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Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Atopic dermatitis (AD) and canine atopic dermatitis (CAD) are common allergic and pruritic skin diseases characterized by immune dysregulation and epidermal barrier dysfunction. To delineate how Th2 cytokines contribute to CAD pathogenesis, canine primary epidermal organoids (cPEOs) were established from keratinocytes, and exposure to IL-4/IL-13 induced morphologic changes characteristic of CAD. RNA sequencing analysis comparing IL-4/IL-13-treated cPEOs to untreated controls identified 224 differentially expressed genes (DEGs). Further rigorous filtering narrowed this down to 69 key DEGs, with the majority being associated with atopic dermatitis in both dogs and humans. Pathway enrichment analyses demonstrated the activation of immune and inflammatory signalling and suppression of epidermal differentiation, keratinisation, and lipid metabolism, recapitulating key features of atopic skin. Additional Th2-driven alterations included dysregulation of neuro-immune signalling, calcium homeostasis, apoptosis, extracellular matrix remodelling, and metabolic/epigenetic regulations. Together, these findings demonstrate that Th2 cytokines orchestrate multifaceted transcriptomic alterations relevant to AD/CAD. By mapping each key DEG to its known or putative role in AD/CAD, this study also provides a gene-level functional framework to inform future mechanistic studies and targeted therapeutic development. These findings also underscore the value of this model as a comparative tool for investigating both human and canine atopic dermatitis.

Background COVID-19 is an infectious disease characterized by multiple respiratory and extrapulmonary manifestations, including gastrointestinal symptoms. Although recent studies have linked gut microbiota to infectious diseases such as influenza, little is known about the role of the gut microbiota in COVID-19 pathophysiology. Methods To better understand the host-gut microbiota interactions in COVID-19, we characterized the gut microbial community and gut barrier function using metagenomic and metaproteomic approaches in 63 COVID-19 patients and 8 non-infected controls. Both immunohematological parameters and transcriptional profiles were measured to reflect the immune response in COVID-19 patients. Results Altered gut microbial composition was observed in COVID-19 patients, which was characterized by decreased commensal species and increased opportunistic pathogenic species. Severe illness was associated with higher abundance of four microbial species (i.e., Burkholderia contaminans , Bacteroides nordii , Bifidobacterium longum , and Blautia sp. CAG 257), six microbial pathways (e.g., glycolysis and fermentation), and 10 virulence genes. These severity-related microbial features were further associated with host immune response. For example, the abundance of Bu. contaminans was associated with higher levels of inflammation biomarkers and lower levels of immune cells. Furthermore, human-origin proteins identified from both blood and fecal samples suggested gut barrier dysfunction in COVID-19 patients. The circulating levels of lipopolysaccharide-binding protein increased in patients with severe illness and were associated with circulating inflammation biomarkers and immune cells. Besides, proteins of disease-related bacteria (e.g., B. longum ) were detectable in blood samples from patients. Conclusions Our results suggest that the dysbiosis of the gut microbiome and the dysfunction of the gut barrier might play a role in the pathophysiology of COVID-19 by affecting host immune homeostasis.

The mouse has been widely used as a model organism for studying human diseases and for evaluating drug safety and efficacy. Many diseases and drug effects exhibit tissue specificity that may be reflected by tissue-specific gene-expression profiles. Here we construct a comprehensive mouse transcriptomic BodyMap across 17 tissues of six-weeks old C57BL/6JJcl mice using RNA-seq. We find different expression patterns between protein-coding and non-coding genes. Liver expressed the least complex transcriptomes, that is, the smallest number of genes detected in liver across all 17 tissues, whereas testis and ovary harbor more complex transcriptomes than other tissues. We report a comprehensive list of tissue-specific genes across 17 tissues, along with a list of 4,781 housekeeping genes in mouse. In addition, we propose a list of 27 consistently and highly expressed genes that can be used as reference controls in expression-profiling analysis. Our study provides a unique resource of mouse gene-expression profiles, which is helpful for further biomedical research.

Adenocarcinoma in situ and minimally invasive adenocarcinoma are the pre-invasive forms of lung adenocarcinoma. The genomic and immune profiles of these lesions are poorly understood. Here we report exome and transcriptome sequencing of 98 lung adenocarcinoma precursor lesions and 99 invasive adenocarcinomas. We have identified EGFR , RBM10 , BRAF , ERBB2 , TP53 , KRAS , MAP2K1 and MET as significantly mutated genes in the pre/minimally invasive group. Classes of genome alterations that increase in frequency during the progression to malignancy are revealed. These include mutations in TP53 , arm-level copy number alterations, and HLA loss of heterozygosity. Immune infiltration is correlated with copy number alterations of chromosome arm 6p, suggesting a link between arm-level events and the tumor immune environment. The genomic and immune landscape of pre-invasive lung adenocarcinoma is poorly understood. Here, the authors perform exome and transcriptome sequencing on precursor legions and invasive lung adenocarcinomas, identifying recurrently mutated genes in pre/minimally invasive cases, and arm level alteration events linked to immune infiltration.

Background Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. Results As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. Conclusions Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

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. Here the authors report on an RNA-seq benchmarking study that demonstrates greater inter-lab variations in detecting subtle differential expression. The study reveals the impact of experimental execution, experimental designs, low-expression gene filtering, and analysis tool selection.

Background Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed. Methods We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs. Results The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected). Conclusion A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Eggs produced by the mature female parasite are responsible for the pathogenesis and transmission of schistosomiasis. Female schistosomes rely on a unique male-induced strategy to accomplish reproductive development, a process that is incompletely understood. Here we map detailed transcriptomic profiles of male and female Schistosoma japonicum across eight time points throughout the sexual developmental process from pairing to maturation. The dynamic gene expression pattern data reveal clear sex-related characteristics, indicative of an unambiguous functional division between males and females during their interplay. Cluster analysis, in situ hybridization and RNAi assays indicate that males likely use biogenic amine neurotransmitters through the nervous system to control and maintain pairing with females. In addition, the analyses indicate that reproductive development of females involves an insect-like hormonal regulation. These data sets and analyses serve as a foundation for deeper study of sexual development in this pathogen and identification of novel anti-schistosomal interventions. For reproduction, the human parasite Schistosoma japonicum relies on a complex and incompletely understood interplay between female and male schistosomes. Here the authors sequence the transcriptome of female and male schistosomes across eight time points during sexual development.

Indoleamine 2,3-dioxygenase 1 (IDO1), indoleamine 2,3-dioxygenase 2 (IDO2), and tryptophan 2,3-dioxygenase (TDO) initiate the first step of the kynurenine pathway (KP), leading to the transformation of l-tryptophan (Trp) into l-kynurenine (Kyn) and other downstream metabolites. Kyn is known as an endogenous ligand of the aryl hydrocarbon receptor (AhR). Activation of AhR through TDO-derived Kyn is a novel mechanism to support tumor growth in gliomas. However, the role of IDO1 and IDO2 in this mechanism is still unknown. Herein, by using clinical samples, we found that the expression and activity of IDO1 and/or TDO (IDO1/TDO) rather than IDO2 were positively correlated with the pathologic grades of gliomas. The expression of IDO1/TDO rather than IDO2 was positively correlated with the Ki67 index and overall survival. The expression of IDO1/TDO was positively correlated with the expression of aquaporin 4 (AQP4), implying the potential involvement of IDO1/TDO in glioma cell motility. Mechanistically, we found that IDO1/TDO accounted for the release of Kyn, which activated AhR to promote cell motility via the Kyn–AhR–AQP4 signaling pathway in U87MG glioma cells. RY103, an IDO1/TDO dual inhibitor, could block the IDO1/TDO–Kyn–AhR–AQP4 signaling pathway and exert anti-glioma effects in GL261 orthotopic glioma mice. Together, our results showed that the IDO1/TDO–Kyn–AhR–AQP4 signaling pathway is a new mechanism underlying the malignancy of gliomas, and suggest that both IDO1 and TDO might be valuable therapeutic targets for gliomas.