Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression

A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease

Precision medicine in immune-mediated inflammatory diseases (IMIDs) requires a cellular understanding of treatment response. We describe a therapeutic atlas for Crohn's disease (CD) and ulcerative colitis (UC) following adalimumab, an anti-tumour necrosis factor (anti-TNF) treatment. We generated ~1 million single-cell transcriptomes, organised into 109 cell states, from 216 gut biopsies (41 subjects), revealing disease-specific differences. A systems biology-spatial analysis identified granuloma signatures in CD and interferon (IFN)-response signatures localising to T cell aggregates and epithelial damage in CD and UC. Pretreatment differences in epithelial and myeloid compartments were associated with remission outcomes in both diseases. Longitudinal comparisons demonstrated disease progression in nonremission: myeloid and T cell perturbations in CD and increased multi-cellular IFN signalling in UC. IFN signalling was also observed in rheumatoid arthritis (RA) synovium with a lymphoid pathotype. Our therapeutic atlas represents the largest cellular census of perturbation with the most common biologic treatment, anti-TNF, across multiple inflammatory diseases.

SGCP: a spectral self-learning method for clustering genes in co-expression networks

BACKGROUND

A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.

RESULTS

We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.

CONCLUSION

Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Exploration of the molecular mechanism of insulin resistance in adipose tissue of patients with type 2 diabetes mellitus through a bioinformatic analysis

BACKGROUND

We aimed to determine the cis-expression Quantitative Trait Loci (cis-eQTL) and trans-eQTL of differentially expressed genes (DEGs) in insulin resistance (IR) related pathways.

METHODS

The expression profile data for insulin sensitivity (IS) and IR in the adipose tissue of patients with type 2 diabetes mellitus (T2DM) were acquired from the Gene Expression Omnibus databases. Then, the Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) methods were performed to identify the significant enrichment of potential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between IS and IR groups, and the Wilcoxon rank sum test was carried out to identify the DEGs related to KEGG pathways. Finally, the cis-eQTLs and trans-eQTLs that can affect the expression of DEGs were screened from the eQTLGen database.

RESULTS

The GSEA and GSVA analysis indicated that the mTOR signaling pathway, insulin signaling pathway and T2DM had a strong correlation with the pathological process of T2DM. Furthermore, six genes (ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA) were found to be significantly differentially expressed in IR-related pathways. Finally, we have identified a total of 1073 cis-eQTLs and 24 trans-eQTLs.

CONCLUSIONS

We screened out six genes that were significantly differentially expressed in IR-related pathways, including ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA. Moreover, we discovered that these six genes were affected by 1073 cis-eQTLs and 24 trans-eQTLs.

Biological, genetic and epigenetic markers in ulcerative colitis

In this review, we have summarized the existing knowledge of ulcerative colitis (UC) markers based on current literature, specifically, the roles of potential new biomarkers, such as circulating, fecal, genetic, and epigenetic alterations, in UC onset, disease activity, and in therapy response. UC is a complex multifactorial inflammatory disease. There are many invasive and non-invasive diagnostic methods in UC, including several laboratory markers which are employed in diagnosis and disease assessment; however, colonoscopy remains the most widely used method. Common laboratory abnormalities currently used in the clinical practice include inflammation-induced alterations, serum autoantibodies, and antibodies against bacterial antigens. Other new serum and fecal biomarkers are supportive in diagnosis and monitoring disease activity and therapy response; and potential salivary markers are currently being evaluated as well. Several UC-related genetic and epigenetic alterations are implied in its pathogenesis and therapeutic response. Moreover, the use of artificial intelligence in the integration of laboratory biomarkers and big data could potentially be useful in clinical translation and precision medicine in UC management.

Current and emerging biomarkers for ulcerative colitis

INTRODUCTION

Ulcerative colitis (UC) is a chronic illness requiring lifelong management that could be enhanced by personalizing care using biomarkers.

AREAS COVERED

The main biomarker discovery modalities are reviewed, highlighting recent results across the spectrum of applications, including diagnostics (serum anti-αvβ6 antibodies achieving an area under the curve [AUC] = 0.99; serum oncostatin M AUC = 0.94), disease activity assessment (fecal calprotectin and serum trefoil factor 3: AUC > 0.90), prognostication of the need for treatment escalation (whole blood transcriptomic panels and CLEC5A/CDH2 ratio: AUC > 0.90), prediction of treatment response, and early identification of patients with subclinical disease. The use of established biomarkers is discussed, along with new evidence regarding autoantibodies, proteins, proteomic panels, transcriptomic signatures, deoxyribonucleic acid methylation patterns, and UC-specific glycomic and metabolic disturbances.

EXPERT OPINION

Novel biomarkers will pave the way for optimized UC care. However, validation, simplification, and direct clinical translation of complex models may prove challenging. Currently, few candidates exist to assess key characteristics, such as UC susceptibility, histological disease activity, drug response, and long-term disease behavior. Further research will likely not only reveal new tools to tackle these issues but also contribute to understanding UC pathogenesis mechanisms.

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Inflammatory bowel diseases (IBD) encompass two main entities including ulcerative colitis and Crohn's disease. Although having a common global pathophysiological mechanism, IBD patients are characterized by a significant interindividual heterogeneity and may differ by their disease type, disease locations, disease behaviours, disease manifestations, disease course as well as treatment needs. Indeed, although the therapeutic armamentarium for these diseases has expanded rapidly in recent years, a proportion of patients remains with a suboptimal response to medical treatment due to primary non-response, secondary loss of response or intolerance to currently available drugs. Identifying, prior to treatment initiation, which patients are likely to respond to a specific drug would improve the disease management, avoid unnecessary side effects and reduce the healthcare expenses. Precision medicine classifies individuals into subpopulations according to clinical and molecular characteristics with the objective to tailor preventative and therapeutic interventions to the characteristics of each patient. Interventions would thus be performed only on those who will benefit, sparing side effects and expense for those who will not. This review aims to summarize clinical factors, biomarkers (genetic, transcriptomic, proteomic, metabolic, radiomic or from the microbiota) and tools that could predict disease progression to guide towards a step-up or top-down strategy. Predictive factors of response or non-response to treatment will then be reviewed, followed by a discussion about the optimal dose of drug required for patients. The time at which these treatments should be administered (or rather can be stopped in case of a deep remission or in the aftermath of a surgery) will also be addressed. IBD remain biologically complex, with multifactorial etiopathology, clinical heterogeneity as well as temporal and therapeutic variabilities, which makes precision medicine especially challenging in this area. Although applied for many years in oncology, it remains an unmet medical need in IBD.

Targeted Assessment of Mucosal Immune Gene Expression Predicts Clinical Outcomes in Children with Ulcerative Colitis

BACKGROUND AND AIMS

We aimed to determine whether a targeted gene expression panel could predict clinical outcomes in paediatric ulcerative colitis [UC] and investigated putative pathogenic roles of predictive genes.

METHODS

In total, 313 rectal RNA samples from a cohort of newly diagnosed paediatric UC patients (PROTECT) were analysed by a real-time PCR microfluidic array for expression of type 1, 2 and 17 inflammation genes. Associations between expression and clinical outcomes were assessed by logistic regression. Identified prognostic markers were further analysed using existing RNA sequencing (RNA-seq) data sets and tissue immunostaining.

RESULTS

IL13RA2 was associated with a lower likelihood of corticosteroid-free remission (CSFR) on mesalamine at week 52 (p = .002). A model including IL13RA2 and only baseline clinical parameters was as accurate as an established clinical model, which requires week 4 remission status. RORC was associated with a lower likelihood of colectomy by week 52. A model including RORC and PUCAI predicted colectomy by 52 weeks (area under the receiver operating characteristic curve 0.71). Bulk RNA-seq identified IL13RA2 and RORC as hub genes within UC outcome-associated expression networks related to extracellular matrix and innate immune response, and lipid metabolism and microvillus assembly, respectively. Adult UC single-cell RNA-seq data revealed IL13RA2 and RORC co-expressed genes were localized to inflammatory fibroblasts and undifferentiated epithelial cells, respectively, which was supported by protein immunostaining.

CONCLUSION

Targeted assessment of rectal mucosal immune gene expression predicts 52-week CSFR in treatment-naïve paediatric UC patients. Further exploration of IL-13Rɑ2 as a therapeutic target in UC and future studies of the epithelial-specific role of RORC in UC pathogenesis are warranted.