A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals.

BACKGROUND: The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20-69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition. RESULTS: Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics. CONCLUSION: In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01699893

Neuroendocrine pathways and breast cancer progression : a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts

Funding Information: Open access funding provided by Karolinska Institute. This work was supported by grants awarded to KH by the China Scholarship Council (No. 201806240005); to FF by the Swedish Cancer Society (20 0846 PjF); to DL by the National Natural Science Foundation of China (No. 8187111500) and the Swedish Research Council (2018–00648). The funding bodies did not play any role in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript. Funding Information: We thank the West China Biobank, Department of Clinical Research Management, West China Hospital, Sichuan University for the bio-sample storage. We thank Dr. Jianming Zeng (University of Macau) and his team biotrainee for generously sharing their experiences and codes. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was presented as an e-Poster (215P) in ESMO Congress 2021, 16-21 September 2021. Publisher Copyright: © 2022, The Author(s).Background: Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. Methods: We conducted an extreme case–control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. Results: In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07–2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2–35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. Conclusion: Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.Peer reviewe

Cardiovascular disease and subsequent risk of psychiatric disorders: a nationwide sibling-controlled study

Background: The association between cardiovascular disease (CVD) and selected psychiatric disorders has frequently been suggested while the potential role of familial factors and comorbidities in such association has rarely been investigated. Methods: We identified 869 056 patients newly diagnosed with CVD from 1987 to 2016 in Sweden with no history of psychiatric disorders, and 910 178 full siblings of these patients as well as 10 individually age- and sex-matched unrelated population controls (N=8 690 560). Adjusting for multiple comorbid conditions, we used flexible parametric models and Cox models to estimate the association of CVD with risk of all subsequent psychiatric disorders, comparing rates of first incident psychiatric disorder among CVD patients with rates among unaffected full siblings and population controls. Results: The median age at diagnosis was 60 years for patients with CVD and 59.2% were male. During up to thirty years of follow-up, the crude incidence rates of psychiatric disorder were 7.1, 4.6 and 4.0 per 1000 person-years for patients with CVD, their siblings and population controls. In the sibling comparison, we observed an increased risk of psychiatric disorder during the first year after CVD diagnosis (hazard ratio [HR], 2.74; 95% confidence interval [CI], 2.62-2.87) and thereafter (1.45; 95% CI, 1.42-1.48). Increased risks were observed for all types of psychiatric disorders and among all diagnoses of CVD. We observed similar associations in the population comparison. CVD patients who developed a comorbid psychiatric disorder during the first year after diagnosis were at elevated risk of subsequent CVD death compared to patients without such comorbidity (HR 1.55; 95% CI 1.44-1.67). Conclusions: Patients diagnosed with CVD are at an elevated risk for subsequent psychiatric disorders independent of shared familial factors and comorbid conditions. Comorbid psychiatric disorders in patients with CVD are associated with higher risk of cardiovascular mortality suggesting that surveillance and treatment of psychiatric comorbidities should be considered as an integral part of clinical management of newly diagnosed CVD patients. Funding: This work was supported by the EU Horizon 2020 Research and Innovation Action Grant (CoMorMent, grant no. 847776 to UV, PFS and FF), Grant of Excellence, Icelandic Research Fund (grant no. 163362-051 to UV), ERC Consolidator Grant (StressGene, grant no: 726413 to UV), Swedish Research Council (grant no. D0886501 to PFS) and US NIMH R01 MH123724 (to PFS)

Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines.

Humoral immune responses to infectious agents or vaccination vary substantially among individuals, and many of the factors responsible for this variability remain to be defined. Current evidence suggests that human genetic variation influences (i) serum immunoglobulin levels, (ii) seroconversion rates, and (iii) intensity of antigen-specific immune responses. Here, we evaluated the impact of intrinsic (age and sex), environmental, and genetic factors on the variability of humoral response to common pathogens and vaccines. We characterized the serological response to 15 antigens from common human pathogens or vaccines, in an age- and sex-stratified cohort of 1000 healthy individuals (Milieu Intérieur cohort). Using clinical-grade serological assays, we measured total IgA, IgE, IgG, and IgM levels, as well as qualitative (serostatus) and quantitative IgG responses to cytomegalovirus, Epstein-Barr virus, herpes simplex virus 1 and 2, varicella zoster virus, Helicobacter pylori, Toxoplasma gondii, influenza A virus, measles, mumps, rubella, and hepatitis B virus. Following genome-wide genotyping of single nucleotide polymorphisms and imputation, we examined associations between ~ 5 million genetic variants and antibody responses using single marker and gene burden tests. We identified age and sex as important determinants of humoral immunity, with older individuals and women having higher rates of seropositivity for most antigens. Genome-wide association studies revealed significant associations between variants in the human leukocyte antigen (HLA) class II region on chromosome 6 and anti-EBV and anti-rubella IgG levels. We used HLA imputation to fine map these associations to amino acid variants in the peptide-binding groove of HLA-DRβ1 and HLA-DPβ1, respectively. We also observed significant associations for total IgA levels with two loci on chromosome 2 and with specific KIR-HLA combinations. Using extensive serological testing and genome-wide association analyses in a well-characterized cohort of healthy individuals, we demonstrated that age, sex, and specific human genetic variants contribute to inter-individual variability in humoral immunity. By highlighting genes and pathways implicated in the normal antibody response to frequently encountered antigens, these findings provide a basis to better understand disease pathogenesis. ClinicalTrials.gov , NCT01699893

Regulation of germinal center B-cell differentiation

Germinal centers (GC) are the main sites where antigen‐activated B‐cell clones expand and undergo immunoglobulin gene hypermutation and selection. Iterations of this process will lead to affinity maturation, replicating Darwinian evolution on the cellular level. GC B‐cell selection can lead to four different outcomes: further expansion and evolution, apoptosis (non‐selection), or output from the GC with differentiation into memory B cells or plasma cells. T‐helper cells in GC have been shown to have a central role in regulating B‐cell selection by sensing the density of major histocompatibility complex (MHC):peptide antigen complexes. Antigen is provided on follicular dendritic cells in the form of immune complex. Antibody on these immune complexes regulates antigen accessibility by shielding antigen from B‐cell receptor access. Replacement of antibody on immune complexes by antibody generated from GC‐derived plasma cell output will gradually reduce the availability of antigen. This antibody feedback can lead to a situation where a slow rise in selection stringency caused by a changing environment leads to directional evolution toward higher affinity antibody

Statistical Modeling and Learning of the Environmental and Genetic Drivers of Variation in Human Immunity

During the last decade the variation in the human genome has been mapped in fine detail. Next generation sequencing has made it possible to cheaply and rapidly aquire vast amounts of biomolecular information on large cohorts of people. This have enabled large-scale epidemiological studies to investigate the relationships between environmental and genetic factors and human biomolecular traits. It is now possible to map variation in the genomic blueprint for human biology to variation in levels of epigenomic marks, gene expression levels and protein expression levels. This development has opened up the possibility of a "phenomic science": the data-driven study of the interactions between all levels of the relationship between the genotype, the environment, and the phenotype. The Milieu Intérieur study of Institut Pasteur, Paris, aims at bringing the techno-logical developments of modern biology to bear on the study of the human immune system in homeostasis. Deep phenotyping has been performed on 1,000 healthy, un-related people of Western European ancestry. The cohort is evenly stratified across sex, and across five decades of life, between 20 and 70 years of age. In this thesis, we combine the standardised flow cytometry of 173 parameters of innate and adaptive immune cells, genome-wide DNA genotyping, detailed information on life-style and environmental factors and MethylationEPIC array data of the Milieu Intérieur cohort, to identify the genetic and environmental drivers of variation in the human immune system. The increasing complexity of biological data requires the development of new statistical tools. In this work, we aim to integrate developments in machine learning, convex optimization, causal inference, and statistical methodology, to build robust and reliable tools for analysing the high-dimensional and highly complex biomolecular data of the Milieu Intérieur study. We construct a pipeline to perform genome-wide association studies on phenotypes with heterogenous distributions, while controlling for arbitrarily many environmental factors. The pipeline is applied to study the genetics of human immune system variation in homeostasis and the genetics of the function of the human thymus. Our pipeline identifies 15 loci that influence immunophenotypes. We show that these loci are enriched in disease-associated variants. We also report a commongenetic variant, situated within the T cell receptor locus, that increases the production of naive T cells within the human thymus. In addition, we find four key non-genetic factors that drive variation in the healthy human immune system: age, sex, latent cytomegalovirus infection and smoking. Age, sex, and smoking have a broad impact on the innate and the adaptive immune subsystems, while cytomegalovirus infection primarily seems to skew the T cell compartment of the adaptive immune subsystem towards inflammatory subsets. We also show that age and sex influence the function of the human thymus. Immunophenotypes are intimately connected to epigenetic markers in whole-blood. We leverage the >850,000 methylation sites probed in the MethylationEPIC array to build high-dimensional predictive models of 70 immune cell subsets and other traits such as age and smoking status. We employ elastic net regression and stability selection to build sparse, regularized models, and show that they are capable of estimating blood cell composition more accurately and cost-effectively than previous methods. The properties of elastic net regression and stability selection also enable us to investigate the relationship between DNA methylation and immune blood cell composition. This thesis develops methods for, and performs, the analysis of parts of the rich and multifaceted data of the Milieu Intérieur study. With the construction and analysis of this rich observational data we contribute to the young fields of population immunology and human phenomic science. We discover novel associations that will help in understanding the differences between people in vaccination efficacy and susceptibility to common autoimmune and infectious disesases. Finally, we present predictive models that will facilitate the application of immunological markers in the clinic

Sensor Fusion for Robotic Workspace State Estimation

We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao-Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling