Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Towards the biogeography of prokaryotic genes.

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1-3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

STAB2: an updated spatio-temporal cell atlas of the human and mouse brain.

The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset.

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics.

Brain imaging genomics is an emerging interdisciplinary field, where integrated analysis of multimodal medical image-derived phenotypes (IDPs) and multi-omics data, bridging the gap between macroscopic brain phenotypes and their cellular and molecular characteristics. This approach aims to better interpret the genetic architecture and molecular mechanisms associated with brain structure, function and clinical outcomes. More recently, the availability of large-scale imaging and multi-omics datasets from the human brain has afforded the opportunity to the discovering of common genetic variants contributing to the structural and functional IDPs of the human brain. By integrative analyses with functional multi-omics data from the human brain, a set of critical genes, functional genomic regions and neuronal cell types have been identified as significantly associated with brain IDPs. Here, we review the recent advances in the methods and applications of multi-omics integration in brain imaging analysis. We highlight the importance of functional genomic datasets in understanding the biological functions of the identified genes and cell types that are associated with brain IDPs. Moreover, we summarize well-known neuroimaging genetics datasets and discuss challenges and future directions in this field.

A survey on computational strategies for genome-resolved gut metagenomics.

Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.

Engineering of cytosine base editors with DNA damage minimization and editing scope diversification.

Cytosine base editors (CBEs), which enable precise C-to-T substitutions, have been restricted by potential safety risks, including DNA off-target edits, RNA off-target edits and additional genotoxicity such as DNA damages induced by double-strand breaks (DSBs). Though DNA and RNA off-target edits have been ameliorated via various strategies, evaluation and minimization of DSB-associated DNA damage risks for most CBEs remain to be resolved. Here we demonstrate that YE1, an engineered CBE variant with minimized DNA and RNA off-target edits, could induce prominent DSB-associated DNA damage risks, manifested as γH2AX accumulation in human cells. We then perform deaminase engineering for two deaminases lamprey LjCDA1 and human APOBEC3A, and generate divergent CBE variants with eliminated DSB-associated DNA damage risks, in addition to minimized DNA/RNA off-target edits. Furthermore, the editing scopes and sequence preferences of APOBEC3A-derived CBEs could be further diversified by internal fusion strategy. Taken together, this study provides updated evaluation platform for DSB-associated DNA damage risks of CBEs and further generates a series of safer toolkits with diversified editing signatures to expand their applications.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing.

MOTIVATION: Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. RESULTS: We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data. AVAILABILITY AND IMPLEMENTATION: SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.

Fast X-ray fluorescence microscopy provides high-throughput phenotyping of element distribution in seeds.

The concentration, chemical speciation, and spatial distribution of essential and toxic mineral elements in cereal seeds have important implications for human health. To identify genes responsible for element uptake, translocation, and storage, high-throughput phenotyping methods are needed to visualize element distribution and concentration in seeds. Here, we used X-ray fluorescence microscopy (µ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed an ImageJ-based pipeline to analyze the spatial distribution of elements. Using this method, we nondestructively scanned 4,190 ethyl methanesulfonate (EMS)-mutagenized M1 rice (Oryza sativa) seeds and 533 diverse rice accessions in a genome-wide association study (GWAS) panel to simultaneously measure concentrations and spatial distribution of elements in the embryo, endosperm, and aleurone layer. A total of 692 putative mutants and 65 loci associated with the spatial distribution of elements in rice seed were identified. This powerful method provides a basis for investigating the genetics and molecular mechanisms controlling the accumulation and spatial variations of mineral elements in plant seeds.

Efficacy and safety of perioperative application of ketamine on postoperative depression: A meta-analysis of randomized controlled studies.

Ketamine, a commonly used general anesthetic, can produce rapid and sustained antidepressant effect. However, the efficacy and safety of the perioperative application of ketamine on postoperative depression remains uncertain. We performed a meta-analysis to determine the effect of perioperative intravenous administration of ketamine on postoperative depression. Randomized controlled trials comparing ketamine with placebo in patients were included. Primary outcome was postoperative depression scores. Secondary outcomes included postoperative visual analog scale (VAS) scores for pain and adverse effects associated with ketamine. Fifteen studies with 1697 patients receiving ketamine and 1462 controls were enrolled. Compared with the controls, the ketamine group showed a reduction in postoperative depression scores, by a standardized mean difference (SMD) of -0.97, 95% confidence interval [CI, -1.27, -0.66], P < 0.001, I2 = 72% on postoperative day (POD) 1; SMD-0.65, 95% CI [-1.12, -0.17], P < 0.001, I2 = 94% on POD 3; SMD-0.30, 95% CI [-0.45, -0.14], P < 0.001, I2 = 0% on POD 7; and SMD-0.25, 95% CI [-0.38, -0.11], P < 0.001, I2 = 59% over the long term. Ketamine reduced VAS pain scores on POD 1 (SMD-0.93, 95% CI [-1.58, -0.29], P = 0.005, I2 = 97%), but no significant difference was found between the two groups on PODs 3 and 7 or over the long term. However, ketamine administration distinctly increased the risk of adverse effects, including nausea and vomiting (risk ratio [RR] 1.40, 95% CI [1.12, 1.75], P = 0.003, I2 = 30%), headache (RR 2.47, 95% CI [1.41, 4.32], P = 0.002, I2 = 19%), hallucination (RR 15.35, 95% CI [6.24, 37.34], P < 0.001, I2 = 89%), and dizziness (RR 3.48, 95% CI [2.68, 4.50], P < 0.001, I2 = 89%) compared with the controls. In conclusion, perioperative application of ketamine reduces postoperative depression and pain scores with increased risk of adverse effects.

Evaluation of models to predict prognosis in patients with advanced hepatocellular carcinoma treated with TACE combined with apatinib.

BACKGROUND: The HAP, Six-and-Twelve, Up to Seven, and ALBI scores have been substantiated as reliable prognostic markers in patients presenting with intermediate and advanced hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) treatment. Given this premise, our research aims to assess the predictive efficacy of these models in patients with intermediate and advanced HCC receiving a combination of TACE and Apatinib. Additionally, we have conducted a meticulous comparative analysis of these four scoring systems to discern their respective predictive capacities and efficacies in combined therapy. METHODS: Performing a retrospective analysis on the clinical data from 200 patients with intermediate and advanced HCC, we studied those who received TACE combined with Apatinib at the First Affiliated Hospital of the University of Science and Technology of China between June 2018 and December 2022. To identify the factors affecting survival, the study performed univariate and multivariate Cox regression analyses, with calculations of four different scores: HAP, Six-and-Twelve, Up to Seven, and ALBI. Lastly, Harrell's C-index was employed to compare the prognostic abilities of these scores. RESULTS: Cox proportional hazards model results revealed that the ALBI score, presence of portal vein tumor thrombus (PVTT, )and tumor size are independent determinants of prognostic survival. The Kaplan-Meier analyses showed significant differences in survival rates among patients classified by the HAP, Six-and-Twelve, Up to Seven, and ALBI scoring methods. Of the evaluated systems, the HAP scoring demonstrated greater prognostic precision, with a Harrell's C-index of 0.742, surpassing the alternative models (P < 0.05). In addition, an analysis of the area under the AU-ROC curve confirms the remarkable superiority of the HAP score in predicting short-term survival outcomes. CONCLUSION: Our study confirms the predictive value of HAP, Six-and-Twelve, Up to Seven, and ALBI scores in intermediate to advanced Hepatocellular Carcinoma (HCC) patients receiving combined Transarterial Chemoembolization (TACE) and Apatinib therapy. Notably, the HAP model excels in predicting outcomes for this specific HCC subgroup.

Associations of fatty acids with the risk of biliary tract calculus and inflammation: a Mendelian randomization study.

BACKGROUND: The presence of gallstones in both the gallbladder and bile ducts is referred to as cholelithiasis. The prevalence of cholecystolithiasis and bile duct stones differs. Observational and Mendelian randomization (MR) studies have elucidated the significant contributing role of numerous fatty acids (FAs) in the development of cholelithiasis. Despite numerous studies about cholelithiasis, evidence on the relationship between serum FA levels and cholecystolithiasis, as well as bile duct stones with or without inflammation, remains insufficient. METHODS: A two-sample MR study was designed to clarify the impact of serum FA levels on various bile duct inflammatory diseases. The summary statistics of single nucleotide polymorphisms (SNPs) associated with fatty acids were obtained from the UK Biobank (UKB) and included data from 114,999 participants. The researchers obtained GWAS summary statistics for cholecystolithiasis and bile duct stones in 463,010 and 361,194 European participants, including cases with and without inflammation. No sample overlap between the exposure and outcome was verified through the "mr-lap" package. The SNPs were screened to identify instrumental variables (IVs). Cochran's Q test was applied for heterogeneity assessment. Inverse variance weighting (IVW) (fixed effects or random effects), MR-Egger regression and weighted median methods were used for MR. Multivariable MR was applied to determine the direct effect of each exposure on the outcome. A false discovery rate (FDR) was applied to adjust for multiple testing correction based on the Benjamini-Hochberg method. Finally, the FinnGen Consortium was used to validate some results. RESULTS: The overall concentration of polyunsaturated fatty acids (PUFAs) in the serum was negatively associated with the risk of calculus of the gallbladder with acute cholecystitis (IVW, OR = 0.996, P = 0.038, CI 0.992-0.999; weighted median, OR = 0.995, P = 0.025, CI 0.991-0.999). The percentage of PUFAs to total monounsaturated fatty acids(MUFAs) (IVW, OR = 0.998, P = 0.045, CI 0.997-0.999) and the percentage of PUFAs to total FAs (IVW, OR = 0.997, P = 0.025, CI 0.995-0.999) had a protective role against cholecystitis. The percentage of PUFAs to total FAs had a protective role against calculus of the gallbladder without cholecystitis (IVW, OR = 0.995, P = 0.026, CI 0.990-0.999; MR Egger, OR = 0.99, P = 0.03, CI 0.982-0.998; weighted median, OR = 0.991, P = 5.41e-06, CI 0.988-0.995). Conversely, the percentage of MUFAs to total FAs increased the risk for cholecystitis (IVW, OR = 1.001, P = 0.034, CI 1.0001-1.002). However, there were no causal effects of the above exposures on the outcomes through multivariable MR and multiple testing correction. Finally, the causal effects of the above exposures on cholecystitis were validated in the FinnGen Consortium, which suggested that the percentage of PUFAs to total FAs (IVW, OR = 0.744, P = 0.021, CI 0.579-0.957) had a protective role against cholecystitis. CONCLUSION: These Mendelian randomization findings suggested that more attention should be focused on people who have low serum PUFA levels, which may have a potential role in the occurrence of calculus of the gallbladder or cholecystitis rather than calculus of the bile duct without cholangitis or cholecystitis.

mBodyMap: a curated database for microbes across human body and their associations with health and diseases.

mBodyMap is a curated database for microbes across the human body and their associations with health and diseases. Its primary aim is to promote the reusability of human-associated metagenomic data and assist with the identification of disease-associated microbes by consistently annotating the microbial contents of collected samples using state-of-the-art toolsets and manually curating the meta-data of corresponding human hosts. mBodyMap organizes collected samples based on their association with human diseases and body sites to enable cross-dataset integration and comparison. To help users find microbes of interest and visualize and compare their distributions and abundances/prevalence within different body sites and various diseases, the mBodyMap database is equipped with an intuitive interface and extensive graphical representations of the collected data. So far, it contains a total of 63 148 runs, including 14 401 metagenomes and 48 747 amplicons related to health and 56 human diseases, from within 22 human body sites across 136 projects. Also available in the database are pre-computed abundances and prevalence of 6247 species (belonging to 1645 genera) stratified by body sites and diseases. mBodyMap can be accessed at: https://mbodymap.microbiome.cloud.

GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.

MiR-221-5p regulates blood-brain barrier dysfunction through the angiopoietin-1/-2/Tie-2 signaling axis after subarachnoid hemorrhage.

AIM: To explore the potential role of microRNA miR-221-5p on the angiopoietin-1 (Ang-1)/Ang-2/Tie-2 signaling axis after subarachnoid hemorrhage (SAH) in a rat model. METHODS: Aspects of the rat's behavior were measured using the Kaoutzanis scoring system to test neurological responses. This included feeding behavior, body contraction, motor, and eye-opening responses. Brain sections were studied using transmission electron microscopy and Evans blue extravasation. Levels of Ang-1, Ang-2, and Tie-2 were determined by Western blot, while miR-221-5p was quantified using stem-loop real-time quantitative PCR (RT-qPCR). RESULTS: The SAH group responded worse to the neurological response test than the sham-operated group. The intercellular space was widened in the SAH group, but not in the sham-operated group. Evans blue dye leaked significantly more into brain tissue cells of the SAH group. Stem-loop qRT-PCR showed elevated miR-221-5p levels. Additionally, Ang-1 and Tie-2 were reduced but Ang-2 expression was increased after SAH. This led to a significant reduction of the Ang-1/Ang-2 ratio in the brain tissue, which was associated with the destruction of the blood-brain barrier. CONCLUSION: The data indicate that miR-221-5p might regulate blood-brain barrier dysfunction through the Ang-1/Ang-2/Tie-2 signaling axis, suggesting that it should be further investigated as a potential novel biomarker.

Unnatural Direct Interspecies Electron Transfer Enabled by Living Cell-Cell Click Chemistry.

Direct interspecies electron transfer (DIET) is essential for maintaining the function and stability of anaerobic microbial consortia. However, only limited natural DIET modes have been identified and DIET engineering remains highly challenging. In this study, an unnatural DIET between Shewanella oneidensis MR-1 (SO, electron donating partner) and Rhodopseudomonas palustris (RP, electron accepting partner) was artificially established by a facile living cell-cell click chemistry strategy. By introducing alkyne- or azide-modified monosaccharides onto the cell outer surface of the target species, precise covalent connections between different species in high proximity were realized through a fast click chemistry reaction. Remarkably, upon covalent connection, outer cell surface C-type cytochromes mediated DIET between SO and RP was achieved and identified, although this was never realized naturally. Moreover, this connection directly shifted the natural H2 mediated interspecies electron transfer (MIET) to DIET between SO and RP, which delivered superior interspecies electron exchange efficiency. Therefore, this work demonstrated a naturally unachievable DIET and an unprecedented MIET shift to DIET accomplished by cell-cell distance engineering, offering an efficient and versatile solution for DIET engineering, which extends our understanding of DIET and opens up new avenues for DIET exploration and applications.

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial.

OBJECTIVE: We aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis. DESIGN: Healthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups. RESULTS: Both interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species like Fusobacterium nucleatum and Streptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users. CONCLUSION: Our study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use. TRIAL REGISTRATION NUMBER: ChiCTR2300072310.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia.

MOTIVATION: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account. RESULTS: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.