Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

Mars, Ruben A. T.; Yang, Yi; Ward, Tonya; Houtti, Mo; Priya, Sambhawa; Lekatz, Heather; Tang, Xiaojia; Sun, Zhifu; Kalari, Krishna R.; Korem, Tal; Bhattarai, Yogesh; Zheng, Tenghao; Bar, Noam; Frost, Gary; Johnson, Abigail; Treuren, Will Van; Han, Shuo; Ördög, Tamás; Grover, Madhusudan; Sonnenburg, Justin L.; D’Amato, Mauro; Camilleri, Michael; Elinav, Eran; Segal, Eran; Blekhman, Ran; Farrugia, Gianrico; Swann, Jonathan R.; Knights, Dan; Kashyap, Purna C.

doi:10.1016/j.cell.2020.10.040

Cited by 101 publications

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“…As mentioned previously, the module of amino acid degradation was decreased in CD patients, while its trend behaved in the opposite way in CRC patients. In CD patients, the elevated module about nucleoside and nucleotide degradation, which was comprised of degradation of purine, would induce the gut metabolic stress and involve in the inflammatory processes (43, 44). As essential pathways for energetic and biosynthetic demands of cancer cells, the carbohydrate biosynthesis, fermentation and glycolysis were enhanced in CRC (45, 46).…”

Section: Resultsmentioning

confidence: 99%

Metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multi-disease diagnostic models

Jiang

Zhao

2019

Preprint

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Intestinal diseases can share clinical symptoms and gut microbiome alterations, necessitating cross-disease comparisons and the use of multi-disease models. Here, we performed meta-analyses on fecal metagenomes of Crohn's disease (CD), ulcerative colitis (UC) and colorectal cancer (CRC), and identified 65 marker species and 74 marker pathways that were significantly altered in >=2 studies of the same disease. We grouped marker species by their co-occurrences into disease-specific and mixed clusters according to whether all the cluster members belong to the same disease, and revealed their distinct phylogenetic relationships: species in disease-specific clusters are phylogenetically related, while those in mixed clusters are more distant; strikingly, UC- and CRC-specific species are phylogenetically closer than to CD-specific ones, likely because UC-patients have higher risk of CRC. Consistent to their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. In CD and CRC, most marker species correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human derived contents. In fact, our results suggested that due to physio-metabolic alterations, the diseased gut may represent a stressed environment and pose stronger selection to gut microbes; consequently, marker species in diseases showed more coordinated changes than the controls. With the marker species and pathways, we constructed three high-performance (including multi-disease) models with AUROC of 0.88 and true positive rates up to 89%, and explained their putative clinical applications. Together, our results improved our understanding on the relationships among common intestinal diseases, and strongly support the feasibility of metagenome-based multi-disease diagnosis.

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Section: Resultsmentioning

confidence: 99%

Metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multi-disease diagnostic models

Jiang

Zhao

2019

Preprint

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“…Variations in the composition of the human gut microbiome have been associated with a wide variety of chronic diseases, including colorectal cancer (CRC), inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). For example, previous studies have reported an increase in abundance of Fusobacterium nucleatum and Parvimonas in CRC 1,2 , reduced abundance of Faecalibacterium prausnitzii and enrichment of enterotoxigenic Bacteroides fragilis in CRC and IBD 3–5 , and overrepresentation of Enterobacteriaceae and Streptococcus in IBD and IBS 6–8 . In addition to the gut microbiome, dysregulation of host gene expression and pathways have also been implicated in these diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have reported disruption of Notch and WNT signalling pathways in CRC 9,10 , activation of toll-like receptors (e.g. TLR4) that induce NF-κB and TNF-α signaling pathways in IBD 11,12 , and dysregulation of immune response and intestinal antibacterial gene expression in IBS 8,13 . While host transcription and gut microbiome have separately been identified as contributing factors to these gastrointestinal (GI) diseases, it is unclear how the two may interact to influence host pathophysiology 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Studies investigating the role of host gene-microbiome interactions in CRC have found correlations between the abundance of pathogenic mucosal bacteria and expression of host genes implicated in gastrointestinal inflammation and tumorigenesis 26,27 . In IBS, host genes implicated in gut barrier function and peptidoglycan binding, such as KIFC3 and PGLYRP1 , are associated with microbial abundance of Peptostreptococcaceae and Intestinibacter 8 . While these studies have revealed important insights about host gene-microbiome crosstalk in GI diseases, they are limited in several aspects.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we comprehensively characterized interactions between mucosal gene expression and microbiome composition in patients with colorectal cancer, inflammatory bowel disease, and irritable bowel syndrome, three GI disorders in which both host gene regulation and gut microbiome have been implicated as contributing factors 1,6,8,10,13 . We developed and applied a machine learning framework that overcomes typical challenges in multi-omic integrations, including high-dimensionality, sparsity, and multicollinearity, to identify biologically meaningful associations between gut microbes and host genes and pathways in each disease.…”

Section: Introductionmentioning

confidence: 99%

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Shared and disease-specific host gene-microbiome interactions across human diseases

Priya

Burns

Ward

et al. 2021

Preprint

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While the gut microbiome and host gene regulation separately contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here, we developed a machine learning-based framework to jointly analyze host transcriptomic and microbiome profiles from 416 colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease, and irritable bowel syndrome. We identified potential interactions between gut microbes and host genes that are disease-specific, as well as interactions that are shared across the three diseases, involving host genes and gut microbes previously implicated in gastrointestinal inflammation, gut barrier protection, energy metabolism, and tumorigenesis. In addition, we found that mucosal gut microbes that have been associated with all three diseases, such as Streptococcus, interact with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes.

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Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

et al. 2021

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rate up to 90%, but below 36% in the case of advanced GC patients. [1a] An effective approach of early GC detection requires the combination of initial pre-selection of high-risk individuals and follow-up endoscopy examination, while its largescale application is facing challenges like low cost-efficiency (e.g., inefficient preselection by non-specific risk factors), poor patient compliance, and shortage of experienced endoscopists, etc. [2b] Therefore, it is pressing to develop a rapid and noninvasive method to discover biomarkers and diagnose early GC with an improved prognosis.Liquid biopsy allows the detection of oncotarget biomarkers through non-invasive techniques from biofluids (e.g., blood), holding promise for early screening of GC in clinics. [4] Various genetic (e.g., circulating tumor DNA (ctDNA)) [5] and protein (e.g., carcino embryonic antigen (CEA)) biomarkers in human blood have been discovered for GC diagnosis, but the applicable performance is limited by suboptimal accuracy (e.g., sensitivity of 13% for CEA), [6] high cost (€140 per test), [7] and lack of validation in early GC patients. [1b,8] Besides genes and proteins, metabolites serve as direct signatures of biochemical activity and are associated with gastric carcinogenesis. [9] Mass spectrometry (MS), particularly laser desorption/ionization (LDI) MS, is a powerful analytical tool capable of the simultaneous detection and spatial mapping of various metabolites. [10] However, it is offset in metabolic profiling with Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

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Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

Cited by 101 publications

References 0 publications

Metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multi-disease diagnostic models

Metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multi-disease diagnostic models

Shared and disease-specific host gene-microbiome interactions across human diseases

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

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