Ileal Transcriptomic Analysis in Paediatric Crohn's Disease Reveals IL17- and NOD-signalling Expression Signatures in Treatment-naïve Patients and Identifies Epithelial Cells Driving Differentially Expressed Genes

Unveiling the Links Between Microbial Alteration and Host Gene Disarray in Crohn's Disease via TAHMC

A compelling correlation method linking microbial communities and host gene expression in tissues is currently absent. A novel pipeline is proposed, dubbed Transcriptome Analysis of Host-Microbiome Crosstalk (TAHMC), designed to concurrently restore both host gene expression and microbial quantification from bulk RNA-seq data. Employing this approach, it discerned associations between the tissue microbiome and host immunity in the context of Crohn's disease (CD). Further, machine learning is utilized to separately construct networks of associations among host mRNA, long non-coding RNA, and tissue microbes. Unique host genes and tissue microbes are extracted from these networks for potential utility in CD diagnosis. Experimental validation of the predicted host gene regulation by microbes from the association network is achieved through the co-culturing of Faecalibacterium prausnitzii with Caco-2 cells. Collectively, the TAHMC pipeline accurately recovers both host gene expression and microbial quantification from CD RNA-seq data, thereby illuminating potential causal links between shifts in microbial composition as well as diversity within CD mucosal tissues and aberrant host gene expression.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions

OBJECTIVES

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome.

METHODS

Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions.

RESULTS

Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players.

CONCLUSION

This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Cytokine expression in subjects with Mycobacterium avium ssp. paratuberculosis positive blood cultures and a meta-analysis of cytokine expression in Crohn's disease

Objectives

1) Culture Mycobacterium avium ssp. paratuberculosis (MAP)from blood, 2) assess infection persistence, 3) determine Crohn's disease (CD) cytokine expression, 4) compare CD cytokine expression to tuberculosis, and 5) perform a meta-analysis of cytokine expression in CD.

Methods

The Temple University/Abilene Christian University (TU/ACU) study had a prospective case control design with 201 subjects including 61 CD patients and 140 non-CD controls. The culture methods included MGIT, TiKa and Pozzato broths, and were deemed MAP positive, if IS900 PCR positive. A phage amplification assay was also performed to detect MAP. Cytokine analysis of the TU/ACU samples was performed using Simple Plex cytokine reagents on the Ella ELISA system. Statistical analyses were done after log transformation using the R software package. The meta-analysis combined three studies.

Results

Most subjects had MAP positive blood cultures by one or more methods in 3 laboratories. In our cytokine study comparing CD to non-CD controls, IL-17, IFNγ and TNFα were significantly increased in CD, but IL-2, IL-5, IL-10 and GM-CSF were not increased. In the meta-analysis, IL-6, IL-8 and IL-12 were significantly increased in the CD patients.

Conclusion

Most subjects in our sample had MAP infection and 8 of 9 subjects remained MAP positive one year later indicating persistent infection. While not identical, cytokine expression patterns in MAP culture positive CD patients in the TU/ACU study showed similarities (increased IL-17, IFNγ and TNFα) to patterns of patients with Tuberculosis in other studies, indicating the possibilities of similar mechanisms of pathogen infection and potential strategies for treatment.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer

INTRODUCTION

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments.

METHOD

IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC.

CONCLUSION

Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

Hypomethylation and Overexpression of Th17-Associated Genes is a Hallmark of Intestinal CD4+ Lymphocytes in Crohn's Disease

BACKGROUND

The development of Crohn's disease [CD] involves immune cell signalling pathways regulated by epigenetic modifications. Aberrant DNA methylation has been identified in peripheral blood and bulk intestinal tissue from CD patients. However, the DNA methylome of disease-associated intestinal CD4+ lymphocytes has not been evaluated.

MATERIALS AND METHODS

Genome-wide DNA methylation sequencing was performed from terminal ileum CD4+ cells from 21 CD patients and 12 age- and sex-matched controls. Data were analysed for differentially methylated CpGs [DMCs] and methylated regions [DMRs]. Integration was performed with RNA-sequencing data to evaluate the functional impact of DNA methylation changes on gene expression. DMRs were overlapped with regions of differentially open chromatin [by ATAC-seq] and CCCTC-binding factor [CTCF] binding sites [by ChIP-seq] between peripherally derived Th17 and Treg cells.

RESULTS

CD4+ cells in CD patients had significantly increased DNA methylation compared to those from the controls. A total of 119 051 DMCs and 8113 DMRs were detected. While hypermethylated genes were mostly related to cell metabolism and homeostasis, hypomethylated genes were significantly enriched within the Th17 signalling pathway. The differentially enriched ATAC regions in Th17 cells [compared to Tregs] were hypomethylated in CD patients, suggesting heightened Th17 activity. There was significant overlap between hypomethylated DNA regions and CTCF-associated binding sites.

CONCLUSIONS

The methylome of CD patients shows an overall dominant hypermethylation yet hypomethylation is more concentrated in proinflammatory pathways, including Th17 differentiation. Hypomethylation of Th17-related genes associated with areas of open chromatin and CTCF binding sites constitutes a hallmark of CD-associated intestinal CD4+ cells.

Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing Data

BACKGROUND

Inflammatory bowel disease [IBD] is a chronic inflammatory disorder with two main subtypes: Crohn's disease [CD] and ulcerative colitis [UC]. Prompt subtype diagnosis enables the correct treatment to be administered. Using genomic data, we aimed to assess machine learning [ML] to classify patients according to IBD subtype.

METHODS

Whole exome sequencing [WES] from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets [80/20]. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed [training data]. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1] all available genes; 2] autoimmune genes; 3] 'IBD' genes. ML results were assessed using area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity on the testing dataset.

RESULTS

A total of 906 patients were included in analysis [600 CD, 306 UC]. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model [AUROC = 0.68], outperforming an IBD gene panel [AUROC = 0.61]. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC.

DISCUSSION

We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.

Recent developments in the assessment and management of inflammatory bowel disease in childhood: a narrative review

Background and Objective

The landscape of paediatric inflammatory bowel disease (pIBD) continues to evolve in an era of increasing incidence. There have been rapid developments in understanding, as we begin to perceive IBD as a spectrum of conditions, alongside advancements in monitoring and treatment. The objective of this article was to provide an overview of recent advances and challenges in the management of pIBD, with a focus on sustainable healthcare, personalised therapy, genomics, new drugs and avenues for future optimisation.

Methods

We present a narrative review that synthesises and summarises recent research (2017-2022) related to pIBD. We undertook a structured search of the literature (PubMed and Medline) and additional articles were identified through manual searches of reference lists. Evidence tables were compiled for disease outcomes.

Key Content and Findings

In this review we outline current practice, integrating clinical guidelines and contemporary research. We discuss initial investigations (including suggested threshold for paediatric faecal calprotectin), specialist investigations for disease monitoring [with reference to video capsule endoscopy (VCE) and therapeutic drug levels] and outline new and established treatment options. Biomarkers and genomic testing are examined as important tools for individualising care and identifying potential therapeutic targets, including for top-down therapy. Despite these advances, significant challenges remain, including the need for further research to understand the mechanisms of disease and the translation of these advances into real-world improvements in practice.

Conclusions

Recent advances in understanding of the pathogenesis of pIBD, alongside genomic and pharmacological developments have added more tools to the armamentarium for the treatment of these conditions and highlighted ongoing areas of research need.

Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.

Distal Fecal Wash Host Transcriptomics Identifies Inflammation Throughout the Colon and Terminal Ileum

BACKGROUND & AIMS

Noninvasive modalities for assessing active endoscopic and histologic inflammation in Crohn's disease and ulcerative colitis patients are critically needed. Fecal wash host shed-cell transcriptomics has been shown to be a robust classifier of endoscopic and histologic inflammation in inflammatory bowel disease patients with distal colitis. Whether such fecal washes can inform on inflammatory processes occurring in more proximal intestinal segments is currently unknown.

METHODS

Fifty-nine inflammatory bowel disease patients and 50 controls were prospectively enrolled. Biopsy specimens and fecal washes from the distal colon, proximal colon, and terminal ileum were compared. Host transcriptomics were performed on the biopsy specimens and fecal washes obtained during colonoscopy at predefined locations throughout the colon and terminal ileum and results were associated with concurrent clinical, endoscopic, and histologic parameters.

RESULTS

We found that host transcriptomics of distal fecal washes robustly classify histologic inflammation in ileal and proximal colonic Crohn's disease, even without distal colonic involvement (area under the receiver operating characteristic curve, 0.94 ± 0.09). We further found that fecal washes consist of modules of co-expressed genes of immune, stromal, and epithelial origin that are indicative of endoscopic disease severity. Fecal wash host transcriptomics also capture expression of gene modules previously associated with a lack of response to biological therapies.

CONCLUSIONS

Our study establishes the accuracy of distal colonic fecal washes for identifying and scoring inflammatory processes throughout the entire ileal-colonic axis.

Overexpression of Ubiquitin-Conjugating Enzyme E2C Is Associated with Worsened Prognosis in Prostate Cancer

To evaluate the role of ubiquitin-conjugating enzyme E2C (UBE2C) in prostate cancer (PCa) progression and prognosis, the TCGA and our PCa tissue microarray cohort were included in the study. Weighted gene co-expression network analysis (WGCNA) and non-negative matrix factorization were used to cluster patients and to screen genes that play a vital role in PCa progression (hub gene). Immunohistochemistry staining was used to evaluate the protein level of UBE2C in prostatic tissues. Through WGCNA, we found a gene co-expression module (named the purple module) that is strongly associated with the Gleason score, pathologic T stage, and biochemical recurrent status. Genes in the purple module are enriched in cell cycle and P53 signaling and help us to cluster patients into two groups with distinctive biochemical recurrent survival rates and TP53 mutation statuses. Further analysis showed UBE2C served as a hub gene in the purple module. The expression of UBE2C in PCa was significantly higher than that in paracancerous tissues and was remarkably associated with pathologic grade, Gleason score, and prognosis in PCa patients. To conclude, UBE2C is a PCa-progress-related gene and a biomarker for PCa patients. Therapy targeting UBE2C may serve as a promising treatment of PCa in the future.

Deleterious Genetic Variation Across the NOD Signaling Pathway Is Associated With Reduced NFKB Signaling Transcription and Upregulation of Alternative Inflammatory Transcripts in Pediatric Inflammatory Bowel Disease

BACKGROUND

Inflammatory bowel disease may arise with inadequate immune response to intestinal bacteria. NOD2 is an established gene in Crohn's disease pathogenesis, with deleterious variation associated with reduced NFKB signaling. We hypothesized that deleterious variation across the NOD2 signaling pathway impacts on transcription.

METHODS

Treatment-naïve pediatric inflammatory bowel disease patients had ileal biopsies for targeted autoimmune RNA-sequencing and blood for whole exome sequencing collected at diagnostic endoscopy. Utilizing GenePy, a per-individual, per-gene score, genes within the NOD signaling pathway were assigned a quantitative score representing total variant burden. Where multiple genes formed complexes, GenePy scores were summed to create a "complex" score. Normalized transcript expression of 95 genes within this pathway was retrieved. Regression analysis was performed to determine the impact of genomic variation on gene transcription.

RESULTS

Thirty-nine patients were included. Limited clustering of patients based on NOD signaling transcripts was related to underlying genomic variation. Patients harboring deleterious variation in NOD2 had reduced NOD2 (β = -0.702, P = 4.3 × 10-5) and increased NFKBIA (β = 0.486, P = .001), reflecting reduced NFKB signal activation. Deleterious variation in the NOD2-RIPK2 complex was associated with increased NLRP3 (β = 0.8, P = 3.1475 × 10-8) and TXN (β = -0.417, P = 8.4 × 10-5) transcription, components of the NLRP3 inflammasome. Deleterious variation in the TAK1-TAB complex resulted in reduced MAPK14 transcription (β = -0.677, P = 1.7 × 10-5), a key signal transduction protein in the NOD2 signaling cascade and increased IFNA1 (β = 0.479, P = .001), indicating reduced transcription of NFKB activators and alternative interferon transcription in these patients.

CONCLUSIONS

Data integration identified perturbation of NOD2 signaling transcription correlated with genomic variation. A hypoimmune NFKB signaling transcription response was observed. Alternative inflammatory pathways were activated and may represent therapeutic targets in specific patients.

Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis

Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify gene modules associated with LN. Gene Ontology (GO) analysis was also applied to explore the biological function of genes and identify the key module. Differentially expressed genes (DEGs) were identified and Maximal Clique Centrality (MCC) values were calculated to screen hub genes. Furthermore, we selected promising biomarkers for real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) validation in independent cohorts. Our results indicated that five hub genes, including IFI44, IFIT3, HERC5, RSAD2, and DDX60 play vital roles in the pathogenesis of LN. Importantly, IFI44 may considered as a key biomarker in LN for its diagnostic capabilities, which is also a promising therapeutic target in the future.