Dataset Information

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.

ABSTRACT: Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub ( https://github.com/welch-lab/MultiVelo ).

SUBMITTER: Li C

PROVIDER: S-EPMC10246490 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.

Li Chen C Virgilio Maria C MC Collins Kathleen L KL Welch Joshua D JD

Nature biotechnology 20221013 3

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibi ...[more]

PMID: 36229609

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Project description:BackgroundThe enteric myxozoan parasite Enteromyxum leei is an important problem in gilthead seabream aquaculture invading the intestinal epithelium and leading to chronic intestinal inflammation, poor food conversion rates, cachexia, and mortalities, with no treatments available, resulting in significant economic losses. It is known that myxozoan infections are affected by factors such as temperature, duration of exposure, stocking densities, and seasonality. Gut microbiota has key effects on host health, including disease resistance and immune system training and development, tightly interacting with the host, affecting systemic and local physiological functions. This study aimed to gain insights into the host-microbiota-parasite interactions integrating metataxonomics, host transcriptomics, and metatranscriptomics within this disease model.ResultsExposure to E. leei together with temperature and age differences led to alterations in gilthead seabream intestinal microbiota. Samples from 240 g fish kept at 18ºC during a winter trial at 10 weeks post-parasite exposure showed the highest significant changes in their microbial composition with Proteobacteria increasing in abundance from 32.3% in the control group up to 89.8% in the infected group, while Firmicutes and Actinobacteria significantly decreased in relative abundance from 23% and 37.8-2.4% and 1.1%, respectively. After LEfSe analysis, Acinetobacter was identified as the best biomarker for the parasite-exposed group. Parasite exposure also altered the expression of 935 host genes, highlighting genes involved in immune responses such as pathways related to Interleukins, MHCI and Interferons. Microbial transcripts, also showed significant changes upon parasite infection. Integration of the results revealed differential effects on the host induced directly by the parasite or indirectly by parasite-induced microbial shift.ConclusionsIntestinal microbiota and local host gene expression showed significant changes upon en enteromyxosis. The detected activation of the host immune response was not exclusively linked to the parasite infection but also to changes in microbiota, demonstrating the key role of the different components of the mucosal system during disease. These results provided different datasets of bacterial taxa and microbial and host transcripts that will allow a better understanding of host-microbiota-parasite interactions and can serve as starting points for studying and evaluating mucosal health in aquaculture during parasitosis or other diseases.

Project description:Aims/hypothesisThe identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.MethodsWe developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.ResultsAmong single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.Conclusions/interpretationOmic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Dataset Information

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.

Publications

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.

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