Impact of gene annotation choice on the quantification of RNA-seq data

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

Transcriptomic Meta-analysis Identifies Long Non-Coding RNAs Mediating Zika's Oncolytic Impact in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is an aggressive and lethal form of brain cancer with few effective treatments. In this context, Zika virus has emerged as a promising therapeutic agent due to its ability to selectively infect and kill GBM cells. To elucidate these mechanisms and expand the landscape of oncolytic virotherapy, we pursued a transcriptomic meta-analysis comparing the molecular signatures of Zika infection in GBM and neuroblastoma (NBM). Over-representation analysis of dysregulated coding genes showed significant enrichment of tumor necrosis factor (TNF), NF-κB, and p53 signaling pathways. A refined list of long non-coding RNAs consistently dysregulated in Zika-infected GBMs was also developed. Functional review of these candidates revealed their potential regulatory role in Zika-mediated oncolysis. We performed validation of the less-researched targets in adult and pediatric GBM cell lines and found significant differential regulation, as predicted. Altogether, our results provide novel insights into the molecular mechanisms underlying the effect of Zika on GBM. We highlight potential therapeutic targets that could be further interrogated to improve the efficacy of tumor cell death and the utility of Zika as an adjuvant virotherapy for GBM and other related cancers.

A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials

Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data

Genome annotation plays a crucial role in providing comprehensive catalog of genes and transcripts for a particular species. As research projects generate new transcriptome data worldwide, integrating this information into existing annotations becomes essential. However, most bioinformatics pipelines are limited in their ability to effectively and consistently update annotations using new RNA-seq data. Here we introduce TAGADA, an RNA-seq pipeline for Transcripts And Genes Assembly, Deconvolution, and Analysis. Given a genomic sequence, a reference annotation and RNA-seq reads, TAGADA enhances existing gene models by generating an improved annotation. It also computes expression values for both the reference and novel annotation, identifies long non-coding transcripts (lncRNAs), and provides a comprehensive quality control report. Developed using Nextflow DSL2, TAGADA offers user-friendly functionalities and ensures reproducibility across different computing platforms through its containerized environment. In this study, we demonstrate the efficacy of TAGADA using RNA-seq data from the GENE-SWiTCH project alongside chicken and pig genome annotations as references. Results indicate that TAGADA can substantially increase the number of annotated transcripts by approximately [Formula: see text] in these species. Furthermore, we illustrate how TAGADA can integrate Illumina NovaSeq short reads with PacBio Iso-Seq long reads, showcasing its versatility. TAGADA is available at github.com/FAANG/analysis-TAGADA.

Detection of microRNAs expression signatures in vitreous humor of intraocular tuberculosis

BACKGROUND

MicroRNA (miRNA) expression analysis has been shown to provide them as biomarkers in several eye diseases and has a regulatory role in pathogenesis. However, miRNA expression analysis in the vitreous humor (VH) of intraocular tuberculosis (IOTB) is not studied. Thus, we aim to find miRNA expression signatures in the VH of IOTB patients to identify their regulatory role in disease pathogenesis and to find them as potential biomarkers for IOTB.

METHODS AND RESULTS

First, we profiled miRNAs in VH of three IOTB and three Macular hole (MH) samples as controls through small-RNA deep sequencing using Illumina Platform. In-house bioinformatics analysis identified 81 dysregulated miRNAs in IOTB. Further validation in VH of IOTB (n = 15) compared to MH (n = 15) using Real-Time quantitative PCR (RT-qPCR) identified three significantly upregulated miRNAs, hsa-miR-150-5p, hsa-miR-26b-5p, and hsa-miR-21-5p. Based on the miRNA target prediction, functional network analysis, and RT-qPCR analysis of target genes, the three miRNAs downregulating WNT5A, PRKCA, MAP3K7, IL7, TGFB2, IL1A, PRKCB, TNFA, and TP53 genes involving MAPK signaling pathway, PI3K-AKT signaling pathway, WNT signaling pathway, Cell cycle, TGF-beta signaling pathway, Long-term potentiation, and Sphingolipid signaling pathways, have a potential role in disease pathogenesis. The ROC analysis of RT-qPCR data showed that hsa-miR-150-5p with AUC = 0.715, hsa-miR-21-5p with AUC = 0.789, and hsa-miR-26b-5p with AUC = 0.738; however, the combination of hsa-miR-21-5p and hsa-miR-26b-5p with AUC = 0.796 could serve as a potential biomarker for IOTB.

CONCLUSIONS

This study provides the first report on miRNA expression signatures detected in VH for IOTB pathogenesis and also provides a potential biomarker for IOTB.

A tuft cell - ILC2 signaling circuit provides therapeutic targets to inhibit gastric metaplasia and tumor development

Although gastric cancer is a leading cause of cancer-related deaths, systemic treatment strategies remain scarce. Here, we report the pro-tumorigenic properties of the crosstalk between intestinal tuft cells and type 2 innate lymphoid cells (ILC2) that is evolutionarily optimized for epithelial remodeling in response to helminth infection. We demonstrate that tuft cell-derived interleukin 25 (IL25) drives ILC2 activation, inducing the release of IL13 and promoting epithelial tuft cell hyperplasia. While the resulting tuft cell - ILC2 feed-forward circuit promotes gastric metaplasia and tumor formation, genetic depletion of tuft cells or ILC2s, or therapeutic targeting of IL13 or IL25 alleviates these pathologies in mice. In gastric cancer patients, tuft cell and ILC2 gene signatures predict worsening survival in intestinal-type gastric cancer where ~40% of the corresponding cancers show enriched co-existence of tuft cells and ILC2s. Our findings suggest a role for ILC2 and tuft cells, along with their associated cytokine IL13 and IL25 as gatekeepers and enablers of metaplastic transformation and gastric tumorigenesis, thereby providing an opportunity to therapeutically inhibit early-stage gastric cancer through repurposing antibody-mediated therapies.

Evolution of a new form of haploid-specific gene regulation appearing in a limited clade of ascomycete yeast species

Over evolutionary timescales, the logic and pattern of cell-type specific gene expression can remain constant, yet the molecular mechanisms underlying such regulation can drift between alternative forms. Here, we document a new example of this principle in the regulation of the haploid-specific genes in a small clade of fungal species. For most ascomycete fungal species, transcription of these genes is repressed in the a/α cell type by a heterodimer of two homeodomain proteins, Mata1 and Matα2. We show that in the species Lachancea kluyveri, most of the haploid-specific genes are regulated in this way, but repression of one haploid-specific gene (GPA1) requires, in addition to Mata1 and Matα2, a third regulatory protein, Mcm1. Model building, based on x-ray crystal structures of the three proteins, rationalizes the requirement for all three proteins: no single pair of the proteins is optimally arranged, and we show that no single pair can bring about repression. This case study exemplifies the idea that the energy of DNA binding can be "shared out" in different ways and can result in different DNA-binding solutions across different genes-while maintaining the same overall pattern of gene expression.

Chitosan degradation products promote healing of burn wounds of rat skin

Burns can impair the barrier function of the skin, and small burns can also cause high mortality. The WHO has described that over 180,000 people die of burns worldwide each year. Thus, the treatment of burn wounds is a major clinical challenge. Chitooligosaccharides (COS) are alkaline amino oligosaccharides with small molecular weights obtained by enzyme or chemical degradation of chitosan. With the characteristics of biocompatibility, water solubility and degradability, it has attracted increasing attention in the fields of biomedicine. In the present study, we used COS to treat deep second-degree burn wounds of rat skin and found that COS was able to promote wound healing. We also revealed that COS could promote fibroblast proliferation. Transcriptome sequencing analysis was performed on COS-treated fibroblasts to identify the underlying mechanisms. The results showed that COS was able to promote wound healing through regulation of the mitogen-activated protein kinase (MAPK) pathway and growth factor Hepatocyte Growth Factor (HGF). Our results provide a potential drug for burn wound therapy and the related molecular mechanism.