Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies

Beneficial Regulatory Effects of Polymethoxyflavone—Rich Fraction from Ougan (Citrus reticulata cv. Suavissima) Fruit on Gut Microbiota and Identification of Its Intestinal Metabolites in Mice

Polymethoxyflavones (PMFs) are special flavonoids in citrus fruits that have been suggested to be beneficial to human health. However, whether PMFs in citrus fruit alter human gut microbiota is not well understood. The aim of the present study was to investigate the effects of PMF-rich fraction from Ougan (Citrus reticulata cv. Suavissima) on gut microbiota and evaluate the intestinal metabolic profile of PMFs in Institute of Cancer Research mice. The main components of the PMF-rich fraction were nobiletin, tangeretin, and 5-demethylnobiletin. The composition of the gut microbiota was analyzed using 16S ribosomal DNA sequencing. The results showed that after oral administration, the composition of mice gut microbiota was significantly altered. The relative abundance of two probiotics, Lactobacillus and Bifidobacterium, were found to increase significantly. A total of 21 metabolites of PMFs were detected in mice intestinal content by high performance liquid chromatography electrospray ionization tandem mass spectrometry, and they were generated through demethylation, demethoxylation, hydroxylation, and glucuronidation. Our results provided evidence that PMFs have potential beneficial regulatory effects on gut microbiota that in turn metabolize PMFs, which warrants further investigation in human clinical trials

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis.

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.Y

Metabolome and Transcriptome Analysis Revealed the Basis of the Difference in Antioxidant Capacity in Different Tissues of <i>Citrus reticulata</i> ‘Ponkan’

Citrus is an important type of fruit, with antioxidant bioactivity. However, the variations in the antioxidant ability of different tissues in citrus and its metabolic and molecular basis remain unclear. Here, we assessed the antioxidant capacities of 12 tissues from Citrus reticulata ‘Ponkan’, finding that young leaves and root exhibited the strongest antioxidant capacity. Secondary metabolites accumulated differentially in parts of the citrus plant, of which flavonoids were enriched in stem, leaf, and flavedo; phenolic acids were enriched in the albedo, while coumarins were enriched in the root, potentially explaining the higher antioxidant capacities of these tissues. The spatially specific accumulation of metabolites was related to the expression levels of biosynthesis-related genes such as chalcone synthase (CHS), chalcone isomerase (CHI), flavone synthase (FNS), O-methyltransferase (OMT), flavonoid-3′-hydroxylase (F3′H), flavonoid-6/8-hydroxylase (F6/8H), p-coumaroyl CoA 2′-hydroxylase (C2′H), and prenyltransferase (PT), among others, in the phenylpropane pathway. Weighted gene co-expression network analysis (WGCNA) identified modules associated with flavonoids and coumarin content, among which we identified an OMT involved in coumarin O-methylation, and related transcription factors were predicted. Our study identifies key genes and metabolites influencing the antioxidant capacity of citrus, which could contribute to the enhanced understanding and utilization of bioactive citrus components

Evaluation of Antioxidant Capacity and Gut Microbiota Modulatory Effects of Different Kinds of Berries

Berries are fairly favored by consumers. Phenolic compounds are the major phytochemicals in berries, among which anthocyanins are one of the most studied. Phenolic compounds are reported to have prebiotic-like effects. In the present study, we identified the anthocyanin profiles, evaluated and compared the antioxidant capacities and gut microbiota modulatory effects of nine common berries, namely blackberry, black goji berry, blueberry, mulberry, red Chinese bayberry, raspberry, red goji berry, strawberry and white Chinese bayberry. Anthocyanin profiles were identified by UPLC-Triple-TOF/MS. In vitro antioxidant capacity was evaluated by four chemical assays (DPPH, ABTS, FRAP and ORAC). In vivo antioxidant capacity and gut microbiota modulatory effects evaluation was carried out by treating healthy mice with different berry extracts for two weeks. The results show that most berries could improve internal antioxidant status, reflected by elevated serum or colonic T-AOC, GSH, T-SOD, CAT, and GSH-PX levels, as well as decreased MDA content. All berries significantly altered the gut microbiota composition. The modulatory effects of the berries were much the same, namely by the enrichment of beneficial SCFAs-producing bacteria and the inhibition of potentially harmful bacteria. Our study shed light on the gut microbiota modulatory effect of different berries and may offer consumers useful consumption guidance

Robust and Accurate Estimation of Cellular Fraction from Tissue Omics Data Via Ensemble Deconvolution

Motivation: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. Results: To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data

Polymethoxyflavone–Enriched Fraction from Ougan (Citrus reticulata cv. Suavissima) Attenuated Diabetes and Modulated Gut Microbiota in Diabetic KK‑A^y Mice

Diabetes mellitus is a serious, chronic disease worldwide; yet it is largely preventable through physical activity and healthy diets. Ougan (Citrus reticulata cv. Suavissima) is a characteristic citrus variety rich in polymethoxyflavones. In the present study, the anti-diabetic effects of the polymethoxyflavone-enriched fraction from Ougan (OG-PMFs) were investigated. Diabetic KK-Ay mice were supplemented with different doses of OG-PMFs for 5 weeks. Our results demonstrated that OG-PMFs exhibited robust protective effects against diabetes symptoms in KK-Ay mice. The potential mechanisms may partially be attributed to the restoration of hepatic GLUT2 and catalase expression. Notably, OG-PMF administration significantly altered the gut microbiota composition in diabetic KK-Ay, indicated by the suppression of metabolic disease-associated genera Desulfovibrio, Lachnoclostridium, Enterorhabdus, and Ralstonia, implying that the gut microbiota might be another target for OG-PMFs to show effects. Taken together, our results provided a supplementation for the metabolic-protective effects of PMFs and highlighted that OG-PMFs hold great potential to be developed as a functional food ingredient

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction

Abstract Background Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. Method To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. Results We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer’s disease). Conclusion We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use