Multi-omics analysis of inflammatory bowel disease. Immunol Lett. 2014;162:62-68. [PMID: 25131220 DOI: 10.1016/j.imlet.2014.07.014]

Multiomics-empowered Deep Phenotyping of Ulcerative Colitis Identifies Biomarker Signatures Reporting Functional Remission States

INTRODUCTION

Ulcerative colitis [UC] is a chronic disease with rising incidence and unclear aetiology. Deep molecular phenotyping by multiomics analyses may provide novel insights into disease processes and characteristic features of remission states.

METHODS

UC pathomechanisms were assessed by proteome profiling of human tissue specimens, obtained from five distinct colon locations for each of the 12 patients included in the study. Systemic disease-associated alterations were evaluated thanks to a cross-sectional setting of mass spectrometry-based multiomics analyses comprising proteins, metabolites, and eicosanoids of plasma obtained from UC patients during acute episodes and upon remission, in comparison with healthy controls.

RESULTS

Tissue proteome profiling indicated colitis-associated activation of neutrophils, macrophages, B and T cells, fibroblasts, endothelial cells and platelets, and hypoxic stress, and suggested a general downregulation of mitochondrial proteins accompanying the establishment of apparent wound healing-promoting activities including scar formation. Whereas pro-inflammatory proteins were apparently upregulated by immune cells, the colitis-associated epithelial cells, fibroblasts, endothelial cells, and platelets seemed to predominantly contribute anti-inflammatory and wound healing-promoting proteins. Blood plasma proteomics indicated chronic inflammation and platelet activation, whereas plasma metabolomics identified disease-associated deregulations of gut and gut microbiome-derived metabolites. Upon remission several, but not all, molecular candidate biomarker levels recovered back to normal.

CONCLUSION

The findings may indicate that microvascular damage and platelet deregulation hardly resolve upon remission, but apparently persist as disease-associated molecular signatures. This study presents local and systemic molecular alterations integrated in a model for UC pathomechanisms, potentially supporting the assessment of disease and remission states in UC patients.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Absence of enterotypes in the human gut microbiomes reanalyzed with non-linear dimensionality reduction methods

Enterotypes of the human gut microbiome have been proposed to be a powerful prognostic tool to evaluate the correlation between lifestyle, nutrition, and disease. However, the number of enterotypes suggested in the literature ranged from two to four. The growth of available metagenome data and the use of exact, non-linear methods of data analysis challenges the very concept of clusters in the multidimensional space of bacterial microbiomes. Using several published human gut microbiome datasets of variable 16S rRNA regions, we demonstrate the presence of a lower-dimensional structure in the microbiome space, with high-dimensional data concentrated near a low-dimensional non-linear submanifold, but the absence of distinct and stable clusters that could represent enterotypes. This observation is robust with regard to diverse combinations of dimensionality reduction techniques and clustering algorithms.

Personalizing therapy selection in inflammatory bowel disease

INTRODUCTION

Inflammatory bowel disease (IBD) is a complex disease, caused by aberrant immune responses to environmental stimuli where genetic, metabolomic, and environmental variables interact to cause mucosal inflammation. This review sheds light on the different drug and patient related factors that affect personalization of biologics in IBD treatment.

AREAS COVERED

We utilized the online research database PubMed to carry out literature search pertaining to therapies in IBD. We incorporated a combination of primary literature as well as review articles and meta-analyses in writing this clinical review. In this paper, we discuss the mechanisms of action for different biologics, the genotype and phenotype of patients, and pharmacokinetics/pharmacodynamics of drugs, as factors that influence response rates. We also touch upon the role of artificial intelligence in treatment personalization.

EXPERT OPINION

The future of IBD therapeutics is one of precision medicine, based on the identification of aberrant signaling pathways unique to individual patients as well as exploring the exposome, diet, viruses, and epithelial cell dysfunction as part of disease pathogenesis. We need global cooperation for pragmatic study designs as well as equitable access to machine learning/artificial intelligence technology to reach the unfulfilled potential of IBD care.

Sequential isolation of metabolites and lipids from a single sample to achieve multiomics by using TRIzol reagent

Simultaneous extraction of various types of biomolecule from a single sample can be beneficial for multiomics studies of unique specimens. An efficient and convenient sample preparation approach must be developed that can comprehensively isolate and extract biomolecules from one sample. TRIzol reagent is widely used in biological studies for DNA, RNA, and protein isolation. This study evaluated the feasibility of using TRIzol reagent for the simultaneous isolation of not only DNA, RNA, and proteins but also metabolites and lipids from a single sample. Through the comparison of known metabolites and lipids obtained using the conventional methanol (MeOH) and methyl-tert-butyl ether (MTBE) extraction methods, we determined the presence of metabolites and lipids in the supernatant during TRIzol sequential isolation. Finally, we performed untargeted metabolomics and lipidomics to examine metabolite and lipid alterations associated with the jhp0417 mutation in Helicobacter pylori by using the TRIzol sequential isolation protocol and MeOH and MTBE extraction methods. Metabolites and lipids with significant differences isolated using the TRIzol sequential isolation protocol were consistent with those obtained using the conventional MeOH and MTBE extraction methods. These results indicated that TRIzol reagent can be used to simultaneously isolate metabolites and lipids from a single sample. Thus, TRIzol reagent can be used in biological and clinical research, especially in multiomics studies.

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile–quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.

Inflammatory Bowel Disease and COVID-19: How Microbiomics and Metabolomics Depict Two Sides of the Same Coin

The integrity of the gastrointestinal tract structure and function is seriously compromised by two pathological conditions sharing, at least in part, several pathogenetic mechanisms: inflammatory bowel diseases (IBD) and coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. IBD and COVID-19 are marked by gut inflammation, intestinal barrier breakdown, resulting in mucosal hyperpermeability, gut bacterial overgrowth, and dysbiosis together with perturbations in microbial and human metabolic pathways originating changes in the blood and fecal metabolome. This review compared the most relevant metabolic and microbial alterations reported from the literature in patients with IBD with those in patients with COVID-19. In both diseases, gut dysbiosis is marked by the prevalence of pro-inflammatory bacterial species and the shortfall of anti-inflammatory species; most studies reported the decrease in Firmicutes, with a specific decrease in obligately anaerobic producers short-chain fatty acids (SCFAs), such as Faecalibacterium prausnitzii. In addition, Escherichia coli overgrowth has been observed in IBD and COVID-19, while Akkermansia muciniphila is depleted in IBD and overexpressed in COVID-19. In patients with COVID-19, gut dysbiosis continues after the clearance of the viral RNA from the upper respiratory tract and the resolution of clinical symptoms. Finally, we presented and discussed the impact of gut dysbiosis, inflammation, oxidative stress, and increased energy demand on metabolic pathways involving key metabolites, such as tryptophan, phenylalanine, histidine, glutamine, succinate, citrate, and lipids.

ECCO Topical Review on Clinicopathological Spectrum and Differential Diagnosis of Inflammatory Bowel Disease

INTRODUCTION

Many diseases can imitate inflammatory bowel disease [IBD] clinically and pathologically. This review outlines the differential diagnosis of IBD and discusses morphological pointers and ancillary techniques that assist with the distinction between IBD and its mimics.

METHODS

European Crohn's and Colitis Organisation [ECCO] Topical Reviews are the result of an expert consensus. For this review, ECCO announced an open call to its members and formed three working groups [WGs] to study clinical aspects, pathological considerations, and the value of ancillary techniques. All WGs performed a systematic literature search.

RESULTS

Each WG produced a draft text and drew up provisional Current Practice Position [CPP] statements that highlighted the most important conclusions. Discussions and a preliminary voting round took place, with subsequent revision of CPP statements and text and a further meeting to agree on final statements.

CONCLUSIONS

Clinicians and pathologists encounter a wide variety of mimics of IBD, including infection, drug-induced disease, vascular disorders, diverticular disease, diversion proctocolitis, radiation damage, and immune disorders. Reliable distinction requires a multidisciplinary approach.

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.

Multiomics analysis of soybean meal induced marine fish enteritis in juvenile pearl gentian grouper, Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂

As an important protein source, soybean products can cause intestinal inflammation and injury in many animals including human beings, particularly infants and juvenile individuals. Research in this field has been performed for terrestrial animals and fish, but still lacks integrity and systematicness. In this study, the main biological processes in the intestinal tract of marine fish juvenile pearl gentian grouper in the state of soybean meal-induced enteritis (SBMIE) were analyzed. A total of 720 groupers with an approximate initial weight of 12.5 g were randomly divided into three groups: the fish meal (FM) control group, the 20% SBM group (SBM20), and the SBM40 group (n = 4). Three iso-nitrogenous and iso-lipidic diets were prepared and fed to fish for 10 weeks. Each barrel contained a water volume of about 1 m3 in and was exposed to natural light and temperature. Results indicated that the growth and physiology of groupers fed with SBM were significantly negatively affected, with the gene expressions of intestinal structural protein abnormal. 16SrDNA high-throughput sequencing showed that the intestinal microflora played an important role in the pathogenesis of pearl gentian grouper SBMIE, which may activate a variety of pathogen pattern recognition receptors, such as toll-like receptors (TLRs), RIG-I-like receptors, and nod-like receptors. Transcriptome analysis revealed that changes of the SBMIE signaling pathway in pearl gentian groupers were conservative to some extent than that of terrestrial animals and freshwater fish. Moreover, the TLRs-nuclear factor kappa-B signaling pathway becomes activated, which played an important role in SBMIE. Meanwhile, the signal pathways related to nutrient absorption and metabolism were generally inhibited. Metabolomics analysis showed that isoflavones and saponins accounted for a large proportion in the potential biomarkers of pearl gentian grouper SBMIE, and most of the biomarkers had significantly positive or negative correlations with each other; 56 metabolites were exchanged between intestinal tissues and contents, which may play an important role in the development of enteritis, including unsaturated fatty acids, organic acids, amino acids, vitamins, small peptides, and nucleotides, etc. These results provide a basic theoretical reference for solving the intestinal issues of fish SBMIE and research of inflammatory bowel disease in mammals.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Multi-omics approaches to improve malaria therapy

Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.

Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis

BACKGROUND

Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease.

RESULTS

We present here a method to identify interactions based on canonical correlation analysis. We show that the model is the most important factor to identify relationships between blocks using a dataset of Crohn's disease patients with longitudinal sampling. First the analysis was tested in two previously published datasets: a glioma and a Crohn's disease and ulcerative colitis dataset where we describe how to select the optimum parameters. Using such parameters, we analyzed our Crohn's disease data set. We selected the model with the highest inner average variance explained to identify relationships between transcriptome, gut microbiome and clinically relevant variables. Adding the clinically relevant variables improved the average variance explained by the model compared to multiple co-inertia analysis.

CONCLUSIONS

The methodology described herein provides a general framework for identifying interactions between sets of omic data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.

Gut microbiome, big data and machine learning to promote precision medicine for cancer

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.

Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.

Characterization of microbiota in systemic-onset juvenile idiopathic arthritis with different disease severities

BACKGROUND

Systemic-onset juvenile idiopathic arthritis (SoJIA) is one of most serious subtypes of juvenile idiopathic arthritis. Although the pathogenesis of SoJIA remains unclear, several studies have suggested a correlation between gut dysbiosis and JIA. Further understanding of the intestinal microbiome may help to establish alternative ways to treat, or even prevent, the disease.

AIM

To explore alterations in fecal microbiota profiles in SoJIA patients and to evaluate the correlations between microbiota and clinical parameters.

METHODS

We conducted an observational single-center study at the Pediatric Department of Peking Union Medical College Hospital. Children who were diagnosed with SoJIA at our institution and followed for a minimum period of six months after diagnosis were recruited for the study. Healthy children were recruited as a control group (HS group) during the same period. Clinical data and stool samples were collected from SoJIA patients when they visited the hospital.

RESULTS

The SoJIA group included 17 active and 15 inactive consecutively recruited children; the control group consisted of 32 children. Firmicutes and Bacteroidetes were the two most abundant phyla among the total sample of SoJIA children and controls. There was a significant difference among the three groups in observed species, which was the highest in the Active-SoJIA group, followed by the Inactive-SoJIA group and then HS group (Active-SoJIA vs HS: P = 0.000; and Inactive-SoJIA vs HS: P = 0.005). We observed a lower Firmicutes/Bacteroidetes ratio in SoJIA patients (3.28 ± 4.47 in Active-SoJIA, 5.36 ± 8.39 in Inactive-SoJIA, and 5.67 ± 3.92 in HS). We also observed decreased abundances of Ruminococcaceae (14.9% in Active-SoJIA, 17.3% in Inactive-SoJIA, and 22.8% in HS; Active-SoJIA vs HS: P = 0.005) and Faecalibacterium (5.1% in Active-SoJIA, 9.9% in Inactive-SoJIA, and 13.0% in HS; Active-SoJIA vs HS: P = 0.000) in SoJIA compared with HS. By contrast, the abundance of Bacteroidaceae was the highest in the Active-SoJIA group, followed by the Inactive-SoJIA and HS groups (16.5% in Active-SoJIA, 12.8% in Inactive-SoJIA, and 9.7% in HS; Active-SoJIA vs HS: P = 0.03). The Spearman correlation analysis revealed a negative correlation between Proteobacteria or Enterobacteriaceae and juvenile arthritis disease activity score on 27 joints (JADAS-27).

CONCLUSION

The composition of the intestinal microbiota is different in SoJIA patients compared with healthy children. The dysbiosis presents partial restoration in inactive status patients.

Serum Metabolomics Identifies Altered Bioenergetics, Signaling Cascades in Parallel with Exposome Markers in Crohn's Disease

: Inflammatory bowel disease (IBD) has stimulated much interest due to its surging incidences and health impacts in the U.S. and worldwide. However, the exact cause of IBD remains incompletely understood, and biomarker is lacking towards early diagnostics and effective therapy assessment. To tackle these, the emerging high-resolution mass spectrometry (HRMS)-based metabolomics shows promise. Here, we conducted a pilot untargeted LC/MS metabolomic profiling in Crohn's disease, for which serum samples of both active and inactive cases were collected, extracted, and profiled by a state-of-the-art compound identification workflow. Results show a distinct metabolic profile of Crohn's from control, with most metabolites downregulated. The identified compounds are structurally diverse, pointing to important pathway perturbations ranging from energy metabolism (e.g., β-oxidation of fatty acids) to signaling cascades of lipids (e.g., DHA) and amino acid (e.g., L-tryptophan). Importantly, an integral role of gut microbiota in the pathogenesis of Crohn's disease is highlighted. Xenobiotics and their biotransformants were widely detected, calling for massive exposomic profiling for future cohort studies as such. This study endorses the analytical capacity of untargeted metabolomics for biomarker development, cohort stratification, and mechanistic interpretation; the findings might be valuable for advancing biomarker research and etiologic inquiry in IBD.

Gut microbiota as important modulator of metabolism in health and disease

The human gastrointestinal tract colonizes a large number of microbial microflora, forms a host-microbiota co-metabolism structure with the host to participate in various metabolic processes in the human body, and plays a major role in the host immune response. In addition, the dysbiosis of intestinal microbial homeostasis is closely related to many diseases. Thus, an in-depth understanding of the relationship between them is of importance for disease pathogenesis, prevention and treatment. The combined use of metagenomics, transcriptomics, proteomics and metabolomics techniques for the analysis of gut microbiota can reveal the relationship between microbiota and the host in many ways, which has become a hot topic of analysis in recent years. This review describes the mechanism of co-metabolites in host health, including short-chain fatty acids (SCFA) and bile acid metabolism. The metabolic role of gut microbiota in obesity, liver diseases, gastrointestinal diseases and other diseases is also summarized, and the research methods for multi-omics combined application on gut microbiota are summarized. According to the studies of the interaction mechanism between gut microbiota and the host, we have a better understanding of the use of intestinal microflora in the treatment of related diseases. It is hoped that the gut microbiota can be utilized to maintain human health, providing a reference for future research.

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.

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.

Immune biomarkers in the spectrum of childhood noncommunicable diseases

A biomarker is an accurately and reproducibly quantifiable biological characteristic that provides an objective measure of health status or disease. Benefits of biomarkers include identification of therapeutic targets, monitoring of clinical interventions, and development of personalized (or precision) medicine. Challenges to the use of biomarkers include optimizing sample collection, processing and storage, validation, and often the need for sophisticated laboratory and bioinformatics approaches. Biomarkers offer better understanding of disease processes and should benefit the early detection, treatment, and management of multiple noncommunicable diseases (NCDs). This review will consider the utility of biomarkers in patients with allergic and other immune-mediated diseases in childhood. Typically, biomarkers are used currently to provide mechanistic insight or an objective measure of disease severity, with their future role in risk stratification/disease prediction speculative at best. There are many lessons to be learned from the biomarker strategies used for cancer in which biomarkers are in routine clinical use and industry-wide standardized approaches have been developed. Biomarker discovery and validation in children with disease lag behind those in adults; given the early onset and therefore potential lifelong effect of many NCDs, there should be more studies incorporating cohorts of children. Many pediatric biomarkers are at the discovery stage, with a long path to evaluation and clinical implementation. The ultimate challenge will be optimization of prevention strategies that can be implemented in children identified as being at risk of an NCD through the use of biomarkers.

Interaction of gut microbiota with bile acid metabolism and its influence on disease states

Primary bile acids serve important roles in cholesterol metabolism, lipid digestion, host-microbe interactions, and regulatory pathways in the human host. While most bile acids are reabsorbed and recycled via enterohepatic cycling, ∼5% serve as substrates for bacterial biotransformation in the colon. Enzymes involved in various transformations have been characterized from cultured gut bacteria and reveal taxa-specific distribution. More recently, bioinformatic approaches have revealed greater diversity in isoforms of these enzymes, and the microbial species in which they are found. Thus, the functional roles played by the bile acid-transforming gut microbiota and the distribution of resulting secondary bile acids, in the bile acid pool, may be profoundly affected by microbial community structure and function. Bile acids and the composition of the bile acid pool have historically been hypothesized to be associated with several disease states, including recurrent Clostridium difficile infection, inflammatory bowel diseases, metabolic syndrome, and several cancers. Recently, however, emphasis has been placed on how microbial communities in the dysbiotic gut may alter the bile acid pool to potentially cause or mitigate disease onset. This review highlights the current understanding of the interactions between the gut microbial community, bile acid biotransformation, and disease states, and addresses future directions to better understand these complex associations.

Reactive oxygen species and antioxidant defense in human gastrointestinal diseases

Crohn's disease and ulcerative colitis, known together as inflammatory bowel diseases (IBDs), and celiac disease are the most common disorders affecting not only adults but also children. Both IBDs and celiac disease are associated with oxidative stress, which may play a significant role in their etiologies. Reactive oxygen species (ROS) such as superoxide radicals (O2•-), hydroxyl radicals (•OH), hydrogen peroxide (H2O2), and singlet oxygen (1O2) are responsible for cell death via oxidation of DNA, proteins, lipids, and almost any other cellular constituent. To protect biological systems from free radical toxicity, several cellular antioxidant defense mechanisms exist to regulate the production of ROS, including enzymatic and nonenzymatic pathways. Superoxide dismutase catalyzes the dismutation of O2•- to H2O2 and oxygen. The glutathione redox cycle involves two enzymes: glutathione peroxidase, which uses glutathione to reduce organic peroxides and H2O2; and glutathione reductase, which reduces the oxidized form of glutathione with concomitant oxidation of nicotinamide adenine dinucleotide phosphate. In addition to this cycle, GSH can react directly with free radicals. Studies into the effects of free radicals and antioxidant status in patients with IBDs and celiac disease are scarce, especially in pediatric patients. It is therefore very necessary to conduct additional research studies to confirm previous data about ROS status and antioxidant activities in patients with IBDs and celiac disease, especially in children.

Urine metabolome profiling of immune-mediated inflammatory diseases

BACKGROUND

Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis.

METHODS

Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls.

RESULTS

In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways.

CONCLUSIONS

This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Danish cohort of monozygotic inflammatory bowel disease twins: Clinical characteristics and inflammatory activity

AIM: To describe the establishment of a Danish inflammatory bowel diseases (IBD) twin cohort with focus on concordance of treatment and inflammatory markers.

METHODS: We identified MZ twins, likely to be discordant or concordant for IBD, by merging information from the Danish Twin Register and the National Patient Register. The twins were asked to provide biological samples, questionnaires, and data access to patient files and public registries. Biological samples were collected via a mobile laboratory, which allowed for immediate centrifugation, fractionation, and storage of samples. The mean time from collection of samples to storage in the -80 °C mobile freezer was less than one hour. The diagnoses where validated using the Copenhagen diagnostic criteria.

RESULTS: We identified 159 MZ IBD twin pairs, in a total of 62 (39%) pairs both twins agreed to participate. Of the supposed 62 IBD pairs, the IBD diagnosis could be confirmed in 54 pairs. The cohort included 10 concordant pairs, whereof some were discordant for either treatment or surgery. The 10 concordant pairs, where both pairs suffered from IBD, included eight CD/CD pairs, one UC/UC pair and one UC/IBDU pair. The discordant pairs comprised 31 UC, 5 IBDU (IBD unclassified), and 8 CD discordant pairs. In the co-twins not affected by IBD, calprotectin was above 100 μg/g in 2 participants, and above 50 μg/g in a further 5 participants.

CONCLUSION: The presented IBD twin cohorts are an excellent resource for bioinformatics studies with proper adjustment for disease-associated exposures including medication and inflammatory activity in the co-twins.

Bugs, genes, fatty acids, and serotonin: Unraveling inflammatory bowel disease?

The annual incidence of the inflammatory bowel diseases (IBDs) ulcerative colitis and Crohn's disease has increased at an alarming rate. Although the specific pathophysiology underlying IBD continues to be elusive, it is hypothesized that IBD results from an aberrant and persistent immune response directed against microbes or their products in the gut, facilitated by the genetic susceptibility of the host and intrinsic alterations in mucosal barrier function. In this review, we will describe advances in the understanding of how the interaction of host genetics and the intestinal microbiome contribute to the pathogenesis of IBD, with a focus on bacterial metabolites such as short chain fatty acids (SCFAs) as possible key signaling molecules.  In particular, we will describe alterations of the intestinal microbiota in IBD, focusing on how genetic loci affect the gut microbial phylogenetic distribution and the production of their major microbial metabolic product, SCFAs. We then describe how enteroendocrine cells and myenteric nerves express SCFA receptors that integrate networks such as the cholinergic and serotonergic neural systems and the glucagon-like peptide hormonal pathway, to modulate gut inflammation, permeability, and growth as part of an integrated model of IBD pathogenesis.  Through this integrative approach, we hope that novel hypotheses will emerge that will be tested in reductionist, hypothesis-driven studies in order to examine the interrelationship of these systems in the hope of better understanding IBD pathogenesis and to inform novel therapies.

Nutritional Modulation of Gene Expression: Might This be of Benefit to Individuals with Crohn's Disease?

The incidence of inflammatory bowel diseases (IBD), including Crohn's disease (CD), is increasing worldwide, especially in young children and adolescents. Although hospitalized patients are usually provided with enteral or parenteral support, continuing care typically requires a trial-and-error approach to suppressing symptoms and maintaining disease remission. Current nutritional advice does not differ from general population guidelines. International collaborative studies have revealed 163 distinct genetic loci affecting susceptibility to IBD, in some of which host-microbe interactions can be seen to play an important role. The nature of these loci enables a rationale for predicting nutritional requirements that may not be evident through standard therapeutic approaches. Certain recognized nutrients, such as vitamin D and long-chain omega-3 polyunsaturated fatty acids, may be required at higher than anticipated levels. Various phytochemicals, not usually considered in the same class as classic nutrients, could play an important role. Prebiotics and probiotics may also be beneficial. Genomic approaches enable proof of principle of nutrient optimization rather than waiting for disease symptoms to appear and/or progress. We suggest a paradigm shift in diagnostic tools and nutritional therapy for CD, involving a systems biology approach for implementation.

Systems medicine: evolution of systems biology from bench to bedside

High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.