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Article ; Online: Immunopathogenic overlap between COVID-19 and tuberculosis identified from transcriptomic meta-analysis and human macrophage infection

Dylan Sheerin / Abhimanyu / Nashied Peton / William Vo / Cody Charles Allison / Xutao Wang / W. Evan Johnson / Anna Kathleen Coussens

iScience, Vol 25, Iss 6, Pp 104464- (2022)

2022

Abstract: Summary: Current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. To identify mechanisms of immunopathogenic interaction between COVID-19 and TB, we performed a systematic review and patient-level meta-analysis ... ...

Abstract	Summary: Current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. To identify mechanisms of immunopathogenic interaction between COVID-19 and TB, we performed a systematic review and patient-level meta-analysis of COVID-19 transcriptomic signatures, spanning disease severity, from whole blood, PBMCs, and BALF. 35 eligible signatures were profiled on 1181 RNA-seq samples from 853 individuals across the spectrum of TB infection. Thirteen COVID-19 gene-signatures had significantly higher “COVID-19 risk scores” in active TB and latent TB progressors compared with non-progressors and uninfected controls (p<0·005), in three independent cohorts. Integrative single-cell-RNAseq analysis identified FCN1- and SPP1-expressing macrophages enriched in severe COVID-19 BALF and active TB blood. Gene ontology and protein-protein interaction networks identified 12-gene disease-exacerbation hot spots between COVID-19 and TB. Finally, we in vitro validated that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of Mtb-infected macrophages, correlating with TMPRSS2, IFNA1, IFNB1, IFNG, TNF, and IL1B induction.
Keywords	Pathophysiology ; Immunology ; Virology ; Transcriptomics ; Science ; Q
Subject code	610
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: scruff

Zhe Wang / Junming Hu / W. Evan Johnson / Joshua D. Campbell

BMC Bioinformatics, Vol 20, Iss 1, Pp 1-

an R/Bioconductor package for preprocessing single-cell RNA-sequencing data

2019 Volume 9

Abstract: Abstract Background Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique ... ...

Abstract	Abstract Background Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking. Results Here we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package. Conclusions scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.
Keywords	Scruff ; Single-cell RNA-sequencing ; Cell barcode demultiplexing ; Unique molecular identifier (UMI) ; Visualization of data quality ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2019-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Yuqing Zhang / David F. Jenkins / Solaiappan Manimaran / W. Evan Johnson

BMC Bioinformatics, Vol 19, Iss 1, Pp 1-

2018 Volume 15

Abstract: Abstract Background Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant ... ...

Abstract	Abstract Background Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. Results Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. Conclusions We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.
Keywords	Batch effects ; Empirical Bayes models ; Data integration ; Biomarker development ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Respiratory syncytial virus M2-1 protein associates non-specifically with viral messenger RNA and with specific cellular messenger RNA transcripts.

Molly R Braun / Sarah L Noton / Emmeline L Blanchard / Afzaal Shareef / Philip J Santangelo / W Evan Johnson / Rachel Fearns

PLoS Pathogens, Vol 17, Iss 5, p e

2021 Volume 1009589

Abstract: Respiratory syncytial virus (RSV) is a major cause of respiratory disease in infants and the elderly. RSV is a non-segmented negative strand RNA virus. The viral M2-1 protein plays a key role in viral transcription, serving as an elongation factor to ... ...

Abstract	Respiratory syncytial virus (RSV) is a major cause of respiratory disease in infants and the elderly. RSV is a non-segmented negative strand RNA virus. The viral M2-1 protein plays a key role in viral transcription, serving as an elongation factor to enable synthesis of full-length mRNAs. M2-1 contains an unusual CCCH zinc-finger motif that is conserved in the related human metapneumovirus M2-1 protein and filovirus VP30 proteins. Previous biochemical studies have suggested that RSV M2-1 might bind to specific virus RNA sequences, such as the transcription gene end signals or poly A tails, but there was no clear consensus on what RSV sequences it binds. To determine if M2-1 binds to specific RSV RNA sequences during infection, we mapped points of M2-1:RNA interactions in RSV-infected cells at 8 and 18 hours post infection using crosslinking immunoprecipitation with RNA sequencing (CLIP-Seq). This analysis revealed that M2-1 interacts specifically with positive sense RSV RNA, but not negative sense genome RNA. It also showed that M2-1 makes contacts along the length of each viral mRNA, indicating that M2-1 functions as a component of the transcriptase complex, transiently associating with nascent mRNA being extruded from the polymerase. In addition, we found that M2-1 binds specific cellular mRNAs. In contrast to the situation with RSV mRNA, M2-1 binds discrete sites within cellular mRNAs, with a preference for A/U rich sequences. These results suggest that in addition to its previously described role in transcription elongation, M2-1 might have an additional role involving cellular RNA interactions.
Keywords	Immunologic diseases. Allergy ; RC581-607 ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: animalcules

Yue Zhao / Anthony Federico / Tyler Faits / Solaiappan Manimaran / Daniel Segrè / Stefano Monti / W. Evan Johnson

Microbiome, Vol 9, Iss 1, Pp 1-

interactive microbiome analytics and visualization in R

2021 Volume 16

Abstract: Abstract Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these ... ...

Abstract	Abstract Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. Results To address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. Conclusions We present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html . Video abstract
Keywords	Microbiome analysis ; Visualization ; Interactive toolkit ; Biomarker identification ; Microbial ecology ; QR100-130
Subject code	004
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Author Correction

Poonam Chitale / Alexander D. Lemenze / Emily C. Fogarty / Avi Shah / Courtney Grady / Aubrey R. Odom-Mabey / W. Evan Johnson / Jason H. Yang / A. Murat Eren / Roland Brosch / Pradeep Kumar / David Alland

Nature Communications, Vol 13, Iss 1, Pp 1-

A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome

2022 Volume 1

Keywords	Science ; Q
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Rui Hong / Yusuke Koga / Shruthi Bandyadka / Anastasia Leshchyk / Yichen Wang / Vidya Akavoor / Xinyun Cao / Irzam Sarfraz / Zhe Wang / Salam Alabdullatif / Frederick Jansen / Masanao Yajima / W. Evan Johnson / Joshua D. Campbell

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 9

Abstract: Quality control (QC) is a crucial step in single-cell RNA-seq data analysis. Here, the authors present the SCTK-QC pipeline which generates and visualizes a comprehensive set of QC metrics to streamline the process of detecting and removing poor quality ... ...

Abstract	Quality control (QC) is a crucial step in single-cell RNA-seq data analysis. Here, the authors present the SCTK-QC pipeline which generates and visualizes a comprehensive set of QC metrics to streamline the process of detecting and removing poor quality cells and other artifacts.
Keywords	Science ; Q
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Shiyi Yang / Sean E. Corbett / Yusuke Koga / Zhe Wang / W Evan Johnson / Masanao Yajima / Joshua D. Campbell

Genome Biology, Vol 21, Iss 1, Pp 1-

2020 Volume 15

Abstract: Abstract Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross- ... ...

Abstract	Abstract Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.
Keywords	Bayesian mixture model ; Decontamination ; Single cell ; scRNA-seq ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Language	English
Publishing date	2020-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluation of computational methods for human microbiome analysis using simulated data

Matthieu J. Miossec / Sandro L. Valenzuela / Marcos Pérez-Losada / W. Evan Johnson / Keith A. Crandall / Eduardo Castro-Nallar

PeerJ, Vol 8, p e

2020 Volume 9688

Abstract: Background Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the ... ...

Abstract	Background Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the acquisition and analysis of sequence data remain understudied. Methods We assessed eight popular taxonomic profiling pipelines; MetaPhlAn2, metaMix, PathoScope 2.0, Sigma, Kraken, ConStrains, Centrifuge and Taxator-tk, against a battery of metagenomic datasets simulated from real data. The metagenomic datasets were modeled on 426 complete or permanent draft genomes stored in the Human Oral Microbiome Database and were designed to simulate various experimental conditions, both in the design of a putative experiment; read length (75–1,000 bp reads), sequence depth (100K–10M), and in metagenomic composition; number of species present (10, 100, 426), species distribution. The sensitivity and specificity of each of the pipelines under various scenarios were measured. We also estimated the relative root mean square error and average relative error to assess the abundance estimates produced by different methods. Additional datasets were generated for five of the pipelines to simulate the presence within a metagenome of an unreferenced species, closely related to other referenced species. Additional datasets were also generated in order to measure computational time on datasets of ever-increasing sequencing depth (up to 6 × 107). Results Testing of eight pipelines against 144 simulated metagenomic datasets initially produced 1,104 discrete results. Pipelines using a marker gene strategy; MetaPhlAn2 and ConStrains, were overall less sensitive, than other pipelines; with the notable exception of Taxator-tk. This difference in sensitivity was largely made up in terms of runtime, significantly lower than more sensitive pipelines that rely on whole-genome alignments such as PathoScope2.0. However, pipelines that used strategies to speed-up alignment between genomic references ...
Keywords	Microbiome ; Taxonomic profiling ; Read-based metagenomics ; Benchmark ; Medicine ; R ; Biology (General) ; QH301-705.5
Subject code	590
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	PeerJ Inc.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Novel temporal and spatial patterns of metastatic colonization from breast cancer rapid-autopsy tumor biopsies

Xiaomeng Huang / Yi Qiao / Samuel W. Brady / Rachel E. Factor / Erinn Downs-Kelly / Andrew Farrell / Jasmine A. McQuerry / Gajendra Shrestha / David Jenkins / W. Evan Johnson / Adam L. Cohen / Andrea H. Bild / Gabor T. Marth

Genome Medicine, Vol 13, Iss 1, Pp 1-

2021 Volume 18

Abstract: Abstract Background Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. Methods Via rapid autopsy, we have collected 30 tumor samples over 3 ... ...

Abstract	Abstract Background Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. Methods Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor’s evolution at subclonal resolution. Results The most unique, previously unreported aspect of the tumor’s evolution that we observed in this patient was the presence of “subclone incubators,” defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient’s metastases for effective clinical intervention. Conclusions Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.
Keywords	Tumor evolution ; Subclone ; Metastatic breast cancer ; Medicine ; R ; Genetics ; QH426-470
Subject code	610
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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