Ulcerative colitis immune cell landscapes and differentially expressed gene signatures
      determine novel regulators and predict clinical response to biologic therapy

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Abstract

The heterogeneous pathobiology underlying Ulcerative Colitis (UC) is not fully understood. Using publicly available transcriptomes from adult UC patients, we identified the immune cell landscape, molecular pathways, and differentially expressed genes (DEGs) across patient cohorts and their association with treatment outcomes. The global immune cell landscape of UC tissue included increased neutrophils, T CD4 memory activated cells, active dendritic cells (DC), and M0 macrophages, as well as reduced trends in T CD8, Tregs, B memory, resting DC, and M2 macrophages. Pathway analysis of DEGs across UC cohorts demonstrated activated bacterial, inflammatory, growth, and cellular signaling. We identified a specific transcriptional signature of one hundred DEGs (UC ₁₀₀) that distinctly separated UC inflamed from uninflamed transcriptomes. Several UC ₁₀₀ DEGs, with unidentified roles in UC, were validated in primary tissue. Additionally, non-responders to anti-TNFα and anti-α4β7 therapy displayed distinct profiles of immune cells and pathways pertaining to inflammation, growth, and metabolism. We identified twenty resistant DEGs in UC non-responders to both therapies of which four had significant predictive power to treatment outcome. We demonstrated the global immune landscape and pathways in UC tissue, highlighting a unique UC signature across cohorts and a UC resistant signature with predictive performance to biologic therapy outcome.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Robust enumeration of cell subsets from tissue expression profiles

Aaron Newman, Chih Long Liu, Michael Green … (2015)

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Author and article information

Contributors

Suzana D. Savkovic: ssavkovi@tulane.edu

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 27 April 2021

Publication date PMC-release: 27 April 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 9010

Affiliations

[1 ]GRID grid.265219.b, ISNI 0000 0001 2217 8588, Department of Pathology and Laboratory Medicine, , Tulane University, ; 1430 Tulane Ave SL-79, New Orleans, LA 70112 USA

[2 ]GRID grid.265219.b, ISNI 0000 0001 2217 8588, Division of Endocrine and Oncologic Surgery, Department of Surgery, , Tulane University, ; New Orleans, LA 70112 USA

[3 ]GRID grid.240416.5, ISNI 0000 0004 0608 1972, Division of Gastroenterology, , Ochsner Clinic Foundation, ; New Orleans, LA 70121 USA

Article

Publisher ID: 88489

DOI: 10.1038/s41598-021-88489-w

PMC ID: 8079702

PubMed ID: 33907256

SO-VID: 220f867e-4efe-4576-b71e-48ef0215380f

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 10 November 2020

Date accepted : 8 April 2021

Funding

Funded by: NIH

Award ID: P01CA214091

Award Recipient : Erik F. Flemington

Funded by: Crohn’s

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ScienceOpen disciplines: Uncategorized

Keywords: inflammation,computational biology and bioinformatics,immunology,gastroenterology

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ScienceOpen disciplines: Uncategorized

Keywords: inflammation, computational biology and bioinformatics, immunology, gastroenterology

Ulcerative colitis immune cell landscapes and differentially expressed gene signatures determine novel regulators and predict clinical response to biologic therapy

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Karger: Gastroenterology

Most cited references 85

limma powers differential expression analyses for RNA-sequencing and microarray studies

Robust enumeration of cell subsets from tissue expression profiles

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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