undergoing derivatization to optimize its pharmacological characteristics.

Serum myostatin as a candidate disease severity and progression biomarker of spinal muscular atrophy

The identification of biomarkers for spinal muscular atrophy is crucial for predicting disease progression, severity, and response to new disease-modifying therapies. This study aimed to investigate the role of serum levels of myostatin and follistatin as biomarkers for spinal muscular atrophy, considering muscle atrophy secondary to denervation as the main clinical manifestation of the disease. The study evaluated the differential gene expression of myostatin and follistatin in a lesional model of gastrocnemius denervation in mice, as well as in a meta-analysis of three datasets in transgenic mice models of spinal muscular atrophy, and in two studies involving humans with spinal muscular atrophy. Subsequently, a case-control study involving 27 spinal muscular atrophy patients and 27 controls was conducted, followed by a 12-month cohort study with 25 spinal muscular atrophy cases. Serum levels of myostatin and follistatin were analysed using enzyme-linked immunosorbent assay at a single centre in southern Brazil. Skeletal muscle gene expression of myostatin decreased and of follistatin increased following lesional muscle denervation in mice, consistent with findings in the spinal muscular atrophy transgenic mice meta-analysis and in the iliopsoas muscle of five patients with spinal muscular atrophy type 1. Median serum myostatin levels were significantly lower in spinal muscular atrophy patients (98 pg/mL; 5-157) compared to controls (412 pg/mL; 299-730) (P < 0.001). Lower myostatin levels were associated with greater disease severity based on clinician-rated outcomes (Rho = 0.493-0.812; P < 0.05). After 12 months, there was a further reduction in myostatin levels among spinal muscular atrophy cases (P = 0.021). Follistatin levels did not differ between cases and controls, and no significant changes were observed over time. The follistatin:myostatin ratio was significantly increased in spinal muscular atrophy subjects and inversely correlated with motor severity. Serum myostatin levels show promise as a novel biomarker for evaluating the severity and progression of spinal muscular atrophy. The decrease in myostatin levels and the subsequent favourable environment for muscle growth may be attributed to denervation caused by motor neuron dysfunction.

AgeMeta: Quantitative Gene Expression Database of Mammalian Aging

AgeMeta is a database that provides systemic and quantitative description of mammalian aging at the level of gene expression. It encompasses transcriptomic changes with age across various tissues of humans, mice, and rats, based on a comprehensive meta-analysis of 122 publicly available gene expression datasets from 26 studies. AgeMeta provides an intuitive visual interface for quantification of aging-associated transcriptomics at the level of individual genes and functional groups of genes, allowing easy comparison among various species and tissues. Additionally, all the data in the database can be downloaded and analyzed independently. Overall, this work contributes to the understanding of the complex network of biological processes underlying mammalian aging and supports future advancements in this field. AgeMeta is freely available at: https://age-meta.com/.

Hypomethylation of Wnt signaling regulator genes in developmental language disorder

Background: Developmental language disorder (DLD) is a neurodevelopmental disorder. Considering the pivotal role of epigenetics in neurodevelopment, we examined any altered DNA methylation between DLD and control subjects. Materials & methods: We looked into genome-wide methylation differences between DLD and control groups. The findings were validated by quantitative PCR (qPCR). Results: In the DLD group, differential methylation of CpG sites was observed in the Wnt signaling regulator genes APCDD1, AMOTL1, LRP5, MARK2, TMEM64, TRABD2B, VEPH1 and WNT2B. Hypomethylation of APCDD1, LRP5 and WNT2B was confirmed by qPCR. Conclusion: This is the first report associating Wnt signaling with DLD. The findings are relevant in the light of the essential role of Wnt in myelination, and of the altered myelination in DLD.

Parameterization of asymmetric sigmoid functions in weighted gene co-expression network analysis

In most the biological contexts, examining gene expressions at the genomic level gives more accurate results than examining genes individually. It can improve understanding of the molecular mechanisms that cause molecular alterations. Weighted gene co-expression network analysis (WGCNA), which has recently been widely used to cluster transcriptomic datasets, implements a soft thresholding procedure using power function. However, these functions may sometimes exaggerate minor differences in expression correlations. We have previously proposed to use asymmetric sigmoid functions in soft thresholding as an alternative solution. However, the number of variables in asymmetric sigmoid functions may vary and parameterization can be problematic. In this study, we have introduced a systematic procedure for parameterizing asymmetric sigmoid function to ease using it as an alternative soft-thresholding solution in WGCNA. The efficiency of the employment was shown on four different COVID-19 datasets, on a yeast dataset, and on an E.Coli dataset. The results indicate that this approach provides biologically plausible associations for the resulting modules.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Network Analysis Performed on Transcriptomes of Parkinson's Disease Patients Reveals Dysfunction in Protein Translation

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra region of the brain. The hallmark pathological feature of PD is the accumulation of misfolded proteins, leading to the formation of intracellular aggregates known as Lewy bodies. Recent data evidenced how disruptions in protein synthesis, folding, and degradation are events commonly observed in PD and may provide information on the molecular background behind its etiopathogenesis. In the present study, we used a publicly available transcriptomic microarray dataset of peripheral blood of PD patients and healthy controls (GSE6613) to investigate the potential dysregulation of elements involved in proteostasis-related processes at the transcriptomic level. Our bioinformatics analysis revealed 375 differentially expressed genes (DEGs), of which 281 were down-regulated and 94 were up-regulated. Network analysis performed on the observed DEGs highlighted a cluster of 36 elements mainly involved in the protein synthesis processes. Different enriched ontologies were related to translation initiation and regulation, ribosome structure, and ribosome components nuclear export. Overall, this data consistently points to a generalized impairment of the translational machinery and proteostasis. Dysregulation of these mechanics has been associated with PD pathogenesis. Understanding the precise regulation of such processes may shed light on the molecular mechanisms of PD and provide potential data for early diagnosis.

Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is a central genome-wide method for in vivo analyses of DNA-protein interactions in various cellular conditions. Numerous studies have demonstrated the complex contextual organization of ChIP-seq peak sequences and the presence of binding sites for transcription factors in them. We assessed the dependence of the ChIP-seq peak score on the presence of different contextual signals in the peak sequences by analyzing these sequences from several ChIP-seq experiments using our fully enumerative GPU-based de novo motif discovery method, Argo_CUDA. Analysis revealed sets of significant IUPAC motifs corresponding to the binding sites of the target and partner transcription factors. For these ChIP-seq experiments, multiple regression models were constructed, demonstrating a significant dependence of the peak scores on the presence in the peak sequences of not only highly significant target motifs but also less significant motifs corresponding to the binding sites of the partner transcription factors. A significant correlation was shown between the presence of the target motifs FOXA2 and the partner motifs HNF4G, which found experimental confirmation in the scientific literature, demonstrating the important contribution of the partner transcription factors to the binding of the target transcription factor to DNA and, consequently, their important contribution to the peak score.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

A novel therapeutic approach to prevent Helicobacter pylori induced gastric cancer using networking biology, molecular docking, and simulation approaches

Helicobacter pylori infects 50% of the world population and in 80% of cases, the infection progresses to the point where an ulcer develops leading to gastric cancer (GC). This study aimed to prevent GC by predicting Hub genes that are inducing GC. Furthermore, the study objective was to screen inhibitory molecules that block the function of predicted genes through several biophysical approaches. These proteins, such as Mucin 4 (MUC4) and Baculoviral IAP repeat containing 3 (BIRC3), had LogFC values of 2.28 and 3.39, respectively, and were found to be substantially expressed in those who had H. pylori infection. The MUC4 and BIRC3 inhibit apoptosis of infected cells and promote cancerous cell survival. The proteins were examined for their Physico-chemical characteristics, 3D structure and secondary structure analysis, solvent assessable surface area (SASA), active site identification, and network analysis. The MUC4 and BIRC3 expression was inhibited by docking eighty different compounds collected from the ZINC database. Fifty-seven compounds were successfully docked into the active site resulting in the lowest binding energy scores. The ZINC585267910 and ZINC585268691 compounds showed the lowest binding energy of -8.5 kcal/mol for MUC4 and -7.1 kcal/mol for BIRC3, respectively, and were considered best-docked solutions for molecular dynamics simulations. The mean root mean square deviation (RMSD) value for the ZINC585267910-MUC4 complex was 0.86 Å and the ZINC585268691-BIRC3 complex was 1.01 Å. The net MM/GBSA energy value of the ZINC585267910-MUC4 complex estimated was -46.84 kcal/mol and that of the ZINC585268691-BIRC3 complex was -44.84 kcal/mol. In a nutshell, the compounds might be investigated further as an inhibitor of the said proteins to stop the progress of GC induced by H. pylori.Communicated by Ramaswamy H. Sarma.