Background and objectivesThe role of B cells in the pathogenic events leading to relapsing multiple sclerosis (R-MS) has only been recently elucidated. A pivotal step in defining this role has been provided by therapeutic efficacy of anti-CD20 monoclonal antibodies. Indeed, treatment with anti-CD20 can also alter number and function of other immune cells not directly expressing CD20 on their cell surface, whose activities can contribute to unknown aspects influencing therapeutic efficacy. We examined the phenotype and function of cytotoxic lymphocytes and Epstein-Barr virus (EBV)-specific immune responses in people with R-MS before and after ocrelizumab treatment.MethodsIn this prospective study, we collected blood samples from people with R-MS (n = 41) before and 6 and 12 months after initiating ocrelizumab to assess the immune phenotype and the indirect impact on cytotoxic functions of CD8+ T and NK cells. In addition, we evaluated the specific anti-EBV proliferative responses of both CD8+ T and NK lymphocytes as surrogate markers of anti-EBV activity.ResultsWe observed that while ocrelizumab depleted circulating B cells, it also reduced the expression of activation and migratory markers on both CD8+ T and NK cells as well as their in vitro cytotoxic activity. A comparable pattern in the modulation of immune molecules by ocrelizumab was observed in cytotoxic cells even when patients with R-MS were divided into groups based on their prior disease-modifying treatment. These effects were accompanied by a significant and selective reduction of CD8+ T-cell proliferation in response to EBV antigenic peptides.DiscussionTaken together, our findings suggest that ocrelizumab-while depleting B cells-affects the cytotoxic function of CD8+ and NK cells, whose reduced cross-activity against myelin antigens might also contribute to its therapeutic efficacy during MS.

Early development of the gut ecosystem is crucial for lifelong health. While infant gut bacterial communities have been studied extensively, the infant gut virome remains under-explored. To study the development of the infant gut virome over time and the factors that shape it, we longitudinally assess the composition of gut viruses and their bacterial hosts in 30 women during and after pregnancy and in their 32 infants during their first year of life. Using shotgun metagenomic sequencing applied to dsDNA extracted from Virus-Like Particles (VLPs) and bacteria, we generate 205 VLP metaviromes and 322 total metagenomes. With this data, we show that while the maternal gut virome composition remains stable during late pregnancy and after birth, the infant gut virome is dynamic in the first year of life. Notably, infant gut viromes contain a higher abundance of active temperate phages compared to maternal gut viromes, which decreases over the first year of life. Moreover, we show that the feeding mode and place of delivery influence the gut virome composition of infants. Lastly, we provide evidence of co-transmission of viral and bacterial strains from mothers to infants, demonstrating that infants acquire some of their virome from their mother's gut.

The lack of standardization in the methods of DNA extraction from fecal samples represents the major source of experimental variation in the microbiome research field. In this study, we aimed to compare the metagenomic profiles and microbiome-phenotype associations obtained by applying two commercially available DNA extraction kits: the AllPrep DNA/RNA Mini Kit (APK) and the QIAamp Fast DNA Stool Mini Kit (FSK). Using metagenomic sequencing data available from 745 paired fecal samples from two independent population cohorts, Lifelines-DEEP (LLD, n = 292) and the 500 Functional Genomics project (500FG, n = 453), we confirmed significant differences in DNA yield and the recovered microbial communities between protocols, with the APK method resulting in a higher DNA concentration and microbial diversity. Further, we observed a massive difference in bacterial relative abundances at species-level between the APK and the FSK protocols, with > 75% of species differentially abundant between protocols in both cohorts. Specifically, comparison with a standard mock community revealed that the APK method provided higher accuracy in the recovery of microbial relative abundances, with the absence of a bead-beating step in the FSK protocol causing an underrepresentation of gram-positive bacteria. This heterogeneity in the recovered microbial composition led to remarkable differences in the association with anthropometric and lifestyle phenotypes. The results of this study further reinforce that the choice of DNA extraction method impacts the metagenomic profile of human gut microbiota and highlight the importance of harmonizing protocols in microbiome studies.

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.

Aims/hypothesisType 1 diabetes is an autoimmune disorder that is characterised by destruction of pancreatic beta cells by autoreactive T lymphocytes. Although islet autoantibodies (AAb) are an indicator of disease progression, specific immune biomarkers that can be used as target molecules to halt development of type 1 diabetes have not been discovered. Soluble immune checkpoint molecules (sICM) play a pivotal role in counteracting excessive lymphocyte responses, but their role in type 1 diabetes is unexplored. In this longitudinal study, we measured sICM levels in AAb-positive (AAb+) children to identify molecules related to type 1 diabetes progression.MethodsWe measured the levels of 14 sICM in the sera of AAb+ children (n=57) compared to those with recent-onset type 1 diabetes (n=79) and healthy children (n=44), obtained from two cohorts. AAb+ children were followed up and divided based on their progression to type 1 diabetes (AAbP) or not (AAbNP) (if they lost islet autoimmunity and did not develop disease in subsequent years). sICM were also measured in the sample taken at the visit closest to disease onset in AAbP children.ResultsWe found that AAb+ children had a distinct sICM profile compared with healthy children and those with recent-onset type 1 diabetes. In addition, AAb+ children who progressed to type 1 diabetes (AAbP) had higher sICM concentrations than non-progressors (AAbNP). Further, sICM levels decreased in AAbP children close to disease onset. Application of Cox regression models highlighted that high concentrations of soluble programmed cell death protein 1 (sPD-1) are associated with type 1 diabetes progression (HR 1.71; 95% CI 1.16, 2.51; p=0.007).Conclusions/interpretationThis study reveals an sICM profile that is dysregulated during the preclinical stage of type 1 diabetes, and identifies sPD-1 as a pathophysiologically-relevant molecule that is associated with disease progression, offering a potential target for early interventions in autoimmune diabetes.

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

The gut microbiome has important roles in host metabolism and immunity, and microbial dysbiosis affects human physiology and health. Maternal immunity and microbial metabolites during pregnancy, microbial transfer during birth, and transfer of immune factors, microorganisms and metabolites via breastfeeding provide critical sources of early-life microbial and immune training, with important consequences for human health. Only a few studies have directly examined the interactions between the gut microbiome and the immune system during pregnancy, and the subsequent effect on offspring development. In this Review, we aim to describe how the maternal microbiome shapes overall pregnancy-associated maternal, fetal and early neonatal immune systems, focusing on the existing evidence and highlighting current gaps to promote further research.

Prenatal exposure to environmental contaminants is a significant health concern because it has the potential to interfere with host metabolism, leading to adverse health effects in early childhood and later in life. Growing evidence suggests that genetic and environmental factors, as well as their interactions, play a significant role in the development of autoimmune diseases. In this study, we hypothesized that prenatal exposure to environmental contaminants impacts cord serum metabolome and contributes to the development of autoimmune diseases. We selected cord serum samples from All Babies in Southeast Sweden (ABIS) general population cohort, from infants who later developed one or more autoimmune-mediated and inflammatory diseases: celiac disease (CD), Crohn’s disease (IBD), hypothyroidism (HT), juvenile idiopathic arthritis (JIA), and type 1 diabetes (T1D) (all cases, N = 62), along with matched controls (N = 268). Using integrated exposomics and metabolomics mass spectrometry (MS) based platforms, we determined the levels of contaminants and metabolites. Differences in exposure levels were found between the controls and those who later developed various diseases. High contaminant exposure levels were associated with changes in metabolome, including amino acids and free fatty acids. Specifically, we identified marked associations between metabolite levels and exposure levels of deoxynivalenol (DON), bisphenol S (BPS), and specific per- and polyfluorinated substances (PFAS). Our study suggests that prenatal exposure to specific environmental contaminants alters the cord serum metabolomes, which, in turn, might increase the risk of various immune-mediated disease later in life.

Aims/hypothesisAppearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody).MethodsLongitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites.ResultsWe observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP.Conclusion/interpretationOur study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes.

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.