Inter-kingdom relationships in Crohn's disease explored using a multi-omics approach.
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ABSTRACT:
SUBMITTER: Frau A
PROVIDER: S-EPMC8274447 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCrohn's disease (CD), an inflammatory bowel disease (IBD) subtype, results from pathologic interactions between host cells and its resident gut microbes. CD manifests in both isolated disease locations (ileum or colon) or a combination of locations (ileocolonic). To date, a comprehensive understanding of how isolated CD subtypes influence molecular profiles remains outstanding. To address this, we sought to define CD location signatures by leveraging a large cross-sectional feature set captured from the stool of over 200 IBD patients and healthy controls using metaproteomics, shotgun metagenomics, 16S rRNA sequencing, metabolomic profiling, and host genetics paired with clinical endoscopic assessments.ResultsNeither metagenomic nor host genetics alone distinguished CD location subtypes. In contrast, ileal and colonic CD were distinguished using mass spectrometry-based methods (metabolomics or metaproteomics) or a combined multi-omic feature set. This multi-omic feature set revealed colonic CD was strongly associated with neutrophil-related proteins. Additionally, colonic CD displayed a disease-severity-related association with Bacteroides vulgatus. Colonic CD and ulcerative colitis profiles harbored strikingly similar feature enrichments compared to ileal CD, including neutrophil-related protein enrichments. Compared to colonic CD, ileal CD profiles displayed increased primary and secondary bile acid levels and concomitant shifts in taxa with noted sensitivities such as Faecalibacterium prausnitzii or affinities for bile acid-rich environments, including Gammaproteobacteria and Blautia sp. Having shown robust molecular and microbial distinctions tied to CD locations, we leveraged these profiles to generate location-specific disease severity biomarkers that surpass the performance of Calprotectin.ConclusionsWhen compared using multi-omics features, colonic- and ileal-isolated CD subtypes display striking differences that suggest separate location-specific pathologies. Colonic CD's strong similarity to ulcerative colitis, including neutrophil and Bacteroides vulgatus involvement, is also evidence of a shared pathology for colonic-isolated IBD subtypes, while ileal CD maintains a unique, bile acid-driven profile. More broadly, this study demonstrates the power of multi-omics approaches for IBD biomarker discovery and elucidating the underlying biology. Video Abstract.
| S-EPMC9400277 | biostudies-literature
Project description:BackgroundCrohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.ResultsWe sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.ConclusionsWe demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.
| S-EPMC5769311 | biostudies-literature
Project description:BackgroundDespite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease.ResultsHere, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively.ConclusionsDIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.
| S-EPMC8793231 | biostudies-literature
Project description:Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial and archaeal members of complex biogas-producing microbial communities. In the present study, the microbiomes of three industrial-scale biogas digesters, each fed with different substrates, were analysed using a machine-learning guided genome-centric metagenomics framework complemented with metatranscriptome data. This data permitted us to elucidate the relationship between abundant core methanogenic communities and their syntrophic bacterial partners. In total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, the assembled 16 S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes possessed the highest copy number, while the representatives of the archaeal domain had the lowest. Further investigation of the three anaerobic microbial communities showed characteristic alterations over time but remained specific to each industrial-scale biogas plant. The relative abundance of various microorganisms as revealed by metagenome data was independent from corresponding metatranscriptome activity data. Archaea showed considerably higher activity than was expected from their abundance. We detected 51 nrMAGs that were present in all three biogas plant microbiomes with different abundances. The core microbiome correlated with the main chemical fermentation parameters, and no individual parameter emerged as a predominant shaper of community composition. Various interspecies H2/electron transfer mechanisms were assigned to hydrogenotrophic methanogens in the biogas plants that ran on agricultural biomass and wastewater. Analysis of metatranscriptome data revealed that methanogenesis pathways were the most active of all main metabolic pathways.
| S-EPMC10356833 | biostudies-literature
Project description:With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.
| S-EPMC7251138 | biostudies-literature
Project description:Apple fruit ring rot (FRR), caused by Botryosphaeria dothidea, is a worldwide disease that impacts Asian apple production regions. However, no substantial progress has thus far been made toward the mapping of candidate genes or the development of effective genetic makers. In this five-year study, the resistance of 1,733 F1 hybrids from the cross 'Jonathan' × 'Golden Delicious' was phenotyped by non-wounding inoculation with four B. dothidea isolates. We first conducted systematic comparison of different analytic strategies for bulk segregant analysis by re-sequencing (BSA-Seq) and obtained suitable one for outbreeding species such as Malus Forty-six quantitative trait loci (QTL) for resistance/susceptibility to the four isolates, including one QTL 'hotspot' on chromosome 14, were identified via BSA-Seq. Using integrated multi-omics strategies including RNA-sequencing, parental re-sequencing, BSA-Seq and meta-analysis of RNA-sequencing, fifty-seven candidate genes and corresponding functional mutations from the QTL were predicted. Functional mutations located on the candidate genes were validated using kompetitive allele-specific PCR in hybrids and Malus germplasm accessions with extremely resistant/susceptible phenotypes. Ten effective markers for apple ring rot were developed. The results provide an example of rapid candidate gene mapping for complex traits in outbreeding species.
| S-EPMC6505150 | biostudies-literature
Project description:Crohn’s disease (CD) is a subtype of inflammatory bowel disease (IBD) characterized by transmural disease. The concept of transmural healing (TH) has been proposed as an indicator of deep clinical remission of CD and as a predictor of favorable treatment endpoints. Understanding the pathophysiology involved in transmural disease is critical to achieving these endpoints. However, most studies have focused on the intestinal mucosa, overlooking the contribution of the intestinal wall in Crohn’s disease. Multi-omics approaches have provided new avenues for exploring the pathogenesis of Crohn’s disease and identifying potential biomarkers. . We aimed to use transcriptomic and proteomic technologies to compare immune and mesenchymal cell profiles and pathways in the mucosal and submucosa/wall compartments to better understand chronic refractory disease elements to achieve transmural healing. The results revealed similarities and differences in gene and protein expression profiles, metabolic mechanisms, and immune and non-immune pathways between these two compartments. Additionally, the identification of protein isoforms highlights the complex molecular mechanisms underlying this disease, such as decreased RTN4 isoforms (RTN4B2 and RTN4C) in the submucosa/wall which may be related to the dysregulation of enteric neural processes. These findings have the potential to inform the development of novel therapeutic strategies to achieve TH
2024-03-15 | GSE261086 | GEO
Project description:Genome-wide association studies have identified numerous loci demonstrating genome-wide significant association with Crohn's disease. However, when many single nucleotide polymorphisms (SNPs) have weak-to-moderate disease risks, genetic risk prediction models based only on those markers that pass the most stringent statistical significance testing threshold may be suboptimal. Haplotype-based predictive models may provide advantages over single-SNP approaches by facilitating detection of associations driven by cis-interactions among nearby SNPs. In addition, these approaches may be helpful in assaying non-genotyped, rare causal variants. In this study, we investigated the use of two-marker haplotypes for risk prediction in Crohn's disease and show that it leads to improved prediction accuracy compared with single-point analyses. With large numbers of predictors, traditional classification methods such as logistic regression and support vector machine approaches may be suboptimal. An alternative approach is to apply the risk-score method calculated as the number of risk haplotypes an individual carries, both within and across loci. We used the area under the curve (AUC) of the receiver operating curve to assess the performance of prediction models in large-scale genetic data, and observed that the prediction performance in the validation cohort continues to improve as thousands of haplotypes are included in the model, with the AUC reaching its plateau at 0.72 at ∼7000 haplotypes, and begins to gradually decline after that point. In contrast, using the SNP as predictors, we only obtained maximum AUC of 0.65. Validation studies in independent cohorts further support improved prediction capacity with multi-marker, as opposed to single marker analyses.
| S-EPMC3298027 | biostudies-literature
Project description:The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.
| S-EPMC7508392 | biostudies-literature
Project description:The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.
| S-EPMC11321629 | biostudies-literature