Biology subjects

Sanna, S.

Publications and source records attributed to Sanna, S..

6 recordsLinked to original sources

bioRxiv2025

Vaginal microbiota changes during a natural menstrual cycle in the Women4Health cohort highlights specific features of the Italian population.

Large sex hormonal fluctuations are thought to influence vaginal microbiota, but little is known about the impact of small, physiological variations. Here we tracked changes in vaginal microbiota during four key menstrual cycle phases in 61 healthy, naturally menstruating Italian women from the Women4Health cohort. The microbiota, characterized using a high depth 16S rRNA amplicon sequencing approach covering four hypervariable regions, was primarily composed of Lactobacillus species, with Lactobacillus iners being the most abundant (average relative abundance 40%) and the most prevalent (prevalence 98%). Individual microbiota were generally stable, but beta diversity was higher during the follicular phase (p=0.007). Only 11 women exhibited compositional shifts, mostly occurring between follicular and ovulatory phases. Finally, using linear mixed models we assessed the association between taxa relative abundance and five sex-hormones along the menstrual cycle. Among these, 17-beta estradiol showed the largest number of significant associations, linking its increase to a decrease in relative abundance of taxa that are more common after menopause. Our study highlights specific features of the Italian population and points to the resilience of the vaginal microbiota to physiological hormonal changes. Noteworthy, the observed high abundance of L. iners contrasts with previous studies in European populations, challenging its proposed pathogenic role and suggesting distinct microbiota profiles within Europe. IMPORTANCEThe vaginal microbiota plays an important role in womens health, yet we know little about how it responds to normal hormonal fluctuations. In this study, we followed 61 healthy Italian women over a natural menstrual cycle to explore microbiota changes across different hormonal phases. We found that Lactobacillus iners was the most common species, unlike previous findings in Northern Europe, suggesting population-specific patterns. The common hypothesis that L. iners is invariably linked with poor health is called into question by our findings. They emphasize the importance of considering population context and hormonal status when assessing vaginal health. The vaginal microbiota was generally stable, with only a few changes observed between the follicular and ovulatory phases. When evaluating association between five sex hormones and taxa abundances, we found that 17-beta estradiol levels had the largest number of significant associations. These highlight an association between increase levels of 17-beta estradiol and increased relative abundance of rare bacterial taxa rather than dominant species like Lactobacillus. Our findings help define what constitutes a "healthy microbiota" in generally healthy Italian women of reproductive age and may inform future strategies for diagnosing or preventing womens health conditions.

microbiology↗

bioRxiv2020

Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function within the Dutch Microbiome Project, a population cohort of 7,738 individuals from the northern Netherlands. Two robust, study-wide significant (p<1.89x10-10) signals near the LCT and ABO genes were found to affect multiple microbial taxa and pathways, and were replicated in two independent cohorts. The LCT locus associations were modulated by lactose intake, while those at ABO reflected participant secretor status determined by FUT2 genotype. Eighteen other loci showed suggestive evidence (p<5x10-8) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome.

genetics↗

bioRxiv2020

The Dutch Microbiome Project defines factors that shape the healthy gut microbiome

The gut microbiome is associated with diverse diseases, but the universal signature of an (un)healthy microbiome remains elusive and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. To fill this gap, we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We then correlated this to 241 host and environmental factors, including physical and mental health, medication use, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is primarily shaped by environment and cohousing. Only [~]13% of taxa are heritable, which are enriched with highly prevalent and health-associated bacteria. By identifying 2,856 associations between microbiome and health, we find that seemingly unrelated diseases share a common signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, of which numerous early-life and current factors are particularly linked to the microbiome. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.

microbiology↗

bioRxiv2020

Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a master regulator of IFNγ signalling in celiac disease

BackgroundCeliac disease (CeD) is a complex T cell-mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD. ResultsWe used four different in silico approaches - Mendelian Randomization inverse variance weighting, COLOC, LD overlap and DEPICT - to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signalling and T cell activation pathways. 51 of these genes are targets of known drug compounds and likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene-combinations that were affected by our CeD-prioritized genes in trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAFD1, and were found to be involved in IFN{gamma} signalling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments that validated the role of TRAFD1 as an immune regulator acting in trans. ConclusionsOur strategy has confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and the IFN{gamma} signalling and MHC I antigen processing pathways, both major players of immune activation and CeD pathogenesis.

genetics↗

bioRxiv2019

A novel Mendelian randomization method identifies causal relationships between gene expression and low-density lipoprotein cholesterol levels.

Robust inference of causal relationships between gene expression and complex traits using Mendelian Randomization (MR) approaches is confounded by pleiotropy and linkage disequilibrium (LD) between gene expression quantitative loci (eQTLs). Here we propose a new MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data. In simulations, MR-link shows false positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other MR methods we tested, even when only one eQTL variant is present. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals and eQTLs summary statistics from whole blood and liver identified 19 genes causally linked to LDL-C. These include the previously functionally validated SORT1 gene, and the PVRL2 gene, located in the APOE locus, for which a causal role in liver was yet unknown. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

genetics↗

bioRxiv2019

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R [≥] 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and assign them to a cell type with Decon-eQTL using the predicted cell proportions from Decon-cell. Deconvoluted eQTLs show excellent allelic directional concordance with those of eQTL([≥] 96%) and chromatin mark QTL ([≥]87%) studies that used either purified cell subpopulations or single-cell RNA-seq. Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).

genomics↗