Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis

Unbiased serum metabolomic analysis in cats with naturally occurring chronic enteropathies before and after medical intervention

Chronic enteropathies (CE) are common disorders in cats and the differentiation between the two main underlying diseases, inflammatory bowel disease (IBD) and low-grade intestinal T-cell lymphoma (LGITL), can be challenging. Characterization of the serum metabolome could provide further information on alterations of disease-associated metabolic pathways and may identify diagnostic or therapeutic targets. Unbiased metabolomics analysis of serum from 28 cats with CE (14 cats with IBD, 14 cats with LGITL) and 14 healthy controls identified 1,007 named metabolites, of which 129 were significantly different in cats with CE compared to healthy controls at baseline. Random Forest analysis revealed a predictive accuracy of 90% for differentiating controls from cats with chronic enteropathy. Metabolic pathways found to be significantly altered included phospholipids, amino acids, thiamine, and tryptophan metabolism. Several metabolites were found to be significantly different between cats with IBD versus LGITL, including several sphingolipids, phosphatidylcholine 40:7, uridine, pinitol, 3,4-dihydroxybenzoic acid, and glucuronic acid. However, random forest analysis revealed a poor group predictive accuracy of 60% for the differentiation of IBD from LGITL. Of 129 compounds found to be significantly different between healthy cats and cats with CE at baseline, 58 remained different following treatment.

An integrated investigation of 16S rRNA gene sequencing and proteomics to elucidate the mechanism of Corydalis bungeana Turcz. on dextran sulfate sodium-induced colitis

Corydalis bungeana Turcz. (CBT) is frequently used to treat inflammatory illnesses, the mechanisms underlying its use to ulcerative colitis (UC) remain unclear. A dextran sulfate sodium (DSS)-induced UC mice model was established. The disease activity index (DAI), colonic length, histological inspection by hematoxylin-eosin staining, the cytokines levels in the colon, proteomics and intestinal flora in mice were investigated to evaluate the effect of CBT. The results showed that CBT can significantly reduce the DAI, increase the length of colon, improve the pathological injury of colon tissue, decrease the level of TNF-α, IL-6, IL-1β and increase the level of IL-10 in UC mice. Gut microbe sequencing showed that CBT could enhance the abundance of the intestinal microbiome, decrease possibly harmful bacteria and promote potentially helpful microbes. Proteomics investigation showed that 20 overlapping differentially expressed proteins (DEPs) were discovered in the control, model, and CBT administration groups. The DEPs in the CBT administration group were connected to biological procedures mainly involving detoxification. Extracellular matrix (ECM) receptor-associated proteins such as Col6a1 and CD36 may be important targets for CBT treatment of UC. Overall, this integrated methodology identified a comprehensive multi-omics network, composed of a certain set of gut microbiota and proteins, which may be potential targets for CBT treatment with UC.

Evaluation of the influences of low dose polybrominated diphenyl ethers exposure on human early retinal development

Increasing evidence in animal models has suggested that polybrominated diphenyl ethers (PBDEs), a class of brominated flame retardants, can cause retinotoxicity. However, data on the influence of PBDE treatment on human retinal development are scarce due to the lack of appropriate models. In the present study, we report the utilization of human embryonic stem cell-derived retinal organoids (hESC-ROs) for toxicity assessment of the most common PBDE congener (BDE-47) during the early stages of retinal development. Exposure to BDE-47 decreased the thickness and area of the neural retina (NR) of hESC-ROs in a dose- and time-dependent manner. Abnormal retinal cell distributions, disordered NR structures, and neural rosette-like structures were found on hESC-ROs after low-level BDE-47 exposure. Moreover, BDE-47 exposure decreased cell proliferation, promoted cell apoptosis, and caused abnormal differentiation. Transcriptomic analysis demonstrated that differentially expressed genes, caused by BDE-47, were enriched in extracellular matrix organization. Metabolomic studies of hESC-ROs revealed significant changes in the metabolism of purine and glutathione after BDE-47 exposure for five weeks. This study clarifies the retinotoxicity of low-level BDE-47 treatment and highlights the powerfulness of the hESC-RO model, deepening our understanding of BDE-47-driven human early retina developmental toxicity.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

IBD metabonomics predicts phenotype, disease course, and treatment response

Metabonomics in inflammatory bowel disease (IBD) characterizes the effector molecules of biological systems and thus aims to describe the molecular phenotype, generate insight into the pathology, and predict disease course and response to treatment. Nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and integrated NMR and MS platforms coupled with multivariate analyses have been applied to create such metabolic profiles. Recent advances have identified quiescent ulcerative colitis as a distinct molecular phenotype and demonstrated metabonomics as a promising clinical tool for predicting relapse and response to treatment with biologics as well as fecal microbiome transplantation, thus facilitating much needed precision medicine. However, understanding this complex research field and how it translates into clinical settings is a challenge. This review aims to describe the current workflow, analytical strategies, and associated bioinformatics, and translate current IBD metabonomic knowledge into new potential clinically applicable treatment strategies, and outline future key translational perspectives.

Advances in the understanding of the intestinal micro-environment and inflammatory bowel disease

The human gastrointestinal tract accommodates an entire micro-environment for divergent physiologic processes, the dysbiosis of this micro-ecology has a strong inter-action with the pathogenesis of inflammatory bowel disease (IBD). In the past few years, with the advances in the understanding of microbiome, its metabolites and further application of next generation sequencing, analysis of dynamic alteration of gut micro-environment was realized, which provides numerous information beyond simple microbiota structure or metabolites differences under chronic colitis status. The subsequent intervention strategies targeting the modulation of intestinal micro-environment have been explored as a potential therapy. In this review, we will summarize the recent knowledge about multi-dimensional dysbiosis, the inter-action between fungus and bacteria under inflamed mucosa, and the clinical application of probiotics and fecal microbiota transplantation as a promising therapeutic approach in IBD.

Metabolomics Coupled with Transcriptomics Approach Deciphering Age Relevance in Sepsis

Sepsis is a severe disease frequently occurred in the Intenisive Care Unit (ICU), which has a very high morbidity and mortality, especially in patients aged over 65 years. Owing to the aging effect and the ensuing deterioration of body function, the elder patients may have atypical responses to sepsis. Diagnosis and pathogenesis of sepsis in this population are thus difficult, which hindered effective treatment and management in clinic. To investigated age effects on sepsis, 158 elderly septic patients and 71 non-septic elderly participants were enrolled, and their plasma samples were collected for transcriptomics (RNA-seq) and metabolomics (NMR and GC-MS) analyses, which are both increasingly being utilized to discover key molecular changes and potential biomarkers for various diseases. Protein-protein interaction (PPI) analysis was subsequently performed to assist cross-platform integration. Real time polymerase chain reaction (RT-PCR) was used for validation of RNA-seq results. For further understanding of the mechanisms, cecal ligation and puncture (CLP) experiment was performed both in young and middle-aged rats, which were subjected to NMR-based metabolomics study and validated for several key inflammation pathways by western blot. Comprehensive analysis of data from the two omics approaches provides a systematic perspective on dysregulated pathways that could facilitate the development of therapy and biomarkers for elderly sepsis. Additionally, the metabolites of lactate, arginine, histamine, tyrosine, glutamate and glucose were shown to be highly specific and sensitive in distinguishing septic patients from healthy controls. Significant increases of arginine, trimethylamine N-oxide and allantoin characterized elderly patient incurred sepsis. Further analytical and biological validations in different subpopulations of septic patients should be carried out, allowing accurate diagnostics and precise treatment of sepsis in clinic.

A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer

Cervical cancer (CC) still remains a common and deadly malignancy among females in developing countries. More accurate and reliable diagnostic methods/biomarkers should be discovered. In this study, we performed a comprehensive analysis of metabolomics (285 samples) and transcriptomics (52 samples) on the potential diagnostic implication and metabolic characteristic description in cervical cancer. Sixty-two metabolites were different between CC and normal controls (NOR), in which 5 metabolites (bilirubin, LysoPC(17:0), n-oleoyl threonine, 12-hydroxydodecanoic acid and tetracosahexaenoic acid) were selected as candidate biomarkers for CC. The AUC value, sensitivity (SE), and specificity (SP) of these 5 biomarkers were 0.99, 0.98 and 0.99, respectively. We further analysed the genes in 7 significantly enriched pathways, of which 117 genes, that were expressed differentially, were mainly involved in catalytic activity. Finally, a fully connected network of metabolites and genes in these pathways was built, which can increase the credibility of our selected metabolites. In conclusion, our biomarkers from metabolomics could set a path for CC diagnosis and screening. Our results also showed that variables of both transcriptomics and metabolomics were associated with CC.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Metabonomics and systems biology

Systems biology represents an integrative research strategy that studies the interactions between DNA, mRNA, protein, and metabolite level in an organism, thereby including the interactions with the physical environment and other organisms. The application of metabonomics, or the quantitative study of metabolites in biological systems, in systems biology is currently an emerging area of research, which can contribute to the discovery of (disease) signatures, drug targeting and design, and the further elucidation of basic and more complex biochemical principles. This chapter covers the contribution of metabonomics in advancing our understanding in systems biology.

Application of metabonomics in research of inflammatory bowel disease

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), is a chronic non-specific inflammatory disorder of the gastrointestinal tract. The etiology and pathogenesis of IBD are still not entirely understood today and are thought to be caused by the interaction of multiple factors, including environmental, genetic, infectious and immune factors. The lack of typical clinical features also leads to a difficult diagnosis of IBD. In recent years, metabonomics is becoming a very important way to find biomarkers and investigate disease mechanisms. In this paper we review the main technologies of metabonomics and their present application in IBD.

Alpha-1 antitrypsin and granulocyte colony-stimulating factor as serum biomarkers of disease severity in ulcerative colitis

BACKGROUND

Initial assessment of patients with ulcerative colitis (UC) is challenging and relies on apparent clinical symptoms and measurements of surrogate markers (e.g., C-reactive protein [CRP] or similar acute phase proteins). As CRP only reliably identifies patients with severe disease, novel biomarkers are currently needed for identification of patients with mild or moderate disease activity. Using a commercially available platform, we aimed at identifying serum biomarkers that are able to grade the disease severity.

METHODS

Serum samples from 65 patients with UC with varying disease activity (Mayo score) and from 40 healthy controls were analyzed by multiplex enzyme-linked immunosorbent assay for 78 potential disease biomarkers. Using the statistical software SIMCA-P+ and GraphPad Prism, multivariate statistical analyses were conducted to identify a limited number of biomarkers to assess disease severity.

RESULTS

Alpha-1 antitrypsin (AAT) differentiated between mild and moderate UC (area under the curve [AUC] = 0.79) with a sensitivity of 0.90 and a specificity of 0.70, thereby exceeding the predictive ability of CRP (AUC = 0.52). Combining alpha-1 antitrypsin and granulocyte colony-stimulating factor produced a predictive model with an AUC of 0.72 when differentiating mild and moderate UC, and an AUC of 0.96 when differentiating moderate and severe UC, the latter being as reliable as CRP.

CONCLUSIONS

Alpha-1 antitrypsin is identified as a potential serum biomarker of mild-to-moderate disease activity in UC. With the ability to differentiate between mild, moderate, and severe stages of UC using a simple serum biomarker that is already commercially available, clinicians can initiate individualized treatment regimens at an earlier stage before endoscopic examinations are available.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Metabonomics: analytical techniques and associated chemometrics at a glance

Without any prior knowledge, it can be an overwhelming task to get an overview of and insight into the field of metabonomics. This chapter introduces the concept of metabonomics, the most commonly applied techniques, and the inevitably indispensable multivariate statistical analyses in an easily digestible yet comprehensive manner.

Metabonomics of human fecal extracts characterize ulcerative colitis, Crohn's disease and healthy individuals

This study employs spectroscopy-based metabolic profiling of fecal extracts from healthy subjects and patients with active or inactive ulcerative colitis (UC) and Crohn's disease (CD) to substantiate the potential use of spectroscopy as a non-invasive diagnostic tool and to characterize the fecal metabolome in inflammatory bowel disease (IBD). Stool samples from 113 individuals (UC 48, CD 44, controls 21) were analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with principal component analysis and orthogonal-projection to latent structure-discriminant analysis using SIMCA-P + 12 and MATLAB. Significant differences were found in the metabolic profiles making it possible to differentiate between active IBD and controls and between UC and CD. The metabolites holding differential power primarily belonged to a range of amino acids, microbiota-related short chain fatty acids, and lactate suggestive of an inflammation-driven malabsorption and dysbiosis of the normal bacterial ecology. However, removal of patients with intestinal surgery and anti-TNF-α antibody treatment eliminated the discriminative power regarding UC versus CD. This study consequently demonstrates that ¹H NMR spectroscopy of fecal extracts is a potential non-invasive diagnostic tool and able to characterize the inflammation-driven changes in the metabolic profiles related to malabsorption and dysbiosis. Intestinal surgery and medication are to be accounted for in future studies, as it seems to be factors of importance in the discriminative process.

Assessment of the validity of a multigene analysis in the diagnostics of inflammatory bowel disease

OBJECTIVES

The findings of a previous multigene study indicated that the expression of a panel of seven specific genes had strong differential power regarding inflammatory bowel disease (IBD) versus non-IBD, as well as ulcerative colitis (UC) versus Crohn's disease (CD). This prospective confirmatory study based on an independent patient cohort from a national Danish IBD centre was conducted in an attempt to verify these earlier observations.

DESIGN, SETTING AND PARTICIPANTS

A total of 119 patients were included in the study (CD, UC and controls). Three mucosal biopsies were retrieved from the left side of the colon of each patient. RNA was extracted, and RT-PCR was performed to retain expression profiles from the seven selected genes. Expression data from the training set (18 CD, 20 UC and 20 controls) were used to build a classification model, using quadratic discriminant analysis, and data from the test set (20 CD, 21 UC and 20 controls) were used to test the validity of the model.

RESULTS

The present investigation did not confirm the previous observation that a panel of seven specific genes is able to distinguish between patients with CD and UC, whereas the discriminative power for IBD versus control subjects was substantiated.

CONCLUSION

Our results fail to demonstrate that the previously identified seven-gene classification model is able to discriminate between CD and UC but suggest that the gene panel merely discriminates between inflamed and noninflamed colonic tissue. Thus, a reliable and simple diagnostic tool is still warranted for optimal diagnosis and treatment of patients with IBD, especially the subgroup with unclassified disease.