scholarly journals Large-scale CRISPRi and transcriptomics of Staphylococcus epidermidis identify genetic factors implicated in commensal-pathogen lifestyle versatility

2021 ◽  
Author(s):  
Michelle Spoto ◽  
Elizabeth Fleming ◽  
Yvette Ondouah Nzutchi ◽  
Changhui Guan ◽  
Julia Oh
Keyword(s):  
Large Scale ◽  
Genetic Factors ◽  
Transcriptional Profiling ◽  
Acid Stress ◽  
Essential Genes ◽  
Rna Seq ◽  
Cell Wall Modification ◽  
Transcriptomics Data ◽  
Skin Bacterium ◽  
Multiple Conditions

Abstract Staphylococcus (S.) epidermidis is a ubiquitous human commensal skin bacterium and prevalent nosocomial pathogen, but genetic factors underlying this lifestyle plasticity are incompletely understood. We developed a large-scale CRISPR interference (CRISPRi) screen complemented by transcriptional profiling (RNA-seq) across 24 conditions and piloted droplet-based CRISPRi. We identified putative essential genes, demonstrated the importance of trace metal uptake under stress, and identified condition-specific essential genes. Additionally, we demonstrate the considerable transcriptional plasticity of S. epidermidis, postulating a mechanism by which nitrogen metabolism is linked to lifestyle versatility in response to hyperosmotic challenges, and performed a comprehensive analysis of survival under acid stress, hypothesizing a role for cell wall modification as crucial. This study represents one of the first large-scale CRISPRi studies in prokaryotes and the first to integrate transcriptomics data across multiple conditions. Our results provide a valuable benchmarking analysis for CRISPRi screens and are rich resource for other staphylococcal researchers.

scholarly journals Large-scale CRISPRi and transcriptomics of Staphylococcus epidermidis identify genetic factors implicated in commensal-pathogen lifestyle versatility

2021 ◽  
Author(s):  
Michelle Spoto ◽  
Elizabeth Fleming ◽  
Yvette Ondouah Nzutchi ◽  
Changhui Guan ◽  
Julia Oh
Keyword(s):  
Human Skin ◽  
Large Scale ◽  
Genetic Factors ◽  
Genetic Manipulation ◽  
Transcriptional Profiling ◽  
Acid Stress ◽  
Model Organism ◽  
Essential Genes ◽  
Skin Microbiome ◽  
Cell Wall Modification

AbstractStaphylococcus (S.) epidermidis is a ubiquitous human commensal skin bacterium that is also one of the most prevalent nosocomial pathogens. The genetic factors underlying this remarkable lifestyle plasticity are incompletely understood, much due to the difficulties of genetic manipulation, precluding high-throughput functional profiling of this species. To probe S. epidermdis’ versatility to survive across a diversity of skin sites and infection niches, we developed a large-scale CRISPR interference (CRISPRi) screen complemented by transcriptional profiling (RNA-seq) across 24 diverse environmental conditions and piloted a droplet-based CRISPRi approach to enhance throughput and sensitivity. We identified putative essential genes, importantly, revealing amino acid metabolism as crucial to survival across diverse environments, demonstrated the importance of trace metal uptake for survival under multiple stress conditions, and identified condition-specific essential genes for each of our 24 different environments. We identified pathways significantly enriched and repressed across our range of stress and nutrient limited conditions, demonstrating the considerable plasticity of S. epidermidis in responding to environmental stressors. In particular, we postulate a mechanism by which nitrogen metabolism is linked to lifestyle versatility in response to hyperosmotic challenges, such as those encountered on human skin. Finally, by leveraging both transcriptomic and CRISPRi data, we performed a comprehensive analysis of S. epidermidis survival under acid stress and hypothesize a role for cell wall modification as a vital component of the survival response in acidic conditions. Taken together, this study represents one of the first large-scale CRISPRi studies in a non-model organism and the first to integrate transcriptomics data across multiple conditions to provide new biological insights into a keystone member of the human skin microbiome. Our results additionally provide a valuable benchmarking analysis for CRISPRi screens and are rich resource for other staphylococcal researchers.

scholarly journals A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

2012 ◽  
Vol 215 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Georg Homuth ◽  
Alexander Teumer ◽  
Uwe Völker ◽  
Matthias Nauck
Keyword(s):  
Blood Cells ◽  
Large Scale ◽  
Genetic Factors ◽  
Association Studies ◽  
Transcriptional Profiling ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Future Developments ◽  
Genome Wide ◽  
Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

scholarly journals Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

2016 ◽  
Author(s):  
Joseph N. Paulson ◽  
Cho-Yi Chen ◽  
Camila M. Lopes-Ramos ◽  
Marieke L Kuijjer ◽  
John Platig ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Transcriptional Profiling ◽  
R Package ◽  
Heterogeneous Data ◽  
Data Sets ◽  
List Type ◽  
Complex Data ◽  
Rna Seq ◽  
Data Set

AbstractAlthough ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.HighlightsOverview of assumptions used in preprocessing and normalizationPipeline for preprocessing, quality control, and normalization of large heterogeneous dataA Bioconductor package for the YARN pipeline and easy manipulation of count dataPreprocessed GTEx data set using the YARN pipeline available as a resource

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

scholarly journals Multiple Alu exonization in 3’UTR of a primate specific isoform of CYP20A1 creates a potential miRNA sponge

2020 ◽  
Author(s):  
Aniket Bhattacharya ◽  
Vineet Jha ◽  
Khushboo Singhal ◽  
Mahar Fatima ◽  
Dayanidhi Singh ◽  
...  
Keyword(s):  
Heat Shock ◽  
Cortical Neurons ◽  
Regulatory Networks ◽  
Large Scale ◽  
Neuronal Development ◽  
Random Sets ◽  
Rna Seq ◽  
Orphan Gene ◽  
Mirna Sponge ◽  
Human Neurons

Abstract Alu repeats contribute to phylogenetic novelties in conserved regulatory networks in primates. Our study highlights how exonized Alus could nucleate large-scale mRNA-miRNA interactions. Using a functional genomics approach, we characterize a transcript isoform of an orphan gene, CYP20A1 (CYP20A1_Alu-LT) that has exonization of 23 Alus in its 3’UTR. CYP20A1_Alu-LT, confirmed by 3’RACE, is an outlier in length (9 kb 3’UTR) and widely expressed. Using publically available datasets, we demonstrate its expression in higher primates and presence in single nucleus RNA-seq of 15928 human cortical neurons. miRanda predicts ∼4700 miRNA recognition elements (MREs) for ∼1000 miRNAs, primarily originated within these 3’UTR-Alus. CYP20A1_Alu-LT could be a potential multi-miRNA sponge as it harbors ≥10 MREs for 140 miRNAs and has cytosolic localization. We further tested whether expression of CYP20A1_Alu-LT correlates with mRNAs harboring similar MRE targets. RNA-seq with conjoint miRNA-seq analysis was done in primary human neurons where we observed CYP20A1_Alu-LT to be downregulated during heat shock response and upregulated in HIV1-Tat treatment. 380 genes were positively correlated with its expression (significantly downregulated in heat shock and upregulated in Tat) and they harbored MREs for nine expressed miRNAs which were also enriched in CYP20A1_Alu-LT. MREs were significantly enriched in these 380 genes compared to random sets of differentially expressed genes (p = 8.134e-12). Gene ontology suggested involvement of these genes in neuronal development and hemostasis pathways thus proposing a novel component of Alu-miRNA mediated transcriptional modulation that could govern specific physiological outcomes in higher primates.

scholarly journals Gene Expression Imputation with Generative Adversarial Imputation Nets

2020 ◽  
Author(s):  
Ramon Viñas ◽  
Tiago Azevedo ◽  
Eric R. Gamazon ◽  
Pietro Liò
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biological Significance ◽  
Predictive Performance ◽  
Cost Effective ◽  
Rna Seq ◽  
Comprehensive Collection ◽  
Genomic Studies ◽  
Biological Discovery ◽  
Cancer Types

AbstractA question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we present GAIN-GTEx, a method for gene expression imputation based on Generative Adversarial Imputation Networks. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We compare our model to several standard and state-of-the-art imputation methods and show that GAIN-GTEx is significantly superior in terms of predictive performance and runtime. Furthermore, our results indicate strong generalisation on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types.

scholarly journals Automated Isoform Diversity Detector (AIDD): A pipeline for investigating transcriptome diversity of RNA-seq data

2020 ◽  
Author(s):  
Noel-Marie Plonski ◽  
Emily Johnson ◽  
Madeline Frederick ◽  
Heather Mercer ◽  
Gail Fraizer ◽  
...  
Keyword(s):  
Rna Editing ◽  
Large Scale ◽  
Neural Progenitor Cells ◽  
Viral Infections ◽  
Variant Calling ◽  
Rna Seq ◽  
Major Mechanism ◽  
Isoform Diversity ◽  
Adar Editing ◽  
Transcriptome Diversity

AbstractBackgroundAs the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides – remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.ResultsHere we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities.ConclusionsAIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

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