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Bell, J. T.

Publications and source records attributed to Bell, J. T..

At least 19 recordsLinked to original sources

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions

Body fat distribution is a strong predictor of cardiometabolic disease risk. Gene-environment and gene-gene interactions can affect body fat distribution, resulting in differential phenotypic variance across genotype groups that can be detected through variance quantitative trait loci (vQTLs). Using UK Biobank MRI data in conjunction with genetic data, we explored evidence for vQTLs for body fat distribution phenotypes aiming to uncover potential genetic interactions. We identified three vQTLs for liver fat distribution, including rs738408 (PNPLA3), rs4293458 (APOE), and rs58542926 (TM6SF2), and one vQTL region (FTO) for abdominal subcutaneous adipose tissue. To dissect putative gene-environment and gene-gene interactions underlying these signals, we identified multiple vQTL-environment interactions and one epistatic effect (rs58542926*rs429358) for liver fat. The vQTLs and interaction results were validated in multiple UK Biobank and external replication cohort datasets (Framingham Heart Study, All of Us, and TwinsUK), showing replication of the three liver vQTLs with the greatest reproducibility for vQTL rs738408. Our findings uncover vQTLs and underlying interaction effects on body fat distribution, especially liver fat, that may be useful for the development of precision medicine approaches.

genetics↗

methbiome: reference-based DNA methylation profiling of long-read metagenomic data

SummaryLong-read DNA sequencing technologies collect base modification information, but their capacity to profile epigenetic signals has not been widely explored within metagenomic context. Here, we present a novel pipeline consolidating key steps of meta-epigenomic profiling. methbiome can process both Oxford Nanopore and PacBio long-reads within a reference-based framework, facilitating cross-platform and between-sample comparison of DNA methylation signals in whole microbiomes. The tool is applicable to both microbial communities and isolates, for analyses within both methylation motifs as well as out-of-motif contexts, and can be used in metaepigenome-wide association studies. Availability and Implementationmethbiome was implemented using Snakemake and is available on GitHub (github.com/ricardocosteira/methbiome).

bioinformatics↗

Genetic impacts on within-pair DNA methylation variance in monozygotic twins capture gene-environment interactions and cell-type effects

BackgroundGenetic variants that are associated with phenotypic variability, or variance quantitative trait loci (vQTLs), have been detected for multiple human traits. Gene-environment interactions can lead to differential phenotypic variability across genotype groups, therefore, genetic variants that interact with environmental exposures can manifest as vQTLs. Although changes in DNA methylation variability have been observed in several diseases, vQTLs for methylation levels (vmeQTL) have not yet been explored in depth. ResultsWe optimize the value of monozygotic (MZ) twin studies to identify and replicate vmeQTLs for blood DNA methylation variance at 358 CpGs in 988 adult MZ twin pairs from two European twin registries. Over a third of vmeQTLs captured identical vmeQTL-environmental factor interactions in both datasets, and the majority of interactions were observed with blood cell counts. Correspondingly, over 60% of CpGs affected by genotype-monocyte and genotype-T cell interactions replicated as CpGs affected by genetic effects in the relevant cell type in an independent dataset. Most vmeQTLs also replicated in 1,348 UK non-twin adults and showed longitudinal stability in a sample subset. Integrating gene expression and phenotype association results identified multiple vmeQTLs that capture GxE effects relevant to human health. Examples include vmeQTLs interacting with blood cell type to influence DNA methylation in FAM65A, NAPRT, and CSGALNACT1 underlying immune disease susceptibility and progression. ConclusionOur findings identify novel genetic effects on human DNA methylation variability within a unique MZ twin study design. The results show the potential of vmeQTLs to identify gene-environment interactions and provide novel insights into complex traits.

genetics↗

Genetic regulation of fatty acid content in adipose tissue

Fatty acids are important as structural components, energy sources, and signaling mediators. While studies have extensively explored genetic regulation of fatty acids in serum and other bodily fluids, their regulation within adipose tissue, a crucial regulator of cardiovascular and metabolic health remains unclear. Here, we investigated the genetic regulation of 18 fatty acids in subcutaneous adipose tissue from 569 female twins from TwinsUK. Using twin models, the heritability of fatty acids ranged from 5% to 59%, indicating a substantial genetic regulation of fatty acid levels within adipose tissue, which was also tissue-specific in many cases. Genome-wide association studies identified ten significant loci, in SCD, ADAMTSL1, ZBTB41, SNTB1, EXOC6B, ACSL3, LINC02055, MKRN2/TSEN2, FADS1 and HAPLN across 13 fatty acids or fatty acid product-to-precursor ratios. Using adipose gene expression and methylation, which were concurrently measured in these samples, we detected five fatty acid-associated signals that colocalized with eQTL and meQTL signals, highlighting fatty acids that are regulated by molecular processes within adipose tissue. We identified strong associations of adipose fatty acids-associated loci with type 2 diabetes, body fat percentage, and cardiovascular disease. We explored links between polygenic scores of common metabolic traits and adipose fatty acid levels, and identified associations between polygenic scores of BMI, body-fat distribution and triglycerides and several fatty acids, indicating these risk scores impact local adipose tissue content. Overall, our results identified local genetic regulation of fatty acids within adipose tissue and highlighted their links with renal and cardio-metabolic health.

genetics↗

Theobromine is Associated with Slower Epigenetic Ageing

Theobromine, a commonly consumed dietary alkaloid derived from cocoa, has been linked to extended lifespan in model organisms and to health benefits in humans. We examined associations between circulating theobromine intake, measured using serum metabolomics, and blood-based epigenetic markers of biological ageing in two European human population-based cohorts. Serum theobromine levels were significantly associated with reduced epigenetic age acceleration, as measured by GrimAge (p<2e-7) and DNAmTL (p<0.001) in over 500 individuals from the TwinsUK cohort, and both signals replicated in 1,160 individuals from the KORA cohort (p = 7.2e-08 and p = 0.007, respectively). Sensitivity analyses including covariates of other cocoa and coffee metabolites suggest that the effect is specific to theobromine. Our findings indicate that the reported beneficial links between theobromine intake on health and ageing extend to the molecular epigenetic level in humans.

genetics↗

DNA methylation and gene expression trajectories of human postprandial metabolism

Human postprandial metabolism is characterised by a highly individualised response to food that is predictive of cardiometabolic health and underexplored at the molecular level. We profiled blood DNA methylation (DNAm) and gene expression trajectories before and after a test meal in 225 European participants. We identify DNAm changes at fasting, 30 minutes and 4 hours after meal challenge, including in metabolically relevant genes INPP4A, GHRL, ASIP and ABCG1, with changes observed as early as 30mins postprandially. Gene expression trajectories also changed postprandially predominantly at 4 hours, with replication of lipid metabolism (CPT1A) and circadian rhythm (PER1) genes. Genetic variants affect postprandial molecular trajectories at genes linked to obesity (PDE9A) and glucose response (GPT2). Multiple signals associated with postprandial glucose and triglyceride levels, with replication of CPT1A methylation. The postprandial DNAm and expression trajectories target metabolically relevant genes, giving insights towards mechanisms underlying inter-individual response to food and cardiometabolic disease risk.

genomics↗

Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. We find that the squared residual value linear model (SVLM) method is optimal when the interacting exposure is discrete, and both SVLM and the deviation regression model (DRM) perform well when the interacting exposure is continuous. Additionally, a larger sample size, smaller minor allele frequency, and more balanced sample distribution in different exposure categories increase power of SVLM and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the type of exposure in the interaction model underlying the vQTL. Author SummaryGenetic background can influence organismal response to changes in the environment, including through effects of gene-environment (GxE) interactions. GxE interactions form a fundamental component of complex phenotypes and disease. The presence of GxE interactions leads to increases in phenotypic variability, where individuals of a certain genotype can display more divergent phenotypes. Therefore, identifying genetic variation that can alter phenotypic variance is an alternative approach to identifying genetic variants that underlie GxE effects. Here, we conduct a systematic assessment of methods that detect genetic variants linked to phenotypic variance under several GxE scenarios. We identify the most optimal approaches for different environmental exposure settings and apply these to validate previously detected GxE signals. We also estimate power of these methods to detect GxE effects. Our results help guide experimental design for future studies aiming to identify genetic variant impacts on phenotypic variance, and their result interpretation in context of gene-environment interactions in complex traits.

genetics↗

Genetic impacts on DNA methylation help elucidate regulatory genomic processes

Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts, with replication. Methylation levels at 34.2% of CpGs were affected by SNPs, and 98% of effects were cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both meQTL SNPs and CpGs with meQTLs were overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identified 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such ICOSLG (inflammatory bowel disease), and USP1 and DOCK7 (total cholesterol levels). Enrichment analysis of meQTLs and integration with expression QTLs gave insights into mechanisms underlying cis-meQTLs, for example through disruption of transcription factor binding sites for CTCF and SMC3, and trans-meQTLs, for example through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites. Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. A results database and viewer are available online.

genomics↗

Hemin availability induces coordinated DNA methylation and gene expression changes in Porphyromonas gingivalis

Periodontal disease is a common chronic inflammatory disease. Porphyromonas gingivalis is an important bacterium in the development of the disease and expresses a variety of virulence determinants. Hemin (iron [III] protopotphyrin IX), an essential nutrient of this organism, whose concentration increases with increasing inflammation, is a global regulator of virulence in P. gingivalis: high hemin levels increase expression of several virulence determinants. However, the mechanism through which hemin influences bacterial gene expression is poorly understood. Bacterial DNA methylation has the potential to fulfil this mechanistic role. Here, we characterised the methylome of P. gingivalis, and compared its variation to transcriptomic changes in response to changes in hemin concentration. Gene expression and DNA methylation profiling of P. gingivalis W50 was performed, following continuous culture in chemostats with excess or limited hemin, using Illumina RNA-Seq and Nanopore DNA sequencing. DNA methylation quantification was carried out for Dam/Dcm motifs and all-context N6-methyladenine (6mA) and 5-methylcytosine (5mC) base pair modifications. Differential expression and methylation in response to excess hemin availability are presented after multiple testing correction (FDR 5%). In excess hemin there were 161 over- and 268 under-expressed genes compared to limited hemin. Genes under-expressed in excess hemin were involved in iron recruitment (the hemophore HmuY) and transport (TonB-dependent receptors), and those over-expressed were involved in iron-sulphur cluster binding. Hemin-dependent differentially methylation was observed for the Dam GATC motif and all-context 6mA and 5mC, with 36, 49 and 47 signals, respectively. Coordinated genome-wide differential expression and methylation effects were observed in 6 genes encoding a Ppx/GppA family phosphatase, a lactate utilization protein, a 4-alpha-glucanotransferase, two ABC transporter proteins, and a hypothetical protein HMPREF1322_RS00730. The findings indicate that altered genome methylation occurs in response to the availability of hemin and give insights into the molecular mechanisms of regulation of virulence in this bacterium. Author SummaryDNA methylation has important roles in bacteria, including in the regulation of transcription. Porphyromonas gingivalis, an oral pathogen in periodontitis, exhibits well-established gene expression changes in response to hemin availability. However, the gene regulatory processes underlying these effects remain unknown. To this end, we profiled the novel P. gingivalis epigenome, and assessed epigenetic and transcriptome variation under limited and excess hemin conditions. As expected, multiple gene expression changes were detected in response to limited and excess hemin conditions that reflect conditions associated with health and disease, respectively. Notably, we also detected differential DNA methylation signatures for the Dam GATC motif and both all-context N6-methyladenine (6mA) and 5-methylcytosine (5mC) in response to hemin availability. Joint analyses identified a subset of coordinated changes in gene expression, 6mA, and 5mC methylation that target genes involved in lactate utilization and ABC transporters. The results identify novel regulatory processes underlying the mechanism of hemin regulated gene expression in P. gingivalis, with phenotypic impacts on its virulence in periodontal disease.

genomics↗

Characterizing the human methylome across the life course: findings from eight UK-based studies

Variation in DNA methylation (DNAm) is associated with multiple biological processes that track growth and development, ageing and age-related diseases. However, there is little understanding of what constitutes typical patterns of DNAm variation and how these patterns change across the life course. In this study, we synthesised a map of the human methylome across the life course, focussing on changes in variability and mean DNAm. Harmonizing DNAm datasets across eight longitudinal and cross-sectional UK-based studies, we meta-analysed n=13,215 blood samples from n=7,037 unique individuals from birth to 98 years of age. Changes in CpG-specific variability and means were described across the life course using a meta-regression framework. CpG-specific associations of variability or mean DNAm in relation to the likelihood of association with 100 traits linked to environmental exposures, health and disease were tested within and across ten developmental age bins across the life course. Age was linked to DNAm variability at 29,212 CpG sites. On average, we observed a 1.26 fold increase in DNAm variability per year across the life course. 33,730 CpGs displayed changes in mean DNAm, with 64% of these loci showing decreases in DNAm over time. CpG sites linked to traits were in general more variable across the life course. Our study provides, for the first time, a map of the human methylome across the life course, which is publicly accessible through a searchable online database. This resource allows researchers to query CpG-specific trajectories from birth to old age and link these to health and disease.

genetics↗

Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study

AO_SCPLOWBSTRACTC_SCPLOWThe Fragile Families Child Wellbeing Study (FFCWS) is a longitudinal cohort of ethnically diverse and primarily low socioeconomic status children and their families in the U.S. Here, we analyze DNA methylation data collected from 748 FFCWS participants in two waves of this study, corresponding to participant ages 9 and 15. Our primary goal is to leverage the DNA methylation data from these two time points to study methylation associated with two key traits in adolescent health that are over-represented in these data: Early puberty and teen depression. We first identify differentially methylated regions (DMRs) for depression and early puberty. We then identify DMRs for the interaction effects between these two conditions and age by including interaction terms in our regression models to understand how age-related changes in methylation are influenced by depression or early puberty. Next, we identify methylation quantitative trait loci (meQTLs) using genotype data from the participants. We also identify meQTLs with epistatic effects with depression and early puberty. We find enrichment of our interaction meQTLs with functional categories of the genome that contribute to the heritability of co-morbid complex diseases. We replicate our meQTLs in data from the GoDMC study. This work leverages the important focus of the FFCWS data on disadvantaged children to shed light on the methylation states associated with teen depression and early puberty, and on how genetic regulation of methylation is affected in adolescents with these two conditions.

genomics↗

Genetics of human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P<5x10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28x10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95x10-10<P<5x10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome has causal effects in ulcerative colitis and rheumatoid arthritis.

genetics↗

Host genetic and environmental factors shape the human gut resistome

BackgroundUnderstanding and controlling the spread of antimicrobial resistance is one of the greatest challenges of modern medicine. To this end many efforts focus on characterising the human resistome or the set of antibiotic resistance determinants within the microbiome of an individual. Aside from antibiotic use, other host environmental and genetic factors that may shape the resistome remain relatively underexplored. MethodsUsing gut metagenome data from 250 TwinsUK female twins, we quantified known antibiotic resistance genes to estimate gut microbiome antibiotic resistance potential for 41 types of antibiotics and resistance mechanisms. Using heritability modelling, we assessed the influence of host genetic and environmental factors on the gut resistome. We then explored links between gut resistome, host health and specific environmental exposures using linear mixed effect models adjusted for age, BMI, alpha diversity and family structure. ResultsWe considered gut microbiome antibiotic resistance to 21 classes of antibiotics, for which resistance genes were detected in over 90% of our population sample. Using twin modelling, we estimated that on average about 25% of resistome variability could be attributed to host genetic influences. Greatest heritability estimates were observed for resistance potential to acriflavine (70%), dalfopristin (51%), clindamycin (48%), aminocoumarin (48%) and the total score summing across all antibiotic resistance genes (38%). As expected, the majority of resistome variability was attributed to host environmental factors specific to an individual. We compared antibiotic resistance profiles to multiple environmental exposures, lifestyle and health factors. The strongest associations were observed with alcohol and vegetable consumption, followed by high cholesterol medication and antibiotic usage. Overall, inter-individual variation in host environment showed modest associations with antibiotic resistance profiles, and host health status had relatively minor signals. ConclusionOur results identify host genetic and environmental influences on the human gut resistome. The findings improve our knowledge of human factors that influence the spread of antibiotic resistance genes and may contribute towards helping to attenuate it.

microbiology↗

Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption

Coffee and tea are extensively consumed beverages worldwide. Observational studies have shown contradictory findings for the association between consumption of these beverages and different health outcomes. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. We conducted epigenome-wide association studies (EWAS) on coffee and tea consumptions in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis revealed 11 CpG sites significantly associated with coffee consumption (P-value <1.1x10-7), nine of them annotated to the genes AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH, and two CpGs suggestively associated with tea consumption (P-value<5.0x10-6). Among these, cg14476101 was significantly associated with expression of its annotated gene PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells showed a correlation with expression levels of lipid-associated genes, suggesting a role of PHGDH in hepatic-lipid metabolism. Collectively, this study indicates that coffee consumption is associated with differential DNA methylation levels at multiple CpGs, and that coffee-associated epigenetic variations may explain the mechanism of action of coffee consumption in conferring disease risk.

genomics↗

Multi-omic analyses reveal antibody-dependent natural killer cell-mediated cytotoxicity in autoimmune thyroid diseases

The pathogenesis of autoimmune thyroid diseases (AITD) is poorly understood. We previously observed systemic depletion of IgG core fucosylation and antennary 1,2 fucosylation of peripheral blood mononuclear cells in AITD, correlated with thyroid peroxidase antibody (TPOAb) levels. We hypothesized that deficiency in IgG core fucose enhances antibody-dependent cell-mediated cytotoxicity of thyrocytes by TPOAb, contributing to thyroid autoimmunity. Multi-omic evaluations in 622 individuals (172 with AITD) from the TwinsUK cohort showed decreased IgG core fucosylation levels associated with a subpopulation of natural killer (NK) cells featuring CD335, CD314, and CD158b immunoreceptors, and increased levels of apoptosis-associated Caspase-2 and Interleukin-1, positively associated with AITD. AITD-associated genetic variants rs1521 and rs3094228 alter expression of thyrocyte ligands of the CD314 and CD158b immunoreceptors on NK cells. The combination of low-core fucose IgG associated with an NK cell subpopulation and genetic variant-promoted ligand activation in thyrocytes may promote antibody-dependent NK cell-mediated cytotoxicity of thyrocytes in AITD.

immunology↗

Heritability of skewed X-inactivation in female twins is tissue-specific and dependent on age

To balance the X-linked transcriptional dosages between the sexes, one of the two X-chromosomes is randomly selected to be inactivated in somatic tissues of female placental mammals. Non-random, or skewed X-chromosome inactivation (XCI) toward one parental X has been observed in female somatic tissues, and this skewing effect has been associated with several complex human traits. However, the extent of the influence of genetic and environmental factors on XCI skewing is largely unknown. Here, we use RNA-seq and DNA-seq data taken from a large cohort of female twins to quantify the degree of skewing of XCI (DS) in multiple tissues and to study the relationship of XCI with age, genetic factors and complex traits. We show that the XCI patterns are highly tissue-specific with a higher prevalence of skewed XCI in blood-derived tissues than in fat or skin tissue. We also show that the DS in blood-derived tissues is associated with age and that the acquired DS occurs uniquely in blood-derived tissues with an inflection point at approximately 55 years of age. Heritability analysis indicates that the heritability of DS is both age and tissue specific; DS is heritable in blood tissue of females >55 years-old (h2 = 0.34) but is not heritable in blood tissues of females <55 years-old (h2 = 0), nor in skin and fat tissues at any age. We find a positive association between the DS and smoking status in blood tissues of older females (P = 0.02). The high tissue specificity of XCI patterns in human indicates the existence of tissue-specific mechanisms influencing XCI patterns, including genetic and environmental factors. We conclude that the heritability of XCI skewing in blood-derived tissues is dependent on age, representing a Gene x Age interaction that can shift the functional allelic dosage of an entire chromosome in a tissue-restricted manner.

genetics↗

Interplay between the human gut microbiome and host metabolism

The human gut is inhabited by a complex and metabolically active microbial ecosystem regulating host health. While many studies have focused on the effect of individual microbial taxa, the metabolic potential of the entire gut microbial ecosystem has been largely under-explored. We characterised the gut microbiome of 1,004 twins via whole shotgun metagenomic sequencing (average 39M reads per sample). We observed greater similarity, across unrelated individuals, for functional metabolic pathways (82%) than for taxonomic composition (43%). We conducted a microbiota-wide association study linking both taxonomic information and microbial metabolic pathways with 673 blood and 713 faecal metabolites (Metabolon, Inc.). Metabolic pathways associated with 34% of blood and 95% of faecal metabolites, with over 18,000 significant associations, while species-level results identified less than 3,000 associations, suggesting that coordinated action of multiple taxa is required to affect the metabolome. Finally, we estimated that the microbiome mediated a crosstalk between 71% of faecal and 15% of blood metabolites, highlighting six key species (unclassified Subdoligranulum spp., Faecalibacterium prausnitzii, Roseburia inulinivorans, Methanobrevibacter smithii, Eubacterium rectale, and Akkermansia muciniphila). Because of the large inter-person variability in microbiome composition, our results underline the importance of studying gut microbial metabolic pathways rather than focusing purely on taxonomy to find therapeutic and diagnostic targets.

microbiology↗

The virome in adult monozygotic twins with concordant or discordant gut microbiomes

The virome is one of the most variable components of the human gut microbiome. Within twin-pairs, viromes have been shown to be similar for infants but not for adults, indicating that as twins age and their environments and microbiomes diverge, so do their viromes. The degree to which the microbiome drives the viromes vast diversity is unclear. Here, we examined the relationship between microbiome diversity and virome diversity in 21 adult monozygotic twin pairs selected for high or low microbiome concordance. Viromes derived from virus-like particles were unique to each subject, dominated by Caudovirales and Microviridae, and exhibited a small core that included crAssphage. Microbiome-discordant twins had more dissimilar viromes compared to microbiome-concordant twins, and the richer the microbiomes, the richer the viromes. These patterns were driven by the bacteriophages, not eukaryotic viruses. These observations support a strong role of the microbiome in patterning the virome.

microbiology↗