The person-to-person transmission landscape of the gut and oral microbiomes.

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

A reference map of potential determinants for the human serum metabolome.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

Escape from X-inactivation in twins exhibits intra- and inter-individual variability across tissues and is heritable.

X-chromosome inactivation (XCI) silences one X in female cells to balance sex-differences in X-dosage. A subset of X-linked genes escape XCI, but the extent to which this phenomenon occurs and how it varies across tissues and in a population is as yet unclear. To characterize incidence and variability of escape across individuals and tissues, we conducted a transcriptomic study of escape in adipose, skin, lymphoblastoid cell lines and immune cells in 248 healthy individuals exhibiting skewed XCI. We quantify XCI escape from a linear model of genes' allelic fold-change and XIST-based degree of XCI skewing. We identify 62 genes, including 19 lncRNAs, with previously unknown patterns of escape. We find a range of tissue-specificity, with 11% of genes escaping XCI constitutively across tissues and 23% demonstrating tissue-restricted escape, including cell type-specific escape across immune cells of the same individual. We also detect substantial inter-individual variability in escape. Monozygotic twins share more similar escape than dizygotic twins, indicating that genetic factors may underlie inter-individual differences in escape. However, discordant escape also occurs within monozygotic co-twins, suggesting environmental factors also influence escape. Altogether, these data indicate that XCI escape is an under-appreciated source of transcriptional differences, and an intricate phenotype impacting variable trait expressivity in females.

Treatment response in rheumatoid arthritis is predicted by the microbiome: a large observational study in UK DMARD-naïve patients.

OBJECTIVES: Disease-modifying antirheumatic drugs (DMARDs) are first line treatment in rheumatoid arthritis (RA). Treatment response to DMARDs is patient-specific, dose efficacy is difficult to predict and long-term results variable. The gut microbiota are known to play a pivotal role in prodromal and early-disease RA, manifested by Prevotella spp. enrichment. The clinical response to therapy may be mediated by microbiota, and large-scale studies assessing the microbiome are few. This study assessed whether microbiome signals were associated with, and predictive of, patient response to DMARD-treatment. Accurate early identification of those who will respond poorly to DMARD therapy would allow selection of alternative treatment (e.g. biologic therapy), and potentially improve patient outcome. METHODS: A multicentre, longitudinal, observational study of stool- and saliva microbiome was performed in DMARD-naïve, newly diagnosed RA patients during introduction of DMARD treatment. Clinical data and samples were collected at baseline (n = 144) in DMARD-naïve patients and at six weeks (n = 117) and 12 weeks (n = 95) into DMARD-therapy. Samples collected (n = 365 stool, n = 365 saliva) underwent shotgun sequencing. Disease activity measures were collected at each timepoint and minimal clinically important improvement determined. RESULTS: In total, 26 stool microbes were found to decrease in those manifesting a minimal clinically important improvement. Prevotella spp. and Streptococcus spp. were the predominant taxa to decline following six weeks and 12 weeks of DMARDs, respectively. Furthermore, baseline microbiota of DMARD-naïve patients were indicative of future response. CONCLUSION: DMARDs appear to restore a perturbed microbiome to a eubiotic state. Moreover, microbiome status can be used to predict likelihood of patient response to DMARD.

Total serum N-glycans associate with response to immune checkpoint inhibition therapy and survival in patients with advanced melanoma.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of melanoma and other cancers. However, no reliable biomarker of survival or response has entered the clinic to identify those patients with melanoma who are most likely to benefit from ICIs. Glycosylation affects proteins and lipids' structure and functions. Tumours are characterized by aberrant glycosylation which may contribute to their progression and hinder an effective antitumour immune response. METHODS: We aim at identifying novel glyco-markers of response and survival by leveraging the N-glycome of total serum proteins collected in 88 ICI-naive patients with advanced melanoma from two European countries. Samples were collected before and during ICI treatment. RESULTS: We observe that responders to ICIs present with a pre-treatment N-glycome profile significantly shifted towards higher abundancy of low-branched structures containing lower abundances of antennary fucose, and that this profile is positively associated with survival and a better predictor of response than clinical variables alone. CONCLUSION: While changes in serum protein glycosylation have been previously implicated in a pro-metastatic melanoma behaviour, we show here that they are also associated with response to ICI, opening new avenues for the stratification of patients and the design of adjunct therapies aiming at improving immune response.

Yoghurt consumption is associated with changes in the composition of the human gut microbiome and metabolome.

BACKGROUND: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. RESULTS: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17 ± 0.34; P = 2.72x10-10) and improved metabolic health characterised by reduced visceral fat (beta = -28.18 ± 11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41 ± 0.051; P = 6.14x10-12) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30 ± 0.052; P = 1.49x10-8) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LifeLines-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed than an increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation. CONCLUSIONS: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species (i.e. S. thermophilus and B. lactis).

Cutaneous manifestations of SARS-CoV-2 infection during the Delta and Omicron waves in 348 691 UK users of the UK ZOE COVID Study app.

BACKGROUND: Symptoms of SARS-CoV-2 infection have differed during the different waves of the pandemic but little is known about how cutaneous manifestations have changed. OBJECTIVES: To investigate the diagnostic value, frequency and duration of cutaneous manifestations of SARS-CoV-2 infection and to explore their variations between the Delta and Omicron waves of the pandemic. METHODS: In this retrospective study, we used self-reported data from 348 691 UK users of the ZOE COVID Study app, matched 1 : 1 for age, sex, vaccination status and self-reported eczema diagnosis between the Delta and Omicron waves, to assess the diagnostic value, frequency and duration of five cutaneous manifestations of SARS-CoV-2 infection (acral, burning, erythematopapular and urticarial rash, and unusual hair loss), and how these changed between waves. We also investigated whether vaccination had any effect on symptom frequency. RESULTS: We show a significant association between any cutaneous manifestations and a positive SARS-CoV-2 test result, with a diagnostic value higher in the Delta compared with the Omicron wave (odds ratio 2·29, 95% confidence interval 2·22-2·36, P < 0·001; and odds ratio 1·29, 95% confidence interval 1·26-1·33, P < 0·001, respectively). Cutaneous manifestations were also more common with Delta vs. Omicron (17·6% vs. 11·4%, respectively) and had a longer duration. During both waves, cutaneous symptoms clustered with other frequent symptoms and rarely (in < 2% of the users) as first or only clinical sign of SARS-CoV-2 infection. Finally, we observed that vaccinated and unvaccinated users showed similar odds of presenting with a cutaneous manifestation, apart from burning rash, where the odds were lower in vaccinated users. CONCLUSIONS: Cutaneous manifestations are predictive of SARS-CoV-2 infection, and their frequency and duration have changed with different variants. Therefore, we advocate for their inclusion in the list of clinically relevant COVID-19 symptoms and suggest that their monitoring could help identify new variants. What is already known about this topic? Several studies during the wildtype COVID-19 wave reported that patients presented with common skin-related symptoms. It has been observed that COVID-19 symptoms differ among variants. No study has focused on how skin-related symptoms have changed across different variants. What does this study add? We showed, in a community-based retrospective study including over 348 000 individuals, that the presence of cutaneous symptoms is predictive of SARS-CoV-2 infection during the Delta and Omicron waves and that this diagnostic value, along with symptom frequency and duration, differs between variants. We showed that infected vaccinated and unvaccinated individuals reported similar skin-related symptoms during the Delta and Omicron waves, with only burning rashes being less common after vaccination.

O- and N-Glycosylation of Serum Immunoglobulin A is Associated with IgA Nephropathy and Glomerular Function.

BACKGROUND: IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide and is a leading cause of renal failure. The disease mechanisms are not completely understood, but a higher abundance of galactose-deficient IgA is recognized to play a crucial role in IgAN pathogenesis. Although both types of human IgA (IgA1 and IgA2) have several N-glycans as post-translational modification, only IgA1 features extensive hinge-region O-glycosylation. IgA1 galactose deficiency on the O-glycans is commonly detected by a lectin-based method. To date, limited detail is known about IgA O- and N-glycosylation in IgAN. METHODS: To gain insights into the complex O- and N-glycosylation of serum IgA1 and IgA2 in IgAN, we used liquid chromatography-mass spectrometry (LC-MS) for the analysis of tryptic glycopeptides of serum IgA from 83 patients with IgAN and 244 age- and sex-matched healthy controls. RESULTS: Multiple structural features of N-glycosylation of IgA1 and IgA2 were associated with IgAN and glomerular function in our cross-sectional study. These features included differences in galactosylation, sialylation, bisection, fucosylation, and N-glycan complexity. Moreover, IgA1 O-glycan sialylation was associated with both the disease and glomerular function. Finally, glycopeptides were a better predictor of IgAN and glomerular function than galactose-deficient IgA1 levels measured by lectin-based ELISA. CONCLUSIONS: Our high-resolution data suggest that IgA O- and N-glycopeptides are promising targets for future investigations on the pathophysiology of IgAN and as potential noninvasive biomarkers for disease prediction and deteriorating kidney function.

Cancer and Risk of COVID-19 Through a General Community Survey.

Individuals with cancer may be at high risk for coronavirus disease 2019 (COVID-19) and adverse outcomes. However, evidence from large population-based studies examining whether cancer and cancer-related therapy exacerbates the risk of COVID-19 infection is still limited. Data were collected from the COVID Symptom Study smartphone application since March 29 through May 8, 2020. Among 23,266 participants with cancer and 1,784,293 without cancer, we documented 10,404 reports of a positive COVID-19 test. Compared with participants without cancer, those living with cancer had a 60% increased risk of a positive COVID-19 test. Among patients with cancer, current treatment with chemotherapy or immunotherapy was associated with a 2.2-fold increased risk of a positive test. The association between cancer and COVID-19 infection was stronger among participants >65 years and males. Future studies are needed to identify subgroups by tumor types and treatment regimens who are particularly at risk for COVID-19 infection and adverse outcomes.

Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people.

BACKGROUND: The association between current tobacco smoking, the risk of developing symptomatic COVID-19 and the severity of illness is an important information gap. METHODS: UK users of the Zoe COVID-19 Symptom Study app provided baseline data including demographics, anthropometrics, smoking status and medical conditions, and were asked to log their condition daily. Participants who reported that they did not feel physically normal were then asked by the app to complete a series of questions, including 14 potential COVID-19 symptoms and about hospital attendance. The main study outcome was the development of 'classic' symptoms of COVID-19 during the pandemic defined as fever, new persistent cough and breathlessness and their association with current smoking. The number of concurrent COVID-19 symptoms was used as a proxy for severity and the pattern of association between symptoms was also compared between smokers and non-smokers. RESULTS: Between 24 March 2020 and 23 April 2020, data were available on 2 401 982 participants, mean (SD) age 43.6 (15.1) years, 63.3% female, overall smoking prevalence 11.0%. 834 437 (35%) participants reported being unwell and entered one or more symptoms. Current smokers were more likely to report symptoms suggesting a diagnosis of COVID-19; classic symptoms adjusted OR (95% CI) 1.14 (1.10 to 1.18); >5 symptoms 1.29 (1.26 to 1.31); >10 symptoms 1.50 (1.42 to 1.58). The pattern of association between reported symptoms did not vary between smokers and non-smokers. INTERPRETATION: These data are consistent with people who smoke being at an increased risk of developing symptomatic COVID-19.

Probable delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults.

BACKGROUND: Frailty, increased vulnerability to physiological stressors, is associated with adverse outcomes. COVID-19 exhibits a more severe disease course in older, comorbid adults. Awareness of atypical presentations is critical to facilitate early identification. OBJECTIVE: To assess how frailty affects presenting COVID-19 symptoms in older adults. DESIGN: Observational cohort study of hospitalised older patients and self-report data for community-based older adults. SETTING: Admissions to St Thomas' Hospital, London with laboratory-confirmed COVID-19. Community-based data for older adults using the COVID Symptom Study mobile application. SUBJECTS: Hospital cohort: patients aged 65 and over (n = 322); unscheduled hospital admission between 1 March 2020 and 5 May 2020; COVID-19 confirmed by RT-PCR of nasopharyngeal swab. Community-based cohort: participants aged 65 and over enrolled in the COVID Symptom Study (n = 535); reported test-positive for COVID-19 from 24 March (application launch) to 8 May 2020. METHODS: Multivariable logistic regression analysis performed on age-matched samples from hospital and community-based cohorts to ascertain association of frailty with symptoms of confirmed COVID-19. RESULTS: Hospital cohort: significantly higher prevalence of probable delirium in the frail sample, with no difference in fever or cough. Community-based cohort: significantly higher prevalence of possible delirium in frailer, older adults and fatigue and shortness of breath. CONCLUSIONS: This is the first study demonstrating higher prevalence of probable delirium as a COVID-19 symptom in older adults with frailty compared to other older adults. This emphasises need for systematic frailty assessment and screening for delirium in acutely ill older patients in hospital and community settings. Clinicians should suspect COVID-19 in frail adults with delirium.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation.

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Self-Reported Symptoms of COVID-19, Including Symptoms Most Predictive of SARS-CoV-2 Infection, Are Heritable.

Susceptibility to infection such as SARS-CoV-2 may be influenced by host genotype. TwinsUK volunteers (n = 3261) completing the C-19 COVID-19 symptom tracker app allowed classical twin studies of COVID-19 symptoms, including predicted COVID-19, a symptom-based algorithm to predict true infection, derived from app users tested for SARS-CoV-2. We found heritability of 49% (32-64%) for delirium; 34% (20-47%) for diarrhea; 31% (8-52%) for fatigue; 19% (0-38%) for anosmia; 46% (31-60%) for skipped meals and 31% (11-48%) for predicted COVID-19. Heritability estimates were not affected by cohabiting or by social deprivation. The results suggest the importance of host genetics in the risk of clinical manifestations of COVID-19 and provide grounds for planning genome-wide association studies to establish specific genes involved in viral infectivity and the host immune response.

Conducting metagenomic studies in microbiology and clinical research.

Owing to the increased cost-effectiveness of high-throughput technologies, the number of studies focusing on the human microbiome and its connections to human health and disease has recently surged. However, best practices in microbiology and clinical research have yet to be clearly established. Here, we present an overview of the challenges and opportunities involved in conducting a metagenomic study, with a particular focus on data processing and analytical methods.

IgA1 Glycosylation Is Heritable in Healthy Twins.

IgA nephropathy (IgAN) is the most common form of primary GN and an important cause of kidney failure. Characteristically, patients with IgAN have increased serum levels of undergalactosylated IgA1 (gd-IgA1). To assess the degree to which serum gd-IgA1 levels are genetically determined in healthy individuals, we determined serum IgA and gd-IgA1 levels by ELISA in a sample of 148 healthy female twins, including 27 monozygotic and 47 dizygotic pairs. Using the classic twin model, we found the heritability of serum gd-IgA1 and IgA levels to be 80% (95% confidence interval, 66% to 89%) and 46% (95% confidence interval, 15% to 69%), respectively. These data indicate that serum gd-IgA1 levels are highly heritable. Elucidating the genetic basis of this heritability will be important in understanding the pathogenesis of IgAN.

PopPAnTe: population and pedigree association testing for quantitative data.

BACKGROUND: Family-based designs, from twin studies to isolated populations with their complex genealogical data, are a valuable resource for genetic studies of heritable molecular biomarkers. Existing software for family-based studies have mainly focused on facilitating association between response phenotypes and genetic markers, and no user-friendly tools are at present available to straightforwardly extend association studies in related samples to large datasets of generic quantitative data, as those generated by current -omics technologies. RESULTS: We developed PopPAnTe, a user-friendly Java program, which evaluates the association of quantitative data in related samples. Additionally, PopPAnTe implements data pre and post processing, region based testing, and empirical assessment of associations. CONCLUSIONS: PopPAnTe is an integrated and flexible framework for pairwise association testing in related samples with a large number of predictors and response variables. It works either with family data of any size and complexity, or, when the genealogical information is unknown, it uses genetic similarity information between individuals as those inferred from genome-wide genetic data. It can therefore be particularly useful in facilitating usage of biobank data collections from population isolates when extensive genealogical information is missing.

Prediction and interpretation of the lipophilicity of small peptides.

Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict [Formula: see text] of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict [Formula: see text] of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

Bacteriophages, gut bacteria, and microbial pathways interplay in cardiometabolic health.

Cardiometabolic diseases are leading causes of mortality in Western countries. Well-established risk factors include host genetics, lifestyle, diet, and the gut microbiome. Moreover, gut bacterial communities and their activities can be altered by bacteriophages (also known simply as phages), bacteria-infecting viruses, making these biological entities key regulators of human cardiometabolic health. The manipulation of bacterial populations by phages enables the possibility of using phages in the treatment of cardiometabolic diseases through phage therapy and fecal viral transplants. First, however, a deeper understanding of the role of the phageome in cardiometabolic diseases is required. In this review, we first introduce the phageome as a component of the gut microbiome and discuss fecal viral transplants and phage therapy in relation to cardiometabolic diseases. We then summarize the current state of phageome research in cardiometabolic diseases and propose how the phageome might indirectly influence cardiometabolic health through gut bacteria and their metabolites.