ArrayExpress update - from bulk to single-cell expression data

Relationships between type 2 diabetes, cell dysfunction, and redox signaling: A meta-analysis of single-cell gene expression of human pancreatic α- and β-cells

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by insulin resistance and failure of β-cells to meet the metabolic demand for insulin. Recent advances in single-cell RNA sequencing (sc-RNA-Seq) have allowed for in-depth studies to further understand the underlying cellular mechanisms of T2DM. In β-cells, redox signaling is critical for insulin production. A meta-analysis of human pancreas islet sc-RNA-Seq data was conducted to evaluate how T2DM may modify the transcriptomes of α- and β-cells.

METHODS

Annotated sc-RNA-Seq data from six studies of human pancreatic islets from metabolically healthy and donors with T2DM were collected. α- and β-cells, subpopulations of proliferating α-cells, immature, and senescent β-cells were identified based on expression levels of key marker genes. Each dataset was analyzed individually before combining, using weighted comparisons. Pathways of significant genes and individual redox-related gene expression were then evaluated to further understand the role that redox signaling may play in T2DM-induced β-cell dysfunction.

RESULTS

α- and β-cells from T2DM donors modified genes involved in energy metabolism, immune response, autophagy, and cellular stress. α- and β-cells also had an increased nuclear factor erythroid 2-related factor 2 (NFE2L2)-mediated antioxidant response in T2DM donors. The proportion of immature and senescent β-cells increased in T2DM donors, and in immature and senescent β-cells, genes regulated by NFE2L2 were further upregulated.

CONCLUSIONS

These findings suggest that NFE2L2 plays a role in β-cell maturation and dysfunction. Redox singling maybe a key pathway for β-cell restoration and T2DM therapeutics.

Improving Analysis and Annotation of Microarray Data with Protein Interactions

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.

Significance of CD47 and Its Association With Tumor Immune Microenvironment Heterogeneity in Ovarian Cancer

Background

It was reported that tumor heterogeneity and the surrounding tumor microenvironment (TME) in ovarian cancer affects immunotherapy efficacy and patient outcomes. And the TME of ovarian cancer is intrinsically heterogeneous. CD47 plays vital roles in cell functional behavior and immune homeostasis relating to cancer prognosis. But how it affects TME and its contribution to heterogeneity in ovarian cancer has not been fully illustrated. Therefore, we aimed to identify a prognostic biomarker which may help explain tumor immune microenvironment heterogeneity of ovarian cancer.

Methods

Cancer single-cell state atlas (CancerSEA) was used to evaluate functional role of CD47. Several bioinformatics database including Oncomine, Gene Expression Profiling Interaction Analysis (GEPIA), Tumor Immune Estimation Resource (TIMER), The Human Protein Atlas (HPA), Ualcan and Kaplan-Meier plotter (KM plotter) were applied to illustrate correlation of CD47 with ovarian cancer prognosis and immune infiltration. Tumor Immune Single-cell Hub (TISCH) single cell database was employed to evaluate correlation of CD47 with tumor microenvironment. GeneMANIA was implemented to identify regulation networks of CD47. Differentially expressed genes (DEGs) between CD47 high and low expression groups were analyzed with R package DESeq2. Kyoto encyclopedia of genes and genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were utilized to explore how CD47 affect the immune related cell signaling pathway.

Results

CD47 expression was upregulated and connected to worse OS and PFS in ovarian cancer. Close relation was found between CD47 expression level and immune infiltration in ovarian cancer, especially with Treg cells, Monocytes, Macrophages and T cell exhaustion (P<0.05). The CD47 expression level was relatively low in plasma cells, dendritic cells and Mono/Macro cells of OV_GSE115007, in myofibroblasts, fibroblasts and endothelial cells of OV_GSE118828, compared to malignant cells of OV_GSE118828 dataset. The cell components and distribution in primary and metastatic ovarian cancer are quite distinct, which may lead to TME heterogeneity of ovarian cancer.

Conclusion

Our results indicated that CD47 is closely correlated to ovarian cancer immune microenvironment and might induce ovarian cancer heterogeneity. Therefore, CD47 may be used as a candidate prognostic biomarker and provide us with new insights into potential immunotherapy in ovarian cancer patients.

Fibroblast growth factor (FGF), FGF receptor (FGFR), and cyclin D1 (CCND1) DNA methylation in head and neck squamous cell carcinomas is associated with transcriptional activity, gene amplification, human papillomavirus (HPV) status, and sensitivity to tyrosine kinase inhibitors

Background

Dysregulation of fibroblast growth factor receptor (FGFR) signaling pathway has been observed in head and neck squamous cell carcinoma (HNSCC) and is a promising therapeutic target for selective tyrosine kinase inhibitors (TKIs). Potential predictive biomarkers for response to FGFR-targeted therapies are urgently needed. Understanding the epigenetic regulation of FGF pathway related genes, i.e. FGFRs, FGFs, and CCND1, could enlighten the way towards biomarker-selected FGFR-targeted therapies.

Methods

We performed DNA methylation analysis of the encoding genes FGFR1, FGFR2, FGFR3, FGFR4, FGF1-14, FGF16-23, and CCND1 at single CpG site resolution (840 CpG sites) employing The Cancer Genome Research Atlas (TCGA) HNSCC cohort comprising N = 530 tumor tissue and N = 50 normal adjacent tissue samples. We correlated DNA methylation to mRNA expression with regard to human papilloma virus (HPV) and gene amplification status. Moreover, we investigated the correlation of methylation with sensitivity to the selective FGFR inhibitors PD 173074 and AZD4547 in N = 40 HPV(−) HNSCC cell lines.

Results

We found sequence-contextually nuanced CpG methylation patterns in concordance with epigenetically regulated genes. High methylation levels were predominantly found in the promoter flank and gene body region, while low methylation levels were present in the central promoter region for most of the analyzed CpG sites. FGFRs, FGFs, and CCND1 methylation differed significantly between tumor and normal adjacent tissue and was associated with HPV and gene amplification status. CCND1 promoter methylation correlated with CCND1 amplification. For most of the analyzed CpG sites, methylation levels correlated to mRNA expression in tumor tissue. Furthermore, we found significant correlations of DNA methylation of specific CpG sites with response to the FGFR1/3–selective inhibitors PD 173074 and AZD4547, predominantly within the transcription start site of CCND1.

Conclusions

Our results suggest an epigenetic regulation of CCND1, FGFRs, and FGFs via DNA methylation in HNSCC and warrants further investigation of DNA methylation as a potential predictive biomarker for response to selective FGFR inhibitors in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01212-4.

Cystic Fibrosis: Systems Biology Analysis from Homozygous p.Phe508del Variant Patients' Samples Reveals Perturbations in Tissue-Specific Pathways

Cystic fibrosis (CF) is an autosomal recessive disorder, caused by diverse genetic variants for the CF transmembrane conductance regulator (CFTR) protein. Among these, p.Phe508del is the most prevalent variant. The effects of this variant on the physiology of each tissue remains unknown. This study is aimed at predicting cell signaling pathways present in different tissues of fibrocystic patients, homozygous for p.Phe508del. The study involved analysis of two microarray datasets, E-GEOD-15568 and E-MTAB-360 corresponding to the rectal and bronchial epithelium, respectively, obtained from the ArrayExpress repository. Particularly, differentially expressed genes (DEGs) were predicted, protein-protein interaction (PPI) networks were designed, and centrality and functional interaction networks were analyzed. The study reported that p.Phe508del-mutated CFTR-allele in homozygous state influenced the whole gene expression in each tissue differently. Interestingly, gene ontology (GO) term enrichment analysis revealed that only “neutrophil activation” was shared between both tissues; however, nonshared DEGs were grouped into the same GO term. For further verification, functional interaction networks were generated, wherein no shared nodes were reported between these tissues. These results suggested that the p.Phe508del-mutated CFTR-allele in homozygous state promoted tissue-specific pathways in fibrocystic patients. The generated data might further assist in prediction diagnosis to define biomarkers or devising therapeutic strategies.

Characterization of SLC34A2 as a Potential Prognostic Marker of Oncological Diseases

The main goal of this study is to consider SLC34A2 as a potential prognostic marker of oncological diseases using the mutational, expression, and survival data of cancer studies which are publicly available online. We collected data from four databases (cBioPortal, The Cancer Genome Atlas; cBioPortal, Genie; International Cancer Genome Consortium; ArrayExpress). In total, 111,283 samples were categorized according to 27 tumor locations. Ninety-nine functionally significant missense mutations and twelve functionally significant indel mutations in SLC34A2 were found. The most frequent mutations were SLC34A2-ROS1, p.T154A, p.P506S/R/L, p.G257A/E/R, p.S318W, p.A396T, p.P410L/S/H, p.S461C, p.A473T/V, and p.Y503H/C/F. The upregulation of SLC34A2 was found in samples of myeloid, bowel, ovarian, and uterine tumors; downregulation was found in tumor samples of breast, liver, lung, and skin cancer tumors. It was found that the life expectancy of breast and thymus cancer patients with an SLC34A2 mutation is lower, and it was revealed that SLC34A2 overexpression reduced the life span of patients with brain, ovarian, and pancreatic tumors. Thereby, for these types of oncological diseases, the mutational profile of SLC34A2 can be a potential prognostic marker for breast and thymus cancers, and the upregulation of SLC34A2 can be a potential prognostic marker for brain, ovarian, and pancreatic cancers.

Chemical biology-whole genome engineering datasets predict new antibacterial combinations

Trimethoprim and sulfamethoxazole are used commonly together as cotrimoxazole for the treatment of urinary tract and other infections. The evolution of resistance to these and other antibacterials threatens therapeutic options for clinicians. We generated and analysed a chemical-biology-whole-genome data set to predict new targets for antibacterial combinations with trimethoprim and sulfamethoxazole. For this we used a large transposon mutant library in Escherichia coli BW25113 where an outward-transcribing inducible promoter was engineered into one end of the transposon. This approach allows regulated expression of adjacent genes in addition to gene inactivation at transposon insertion sites, a methodology that has been called TraDIS-Xpress. These chemical genomic data sets identified mechanisms for both reduced and increased susceptibility to trimethoprim and sulfamethoxazole. The data identified that over-expression of FolA reduced trimethoprim susceptibility, a known mechanism for reduced susceptibility. In addition, transposon insertions into the genes tdk, deoR, ybbC, hha, ldcA, wbbK and waaS increased susceptibility to trimethoprim and likewise for rsmH, fadR, ddlB, nlpI and prc with sulfamethoxazole, while insertions in ispD, uspC, minC, minD, yebK, truD and umpG increased susceptibility to both these antibiotics. Two of these genes’ products, Tdk and IspD, are inhibited by AZT and fosmidomycin respectively, antibiotics that are known to synergise with trimethoprim. Thus, the data identified two known targets and several new target candidates for the development of co-drugs that synergise with trimethoprim, sulfamethoxazole or cotrimoxazole. We demonstrate that the TraDIS-Xpress technology can be used to generate information-rich chemical-genomic data sets that can be used for antibacterial development.

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

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

Beck-Sickinger A, Hoffmann A, Weiner J, T. Heiker J. Cleavage of the vaspin N-terminus releases cell-penetrating peptides that affect early stages of adipogenesis and inhibit lipolysis in mature adipocytes

Vaspin expression and function is related to metabolic disorders and comorbidities of obesity. In various cellular and animal models of obesity, diabetes and atherosclerosis vaspin has shown beneficial, protective and/or compensatory action. While testing proteases for inhibition by vaspin, we noticed specific cleavage within the vaspin N-terminus and sequence analysis predicted cell-penetrating activity for the released peptides. These findings raised the question whether these proteolytic peptides exhibit biological activity.

We synthesized various N-terminal vaspin peptides to investigate cell-penetrating activity and analyse uptake mechanisms. Focusing on adipocytes, we performed microarray analysis and functional assays to elucidate biological activities of the vaspin–derived peptide, which is released by KLK7 cleavage (vaspin residues 21-30; VaspinN). Our study provides first evidence that proteolytic processing of the vaspin N-terminus releases cell-penetrating and bioactive peptides with effects on adipocyte biology. The VaspinN peptide increased preadipocyte proliferation, interfered with clonal expansion during the early stage of adipogenesis and blunted adrenergic cAMP-signalling, downstream lipolysis as well as insulin signalling in mature adipocytes.

Protease-mediated release of functional N-terminal peptides presents an additional facet of vaspin action. Future studies will address the mechanisms underlying the biological activities and clarify, if vaspin-derived peptides may have potential as therapeutic agents for the treatment of metabolic diseases.

Shortening of 3' UTRs in most cell types composing tumor tissues implicates alternative polyadenylation in protein metabolism

During pre-mRNA maturation 3' end processing can occur at different polyadenylation sites in the 3' untranslated region (3' UTR) to give rise to transcript isoforms that differ in the length of their 3' UTRs. Longer 3' UTRs contain additional cis-regulatory elements that impact the fate of the transcript and/or of the resulting protein. Extensive alternative polyadenylation (APA) has been observed in cancers, but the mechanisms and roles remain elusive. In particular, it is unclear whether the APA occurs in the malignant cells or in other cell types that infiltrate the tumor. To resolve this, we developed a computational method, called SCUREL, that quantifies changes in 3' UTR length between groups of cells, including cells of the same type originating from tumor and control tissue. We used this method to study APA in human lung adenocarcinoma (LUAD). SCUREL relies solely on annotated 3' UTRs and on control systems such as T cell activation, and spermatogenesis gives qualitatively similar results at much greater sensitivity compared to the previously published scAPA method. In the LUAD samples, we find a general trend toward 3' UTR shortening not only in cancer cells compared to the cell type of origin, but also when comparing other cell types from the tumor vs. the control tissue environment. However, we also find high variability in the individual targets between patients. The findings help in understanding the extent and impact of APA in LUAD, which may support improvements in diagnosis and treatment.

Defective cytokinin signaling reprograms lipid and flavonoid gene-to-metabolite networks to mitigate high salinity in Arabidopsis

Cytokinin (CK) in plants regulates both developmental processes and adaptation to environmental stresses. Arabidopsis histidine phosphotransfer ahp2,3,5 and type-B Arabidopsis response regulator arr1,10,12 triple mutants are almost completely defective in CK signaling, and the ahp2,3,5 mutant was reported to be salt tolerant. Here, we demonstrate that the arr1,10,12 mutant is also more tolerant to salt stress than wild-type (WT) plants. A comprehensive metabolite profiling coupled with transcriptome analysis of the ahp2,3,5 and arr1,10,12 mutants was conducted to elucidate the salt tolerance mechanisms mediated by CK signaling. Numerous primary (e.g., sugars, amino acids, and lipids) and secondary (e.g., flavonoids and sterols) metabolites accumulated in these mutants under nonsaline and saline conditions, suggesting that both prestress and poststress accumulations of stress-related metabolites contribute to improved salt tolerance in CK-signaling mutants. Specifically, the levels of sugars (e.g., trehalose and galactinol), amino acids (e.g., branched-chain amino acids and γ-aminobutyric acid), anthocyanins, sterols, and unsaturated triacylglycerols were higher in the mutant plants than in WT plants. Notably, the reprograming of flavonoid and lipid pools was highly coordinated and concomitant with the changes in transcriptional levels, indicating that these metabolic pathways are transcriptionally regulated by CK signaling. The discovery of the regulatory role of CK signaling on membrane lipid reprogramming provides a greater understanding of CK-mediated salt tolerance in plants. This knowledge will contribute to the development of salt-tolerant crops with the ability to withstand salinity as a key driver to ensure global food security in the era of climate crisis.

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio .

Identification of an Autophagy-Related Risk Signature Correlates With Immunophenotype and Predicts Immune Checkpoint Blockade Efficacy of Neuroblastoma

Neuroblastoma is one of the malignant solid tumors with the highest mortality in childhood. Targeted immunotherapy still cannot achieve satisfactory results due to heterogeneity and tolerance. Exploring markers related to prognosis and evaluating the immune microenvironment remain the major obstacles. Herein, we constructed an autophagy-related gene (ATG) risk model by multivariate Cox regression and least absolute shrinkage and selection operator regression, and identified four prognostic ATGs (BIRC5, GRID2, HK2, and RNASEL) in the training cohort, then verified the signature in the internal and external validation cohorts. BIRC5 and HK2 showed higher expression in MYCN amplified cell lines and tumor tissues consistently, whereas GRID2 and RNASEL showed the opposite trends. The correlation between the signature and clinicopathological parameters was further analyzed and showing consistency. A prognostic nomogram using risk score, International Neuroblastoma Staging System stage, age, and MYCN status was built subsequently, and the area under curves, net reclassification improvement, and integrated discrimination improvement showed more satisfactory prognostic predicting performance. The ATG prognostic signature itself can significantly divide patients with neuroblastoma into high- and low-risk groups; differentially expressed genes between the two groups were enriched in autophagy-related behaviors and immune cell reactions in gene set enrichment analysis (false discovery rate q -value < 0.05). Furthermore, we evaluated the relationship of the signature risk score with immune cell infiltration and the cancer-immunity cycle. The low-risk group was characterized by more abundant expression of chemokines and higher immune checkpoints (PDL1, PD1, CTLA-4, and IDO1). The risk score was significantly correlated with the proportions of CD8+ T cells, CD4+ memory resting T cells, follicular helper T cells, memory B cells, plasma cells, and M2 macrophages in tumor tissues. In conclusion, we developed and validated an autophagy-related signature that can accurately predict the prognosis, which might be meaningful to understand the immune microenvironment and guide immune checkpoint blockade.

DNA Data Bank of Japan (DDBJ) update report 2021

The Bioinformation and DDBJ (DNA Data Bank of Japan) Center (DDBJ Center; https://www.ddbj.nig.ac.jp) operates archival databases that collect nucleotide sequences, study and sample information, and distribute them without access restriction to progress life science research as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute. Besides the INSDC databases, the DDBJ Center also provides the Genomic Expression Archive for functional genomics data and the Japanese Genotype-phenotype Archive for human data requiring controlled access. Additionally, the DDBJ Center started a new public repository, MetaboBank, for experimental raw data and metadata from metabolomics research in October 2020. In response to the COVID-19 pandemic, the DDBJ Center openly shares SARS-CoV-2 genome sequences in collaboration with Shizuoka Prefecture and Keio University. The operation of DDBJ is based on the National Institute of Genetics (NIG) supercomputer, which is open for large-scale sequence data analysis for life science researchers. This paper reports recent updates on the archival databases and the services of DDBJ.

BioSamples database: FAIRer samples metadata to accelerate research data management

The BioSamples database at EMBL-EBI is the central institutional repository for sample metadata storage and connection to EMBL-EBI archives and other resources. The technical improvements to our infrastructure described in our last update have enabled us to scale and accommodate an increasing number of communities, resulting in a higher number of submissions and more heterogeneous data. The BioSamples database now has a valuable set of features and processes to improve data quality in BioSamples, and in particular enriching metadata content and following FAIR principles. In this manuscript, we describe how BioSamples in 2021 handles requirements from our community of users through exemplar use cases: increased findability of samples and improved data management practices support the goals of the ReSOLUTE project, how the plant community benefits from being able to link genotypic to phenotypic information, and we highlight how cumulatively those improvements contribute to more complex multi-omics data integration supporting COVID-19 research. Finally, we present underlying technical features used as pillars throughout those use cases and how they are reused for expanded engagement with communities such as FAIRplus and the Global Alliance for Genomics and Health. Availability: The BioSamples database is freely available at http://www.ebi.ac.uk/biosamples. Content is distributed under the EMBL-EBI Terms of Use available at https://www.ebi.ac.uk/about/terms-of-use. The BioSamples code is available at https://github.com/EBIBioSamples/biosamples-v4 and distributed under the Apache 2.0 license.

Band-based similarity indices for gene expression classification and clustering

The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its (partial) inclusion in all possible bands formed by elements of the data set. We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance.

SysInflam HuDB, a Web Resource for Mining Human Blood Cells Transcriptomic Data Associated with Systemic Inflammatory Responses to Sepsis

Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.

Identification of Key eRNAs for Spinal Cord Injury by Integrated Multinomial Bioinformatics Analysis

Background: Spinal cord injury (SCI) is a severe neurological deficit affecting both young and older people worldwide. The potential role of key enhancer RNAs (eRNAs) in SCI remains elusive, which is a prominent challenge in the trauma repair process. This study aims to investigate the roles of key eRNAs, transcription factors (TFs), signaling pathways, and small-molecule inhibitors in SCI using multi-omics bioinformatics analysis. Methods: Microarray data of peripheral blood mononuclear cell (PBMC) samples from 27 healthy volunteers and 25 chronic-phase SCI patients were retrieved from the Gene Expression Omnibus database. Differentially expressed transcription factors (DETFs), differentially expressed enhancer RNAs (DEeRNAs), and differentially expressed target genes (DETGs) were identified using the Linear Models for Microarray Data (limma) package. Fraction of immune cells was estimated using CIBERSORT algorithm. Gene Set Variation Analysis (GSVA) was applied to identify the downstream signaling pathways. The eRNA regulatory network was constructed based on the correlation results. Connectivity Map (CMap) database was used to find potential drugs for SCI patients. The cellular communication analysis was performed to explore the molecular regulation mechanism of SCI based on single-cell RNA sequencing (scRNA-seq) data. Chromatin immunoprecipitation sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data were used to validate the key regulatory mechanisms. scRNA-seq dataset was used to validate the cell subtype localization of the key eRNAs. Results: In total, 21 DETFs, 24 DEeRNAs, and 829 DETGs were identified. A regulatory network of 13 DETFs, six DEeRNAs, seven DETGs, two hallmark pathways, two immune cells, and six immune pathways was constructed. The link of Splicing factor proline and glutamine rich (SFPQ) (TF) and vesicular overexpressed in cancer prosurvival protein 1 (VOPP1) (eRNA) (R = 0.990, p < 0.001, positive), VOPP1 (eRNA) and epidermal growth factor receptor (EGFR) (target gene) (R = 0.974, p < 0.001, positive), VOPP1, and T helper (Th) cells (R = -0.987, p < 0.001, negative), and VOPP1 and hallmark coagulation (R = 0.937, p < 0.001, positive) was selected. Trichostatin A was considered the best compound target to SCI-related eRNAs (specificity = 0.471, p < 0.001). Conclusion: VOPP1, upregulated by SFPQ, strengthened the transient expression of EGFR. Th cells and coagulation were the potential downstream pathways of VOPP1. This regulatory network and potential inhibitors provide novel diagnostic biomarkers and therapeutic targets for SCI.

Mouse Genome Informatics (MGI): latest news from MGD and GXD

The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .

Automatic cell type identification methods for single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many research disciplines due to its ability to elucidate the heterogeneous and complex cell-type compositions of different tissues and cell populations. Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. By contrast, automatic cell-type identification methods may have the advantages of being fast, accurate, and more user friendly. Here, we discuss and evaluate thirty-two published automatic methods for scRNA-seq data analysis in terms of their prediction accuracy, F1-score, unlabeling rate and running time. We highlight the advantages and disadvantages of these methods and provide recommendations of method choice depending on the available information. The challenges and future applications of these automatic methods are further discussed. In addition, we provide a free scRNA-seq data analysis package encompassing the discussed automatic methods to help the easy usage of them in real-world applications.

Investigations on the Degradation of the Bile Salt Cholate via the 9,10-Seco-Pathway Reveals the Formation of a Novel Recalcitrant Steroid Compound by a Side Reaction in Sphingobium sp. Strain Chol11

Bile salts such as cholate are steroid compounds from the digestive tracts of vertebrates, which enter the environment upon excretion, e.g., in manure. Environmental bacteria degrade bile salts aerobically via two pathway variants involving intermediates with Δ^1,4- or Δ^4,6-3-keto-structures of the steroid skeleton. Recent studies indicated that degradation of bile salts via Δ^4,6-3-keto intermediates in Sphingobium sp. strain Chol11 proceeds via 9,10-seco cleavage of the steroid skeleton. For further elucidation, the presumptive product of this cleavage, 3,12β-dihydroxy-9,10-seco-androsta-1,3,5(10),6-tetraene-9,17-dione (DHSATD), was provided to strain Chol11 in a co-culture approach with Pseudomonas stutzeri Chol1 and as purified substrate. Strain Chol11 converted DHSATD to the so far unknown compound 4-methyl-3-deoxy-1,9,12-trihydroxyestra-1,3,5(10)7-tetraene-6,17-dione (MDTETD), presumably in a side reaction involving an unusual ring closure. MDTETD was neither degraded by strains Chol1 and Chol11 nor in enrichment cultures. Functional transcriptome profiling of zebrafish embryos after exposure to MDTETD identified a significant overrepresentation of genes linked to hormone responses. In both pathway variants, steroid degradation intermediates transiently accumulate in supernatants of laboratory cultures. Soil slurry experiments indicated that bacteria using both pathway variants were active and also released their respective intermediates into the environment. This instance could enable the formation of recalcitrant steroid metabolites by interspecies cross-feeding in agricultural soils.

CancerSCEM: a database of single-cell expression map across various human cancers

With the proliferating studies of human cancers by single-cell RNA sequencing technique (scRNA-seq), cellular heterogeneity, immune landscape and pathogenesis within diverse cancers have been uncovered successively. The exponential explosion of massive cancer scRNA-seq datasets in the past decade are calling for a burning demand to be integrated and processed for essential investigations in tumor microenvironment of various cancer types. To fill this gap, we developed a database of Cancer Single-cell Expression Map (CancerSCEM, https://ngdc.cncb.ac.cn/cancerscem), particularly focusing on a variety of human cancers. To date, CancerSCE version 1.0 consists of 208 cancer samples across 28 studies and 20 human cancer types. A series of uniformly and multiscale analyses for each sample were performed, including accurate cell type annotation, functional gene expressions, cell interaction network, survival analysis and etc. Plus, we visualized CancerSCEM as a user-friendly web interface for users to browse, search, online analyze and download all the metadata as well as analytical results. More importantly and unprecedentedly, the newly-constructed comprehensive online analyzing platform in CancerSCEM integrates seven analyze functions, where investigators can interactively perform cancer scRNA-seq analyses. In all, CancerSCEM paves an informative and practical way to facilitate human cancer studies, and also provides insights into clinical therapy assessments.

The network interplay of interferon and Toll-like receptor signaling pathways in the anti-Candida immune response

Fungal infections represent a major global health problem affecting over a billion people that kills more than 1.5 million annually. In this study, we employed an integrative approach to reveal the landscape of the human immune responses to Candida spp. through meta-analysis of microarray, bulk, and single-cell RNA sequencing (scRNA-seq) data for the blood transcriptome. We identified across these different studies a consistent interconnected network interplay of signaling molecules involved in both Toll-like receptor (TLR) and interferon (IFN) signaling cascades that is activated in response to different Candida species (C. albicans, C. auris, C. glabrata, C. parapsilosis, and C. tropicalis). Among these molecules are several types I IFN, indicating an overlap with antiviral immune responses. scRNA-seq data confirmed that genes commonly identified by the three transcriptomic methods show cell type-specific expression patterns in various innate and adaptive immune cells. These findings shed new light on the anti-Candida immune response, providing putative molecular pathways for therapeutic intervention.

A proteomics sample metadata representation for multiomics integration and big data analysis

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta-analysis of such datasets is, however, very complicated for various reasons. Here, we developed an integrating statistical and machine-learning model approach for the meta-analysis of bisphenol A (BPA) exposure datasets from different mouse tissues. We constructed three joint datasets following three different strategies for dataset integration: in particular, using all common genes from the datasets, uncorrelated, and not co-expressed genes, respectively. By applying machine learning methods to these datasets, we identified genes whose expression was significantly affected in all of the BPA microanalysis data tested; those involved in the regulation of cell survival include: Tnfr2, Hgf-Met, Agtr1a, Bdkrb2; signaling through Mapk8 (Jnk1)); DNA repair (Hgf-Met, Mgmt); apoptosis (Tmbim6, Bcl2, Apaf1); and cellular junctions (F11r, Cldnd1, Ctnd1 and Yes1). Our results highlight the benefit of combining existing datasets for the integrated analysis of a specific topic when individual datasets are limited in size.

PIK3CB is involved in metastasis through the regulation of cell adhesion to collagen I in pancreatic cancer

Introduction

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy, with a major mortality resulting from the rapid progression of metastasis. Unfortunately, no effective treatment strategy has been developed for PAAD metastasis to date. Thus, unraveling the mechanisms involved in PAAD metastatic phenotype may facilitate the treatment for PAAD patients.

Objectives

PIK3CB is an oncogene implicated in cancer development and progression but less is known about whether PIK3CB participates in PAAD metastasis. Therefore, the objective of this study is to explore the mechanism(s) of PIK3CB in PAAD metastasis.

Methods

In our study, we examined the PIK3CB expression pattern using bioinformatic analysis and clinical material derived from patients with PAAD. Subsequently, a series of biochemical experiments were conducted to investigate the role of PIK3CB as potential mechanism(s) underlying PAAD metastasis in vivo using nude mice and in vitro using cell lines.

Results

We observed that PIK3CB was involved in PAAD progression. Notably, we identified that PIK3CB was involved in PAAD metastasis. Downregulation of PIK3CB significantly reduced PAAD metastatic potential in vivo. Furthermore, a series of bioinformatic analyses showed that PIK3CB was involved in cell adhesion in PAAD. Notably, PIK3CB depletion inhibited invasion potential specifically via suppressing cell adhesion to collagen I in PAAD cells.

Conclusion

Collectively, our findings indicate that PIK3CB is involved in PAAD metastasis through cell-matrix adhesion. We proposed that PIK3CB is a potential therapeutic target for PAAD therapy.

Computational workflow for functional characterization of COVID-19 through secondary data analysis

Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021).

Prediction of Biochemical Recurrence-Free Survival of Prostate Cancer Patients Leveraging Multiple Gene Expression Profiles in Tumor Microenvironment

Tumor-adjacent normal (TAN) tissues, which constitute tumor microenvironment and are different from healthy tissues, provide critical information at molecular levels that can be used to differentiate aggressive tumors from indolent tumors. In this study, we analyzed 52 TAN samples from the Cancer Genome Atlas (TCGA) prostate cancer patients and developed a 10-gene prognostic model that can accurately predict biochemical recurrence-free survival based on the profiles of these genes in TAN tissues. The predictive ability was validated using TAN samples from an independent cohort. These 10 prognostic genes in tumor microenvironment are different from the prognostic genes detected in tumor tissues, indicating distinct progression-related mechanisms in two tissue types. Bioinformatics analysis showed that the prognostic genes in tumor microenvironment were significantly enriched by p53 signaling pathway, which may represent the crosstalk tunnels between tumor and its microenvironment and pathways involving cell-to-cell contact and paracrine/endocrine signaling. The insight acquired by this study has advanced our knowledge of the potential role of tumor microenvironment in prostate cancer progression.

ISA API: An open platform for interoperable life science experimental metadata

BACKGROUND

The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed.

RESULTS

In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community.

CONCLUSIONS

The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

KCa channel blockers increase effectiveness of the EGF receptor TK inhibitor erlotinib in non-small cell lung cancer cells (A549)

Non-small cell lung cancer (NSCLC) has a poor prognosis with a 5 year survival rate of only ~ 10%. Important driver mutations underlying NSCLC affect the epidermal growth factor receptor (EGFR) causing the constitutive activation of its tyrosine kinase domain. There are efficient EGFR tyrosine kinase inhibitors (TKIs), but patients develop inevitably a resistance against these drugs. On the other hand, K_Ca3.1 channels contribute to NSCLC progression so that elevated K_Ca3.1 expression is a strong predictor of poor NSCLC patient prognosis. The present study tests whether blocking K_Ca3.1 channels increases the sensitivity of NSCLC cells towards the EGFR TKI erlotinib and overcomes drug resistance. mRNA expression of K_Ca3.1 channels in erlotinib-sensitive and -resistant NSCLC cells was analysed in datasets from Gene expression omnibus (GEO) and ArrayExpress. We assessed proliferation and migration of NSCLC cells. These (live cell-imaging) experiments were complemented by patch clamp experiments and Western blot analyses. We identified three out of four datasets comparing erlotinib-sensitive and -resistant NSCLC cells which revealed an altered expression of K_Ca3.1 mRNA in erlotinib-resistant NSCLC cells. Therefore, we evaluated the combined effect of erlotinib and the K_Ca3.1 channel inhibition with sencapoc. Erlotinib elicits a dose-dependent inhibition of migration and proliferation of NSCLC cells. The simultaneous application of the K_Ca3.1 channel blocker senicapoc increases the sensitivity towards a low dose of erlotinib (300 nmol/L) which by itself has no effect on migration and proliferation. Partial erlotinib resistance can be overcome by K_Ca3.1 channel blockade. The sensitivity towards erlotinib as well as the potentiating effect of K_Ca3.1 blockade is further increased by mimicking hypoxia. Our results suggest that K_Ca3.1 channel blockade may constitute a therapeutic concept for treating NSCLC and overcome EGFR TKI resistance. We propose that this is due to complementary mechanisms of action of both blockers.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations

Principal Component Analysis (PCA) projects high-dimensional genotype data into a few components that discern populations. Ancestry Informative Markers (AIMs) are a small subset of SNPs capable of distinguishing populations. We integrate these two approaches by proposing an algorithm to identify necessary informative loci whose removal from the data deteriorates the PCA structure. Unlike classical AIMs, necessary informative loci densely cover the genome, hence can illuminate the evolution and mixing history of populations. We conduct a comprehensive analysis to the genotype data of the 1000 Genomes Project using necessary informative loci. Projections along the top seven principal components demarcate populations at distinct geographic levels. Millions of necessary informative loci along each PC are identified. Population identities along each PC are approximately determined by weighted sums of minor (or major) alleles over the informative loci. Variations of allele frequencies are aligned with the history and direction of population evolution. The population distribution of projections along the top three PCs is recapitulated by a simple demographic model based on several waves of founder population separation and mixing. Informative loci possess locational concentration in the genome and functional enrichment. Genes at two hot spots encompassing dense PC 7 informative loci exhibit differential expressions among European populations. The mosaic of local ancestry in the genome of a mixed descendant from multiple populations can be inferred from partial PCA projections of informative loci. Finally, informative loci derived from the 1000 Genomes data well predict the projections of an independent genotype data of South Asians. These results demonstrate the utility and relevance of informative loci to investigate human evolution.

ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment

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TempO-Seq assays of rat hepatocytes in collagen sandwich are highly reproducible.

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Gene expression analysis shows S1500+ is representative of the whole transcriptome.

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Connectivity mapping shows consistency between TempO-Seq and Affymetrix data.

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Gene set enrichment shows consistency between S1500+ and the whole transcriptome.

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Gene set enrichment using hallmark gene sets informs hepatotoxicity.

Cell-based in vitro models coupled with high-throughput transcriptomics (HTTr) are increasingly utilized as alternative methods to animal-based toxicity testing. Here, using a panel of 14 chemicals with different risks of human drug-induced liver injury (DILI) and two dosing concentrations, we evaluated an HTTr platform comprised of collagen sandwich primary rat hepatocyte culture and the TempO-Seq surrogate S1500+ (ST) assay. First, the HTTr platform was found to exhibit high reproducibility between technical and biological replicates (r greater than 0.85). Connectivity mapping analysis further demonstrated a high level of inter-platform reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project. Second, the TempO-Seq ST assay was shown to be a robust surrogate to the whole transcriptome (WT) assay in capturing chemical-induced changes in gene expression, as evident from correlation analysis, PCA and unsupervised hierarchical clustering. Gene set enrichment analysis (GSEA) using the Hallmark gene set collection also demonstrated consistency in enrichment scores between ST and WT assays. Lastly, unsupervised hierarchical clustering of hallmark enrichment scores broadly divided the samples into hepatotoxic, intermediate, and non-hepatotoxic groups. Xenobiotic metabolism, bile acid metabolism, apoptosis, p53 pathway, and coagulation were found to be the key hallmarks driving the clustering. Taken together, our results established the reproducibility and performance of collagen sandwich culture in combination with TempO-Seq S1500+ assay, and demonstrated the utility of GSEA using the hallmark gene set collection to identify potential hepatotoxicants for further validation.

A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

CD40L modulates transcriptional signatures of neutrophils in the bone marrow associated with development and trafficking

Neutrophils are produced in the BM in a process called granulopoiesis, in which progenitor cells sequentially develop into mature neutrophils. During the developmental process, which is finely regulated by distinct transcription factors, neutrophils acquire the ability to exit the BM, properly distribute throughout the body, and migrate to infection sites. Previous studies have demonstrated that CD40 ligand (CD40L) influences hematopoiesis and granulopoiesis. Here, we investigate the effect of CD40L on neutrophil development and trafficking by performing functional and transcriptome analyses. We found that CD40L signaling plays an essential role in the early stages of neutrophil generation and development in the BM. Moreover, CD40L modulates transcriptional signatures, indicating that this molecule enables neutrophils to traffic throughout the body and to migrate in response to inflammatory signals. Thus, our study provides insights into the complex relationships between CD40L signaling and granulopoiesis, and it suggests a potentially novel and nonredundant role of CD40L signaling in neutrophil development and function.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Clinical significance and molecular mechanism of angiotensin-converting enzyme 2 in hepatocellular carcinoma tissues

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = −0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan–Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.

A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression

Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types.

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

The circadian clock is an internal molecular 24-h timer that is critical to life on Earth. We describe a series of artificial intelligence (AI)– and machine learning (ML)–based approaches that enable more cost-effective analysis and insight into circadian regulation and function. Throughout the manuscript, we illuminate what is inside the ML “black box” via explanation or interpretation of predictive ML models. Using this interpretation of our models, we derive biological insights into why a prediction was made, alongside accurate predictions. Most innovatively, we use only DNA sequence features for accurate circadian gene expression prediction. Using explainable AI, we define possible, responsible regulatory elements as we make these predictions; this critically requires no prior knowledge of regulatory elements.

The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis. Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.

Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study

Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.

Toxicogenomic differentiation of functional responses to fipronil and imidacloprid in Daphnia magna

Active substances of pesticides, biocides or pharmaceuticals can induce adverse side effects in the aquatic ecosystem, necessitating environmental hazard and risk assessment prior to substance registration. The freshwater crustacean Daphnia magna is a model organism for acute and chronic toxicity assessment representing aquatic invertebrates. However, standardized tests involving daphnia are restricted to the endpoints immobility and reproduction and thus provide only limited insights into the underlying modes-of-action. Here, we applied transcriptome profiling to a modified D. magna Acute Immobilization test to analyze and compare gene expression profiles induced by the GABA-gated chloride channel blocker fipronil and the nicotinic acetylcholine receptor (nAChR) agonist imidacloprid. Daphnids were expose to two low effect concentrations of each substance followed by RNA sequencing and functional classification of affected gene ontologies and pathways. For both insecticides, we observed a concentration-dependent increase in the number of differentially expressed genes, whose expression changes were highly significantly positively correlated when comparing both test concentrations. These gene expression fingerprints showed virtually no overlap between the test substances and they related well to previous data of diazepam and carbaryl, two substances targeting similar molecular key events. While, based on our results, fipronil predominantly interfered with molecular functions involved in ATPase-coupled transmembrane transport and transcription regulation, imidacloprid primarily affected oxidase and oxidoreductase activity. These findings provide evidence that systems biology approaches can be utilized to identify and differentiate modes-of-action of chemical stressors in D. magna as an invertebrate aquatic non-target organism. The mechanistic knowledge extracted from such data will in future contribute to the development of Adverse Outcome Pathways (AOPs) for read-across and prediction of population effects.

Functional genomics meta-analysis to identify gene set enrichment networks in cardiac hypertrophy

In order to take advantage of the continuously increasing number of transcriptome studies, it is important to develop strategies that integrate multiple expression datasets addressing the same biological question to allow a robust analysis. Here, we propose a meta-analysis framework that integrates enriched pathways identified through the Gene Set Enrichment Analysis (GSEA) approach and calculates for each meta-pathway an empirical p-value. Validation of our approach on benchmark datasets showed comparable or even better performance than existing methods and an increase in robustness with increasing number of integrated datasets. We then applied the meta-analysis framework to 15 functional genomics datasets of physiological and pathological cardiac hypertrophy. Within these datasets we grouped expression sets measured at time points that represent the same hallmarks of heart tissue remodeling ('aggregated time points') and performed meta-analysis on the expression sets assigned to each aggregated time point. To facilitate biological interpretation, results were visualized as gene set enrichment networks. Here, our meta-analysis framework identified well-known biological mechanisms associated with pathological cardiac hypertrophy (e.g., cardiomyocyte apoptosis, cardiac contractile dysfunction, and alteration in energy metabolism). In addition, results highlighted novel, potentially cardioprotective mechanisms in physiological cardiac hypertrophy involving the down-regulation of immune cell response, which are worth further investigation.

PSMB7 Is a Key Gene Involved in the Development of Multiple Myeloma and Resistance to Bortezomib

Multiple myeloma (MM), the second most commonly diagnosed hematologic neoplasm, is the most significant clinical manifestation in a series of plasma cell (PC) dyscrasia. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM), approximately 1% or 10% of which, respectively, can progress to MM per year, are the premalignant stages of MM. The overall survival (OS) of MM is significantly improved by the introduction of proteasome inhibitors (PIs), but almost all MM patients eventually relapse and resist anti-MM drugs. Therefore, it is crucial to explore the progression of MM and the mechanisms related to MM drug resistance. In this study, we used weighted gene co-expression network analysis (WGCNA) to analyze the gene expression of the dynamic process from normal plasma cells (NPC) to malignant profiling PC, and found that the abnormal gene expression was mainly concentrated in the proteasome. We also found that the expression of one of the proteasomal subunits PSMB7 was capable of distinguishing the different stages of PC dyscrasia and was the highest in ISS III. In the bortezomib (BTZ) treated NDMM patients, higher PSMB7 expression was associated with shorter survival time, and the expression of PSMB7 in the BTZ treatment group was significantly higher than in the thalidomide (Thai) treatment group. In summary, we found that PSMB7 is the key gene associated with MM disease progression and drug resistance.

Non-Tumor CCAAT/Enhancer-Binding Protein Delta Potentiates Tumor Cell Extravasation and Pancreatic Cancer Metastasis Formation

CCAAT/enhancer-binding protein delta (C/EBPδ) is a transcription factor involved in apoptosis and proliferation, which is downregulated in pancreatic ductal adenocarcinoma (PDAC) cells. Loss of nuclear C/EBPδ in PDAC cells is associated with decreased patient survival and pro-tumorigenic properties in vitro. Interestingly however, next to C/EBPδ expression in tumor cells, C/EBPδ is also expressed by cells constituting the tumor microenvironment and by cells comprising the organs and parenchyma. However, the functional relevance of systemic C/EBPδ in carcinogenesis remains elusive. Here, we consequently assessed the potential importance of C/EBPδ in somatic tissues by utilizing an orthotopic pancreatic cancer model. In doing so, we show that genetic ablation of C/EBPδ does not significantly affect primary tumor growth but has a strong impact on metastases; wildtype mice developed metastases at multiple sites, whilst this was not the case in C/EBPδ^-/- mice. In line with reduced metastasis formation in C/EBPδ^-/- mice, C/EBPδ-deficiency also limited tumor cell dissemination in a specific extravasation model. Tumor cell extravasation was dependent on the platelet-activating factor receptor (PAFR) as a PAFR antagonist inhibited tumor cell extravasation in wildtype mice but not in C/EBPδ^-/- mice. Overall, we show that systemic C/EBPδ facilitates pancreatic cancer metastasis, and we suggest this is due to C/EBPδ-PAFR-dependent tumor cell extravasation.

African Global Representation in Biomedical Sciences

African populations are diverse in their ethnicity, language, culture, and genetics. Although plagued by high disease burdens, until recently the continent has largely been excluded from biomedical studies. Along with limitations in research and clinical infrastructure, human capacity, and funding, this omission has resulted in an underrepresentation of African data and disadvantaged African scientists. This review interrogates the relative abundance of biomedical data from Africa, primarily in genomics and other omics. The visibility of African science through publications is also discussed. A challenge encountered in this review is the relative lack of annotation of data on their geographical or population origin, with African countries represented as a single group. In addition to the abovementioned limitations,the global representation of African data may also be attributed to the hesitation to deposit data in public repositories. Whatever the reason, the disparity should be addressed, as African data have enormous value for scientists in Africa and globally.

Transcriptomic changes in peripheral blood mononuclear cells with weight loss: systematic literature review and primary data synthesis

Background and objectives

Peripheral blood mononuclear cells (PBMCs) have shown promise as a tissue sensitive to subtle and possibly systemic transcriptomic changes, and as such may be useful in identifying responses to weight loss interventions. The primary aim was to comprehensively evaluate the transcriptomic changes that may occur during weight loss and to determine if there is a consistent response across intervention types in human populations of all ages.

Methods

Included studies were randomised control trials or cohort studies that administered an intervention primarily designed to decrease weight in any overweight or obese human population. A systematic search of the literature was conducted to obtain studies and gene expression databases were interrogated to locate corresponding transcriptomic datasets. Datasets were normalised using the ArrayAnalysis online tool and differential gene expression was determined using the limma package in R. Over-represented pathways were explored using the PathVisio software. Heatmaps and hierarchical clustering were utilised to visualise gene expression.

Results

Seven papers met the inclusion criteria, five of which had raw gene expression data available. Of these, three could be grouped into high responders (HR, ≥ 5% body weight loss) and low responders (LR). No genes were consistently differentially expressed between high and low responders across studies. Adolescents had the largest transcriptomic response to weight loss followed by adults who underwent bariatric surgery. Seven pathways were altered in two out of four studies following the intervention and the pathway ‘cytoplasmic ribosomal proteins’ (WikiPathways: WP477) was altered between HR and LR at baseline in the two datasets with both groups. Pathways related to ‘toll-like receptor signalling’ were altered in HR response to the weight loss intervention in two out of three datasets.

Conclusions

Transcriptomic changes in PBMCs do occur in response to weight change. Transparent and standardised data reporting is needed to realise the potential of transcriptomics for investigating phenotypic features.

Registration number

PROSPERO: CRD42019106582

Supplementary Information

The online version contains supplementary material available at 10.1186/s12263-021-00692-6.

Optimized Molecular Interaction Networks for the Study of Skeletal Muscle

BACKGROUND

Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue.

OBJECTIVE

The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs.

METHODS

MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed.

RESULTS

The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks.

CONCLUSION

MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.

The Progenetix oncogenomic resource in 2021

In cancer, copy number aberrations (CNAs) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138 663 (including 115 357 tumor) copy number variation (CNV) profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive National Cancer Institute Thesaurus cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org.