Inferring Crohn's disease association from exome sequences by integrating biological knowledge

Investigating regulatory patterns of NLRP3 Inflammasome features and association with immune microenvironment in Crohn's disease

INTRODUCTION

Crohn's disease is characterized of dysregulated inflammatory and immune reactions. The role of the NOD-like receptor family, pyrin domain-containing 3 (NLRP3) inflammasome in Crohn's disease remains largely unknown.

METHODS

The microarray-based transcriptomic data and corresponding clinical information of GSE100833 and GSE16879 were obtained from the Gene Expression Omnibus (GEO) database. Identification of in the NLRP3 inflammasome-related genes and construction of LASSO regression model. Immune landscape analysis was evaluated with ssGSEA. Classification of Crohn's-disease samples based on NLRP3 inflammasome-related genes with ConsensusClusterPlus. Functional enrichment analysis, gene set variation analysis (GSVA) and drug-gene interaction network.

RESULTS

The expressions of NLRP3 inflammasome-related genes were increased in diseased tissues, and higher expressions of NLRP3 inflammasome-related genes were correlated with generally enhanced immune cell infiltration, immune-related pathways and human leukocyte antigen (HLA)-gene expressions. The gene-based signature showed well performance in the diagnosis of Crohn's disease. Moreover, consensus clustering identified two Crohn's disease clusters based on NLRP3 inflammasome-related genes, and cluster 2 was with higher expressions of the genes. Cluster 2 demonstrated upregulated activities of immune environment in Crohn's disease. Furthermore, four key hub genes were identified and potential drugs were explored for the treatment of Crohn's disease.

CONCLUSIONS

Our findings indicate that NLRP3 inflammasome and its related genes could regulate immune cells and responses, as well as involve in the pathogenesis of Crohn's disease from transcriptomic aspects. These findings provide in silico insights into the diagnosis and treatment of Crohn's disease and might assist in the clinical decision-making process.

Mass-cytometry-based quantitation of global histone post-translational modifications at single-cell resolution across peripheral immune cells in IBD

BACKGROUND AND AIMS

Current understanding of histone post-translational modifications (histone modifications) across immune cell types in patients with inflammatory bowel disease (IBD) during remission and flare is limited. The study aimed to quantify histone modifications at a single-cell resolution in IBD patients during remission and flare and how they differ compared to healthy controls.

METHODS

We performed a case-control study of 94 subjects (83 IBD patients and 11 healthy controls). IBD patients had either UC (n=38) or CD (n=45) in clinical remission or flare. We used epigenetic profiling by time-of-flight (EpiTOF) to investigate changes in histone modifications within peripheral blood mononuclear cells from IBD patients.

RESULTS

We discovered substantial heterogeneity in histone modifications across multiple immune cell types in IBD patients. They had a higher proportion of less differentiated CD34 + hematopoietic progenitors, and a subset of CD56 bright NK cells and γδ T cells characterized by distinct histone modifications associated with the gene transcription. The subset of CD56 bright NK cells had increased several histone acetylations. An epigenetically defined subset of NK was associated with higher levels of CRP in peripheral blood. CD14+ monocytes from IBD patients had significantly decreased cleaved H3T22, suggesting they were epigenetically primed for macrophage differentiation.

CONCLUSION

We describe the first systems-level quantification of histone modifications across immune cells from IBD patients at a single-cell resolution revealing the increased epigenetic heterogeneity that is not possible with traditional ChIP-seq profiling. Our data open new directions in investigating the association between histone modifications and IBD pathology using other epigenomic tools.

Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review

Inflammatory bowel diseases (IBD), subdivided into Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population.

Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.

An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn's disease patients

Whole exome sequencing (WES) data are allowing researchers to pinpoint the causes of many Mendelian disorders. In time, sequencing data will be crucial to solve the genome interpretation puzzle, which aims at uncovering the genotype-to-phenotype relationship, but for the moment many conceptual and technical problems need to be addressed. In particular, very few attempts at the in-silico diagnosis of oligo-to-polygenic disorders have been made so far, due to the complexity of the challenge, the relative scarcity of the data and issues such as batch effects and data heterogeneity, which are confounder factors for machine learning (ML) methods. Here, we propose a method for the exome-based in-silico diagnosis of Crohn’s disease (CD) patients which addresses many of the current methodological issues. First, we devise a rational ML-friendly feature representation for WES data based on the gene mutational burden concept, which is suitable for small sample sizes datasets. Second, we propose a Neural Network (NN) with parameter tying and heavy regularization, in order to limit its complexity and thus the risk of over-fitting. We trained and tested our NN on 3 CD case-controls datasets, comparing the performance with the participants of previous CAGI challenges. We show that, notwithstanding the limited NN complexity, it outperforms the previous approaches. Moreover, we interpret the NN predictions by analyzing the learned patterns at the variant and gene level and investigating the decision process leading to each prediction.

A novel method for data fusion over Entity-Relation graphs and its application to protein-protein interaction prediction

MOTIVATION

Modern Bioinformatics is facing increasingly complex problems to solve, and we are indeed rapidly approaching an era in which the ability to seamlessly integrate heterogeneous sources of information will be crucial for the scientific progress. Here we present a novel non-linear data fusion framework that generalizes the conventional Matrix Factorization paradigm allowing inference over arbitrary Entity-Relation graphs, and we applied it to the prediction of Protein-Protein Interactions (PPIs). Improving our knowledge of Protein Protein Interaction (PPI) networks at the proteome scale is indeed crucial to understand protein function, physiological and disease states and cell life in general.

RESULTS

We devised three data-fusion based models for the proteome-level prediction of PPIs, and we show that our method outperforms state of the art approaches on common benchmarks. Moreover, we investigate its predictions on newly published PPIs, showing that this new data has a clear shift in its underlying distributions and we thus train and test our models on this extended dataset.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Emerging use of artificial intelligence in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations. 

Big data in IBD: big progress for clinical practice

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.