Y-ECCO Literature Review: Valentina Petito

Introduction

In the -omics age it is important to change the approach to the study of diseases: The complexity of interactions in Inflammatory Bowel Disease (IBD) goes beyond the genome and the epigenome, and involves other components of gene regulation, including the transcriptome, the proteome and the metabolome (also called the “IBD interactome”) [1].

The study by Häsler et al. is a great piece of work that shows us how to study the IBD interactome.  Their article analyses the mucosal transcript levels, splicing architecture and mucosal-attached microbial communities of IBD patients to obtain a comprehensive view.

Methods

Mucosal biopsies from patients with Crohn’s Disease (CD, n=19) and Ulcerative Colitis (UC, n=17), disease controls (n=15) and healthy individuals (n=12) were obtained endoscopically during routine diagnosis. For the purpose of mucosa-attached microbiota analysis, the 16S rRNA variable region V3–V4 was amplified using bacterial 16S rRNA gene-specific composite primers (319F and 806R), as described elsewhere [2]. Pooled amplicon libraries were sequenced employing an Illumina MiSeq platform [2]. Host transcriptome analysis was performed by RNA sequencing on an Illumina HiSeq2000.  Reads were quality controlled, aligned and processed further using Bioconductor package DeSeq2. RNA splicing products were identified and quantified using vast tools [3]. To obtain a quantitative measure of host–microbe crosstalk, the Spearman’s rank correlation coefficient was calculated, for all differentially expressed genes and all operational taxonomical units (OTUs) present in at least 50% of all samples, between the respective gene expression level and the respective OTU abundance. Group-wise differences in Spearman’s rank correlation coefficient were assessed for statistical significance employing the Mann-Whitney U test. In order to create context-specific metabolic models of transcriptional activity, the gene expression data were mapped to the human metabolic model Recon 2.04 [4] and analysed using the constraint-based reconstruction and analysis (COBRA) toolbox [5]. The Kyoto Encyclopedia of Genes and Genomes-GenomeNet (KEGG) pathway analysis identified several functional categories comprising differentially expressed genes.

Key findings

The study by Häsler et al. demonstrated a significant uncoupling of host gene expression and microbial signature in patients with IBD that was accompanied by substantial changes in splicing patterns. These findings seem to be an IBD-specific phenomenon because in the disease control group (acute non-IBD intestinal inflammation) a much weaker uncoupling was observed than in patients with IBD.

Several genes were upregulated or downregulated in IBD patients, and many of them are of pathophysiological importance, including IL1RII, IL1-α, IL6 and IL8. At the same time, many known IBD-relevant genes were observed to be alternatively spliced in IBD, including ATG16L1, FOxP1, various interleukins (IL22RA1, IL10RA/B, IL17RA/B/C/E, IL1R1/2, IL2RG, IL4R, IL6R, IL6ST, IL18, IL32) and dual oxidase 2 (DUOX2). However, in concordance with previous studies [6, 7], the authors found only a weak correlation between alternative splicing and differential gene expression. A stronger correlation was observed in the IBD-associated mucosal transcriptome: transcription factor analysis indicated STAT1, NF-κB and NF-kB subunit (REL) binding motifs to be major inflammation-associated sites, regardless of diagnosis, and HSF1 and Sox5 binding motifs were the most prevalent sites for CD and UC, respectively.

Analysis of the mucosa-attached microbiome detected 19 phyla and 3,120 OTUs, including the most dominant phyla, Firmicutes (62.23%), Bacteroidetes (26.62%), Proteobacteria (8.71%) and Actinobacteria (1.51%). Bacterial community richness and diversity did not differ significantly between patients with CD or UC and controls. Only a small number of OTUs and low Shannon diversity were observed in patients with CD, while multivariate analysis of abundance-based (Bray-Curtis) and non-abundance-based (Jaccard) distance matrices revealed significant differences between patient and control microbial communities.

Regardless of tissue type (terminal ileum or sigma), inflammation status was significantly associated with altered microbiota. Principal coordinate analysis of the two distance matrices showed distinct clusters based upon diagnosis, but not upon tissue or inflammation status.

The role of cellular metabolism in regulating the gut microbiome is receiving increasing attention. In the study by Häsler et al., coherence analysis showed a striking “connectedness” (i.e. high coherence) of transcriptome changes in IBD, particularly for downregulated genes, including decreased biosynthesis of bile acids and downregulation of the short-chain fatty acid propanoate metabolism. Both metabolites are key components of host–microbiome crosstalk [8, 9].

The tryptophan metabolism represents another hallmark of inflammation [10].  Häsler et al.’s results suggest that the previously reported reduction in serum tryptophan levels in IBD [11] may be due to an active upregulation of tryptophan degradation in the intestine during inflammation. 

Conclusion

Häsler et al. investigated three closely related ‘omic’ layers in the context of IBD:  host gene expression level, host transcript splicing pattern and mucosa-associated active microbiota. They described for the first time how these three components interact quantitatively and how the interactions are altered in the presence of IBD. Understanding the precise nature of these interactions will be a challenge for the future, but the presented findings emphasise the need to study intestinal microbiota and host mucosa as a “meta-organism” to allow a better understanding of the aetiology of IBD.

References

1. de Souza HSP, Fiocchi C, Iliopoulos D. The IBD interactome: an integrated view of aetiology, pathogenesis and therapy. Nat Rev Gastroenterol Hepatol 2017;14(12):739-49. 
2. Fadrosh DW, Ma B, Gajer P, et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014;2(1):6.
3. Irimia M, Weatheritt RJ, Ellis JD, et al. A highly conserved program of neuronalmicroexons is misregulated in autistic brains. Cell 2014;159:1511–23.
4. Thiele I, Swainston N, Fleming RMT, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013;31:419–25.
5. Schellenberger J, Que R, Fleming RMT, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011;6:1290–307.
6. Dhillon SS, Fattouh R, Elkadri A, et al. Variants in nicotinamide adenine dinucleotide phosphate oxidase complex components determine susceptibility to very early onset inflammatory bowel disease. Gastroenterology 2014;147:680–689.
7. Danan-Gotthold M, Golan-Gerstl R, Eisenberg E, et al. Identification of recurrent regulated alternative splicing events across human solid tumors. Nucleic Acids Res 2015;43:5130–44.
8. Tedelind S, Westberg F, Kjerrulf M, et al. Anti-inflammatory properties of the short-chain fatty acids acetate and propionate: a study with relevance to inflammatory bowel disease. World J Gastroenterol 2007;13:2826–32.
9. Ridlon JM, Kang DJ, Hylemon PB, et al. Bile acids and the gut microbiome. Curr Opin Gastroenterol 2014;30:332–8.
10. Moffett JR, Namboodiri MA. Tryptophan and the immune response. Immunol Cell Biol 2003;81:247-65.
11. Gupta NK, Thaker AI, Kanuri N, et al. Serum analysis of tryptophan catabolism pathway: correlation with Crohn’s disease activity. Inflamm Bowel Dis 2012;18:1214–20.

Valentina Petito  is a biologist and postdoc in the field of Gastroenterology. She has spent a part of her PhD programme at Case Western Reserve University (Cleveland, Ohio, USA) and is currently at the Catholic University of Sacred Heart of Rome, where she is studying the cross-talk between gut microbiota and the immune system.

Valentina Petito © Valentina Petito