Systems analysis of inflammatory bowel disease based on comprehensive gene information

The Anti-Inflammatory Effect of Bovine Bone-Gelatin-Derived Peptides in LPS-Induced RAW264.7 Macrophages Cells and Dextran Sulfate Sodium-Induced C57BL/6 Mice

The bioactive peptides hydrolyzed from bone collagen have been found to possess health-promoting effects by regulating chronic diseases such as arthritis and hypertension. In the current study, the anti-inflammatory effect of bovine bone gelatin peptides (GP) was evaluated in 264.7 macrophages cells and followed by animal trials to investigate their interference on inflammatory cytokines and gut microbiota compositions in dextran sodium sulfate (DSS)-induced C57BL/6 mice. The GP was demonstrated to alleviate the extra secretion of interleukin-6 (IL-6), nitric oxide (NO) and tumor necrosis factor-α(TNF-α) in lipopolysaccharide (LPS)-induced RAW264.7 cells. In DSS-induced colitis mice, the gavage of GP was demonstrated to ameliorate the IBD symptoms of weight loss, hematochezia and inflammatory infiltration in intestinal tissues. In serum, the proinflammatory cytokines (TNF-α,IL-6, MCP-1, IL-1β) were suppressed along with the decreasing effect on toll-like receptor 4 and cyclooxygenase-2 by GP treatment. In the analysis of gut microbiota, the GP was checked to modulate the abundance of Akkermansia, Parasutterella, Peptococcus, Bifidobacterium and Saccharibacteria. The above results imply that GP could attenuate DSS-induced colitis by suppressing the inflammatory cytokines and regulating the gut microbiota.

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.

Elucidating potential molecular signatures through host-microbe interactions for reactive arthritis and inflammatory bowel disease using combinatorial approach

Reactive Arthritis (ReA), a rare seronegative inflammatory arthritis, lacks exquisite classification under rheumatic autoimmunity. ReA is solely established using differential clinical diagnosis of the patient cohorts, where pathogenic triggers linked to enteric and urogenital microorganisms e.g. Salmonella, Shigella, Yersinia, Campylobacter, Chlamydia have been reported. Inflammatory Bowel Disease (IBD), an idiopathic enteric disorder co-evolved and attuned to present gut microbiome dysbiosis, can be correlated to the genesis of enteropathic arthropathies like ReA. Gut microbes symbolically modulate immune system homeostasis and are elementary for varied disease patterns in autoimmune disorders. The gut-microbiota axis structured on the core host-microbe interactions execute an imperative role in discerning the etiopathogenesis of ReA and IBD. This study predicts the molecular signatures for ReA with co-evolved IBD through the enveloped host-microbe interactions and microbe-microbe 'interspecies communication', using synonymous gene expression data for selective microbes. We have utilized a combinatorial approach that have concomitant in-silico work-pipeline and experimental validation to corroborate the findings. In-silico analysis involving text mining, metabolic network reconstruction, simulation, filtering, host-microbe interaction, docking and molecular mimicry studies results in robust drug target/s and biomarker/s for co-evolved IBD and ReA. Cross validation of the target/s or biomarker/s was done by targeted gene expression analysis following a non-probabilistic convenience sampling. Studies were performed to substantiate the host-microbe disease network consisting of protein-marker-symptom/disease-pathway-drug associations resulting in possible identification of vital drug targets, biomarkers, pathways and inhibitors for IBD and ReA.Our study identified Na(+)/H(+) anti-porter (NHAA) and Kynureninase (KYNU) to be robust early and essential host-microbe interacting targets for IBD co-evolved ReA. Other vital host-microbe interacting genes, proteins, pathways and drugs include Adenosine Deaminase (ADA), Superoxide Dismutase 2 (SOD2), Catalase (CAT), Angiotensin I Converting Enzyme (ACE), carbon metabolism (folate biosynthesis) and methotrexate. These can serve as potential prognostic/theranostic biomarkers and signatures that can be extrapolated to stratify ReA and related autoimmunity patient cohorts for further pilot studies.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Inflammatory Bowel Disease Meets Systems Biology: A Multi-Omics Challenge and Frontier

The inflammatory bowel disease (IBD) is a systemic disease that is characterized by the inflammation of the gastrointestinal tract. It includes ulcerative colitis and the Crohn's disease. Presently, IBD is one of the most investigated common complex human disorders, although its causes remain unclear. Multi-omics mechanisms involving genomic, transcriptomic, proteomic, and epigenomic variations, not to forget the miRNome, together with environmental contributions, result in an impairment of the immune system in persons with IBD. Such interactions at multiple levels of biology and in concert with the environment constitute the actual engine of this complex disease, demanding a multifactorial and multi-omics perspective to better understand the root causes of IBD. This expert analysis reviews and examines the latest literature and underscores, from the perspective of systems biology, the value of multi-omics technologies as opportunities to unravel the "IBD integrome." We anticipate that multi-omics research will accelerate the new discoveries and insights on IBD in the near future. It shall also pave the way for early diagnosis and help clinicians and families with IBD to forecast and make informed decisions about the prognosis and, possibly, personalized therapeutics in the future.

Identification of inflammatory bowel disease-related proteins using a reverse k-nearest neighbor search

Inflammatory bowel disease (IBD) is a chronic disease whose incidence and prevalence increase every year; however, the pathogenesis of IBD is still unclear. Thus, identifying IBD-related proteins is important for understanding its complex disease mechanism. Here, we propose a new and simple network-based approach using a reverse k-nearest neighbor ( R k NN ) search to identify novel IBD-related proteins. Protein–protein interactions (PPI) and Genome-Wide Association Studies (GWAS) were used in this study. After constructing the PPI network, the R k NN search was applied to all of the proteins to identify sets of influenced proteins among their k-nearest neighbors ( R k NNs ). An observed protein whose influenced proteins were mostly known IBD-related proteins was statistically identified as a novel IBD-related protein. Our method outperformed a random aspect, k NN search, and centrality measures based on the network topology. A total of 39 proteins were identified as IBD-related proteins. Of these proteins, 71% were reported at least once in the literature as related to IBD. Additionally, these proteins were found over-represented in the IBD pathway and enriched in importantly functional pathways in IBD. In conclusion, the R k NN search with the statistical enrichment test is a great tool to identify IBD-related proteins to better understand its complex disease mechanism.