Phenotype-Genotype Correlation Applying a Cocktail Approach and an Exome Chip Analysis Reveals Further Variants Contributing to Variation of Drug Metabolism.

Although great progress has been made in the fine-tuning of diplotypes, there is still a need to further improve the predictability of individual phenotypes of pharmacogenetically relevant enzymes. The aim of this study was to analyze the additional contribution of sex and variants identified by exome chip analysis to the metabolic ratio of five probe drugs. A cocktail study applying dextromethorphan, losartan, omeprazole, midazolam, and caffeine was conducted on 200 healthy volunteers. CYP2D6, 2C9, 2C19, 3A4/5, and 1A2 genotypes were analyzed and correlated with metabolic ratios. In addition, an exome chip analysis was performed. These SNPs correlating with metabolic ratios were confirmed by individual genotyping. The contribution of various factors to metabolic ratios was assessed by multiple regression analysis. Genotypically predicted phenotypes defined by CPIC discriminated very well the log metabolic ratios with the exception of caffeine. There were minor sex differences in the activity of CYP2C9, 2C19, 1A2, and CYP3A4/5. For dextromethorphan (CYP2D6), IP6K2 (rs61740999) and TCF20 (rs5758651) affected metabolic ratios, but only IP6K2 remained significant after multiple regression analysis. For losartan (CYP2C9), FBXW12 (rs17080138), ZNF703 (rs79707182), and SLC17A4 (rs11754288) together with CYP diplotypes, and sex explained 50% of interindividual variability. For omeprazole (CYP2C19), no significant influence of CYP2C:TG haplotypes was observed, but CYP2C19 rs12777823 improved the predictability. The comprehensive genetic analysis and inclusion of sex in a multiple regression model significantly improved the explanation of variability of metabolic ratios, resulting in further improvement of algorithms for the prediction of individual phenotypes of drug-metabolizing enzymes.

Assessment of the Inflammatory Effects of Gut Microbiota from Human Twins Discordant for Ulcerative Colitis on Germ-free Mice.

Disturbances in gut microbiota are prevalent in inflammatory bowel disease (IBD), which includes ulcerative colitis (UC). However, whether these disturbances contribute to development of the disease or are a result of the disease is unclear. In pairs of human twins discordant for IBD, the healthy twin has a higher risk of developing IBD and a gut microbiota that is more similar to that of IBD patients as compared with healthy individuals. Furthermore, appropriate medical treatment may mitigate these disturbances. To study the correlation between microbiota and IBD, we transferred stool samples from a discordant human twin pair: one twin being healthy and the other receiving treatment for UC. The stool samples were transferred from the disease-discordant twins to germ-free pregnant dams. Colitis was induced in the offspring using dextran sodium sulfate. As compared with offspring born to mice dams inoculated with stool from the healthy cotwin, offspring born to dams inoculated with stool from the UC-afflicted twin had a lower disease activity index, less gut inflammation, and a microbiota characterized by higher α diversity and a more antiinflammatory profile that included the presence and higher abundance of antiinflammatory species such as Akkermansia spp., Bacteroides spp., and Parabacteroides spp. These findings suggest that the microbiota from the healthy twin may have had greater inflammatory properties than did that of the twin undergoing UC treatment.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins.

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

Tryptophan degradation as a systems phenomenon in inflammation - an analysis across 13 chronic inflammatory diseases.

BACKGROUND: Chronic inflammatory diseases (CIDs) are systems disorders that affect diverse organs including the intestine, joints and skin. The essential amino acid tryptophan (Trp) can be broken down to various bioactive derivatives important for immune regulation. Increased Trp catabolism has been observed in some CIDs, so we aimed to characterise the specificity and extent of Trp degradation as a systems phenomenon across CIDs. METHODS: We used high performance liquid chromatography and targeted mass spectrometry to assess the serum and stool levels of Trp and Trp derivatives. Our retrospective study incorporates both cross-sectional and longitudinal components, as we have included a healthy population as a reference and there are also multiple observations per patient over time. FINDINGS: We found reduced serum Trp levels across the majority of CIDs, and a prevailing negative relationship between Trp and systemic inflammatory marker C-reactive protein (CRP). Notably, serum Trp was low in several CIDs even in the absence of measurable systemic inflammation. Increases in the kynurenine-to-Trp ratio (Kyn:Trp) suggest that these changes result from increased degradation along the kynurenine pathway. INTERPRETATION: Increases in Kyn:Trp indicate the kynurenine pathway as a major route for CID-related Trp metabolism disruption and the specificity of the network changes indicates excessive Trp degradation relative to other proteogenic amino acids. Our results suggest that increased Trp catabolism is a common metabolic occurrence in CIDs that may directly affect systemic immunity. FUNDING: This work was supported by the DFG Cluster of Excellence 2167 "Precision medicine in chronic inflammation" (KA, SSchr, PR, BH, SWa), the BMBF (e:Med Juniorverbund "Try-IBD" 01ZX1915A and 01ZX2215, the e:Med Network iTREAT 01ZX2202A, and GUIDE-IBD 031L0188A), EKFS (2020_EKCS.11, KA), DFG RU5042 (PR, KA), and Innovative Medicines Initiative 2 Joint Undertakings ("Taxonomy, Treatments, Targets and Remission", 831434, "ImmUniverse", 853995, "BIOMAP", 821511).

The archaeome in metaorganism research, with a focus on marine models and their bacteria-archaea interactions.

Metaorganism research contributes substantially to our understanding of the interaction between microbes and their hosts, as well as their co-evolution. Most research is currently focused on the bacterial community, while archaea often remain at the sidelines of metaorganism-related research. Here, we describe the archaeome of a total of eleven classical and emerging multicellular model organisms across the phylogenetic tree of life. To determine the microbial community composition of each host, we utilized a combination of archaea and bacteria-specific 16S rRNA gene amplicons. Members of the two prokaryotic domains were described regarding their community composition, diversity, and richness in each multicellular host. Moreover, association with specific hosts and possible interaction partners between the bacterial and archaeal communities were determined for the marine models. Our data show that the archaeome in marine hosts predominantly consists of Nitrosopumilaceae and Nanoarchaeota, which represent keystone taxa among the porifera. The presence of an archaeome in the terrestrial hosts varies substantially. With respect to abundant archaeal taxa, they harbor a higher proportion of methanoarchaea over the aquatic environment. We find that the archaeal community is much less diverse than its bacterial counterpart. Archaeal amplicon sequence variants are usually host-specific, suggesting adaptation through co-evolution with the host. While bacterial richness was higher in the aquatic than the terrestrial hosts, a significant difference in diversity and richness between these groups could not be observed in the archaeal dataset. Our data show a large proportion of unclassifiable archaeal taxa, highlighting the need for improved cultivation efforts and expanded databases.

Whole blood RNA sequencing identifies transcriptional differences between primary sclerosing cholangitis and ulcerative colitis.

Background & Aims: Genetic and microbiome studies across patients with primary sclerosing cholangitis (PSC) and ulcerative colitis (UC) have indicated that UC in PSC is a separate disease entity to primary UC, but expression studies for PSC are lacking. Methods: We conducted whole blood RNA sequencing experiments for 495 patients with UC, 220 patients with PSC (including 177 with UC), and 320 healthy controls from Germany and Norway. Differential expression analyses, gene ontology and coexpression analyses and random forest machine learning were performed to identify genes, ontologies and transcriptional features that discriminate diagnoses. Results: The blood transcriptome in UC and PSC is dominated by neutrophil activation genes (e.g. S100A12). In UC, but not in PSC (neither PSC alone nor patients with an additional diagnosis of UC [PSC/UC]), ribosomal, mitochondrial, and energy metabolism genes are upregulated in conjunction with antibody transcript expression (MZB1, IGJ). In PSC, there is an increase in modules related to apoptosis and expression of genes of interferon-I-related ontologies. Random forest analysis could poorly discriminate PSC alone from PSC/UC (AUROC 0.56), but could discriminate PSC, UC, and controls with high accuracy (AUROC UC vs. controls 0.95, PSC vs. controls 0.88, UC vs. PSC 0.986). The main coexpression modules relevant for distinguishing PSC, UC, and controls are enriched in neutrophil degranulation and antibody production genes. Conclusions: Supported by machine learning results, PSC and UC appear to be separate entities on a molecular level, while PSC/UC and PSC are indistinguishable. Impact and implications: Clinical and genetic studies suggest that the colitis-like symptoms in primary sclerosing cholangitis (PSC) represent a different disease entity from primary ulcerative colitis (UC). The present study supports this assumption with transcriptomic data from whole blood and describes notable differences in gene expression between primary UC and PSC, providing insights into the still unclear pathophysiology of both diseases. These findings are of interest to scientists seeking to decipher the molecular pathophysiology of both diseases and provide evidence that a redefinition of the PSC-UC phenotype should be considered. The study practically supports future molecular research by providing a large transcriptomic whole blood reference cohort.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

Disease- and stage-specific alterations of the oral and fecal microbiota in Alzheimer's disease.

Microbial communities in the intestinal tract are suggested to impact the ethiopathogenesis of Alzheimer's disease (AD). The human microbiome might modulate neuroinflammatory processes and contribute to neurodegeneration in AD. However, the microbial compositions in patients with AD at different stages of the disease are still not fully characterized. We used 16S rRNA analyses to investigate the oral and fecal microbiota in patients with AD and mild cognitive impairment (MCI; n = 84), at-risk individuals (APOE4 carriers; n = 17), and healthy controls (n = 50) and investigated the relationship of microbial communities and disease-specific markers via multivariate- and network-based approaches. We found a slightly decreased diversity in the fecal microbiota of patients with AD (average Chao1 diversity for AD = 212 [SD = 66]; for controls = 215 [SD = 55]) and identified differences in bacterial abundances including Bacteroidetes, Ruminococcus, Sutterella, and Porphyromonadaceae. The diversity in the oral microbiota was increased in patients with AD and at-risk individuals (average Chao1 diversity for AD = 174 [SD = 60], for at-risk group = 195 [SD = 49]). Gram-negative proinflammatory bacteria including Haemophilus, Neisseria, Actinobacillus, and Porphyromonas were dominant oral bacteria in patients with AD and MCI and the abundance correlated with the cerebrospinal fluid biomarker. Taken together, we observed a strong shift in the fecal and the oral communities of patients with AD already prominent in prodromal and, in case of the oral microbiota, in at-risk stages. This indicates stage-dependent alterations in oral and fecal microbiota in AD which may contribute to the pathogenesis via a facilitated intestinal and systemic inflammation leading to neuroinflammation and neurodegeneration.

18th International HLA and Immunogenetics Workshop: Report on the SNP-HLA Reference Consortium (SHLARC) component.

The SNP-HLA Reference Consortium (SHLARC), a component of the 18th International HLA and Immunogenetics Workshop, is aimed at collecting diverse and extensive human leukocyte antigen (HLA) data to create custom reference panels and enhance HLA imputation techniques. Genome-wide association studies (GWAS) have significantly contributed to identifying genetic associations with various diseases. The HLA genomic region has emerged as the top locus in GWAS, particularly in immune-related disorders. However, the limited information provided by single nucleotide polymorphisms (SNPs), the hallmark of GWAS, poses challenges, especially in the HLA region, where strong linkage disequilibrium (LD) spans several megabases. HLA imputation techniques have been developed using statistical inference in response to these challenges. These techniques enable the prediction of HLA alleles from genotyped GWAS SNPs. Here we present the SHLARC activities, a collaborative effort to create extensive, and multi-ethnic reference panels to enhance HLA imputation accuracy.

The gut microbiome modulates associations between adherence to a Mediterranean-style diet, abdominal adiposity, and C-reactive protein in population-level analysis.

BACKGROUND: Adherence to a Mediterranean-style dietary pattern is likely to have variable effects on body composition, but the impact of gut microbiome on this relationship is unknown. OBJECTIVES: To examine the potential mediating effect of the gut microbiome on the associations between Alternate Mediterranean Diet (aMed) scores, abdominal adiposity, and inflammation in population-level analysis. DESIGN: In a community-based sample aged 25 to 83 y (n = 620; 41% female) from Northern Germany, we assessed the role of the gut microbiome, sequenced from 16S rRNA genes, on the associations between aMed scores, estimated using validated food-frequency questionnaires, magnetic resonance imaging-determined visceral (VAT) and subcutaneous (SAT) adipose tissue and C-reactive protein (CRP). RESULTS: Higher aMed scores were associated with lower SAT (-0.86 L (95% CI: -1.56, -0.17), P = 0.01), VAT (-0.65 L (95% CI: -1.03,-0.27), P = 0.01) and CRP concentrations (-0.35 mg/L; ß: -20.1% (95% CI: 35.5, -1.09), P = 0.04) in the highest versus lowest tertile after multivariate adjustment. Of the taxa significantly associated with aMed scores, higher abundance of Porphyromonadaceae mediated 11.6%, 9.3%, and 8.7% of the associations with lower SAT, VAT, and CRP, respectively. Conversely, a lower abundance of Peptostreptococcaceae mediated 13.1% and 18.2% of the association with SAT and CRP levels. Of the individual components of the aMed score, moderate alcohol intake was associated with lower VAT (-0.2 (95% CI: -0.4, -0.1), P =0.01) with a higher abundance of Oxalobacteraceae and lower abundance of Burkholderiaceae explaining 8.3% and 9.6% of this association, respectively. CONCLUSION: These novel data suggest that abundance of specific taxa in the Porphyromonadaceae and Peptostreptococcaceae families may contribute to the association between aMed scores, lower abdominal adipose tissue, and inflammation.

Human Leukocyte Antigen Signatures as Pathophysiological Discriminants of Microscopic Colitis Subtypes.

BACKGROUND AND AIMS: Microscopic colitis [MC] is currently regarded as an inflammatory bowel disease that manifests as two subtypes: collagenous colitis [CC] and lymphocytic colitis [LC]. Whether these represent a clinical continuum or distinct entities is, however, an open question. Genetic investigations may contribute important insight into their respective pathophysiologies. METHODS: We conducted a genome-wide association study [GWAS] meta-analysis in 1498 CC, 373 LC patients, and 13 487 controls from Europe and the USA, combined with publicly available MC GWAS data from UK Biobank and FinnGen [2599 MC cases and 552 343 controls in total]. Human leukocyte antigen [HLA] alleles and polymorphic residues were imputed and tested for association, including conditional analyses for the identification of key causative variants and residues. Genetic correlations with other traits and diagnoses were also studied. RESULTS: We detected strong HLA association with CC, and conditional analyses highlighted the DRB1*03:01 allele and its residues Y26, N77, and R74 as key to this association (best p = 1.4 × 10-23, odds ratio [OR] = 1.96). Nominally significant genetic correlations were detected between CC and pneumonia [rg = 0.77; p = 0.048] and oesophageal diseases [rg = 0.45, p = 0.023]. An additional locus was identified in MC GWAS analyses near the CLEC16A and RMI2 genes on chromosome 16 [rs35099084, p = 2.0 × 10-8, OR = 1.31]. No significant association was detected for LC. CONCLUSION: Our results suggest CC and LC have distinct pathophysiological underpinnings, characterised by an HLA predisposing role only in CC. This challenges existing classifications, eventually calling for a re-evaluation of the utility of MC umbrella definitions.

HLA-DPA1*02:01~B1*01:01 is a risk haplotype for primary sclerosing cholangitis mediating activation of NKp44+ NK cells.

OBJECTIVE: Primary sclerosing cholangitis (PSC) is characterised by bile duct strictures and progressive liver disease, eventually requiring liver transplantation. Although the pathogenesis of PSC remains incompletely understood, strong associations with HLA-class II haplotypes have been described. As specific HLA-DP molecules can bind the activating NK-cell receptor NKp44, we investigated the role of HLA-DP/NKp44-interactions in PSC. DESIGN: Liver tissue, intrahepatic and peripheral blood lymphocytes of individuals with PSC and control individuals were characterised using flow cytometry, immunohistochemical and immunofluorescence analyses. HLA-DPA1 and HLA-DPB1 imputation and association analyses were performed in 3408 individuals with PSC and 34 213 controls. NK cell activation on NKp44/HLA-DP interactions was assessed in vitro using plate-bound HLA-DP molecules and HLA-DPB wildtype versus knock-out human cholangiocyte organoids. RESULTS: NKp44+NK cells were enriched in livers, and intrahepatic bile ducts of individuals with PSC showed higher expression of HLA-DP. HLA-DP haplotype analysis revealed a highly elevated PSC risk for HLA-DPA1*02:01~B1*01:01 (OR 1.99, p=6.7×10-50). Primary NKp44+NK cells exhibited significantly higher degranulation in response to plate-bound HLA-DPA1*02:01-DPB1*01:01 compared with control HLA-DP molecules, which were inhibited by anti-NKp44-blocking. Human cholangiocyte organoids expressing HLA-DPA1*02:01-DPB1*01:01 after IFN-γ-exposure demonstrated significantly increased binding to NKp44-Fc constructs compared with unstimulated controls. Importantly, HLA-DPA1*02:01-DPB1*01:01-expressing organoids increased degranulation of NKp44+NK cells compared with HLA-DPB1-KO organoids. CONCLUSION: Our studies identify a novel PSC risk haplotype HLA-DP A1*02:01~DPB1*01:01 and provide clinical and functional data implicating NKp44+NK cells that recognise HLA-DPA1*02:01-DPB1*01:01 expressed on cholangiocytes in PSC pathogenesis.

Acceptability Criteria of Precision Medicine: Lessons From Patients' Experiences With the GUIDE-IBD Trial Regarding the Use of Mobile Health Technology.

Background: Research about mobile health technologies for inflammatory bowel diseases reveals that these devices are mainly used to predict or self-report disease activity. However, in the near future these tools can be used to integrate large data sets into machine learning for the development of personalized treatment algorithms. The impact of these technologies on patients' well-being and daily lives has not yet been investigated. Methods: We conducted 10 qualitative interviews with patients who used the GUIDE-IBD mHealth technology. This is a special smartphone app for patients to record patient-reported outcomes and a wearable to track physical activity, heart rate, and sleep quality. For data analysis, we used interpretative phenomenological analysis. This method is ideally suited for studying people's lived experiences. Results: The analysis of the data revealed 11 themes that were mentioned by at least 3 participants. These themes were: Self-tracking with wearable devices as normality; variable value of the data from the wearable; risk of putting people under pressure; stimulus to reflect on their own well-being and illness; risk of psychological distress; discussion about app data in the medical consultation is very brief or nonexistent; easier to be honest with an app than with a doctor; questionnaires do not always adequately capture the patient's condition; need for support; the possibility to look at the data retrospectively; and annoyed by additional tasks. Conclusions: Patients identified benefits, risks, and potentials for improvement, which should be considered in the further development of the devices and patient-reported outcome scales, and in the implementation of usual care.

Effects of plant-based proteins and handling stress on intestinal mucus microbiota in rainbow trout.

Via 16S rRNA gene amplicon sequencing, this study explores whether the gut mucus microbiota of rainbow trout is affected by the interaction of a plant-protein-based diet and a daily handling stressor (chasing with a fishing net) across two genetic lines (A, B). Initial body weights of fish from lines A and B were 124.7 g and 147.2 g, respectively. Fish were fed 1.5% of body weight per day for 59 days either of two experimental diets, differing in their fish meal [fishmeal-based diet (F): 35%, plant-based diet (V): 7%] and plant-based protein content (diet F: 47%, diet V: 73%). No diet- or stress-related effect on fish performance was observed at the end of the trial. However, we found significantly increased observed ASVs in the intestinal mucus of fish fed diet F compared to diet V. No significant differences in Shannon diversity could be observed between treatments. The autochthonous microbiota in fish fed with diet V was dominated by representatives of the genera Mycoplasma, Cetobacterium, and Ruminococcaceae, whereas Enterobacteriaceae and Photobacterium were significantly associated with diet F. The mucus bacteria in both genetic lines were significantly separated by diet, but neither by stress nor an interaction, as obtained via PERMANOVA. However, pairwise comparisons revealed that the diet effect was only significant in stressed fish. Therefore, our findings indicate that the mucus-associated microbiota is primarily modulated by the protein source, but this modulation is mediated by the stress status of the fish.

Genome-wide association analysis reveals the associations of NPHP4, TYW1-AUTS2 and SEMA6D for Behçet's disease and HLA-B*46:01 for its intestinal involvement.

BACKGROUND: Intestinal involvement in Behçet's disease (BD) is associated with poor prognosis and is more prevalent in East Asian than in Mediterranean populations. Identifying the genetic causes of intestinal BD is important for understanding the pathogenesis and for appropriate treatment of BD patients. METHODS: We performed genome-wide association studies (GWAS) and imputation/replication genotyping of human leukocyte antigen (HLA) alleles for 1,689 Korean and Turkish patients with BD (including 379 patients with intestinal BD) and 2,327 healthy controls, followed by replication using 593 Japanese patients with BD (101 patients with intestinal BD) and 737 healthy controls. Stratified cross-phenotype analyses were performed for 1) overall BD, 2) intestinal BD, and 3) intestinal BD without association of overall BD. RESULTS: We identified three novel genome-wide significant susceptibility loci including NPHP4 (rs74566205; P=1.36 × 10-8), TYW1-AUTS2 (rs60021986; P=1.14 × 10-9), and SEMA6D (rs4143322; P=5.54 × 10-9) for overall BD, and a new association with HLA-B*46:01 for intestinal BD (P=1.67 × 10-8) but not for BD without intestinal involvement. HLA peptide binding analysis revealed that Mycobacterial peptides, have a stronger binding affinity to HLA-B*46:01 compared to the known risk allele HLA-B*51:01. CONCLUSIONS: HLA-B*46:01 is associated with the development of intestinal BD; NPHP4, TYW1-AUTS2, and SEMA6D are susceptibility loci for overall BD.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability.

Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.