Reuse of public genome-wide gene expression data

Pictures at an exhibition: How to share your imaging data

Open access to data underpinning published results is a key pillar of scientific reproducibility. Making data available at scale also provides opportunities for data reuse, encouraging the development of new analysis approaches. In this poster article, accompanying a recorded talk, we will explain the benefits of publicly archiving your image data alongside your published manuscripts, as well as highlight what resources are available to do this. This will include the BioImage Archive, EMBL-EBI's new resource for biological image data, https://www.ebi.ac.uk/bioimage-archive/. We will look at how image data submission works, how to prepare in advance for archiving your data and upcoming developments.

Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis

BACKGROUND

Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets.

METHODS

After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR).

RESULTS

The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition.

CONCLUSION

In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

The evolution of computational research in a data-centric world

Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

A detailed multi-omics analysis of GNB2 gene in human cancers

Abstract The Guanine-nucleotide binding protein 2 (GNB2) encodes for β2 subunit (Gβ2) of the G-protein complex. Keeping in view the increased demand of reliable biomarkers in cancer, the current study was planned to extensively explored GNB2 expression variation and its roles in different cancers using online available databases and diverse methodology. In view of our results, the GNB2 was notably up-regulated relative to corresponding controls in twenty three cancer types. As well, the elevated expression of GNB2 was found to be associated with the reduced overall survival (OS) of the Liver Hepatocellular Carcinoma (LIHC) and Rectum Adenocarcinoma (READ) only out of all analyzed cancer types. This implies GNB2 plays vital role in the tumorigenesis of LIHC and READ. Several additional analysis also explored six critical pathways and few important correlations related to GNB2 expression and different other parameters such as promoter methylation, tumor purity, CD8+ T immune cells infiltration, and genetic alteration, and chemotherapeutic drugs. In conclusion, GNB2 gene has been identified in this study as a shared potential biomarker (diagnostic and prognostic) of LIHC and READ.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

A Transcriptome Array-Based Approach to Link SGLT-2 and Intrarenal Complement C5 Synthesis in Diabetic Nephropathy

Diabetic nephropathy is a common microvascular complication of diabetes mellitus. It is characterized by progressive chronic kidney disease (CKD) with decline of kidney function by hyperfiltration. On a mechanistic level, activation of the complement system has been implicated in the pathogenesis of diabetic nephropathy. Therefore, here we pursued a transcriptome array-based approach to link intrarenal SGLT-2 and the synthesis of distinct complement components in diabetic nephropathy. Publicly available datasets for SLC5A2 (encoding SGLT-2) and complement system components were extracted specifically from microdissected tubulointerstitial (healthy controls: n = 31, diabetic nephropathy: n = 17) and glomerular compartments (healthy controls: n = 21, diabetic nephropathy: n = 12). First, we compared tubulointerstitial and glomerular log2SLC5A2 mRNA expression levels and confirmed a predominant synthesis within the tubulointerstitial compartment. Among various complement components and receptors, the only significant finding was a positive association between SLC5A2 and the tubulointerstitial synthesis of the complement component C5 in diabetic nephropathy (p = 0.0109). Finally, intrarenal expression of SLC5A2 was associated predominantly with pathways involved in metabolic processes. Interestingly, intrarenal complement C5 synthesis was also associated with enrichment of metabolic signaling pathways, overlapping with SLC5A2 for "metabolism" and "biological oxidations". These observations could be of relevance in the pathogenesis of diabetic nephropathy and implicate a mechanistic link between SGLT-2 and intrarenal complement synthesis.

Transcriptomic characterization of Trichoderma harzianum T34 primed tomato plants: assessment of biocontrol agent induced host specific gene expression and plant growth promotion

In this study, we investigated the intricate interplay between Trichoderma and the tomato genome, focusing on the transcriptional and metabolic changes triggered during the late colonization event. Microarray probe set (GSE76332) was utilized to analyze the gene expression profiles changes of the un-inoculated control (tomato) and Trichoderma-tomato interactions for identification of the differentially expressed significant genes. Based on principal component analysis and R-based correlation, we observed a positive correlation between the two cross-comaparable groups, corroborating the existence of transcriptional responses in the host triggered by Trichoderma priming. The statistically significant genes based on different p-value cut-off scores [(padj-values or q-value); padj-value < 0.05], [(pcal-values); pcal-value < 0.05; pcal < 0.01; pcal < 0.001)] were cross compared. Through cross-comparison, we identified 156 common genes that were consistently significant across all probability thresholds, and showing a strong positive corelation between p-value and q-value in the selected probe sets. We reported TD2, CPT1, pectin synthase, EXT-3 (extensin-3), Lox C, and pyruvate kinase (PK), which exhibited upregulated expression, and Glb1 and nitrate reductase (nii), which demonstrated downregulated expression during Trichoderma-tomato interaction. In addition, microbial priming with Trichoderma resulted into differential expression of transcription factors related to systemic defense and flowering including MYB13, MYB78, ERF2, ERF3, ERF5, ERF-1B, NAC, MADS box, ZF3, ZAT10, A20/AN1, polyol sugar transporter like zinc finger proteins, and a novel plant defensin protein. The potential bottleneck and hub genes involved in this dynamic response were also identified. The protein-protein interaction (PPI) network analysis based on 25 topmost DEGS (pcal-value < 0.05) and the Weighted Correlation Gene Network Analysis (WGCNA) of the 1786 significant DEGs (pcal-value < 0.05) we reported the hits associated with carbohydrate metabolism, secondary metabolite biosynthesis, and the nitrogen metabolism. We conclude that the Trichoderma-induced microbial priming re-programmed the host genome for transcriptional response during the late colonization event and were characterized by metabolic shifting and biochemical changes specific to plant growth and development. The work also highlights the relevance of statistical parameters in understanding the gene regulatory dynamics and complex regulatory networks based on differential expression, co-expression, and protein interaction networks orchestrating the host responses to beneficial microbial interactions.

Identification of the biological processes, immune cell landscape, and hub genes shared by acute anaphylaxis and ST-segment elevation myocardial infarction

Background: Patients with anaphylaxis are at risk for ST-segment elevation myocardial infarction (STEMI). However, the pathological links between anaphylaxis and STEMI remain unclear. Here, we aimed to explore shared biological processes, immune effector cells, and hub genes of anaphylaxis and STEMI. Methods: Gene expression data for anaphylactic (GSE69063) and STEMI (GSE60993) patients with corresponding healthy controls were pooled from the Gene Expression Omnibus database. Differential expression analysis, enrichment analysis, and CIBERSORT were used to reveal transcriptomic signatures and immune infiltration profiles of anaphylaxis and STEMI, respectively. Based on common differentially expressed genes (DEGs), Gene Ontology analysis, cytoHubba algorithms, and correlation analyses were performed to identify biological processes, hub genes, and hub gene-related immune cells shared by anaphylaxis and STEMI. The robustness of hub genes was assessed in external anaphylactic (GSE47655) and STEMI (GSE61144) datasets. Furthermore, a murine model of anaphylaxis complicated STEMI was established to verify hub gene expressions. The logistic regression analysis was used to evaluate the diagnostic efficiency of hub genes. Results: 265 anaphylaxis-related DEGs were identified, which were associated with immune-inflammatory responses. 237 STEMI-related DEGs were screened, which were involved in innate immune response and myeloid leukocyte activation. M0 macrophages and dendritic cells were markedly higher in both anaphylactic and STEMI samples compared with healthy controls, while CD4⁺ naïve T cells and CD8⁺ T cells were significantly lower. Enrichment analysis of 33 common DEGs illustrated shared biological processes of anaphylaxis and STEMI, including cytokine-mediated signaling pathway, response to reactive oxygen species, and positive regulation of defense response. Six hub genes were identified, and their expression levels were positively correlated with M0 macrophage abundance and negatively correlated with CD4⁺ naïve T cell abundance. In external anaphylactic and STEMI samples, five hub genes (IL1R2, FOS, MMP9, DUSP1, CLEC4D) were confirmed to be markedly upregulated. Moreover, experimentally induced anaphylactic mice developed impaired heart function featuring STEMI and significantly increased expression of the five hub genes. DUSP1 and CLEC4D were screened as blood diagnostic biomarkers of anaphylaxis and STEMI based on the logistic regression analysis. Conclusion: Anaphylaxis and STEMI share the biological processes of inflammation and defense responses. Macrophages, dendritic cells, CD8⁺ T cells, and CD4⁺ naïve T cells constitute an immune cell population that acts in both anaphylaxis and STEMI. Hub genes (DUSP1 and CLEC4D) identified here provide candidate genes for diagnosis, prognosis, and therapeutic targeting of STEMI in anaphylactic patients.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Simultaneous blockade of VEGF-B and IL-17A ameliorated diabetic kidney disease by reducing ectopic lipid deposition and alleviating inflammation response

The pathogenesis of diabetic kidney disease (DKD) is complicated. Current clinical treatments fail to achieve satisfactory efficacy in the prevention of DKD progression, it urgently needs novel and effective treatment for DKD. In this study, we firstly demonstrated that renal lipid metabolism abnormality and inflammation significantly changed in DKD conditions by mining public transcriptomic data of DKD patient samples. KEGG analysis further exhibited the critical role of vascular endothelial growth factor B (VEGF-B) and interleukin 17A (IL-17A) signal pathways in DKD progression, indicating that VEGF-B and IL-17A might be the promising targets for DKD treatment. Then the potential of a novel combination therapy, anti-VEGF-B plus anti-IL-17A antibody, was evaluated for DKD treatment. Our results demonstrated that simultaneous blockade of VEGF-B and IL-17A signaling with their neutralizing antibodies alleviated renal damage and ameliorated renal function. The therapeutic effectiveness was not only related to the reduced lipid deposition especially the neutral lipids in kidney but also associated with the decreased inflammation response. Moreover, the therapy alleviated renal fibrosis by reducing collagen deposition and the expression of fibronectin and α-SMA in kidney tissues. RNA-seq analysis indicated that differential expression genes (DEGs) in db/db mice were significantly clustered into lipid metabolism, inflammation, fibrosis and DKD pathology-related pathways, and 181 of those DEGs were significantly reversed by the combinatory treatment, suggesting the underlying mechanism of administration of anti-VEGF-B and anti-IL-17A antibodies in DKD treatment. Taken together, this study identified that renal lipid metabolism abnormality and inflammation were critically involved in the progression of DKD, and simultaneous blockade of VEGF-B and IL-17A signaling represents a potential DKD therapeutic strategy.

Integrated bioinformatics analysis of IFITM1 as a prognostic biomarker and investigation of its immunological role in prostate adenocarcinoma

Introduction

Prostate adenocarcinoma (PRAD) is a highly aggressive malignancy with high mortality and poor prognosis, and its potential mechanism remains unclear. Our study aimed to identify novel markers for the prognosis of PRAD using bioinformatics technology.

Methods

The GSE32571 dataset was downloaded from the GEO database, and analyzed via the limma R package to identify differentially expressed genes (DEGs) and differentially expressed immune score-related genes (DEISRGs). The immune-related genes (IRGs) were further obtained by overlapping DEISRGs and DEGs, and the core gene was identified via survival analysis. Furthermore, the expression level, prognostic value, and potential functions of the core gene were evaluated via multiple bioinformatics databases.

Results

A total of 301 IRGs were identified from the GSE32571 dataset, and IFITM1 was a down-regulated gene in several types of cancer, including PRAD. Besides, low expression of IFITM1 was associated with a poor prognosis in PRAD. GSEA indicated that the vital pathways of IFITM1-associated genes were mainly enriched in primary immunodeficiency, Th17 cell differentiation, Th1, and Th2 cell differentiation, natural killer cell-mediated cytotoxicity, myeloid dendritic cell activation, regulation of leukocyte activation, etc. Furthermore, IFITM1 was closely correlated with 22 types of tumor-infiltrating immune cells.

Discussion

IFITM1 was a prognostic biomarker for PRAD patients, and it can be acted as a potential immune therapy target in PRAD.

OMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data

OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-seq datasets, compendia sharing, RESTful API support, and an additional meta-analysis method based on random effects. Here, we provide a step-by-step guide for using OMiCC.

For complete details on the use and execution of this protocol, please refer to Shah et al. (2016).

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OMiCC (OMics Compendia Commons) is a free web-based tool for gene expression data reuse

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Search publicly available studies to perform sample group comparisons to explore a disease

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In meta-analysis, multiple studies are combined to identify coherent signals

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OMiCC supports crowd-sharing and users can share their own analyses with the community

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Comprehensive Multiomics Analysis Reveals Potential Diagnostic and Prognostic Biomarkers in Adrenal Cortical Carcinoma

Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.

THP-1 cell line model for tuberculosis: A platform for in vitro macrophage manipulation

Macrophages are large mononuclear phagocytic cells that play a vital role in the immune response. They are present in all body tissues with extremely heterogeneous and plastic phenotypes that adapt to the organs and tissues in which they live and respond in the first-line against invading microorganisms. Tuberculosis (TB) is caused by the pathogenic bacteria Mycobacterium tuberculosis (Mtb), which is among the top 10 global infectious agents and the leading cause of mortality, ranking above human immunodeficiency virus (HIV), as a single infectious agent. Macrophages, upon Mtb infection, not only phagocytose the bacteria and present the antigens to T-cells, but also react rapidly by developing antimycobacterial immune response depending highly on the production of cytokines. However, Mtb is also capable of intracellular survival in instances of sub-optimal activation of macrophages. Hence, several systems have been established to evaluate the Mtb-macrophage interaction, where the THP-1 monocytes have been developed as an attractive model for in vitro polarized monocyte-derived macrophages. This model is extensively used for Mtb as well as other intracellular bacterial studies. Herein, we have summarized the updated implications of the THP-1 model for TB-related studies and discussed the pros and cons compared to other cell models of TB.

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/ , respectively and developed ContNeXt ( https://contnext.scai.fraunhofer.de/ ), a web application to explore the networks generated in this work.

The BioImage Archive - Building a Home for Life-Sciences Microscopy Data

Despite the huge impact of data resources in genomics and structural biology, until now there has been no central archive for biological data for all imaging modalities. The BioImage Archive is a new data resource at the European Bioinformatics Institute (EMBL-EBI) designed to fill this gap. In its initial development BioImage Archive accepts bioimaging data associated with publications, in any format, from any imaging modality from the molecular to the organism scale, excluding medical imaging. The BioImage Archive will ensure reproducibility of published studies that derive results from image data and reduce duplication of effort. Most importantly, the BioImage Archive will help scientists to generate new insights through reuse of existing data to answer new biological questions, and provision of training, testing and benchmarking data for development of tools for image analysis. The archive is available at https://www.ebi.ac.uk/bioimage-archive/.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

Compartmentalization of Intrarenal Programmed Cell Death Protein 1-Ligand 1 and Its Receptor in Kidney Injury Related to Immune Checkpoint Inhibitor Nephrotoxicity

Background

Due to advances in cancer therapy, immune checkpoint inhibitors (ICIs) are new classes of drugs targeting programmed cell death protein 1-ligand 1 (PD-L1) or its receptor (PD-1) used in many cancer therapies. Acute interstitial nephritis (AIN) is a potential and deleterious immune-related adverse events (irAE) and the most common biopsy-proven diagnosis in ICI-related nephrotoxicity. AIN in patients receiving ICIs is was only seen in cases with tubular PD-L1 positivity, while PD-1 expression is limited to inflammatory cells and also observed in injured kidneys independent of ICI therapy. We have previously described that PD-L1 positivity can also be detected in glomerular and endothelial compartments. We here aimed to describe compartmentalization of renal PD-L1 expression specifically in injured kidneys with confirmed nephrotoxicity related to ICIs, its association with presence of PD-1, and clinical findings.

Methods

We included human kidney samples with AIN related to ICI therapy to describe PD-L1 and PD-1 expression localized to different renal compartments in association with clinical and laboratory parameters.

Results

We herein report compartmentalization of PD-L1 with tubular positivity in all cases, partially overlapping with glomerular and endothelial PD-L1 positivity. Furthermore, we provide evidence that tubular PD-L1 in ICI-related nephrotoxicity correlates with levels of C-reactive protein (CRP), while glomerular and endothelial PD-L1 positivity with lower serum levels of complement component C4. Interestingly, glomerular PD-L1 correlated with kidney function, while interstitial cell PD-1 positivity specifically with severity of kidney injury. Finally, we provide evidence for signaling pathways associated with intrarenal PD-L1/PD-1 expression.

Conclusion

Our findings implicate that that AIN related to ICI therapy requires presence of interstitial cells positive for PD-1, and that blocking PD-L1/PD-1 signaling may contribute to nephrotoxicity specifically related to these agents.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Identification of potential biomarkers of vascular calcification using bioinformatics analysis and validation in vivo

Background

Vascular calcification (VC) is the most widespread pathological change in diseases of the vascular system. However, we know poorly about the molecular mechanisms and effective therapeutic approaches of VC.

Methods

The VC dataset, GSE146638, was downloaded from the Gene Expression Omnibus (GEO) database. Using the edgeR package to screen Differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to find pathways affecting VC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the DEGs. Meanwhile, using the String database and Cytoscape software to construct protein-protein interaction (PPI) networks and identify hub genes with the highest module scores. Correlation analysis was performed for hub genes. Receiver operating characteristic (ROC) curves, expression level analysis, GSEA, and subcellular localization were performed for each hub gene. Expression of hub genes in normal and calcified vascular tissues was verified by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC) experiments. The hub gene-related miRNA-mRNA and TF-mRNA networks were constructed and functionally enriched for analysis. Finally, the DGIdb database was utilized to search for alternative drugs targeting VC hub genes.

Results

By comparing the genes with normal vessels, there were 64 DEGs in mildly calcified vessels and 650 DEGs in severely calcified vessels. Spp1, Sost, Col1a1, Fn1, and Ibsp were central in the progression of the entire VC by the MCODE plug-in. These hub genes are primarily enriched in ossification, extracellular matrix, and ECM-receptor interactions. Expression level results showed that Spp1, Sost, Ibsp, and Fn1 were significantly highly expressed in VC, and Col1a1 was incredibly low. RT-qPCR and IHC validation results were consistent with bioinformatic analysis. We found multiple pathways of hub genes acting in VC and identified 16 targeting drugs.

Conclusions

This study perfected the molecular regulatory mechanism of VC. Our results indicated that Spp1, Sost, Col1a1, Fn1, and Ibsp could be potential novel biomarkers for VC and promising therapeutic targets.

Transcriptional Landscape of Cotton Fiber Development and Its Alliance With Fiber-Associated Traits

Cotton fiber development is still an intriguing question to understand fiber commitment and development. At different fiber developmental stages, many genes change their expression pattern and have a pivotal role in fiber quality and yield. Recently, numerous studies have been conducted for transcriptional regulation of fiber, and raw data were deposited to the public repository for comprehensive integrative analysis. Here, we remapped > 380 cotton RNAseq data with uniform mapping strategies that span ∼400 fold coverage to the genome. We identified stage-specific features related to fiber cell commitment, initiation, elongation, and Secondary Cell Wall (SCW) synthesis and their putative cis-regulatory elements for the specific regulation in fiber development. We also mined Exclusively Expressed Transcripts (EETs) that were positively selected during cotton fiber evolution and domestication. Furthermore, the expression of EETs was validated in 100 cotton genotypes through the nCounter assay and correlated with different fiber-related traits. Thus, our data mining study reveals several important features related to cotton fiber development and improvement, which were consolidated in the "CottonExpress-omics" database.

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Transcriptomics and network analysis highlight potential pathways in the pathogenesis of pterygium

Pterygium is a common ocular surface condition frequently associated with irritative symptoms. The precise identity of its critical triggers as well as the hierarchical relationship between all the elements involved in the pathogenesis of this disease are not yet elucidated. Meta-analysis of gene expression studies represents a novel strategy capable of identifying key pathogenic mediators and therapeutic targets in complex diseases. Samples from nine patients were collected during surgery after photo documentation and clinical characterization of pterygia. Gene expression experiments were performed using Human Clariom D Assay gene chip. Differential gene expression analysis between active and atrophic pterygia was performed using limma package after adjusting variables by age. In addition, a meta-analysis was performed including recent gene expression studies available at the Gene Expression Omnibus public repository. Two databases including samples from adults with pterygium and controls fulfilled our inclusion criteria. Meta-analysis was performed using the Rank Production algorithm of the RankProd package. Gene set analysis was performed using ClueGO and the transcription factor regulatory network prediction was performed using appropriate bioinformatics tools. Finally, miRNA-mRNA regulatory network was reconstructed using up-regulated genes identified in the gene set analysis from the meta-analysis and their interacting miRNAs from the Brazilian cohort expression data. The meta-analysis identified 154 up-regulated and 58 down-regulated genes. A gene set analysis with the top up-regulated genes evidenced an overrepresentation of pathways associated with remodeling of extracellular matrix. Other pathways represented in the network included formation of cornified envelopes and unsaturated fatty acid metabolic processes. The miRNA-mRNA target prediction network, also reconstructed based on the set of up-regulated genes presented in the gene ontology and biological pathways network, showed that 17 target genes were negatively correlated with their interacting miRNAs from the Brazilian cohort expression data. Once again, the main identified cluster involved extracellular matrix remodeling mechanisms, while the second cluster involved formation of cornified envelope, establishment of skin barrier and unsaturated fatty acid metabolic process. Differential expression comparing active pterygium with atrophic pterygium using data generated from the Brazilian cohort identified differentially expressed genes between the two forms of presentation of this condition. Our results reveal differentially expressed genes not only in pterygium, but also in active pterygium when compared to the atrophic ones. New insights in relation to pterygium's pathophysiology are suggested.

The impact of chronic stress on the PFC transcriptome: a bioinformatic meta-analysis of publicly available RNA-sequencing datasets

The prefrontal cortex (PFC) is one of several brain structures that are sensitive to chronic stress exposure. There have been several studies which have examined the effects on chronic stress, using various protocols such as chronic unpredictable stress and chronic social defeat stress, on the PFC transcriptome. In this report, a bioinformatic meta-analysis of publicly available RNA sequencing datasets (101 samples) from seven chronic stress studies was carried out to identify core PFC transcriptional signatures that underpin behavioral phenotypes including resilience and susceptibility. The results showed 160 differentially expressed genes in chronic stress mice compared to controls with significant enrichment in mechanisms associated with translation and localization of membrane-bound proteins with a putative effect on synaptic plasticity in glutamatergic neurons. Moreover, the meta-analysis revealed no differentially expressed genes in resilient mice but 144 in susceptible mice compared to controls, of which 44 were not identified in the individual studies. Enrichment analysis revealed that susceptibility genes were most affected in oligodendrocytes and linked to mechanisms which mediate biochemical, bidirectional communication between this cell-type and myelinated axons. These results provide new avenues for further research into the neurobiology and treatment of chronic stress-induced disorders.

REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.

Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Immune infiltration landscape and immune-marker molecular typing of pulmonary fibrosis with pulmonary hypertension

BACKGROUND

Pulmonary arterial hypertension (PH) secondary to pulmonary fibrosis (PF) is one of the most common complications in PF patients, it causes severe disease and usually have a poor prognosis. Whether the combination of PH and PF is a unique disease phenotype is unclear. We aimed to screen the key modules associated with PH-PF immune infiltration based on WGCNA and identify the hub genes for molecular typing.

METHOD

Using the gene expression profile GSE24988 of PF patients with or without PH from the Gene Expression Omnibus (GEO) database, we evaluated immune cell infiltration using Cibersortx and immune cell gene signature files. Different immune cell types were screened using the Wilcoxon test; differentially expressed genes were screened using samr. The molecular pathways implicated in these differential responses were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. A weighted co-expression network of the differential genes was constructed, relevant co-expression modules were identified, and relationships between modules and differential immune cell infiltration were calculated. The modules most relevant to this disease were identified using weighted correlation network analysis. From these, we constructed a co-expression network; using the STRING database, we integrated the values into the human protein-protein interaction network before constructing a co-expression interaction subnet, screening genes associated with immunity and unsupervised molecular typing, and analyzing the immune cell infiltration and expression of key genes in each disease type.

RESULTS

Of the 22 immune cell types from the PF GEO data, 20 different immune cell types were identified. There were 1622 differentially expressed genes (295 upregulated and 1327 downregulated). The resulting weighted co-expression network identified six co-expression modules. These were screened to identify the modules most relevant to the disease phenotype (the green module). By calculating the correlations between modules and the differentially infiltrated immune cells, extracting the green module co-expression network (46 genes), extracting 25 key genes using gene significance and module-membership thresholds, and combining these with the 10 key genes in the human protein-protein interaction network, we identified five immune cell-related marker genes that might be applied as biomarkers. Using these marker genes, we evaluated these disease samples using unsupervised clustering molecular typing.

CONCLUSION

Our results demonstrated that all PF combined with PH samples belonged to four categories. Studies on the five key genes are required to validate their diagnostic and prognostic value.

The importance of adherence to international standards for depositing open data in public repositories

There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.

Tissue heterogeneity is prevalent in gene expression studies

Lack of reproducibility in gene expression studies is a serious issue being actively addressed by the biomedical research community. Besides established factors such as batch effects and incorrect sample annotations, we recently reported tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, as a source of variance. Although tissue heterogeneity exacerbates irreproducibility, its prevalence in gene expression data remains unknown. Here, we systematically analyse 2 667 publicly available gene expression datasets covering 76 576 samples. Using two independent data compendia and a reproducible, open-source software pipeline, we find a prevalence of tissue heterogeneity in gene expression data that affects between 1 and 40% of the samples, depending on the tissue type. We discover both cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, and cases of moderate heterogeneity, which are likely caused by tissue infiltration or sample contamination. Our analysis establishes tissue heterogeneity as a widespread phenomenon in publicly available gene expression datasets, which constitutes an important source of variance that should not be ignored. Consequently, we advocate the application of quality-control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.

Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia

Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.

Comprehensive Analysis of Differentially Expressed Genes in Clinically Diagnosed Irreversible Pulpitis by Multiplatform Data Integration Using a Robust Rank Aggregation Approach

INTRODUCTION

Molecular diagnosis may overcome the limitations of clinical and histologic diagnosis in pulpitis, thereby benefiting many treatment techniques, such as vital pulp therapies. In this study, integrated microarray data on pulpitis were used to obtain a list of normalized differentially expressed (DE) genes for analyzing the molecular mechanisms underlying pulpitis and identifying potential diagnostic biomarkers.

METHODS

A systematic search of public microarray and sequencing databases was performed to obtain expression data of pulpitis. Robust rank aggregation (RRA) was used to obtain DE gene lists (RRA_DEmRNAs and RRA_DElncRNAs) between inflamed pulp and normal samples. DE genes were evaluated by functional enrichment analyses, correlation analyses for inflammation-related RRA_DEmRNAs, and protein-protein interaction and competing endogenous RNA network construction. Quantitative real-time polymerase chain reaction validation was applied in snap-frozen pulp tissues.

RESULTS

Using the GSE77459 and GSE92681 data sets, 280 RRA_DEmRNAs and 90 RRA_DElncRNAs were identified. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes and osteoclast differentiation and tumor necrosis factor, chemokine, and B-cell receptor signaling pathways. The molecular complex detection and cytoHubba methods identified 2 clusters and 10 hub genes in the protein-protein interaction network. The competing endogenous RNA network was composed of 2 long noncoding RNAs (ADAMTS9-AS2 and LINC00290), 2 microRNAs (hsa-miR-30a-5p and hsa-miR-128-3p), and 3 messenger RNAs (ABCA1, FBLN5, and SOCS3). The expression between most top inflammation-related RRA_DEmRNAs in pulpitis showed positive correlations. Quantitative real-time polymerase chain reacation validated the expression trends of selected genes, including ITGAX, TREM1, CD86, FCGR2A, ADAMTS9-AS2, LINC00290, hsa-miR-30a-5p, hsa-miR-128-3p, RASGRP3, IL3RA, CCDC178, CRISPLD1, LINC01857, AC007991.2, ARHGEF26-AS1, and AL021408.1.

CONCLUSIONS

The identified biomarkers provide insight into the pathology and may aid in the molecular diagnosis of pulpitis.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method

SUMMARY

Processing raw reads of RNA-sequencing (RNA-seq) data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Here, we develop the R package BP4RNAseq, which integrates the state-of-art tools from both alignment-based and alignment-free quantification workflows. The BP4RNAseq package is a highly automated tool using an optimized pipeline to improve the sensitivity and accuracy of RNA-seq analyses. It can take only two non-technical parameters and output six formatted gene expression quantification at gene and transcript levels. The package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses. It, therefore, greatly facilitates the application of RNA-seq.

AVAILABILITY AND IMPLEMENTATION

The BP4RNAseq package for R and its documentation are freely available at https://github.com/sunshanwen/BP4RNAseq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-label Learning for the Diagnosis of Cancer and Identification of Novel Biomarkers with High-throughput Omics

Background:

The advancement of bioinformatics and machine learning has facilitated the diagnosis of cancer and the discovery of omics-based biomarkers.

Objective:

Our study employed a novel data-driven approach to classifying the normal samples and different types of gastrointestinal cancer samples, to find potential biomarkers for effective diagnosis and prognosis assessment of gastrointestinal cancer patients.

Methods:

Different feature selection methods were used, and the diagnostic performance of the proposed biosignatures was benchmarked using support vector machine (SVM) and random forest (RF) models.

Results:

All models showed satisfactory performance in which Multilabel-RF appeared to be the best. The accuracy of the Multilabel-RF based model was 83.12%, with precision, recall, F1, and Hamming- Loss of 79.70%, 68.31%, 0.7357 and 0.1688, respectively. Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Functional enrichment analysis and impact of the biomarker candidates in the prognosis of the patients were also examined.

Conclusion:

We successfully introduced a solid workflow based on multi-label learning with High- Throughput Omics for diagnosis of cancer and identification of novel biomarkers. Novel transcriptome biosignatures that may improve the diagnostic accuracy in gastrointestinal cancer are introduced for further validations in various clinical settings.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Bioinformatics identification of CCL8/21 as potential prognostic biomarkers in breast cancer microenvironment

BACKGROUND

Breast cancer (BC) is the most common malignancy among females worldwide. The tumor microenvironment usually prevents effective lymphocyte activation and infiltration, and suppresses infiltrating effector cells, leading to a failure of the host to reject the tumor. CC chemokines play a significant role in inflammation and infection.

METHODS

In our study, we analyzed the expression and survival data of CC chemokines in patients with BC using several bioinformatics analyses tools.

RESULTS

The mRNA expression of CCL2/3/4/5/7/8/11/17/19/20/22 was remarkably increased while CCL14/21/23/28 was significantly down-regulated in BC tissues compared with normal tissues. Methylation could down-regulate expression of CCL2/5/15/17/19/20/22/23/24/25/26/27 in BC. Low expression of CCL3/4/23 was found to be associated with drug resistance in BC. Results from Kaplan-Meier plotter and BC Gene-Expression Miner v4.2 (bcGenExMiner) v4.2 demonstrated that BC patients with high CCL8 and low CCL19/21/22 expression were more likely to have a worse prognosis. CCL8 expression was significantly up-regulated in BC tissues compared with normal tissues. High CCL8 expression was significantly correlated with negative PR, negative ER, positive nodal status, triple-negative BC subtype, basal-like BC subtype, triple-negative and basal-like BC subtype and high grades. CCL21 was down-regulated in BC, while high levels of CCL21 was associated with negative PR, triple-negative subtype, basal-like subtype and low tumor grade. Functional analysis demonstrated that CCL8 and CCL21 were involved in carcinogenesis, tumor immune escape and chemoresistance in BC.

CONCLUSION

Integrative bioinformatics analysis demonstrated CCL8/21 as potential prognostic biomarkers in BC microenvironment.

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.

Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.

Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.

Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

Comparative transcriptomics reveals hidden issues in the plant response to arthropod herbivores

Plants experience different abiotic/biotic stresses, which trigger their molecular machinery to cope with them. Besides general mechanisms prompted by many stresses, specific mechanisms have been introduced to optimize the response to individual threats. However, these key mechanisms are difficult to identify. Here, we introduce an in-depth species-specific transcriptomic analysis and conduct an extensive meta-analysis of the responses to related species to gain more knowledge about plant responses. The spider mite Tetranychus urticae was used as the individual species, several arthropod herbivores as the related species for meta-analysis, and Arabidopsis thaliana plants as the common host. The analysis of the transcriptomic data showed typical common responses to herbivory, such as jasmonate signaling or glucosinolate biosynthesis. Also, a specific set of genes likely involved in the particularities of the Arabidopsis-spider mite interaction was discovered. The new findings have determined a prominent role in this interaction of the jasmonate-induced pathways leading to the biosynthesis of anthocyanins and tocopherols. Therefore, tandem individual/general transcriptomic profiling has been revealed as an effective method to identify novel relevant processes and specificities in the plant response to environmental stresses.

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

LAPTM5 Plays a Key Role in the Diagnosis and Prognosis of Testicular Germ Cell Tumors

OBJECTIVE

Testicular germ cell tumors (TGCT) are a serious malignant tumor with low early diagnosis rates and high mortality.

METHODS

To investigate novel biomarkers to predict the diagnosis and prognosis of this cancer, bioinformatics analysis was used as an accurate, efficient, and economical method.

RESULTS

Our study detected 39 upregulated and 589 downregulated differentially expressed genes (DEGs) using the GEO and TCGA databases. To identify the function of DEGs, GO functional analysis, three pathway analysis (KEGG, REACTOME, and PANTHER), and protein-protein interaction network were performed using the KOBAS website, as well as the String database. After a series of analyses in GEPIA and TIMER, including differential expression, we found one candidate gene related to the prognosis and diagnosis of TGCT. LAPTM5 was also associated with CD8+ T cell and PDCD1 expression, which suggests that it may affect immune infiltration.

CONCLUSIONS

LAPTM5 was identified as a hub gene, which could be used as a potential biomarker for TGCT diagnosis and prognosis.

Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.

A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96–570 and LINCS), and multiple imputation tasks (within and across microarray/RNA-seq datasets) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.