Reconstruction of the personal information from human genome reads in gut metagenome sequencing data

SWGTS-a platform for stream-based host DNA depletion

MOTIVATION

Microbial sequencing data from clinical samples is often contaminated with human sequences, which have to be removed prior to sharing. Existing methods for human read removal, however, are applicable only after the target dataset has been retrieved in its entirety, putting the recipient at least temporarily in control of a potentially identifiable genetic dataset with potential implications under regulatory frameworks such as the GDPR. In some instances, the ability to carry out stream-based host depletion as part of the data transfer process may be preferable.

RESULTS

We present SWGTS, a client-server application for the transfer and stream-based host depletion of sequencing reads. SWGTS enforces a robust upper bound on the maximum amount of human genetic data from any one client held in memory at any point in time by storing all incoming sequencing data in a limited-size, client-specific intermediate processing buffer, and by throttling the rate of incoming data if it exceeds the speed of host depletion carried out on the SWGTS server in the background. SWGTS exposes a HTTP-REST interface, is implemented using docker-compose, Redis and traefik, and requires less than 8 Gb of RAM for deployment. We demonstrate high filtering accuracy of SWGTS; incoming data transfer rates of up to 1.65 megabases per second in a conservative configuration; and mitigation of re-identification risks by the ability to limit the number of SNPs present on a popular population-scale genotyping array covered by reads in the SWGTS buffer to a low user-defined number, such as 10 or 100.

AVAILABILITY AND IMPLEMENTATION

SWGTS is available on GitHub: https://github.com/AlBi-HHU/swgts (https://doi.org/10.5281/zenodo.10891052). The repository also contains a jupyter notebook that can be used to reproduce all the benchmarks used in this article. All datasets used for benchmarking are publicly available.

Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Ten common issues with reference sequence databases and how to mitigate them

Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome

The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.

Mouse Adaptation of Human Inflammatory Bowel Diseases Microbiota Enhances Colonization Efficiency and Alters Microbiome Aggressiveness Depending on Recipient Colonic Inflammatory Environment

Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis. Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10-/- mice. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10-/- host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts. Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer.

Analysis of gut microbiome, host genetics, and plasma metabolites reveals gut microbiome-host interactions in the Japanese population

Interaction between the gut microbiome and host plays a key role in human health. Here, we perform a metagenome shotgun-sequencing-based analysis of Japanese participants to reveal associations between the gut microbiome, host genetics, and plasma metabolome. A genome-wide association study (GWAS) for microbial species (n = 524) identifies associations between the PDE1C gene locus and Bacteroides intestinalis and between TGIF2 and TGIF2-RAB5IF gene loci and Bacteroides acidifiaciens. In a microbial gene ortholog GWAS, agaE and agaS, which are related to the metabolism of carbohydrates forming the blood group A antigen, are associated with blood group A in a manner depending on the secretor status determined by the East Asian-specific FUT2 variant. A microbiome-metabolome association analysis (n = 261) identifies associations between bile acids and microbial features such as bile acid metabolism gene orthologs including bai and 7β-hydroxysteroid dehydrogenase. Our publicly available data will be a useful resource for understanding gut microbiome-host interactions in an underrepresented population.

[DNA everywhere]

Advanced analysis of environmental DNA for diversity monitoring using deep sequencing reveals the presence of human DNA in many samples connected to human activity.Moreover, this DNA is in relatively good condition and can be used for genetic survey of populations and even individuals. This opens many interesting scientific opportunities but also raises serious privacy issues.