GIMICA: host genetic and immune factors shaping human microbiota

SOD3 suppresses early cellular immune responses to parasite infection

Host immune responses are tightly controlled by various immune factors during infection, and protozoan parasites also manipulate the immune system to evade surveillance, leading to an evolutionary arms race in host‒pathogen interactions; however, the underlying mechanisms are not fully understood. We observed that the level of superoxide dismutase 3 (SOD3) was significantly elevated in both Plasmodium falciparum malaria patients and mice infected with four parasite species. SOD3-deficient mice had a substantially longer survival time and lower parasitemia than control mice after infection, whereas SOD3-overexpressing mice were much more vulnerable to parasite infection. We revealed that SOD3, secreted from activated neutrophils, bound to T cells, suppressed the interleukin-2 expression and concomitant interferon-gamma responses crucial for parasite clearance. Overall, our findings expose active fronts in the arms race between the parasites and host immune system and provide insights into the roles of SOD3 in shaping host innate immune responses to parasite infection.

A Comprehensive Pilot Study to Elucidate the Distinct Gut Microbial Composition and Its Functional Significance in Cardio-Metabolic Disease

Cardio-metabolic disease is a significant global health challenge with increasing prevalence. Recent research underscores the disruption of gut microbial balance as a key factor in disease susceptibility. We aimed to characterize the gut microbiota composition and function in cardio-metabolic disease and healthy controls. For this purpose, we collected stool samples of 18 subjects (12 diseased, 6 healthy) and we performed metagenomics analysis and functional prediction using QIIME2 and PICRUSt. Furthermore, we carried out assessments of microbe-gene interactions, gene ontology, and microbe-disease associations. Our findings revealed distinct microbial patterns in the diseased group, particularly evident in lower taxonomic levels with significant variations in 14 microbial features. The diseased cohort exhibited an enrichment of Lachnospiraceae family, correlating with obesity, insulin resistance, and metabolic disturbances. Conversely, reduced levels of Clostridium, Gemmiger, and Ruminococcus genera indicated a potential inflammatory state, linked to compromised butyrate production and gut permeability. Functional analyses highlighted dysregulated pathways in amino acid metabolism and energy equilibrium, with perturbations correlating with elevated branch-chain amino acid levels-a known contributor to insulin resistance and type 2 diabetes. These findings were consistent across biomarker assessments, microbe-gene associations, and gene ontology analyses, emphasizing the intricate interplay between gut microbial dysbiosis and cardio-metabolic disease progression. In conclusion, our study unveils significant shifts in gut microbial composition and function in cardio-metabolic disease, emphasizing the broader implications of microbial dysregulation. Addressing gut microbial balance emerges as a crucial therapeutic target in managing cardio-metabolic disease burden.

Distribution and roles of Ligilactobacillus murinus in hosts

Ligilactobacillus murinus, a member of the Ligilactobacillus genus, holds significant potential as a probiotic. While research on Ligilactobacillus murinus has been relatively limited compared to well-studied probiotic lactic acid bacteria such as Limosilactobacillus reuteri and Lactobacillus gasseri, a mounting body of evidence highlights its extensive involvement in host intestinal metabolism and immune activities. Moreover, its abundance exhibits a close correlation with intestinal health. Notably, beyond the intestinal context, Ligilactobacillus murinus is gaining recognition for its contributions to metabolism and regulation in the oral cavity, lungs, and vagina. As such, Ligilactobacillus murinus emerges as a potential probiotic candidate with a pivotal role in supporting host well-being. This review delves into studies elucidating the multifaceted roles of Ligilactobacillus murinus. It also examines its medicinal potential and associated challenges, underscoring the imperative to delve deeper into unraveling the mechanisms of its actions and exploring its health applications.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Mechanisms and Clinical Implications of Human Gut Microbiota-Drug Interactions in the Precision Medicine Era

The human gut microbiota, comprising trillions of microorganisms residing in the gastrointestinal tract, has emerged as a pivotal player in modulating various aspects of human health and disease. Recent research has shed light on the intricate relationship between the gut microbiota and pharmaceuticals, uncovering profound implications for drug metabolism, efficacy, and safety. This review depicted the landscape of molecular mechanisms and clinical implications of dynamic human gut Microbiota-Drug Interactions (MDI), with an emphasis on the impact of MDI on drug responses and individual variations. This review also discussed the therapeutic potential of modulating the gut microbiota or harnessing its metabolic capabilities to optimize clinical treatments and advance personalized medicine, as well as the challenges and future directions in this emerging field.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.

CDEMI: characterizing differences in microbial composition and function in microbiome data

Microbial communities influence host phenotypes through microbiota-derived metabolites and interactions between exogenous active substances (EASs) and the microbiota. Owing to the high dynamics of microbial community composition and difficulty in microbial functional analysis, the identification of mechanistic links between individual microbes and host phenotypes is complex. Thus, it is important to characterize variations in microbial composition across various conditions (for example, topographical locations, times, physiological and pathological conditions, and populations of different ethnicities) in microbiome studies. However, no web server is currently available to facilitate such characterization. Moreover, accurately annotating the functions of microbes and investigating the possible factors that shape microbial function are critical for discovering links between microbes and host phenotypes. Herein, an online tool, CDEMI, is introduced to discover microbial composition variations across different conditions, and five types of microbe libraries are provided to comprehensively characterize the functionality of microbes from different perspectives. These collective microbe libraries include (1) microbial functional pathways, (2) disease associations with microbes, (3) EASs associations with microbes, (4) bioactive microbial metabolites, and (5) human body habitats. In summary, CDEMI is unique in that it can reveal microbial patterns in distributions/compositions across different conditions and facilitate biological interpretations based on diverse microbe libraries. CDEMI is accessible at http://rdblab.cn/cdemi/.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Microbes communicate with each other by Quorum sensing (QS) languages. Here the authors construct a QS database and the QS communication network to decipher intricate QSbased communications and form one of the key knowledge maps for human gut microbiota.

MIAOME: Human Microbiome Affect The Host Epigenome

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/

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.

AMDB: a database of animal gut microbial communities with manually curated metadata

Variations in gut microbiota can be explained by animal host characteristics, including host phylogeny and diet. However, there are currently no databases that allow for easy exploration of the relationship between gut microbiota and diverse animal hosts. The Animal Microbiome Database (AMDB) is the first database to provide taxonomic profiles of the gut microbiota in various animal species. AMDB contains 2530 amplicon data from 34 projects with manually curated metadata. The total data represent 467 animal species and contain 10 478 bacterial taxa. This novel database provides information regarding gut microbiota structures and the distribution of gut bacteria in animals, with an easy-to-use interface. Interactive visualizations are also available, enabling effective investigation of the relationship between the gut microbiota and animal hosts. AMDB will contribute to a better understanding of the gut microbiota of animals. AMDB is publicly available without login requirements at http://leb.snu.ac.kr/amdb.

Association between Human Genetic Variants and the Vaginal Bacteriome of Pregnant Women

The influence of human genetic variants on the vaginal bacterial traits (VBTs) of pregnant women is still unknown. Using a genome-wide association approach based on the 16S rRNA bacteriome analysis, a total of 72 host genetic variant (single nucleotide polymorphisms [SNPs], indels, or copy number variations [CNVs])-VBT associations were found that reached the genome-wide significance level (P < 5 × 10^-8) with an acceptable genomic inflation factor λ of <1.1. The majority of these SNPs that reached the genome-wide significance level had a relatively low minor allele frequency (MAF), and only seven of them had MAFs greater than 0.05. rs303212, located at the IFIT1 gene on chromosome 10, was the most eye-catching variant, which had a genome-wide association with the relative abundance (RAB) of Actinobacteria and Bifidobacteriaceae and also had a suggestive association with the RAB of a few common vaginal bacteria including Actinobacteriota, Firmicutes, Lactobacillus, and Gardnerella vaginalis and the beta diversity weighted UniFrac (P < 1 × 10^-5). The findings of the study suggest that the vaginal bacteriome may be influenced by a number of genetic variants across the human genome and that interferon signaling may have an important influence on vaginal bacterial communities during pregnancy. IMPORTANCE Knowledge about the influence of host genetics on the vaginal bacteriome in pregnancy is still limited. Although a number of environmental and behavioral factors may exert influences on the structure of vaginal bacterial communities, the vaginal bacteriome often undergoes a relatively fixed transition to a more stable and less diverse state as the menstrual cycle stops, which raises questions on the effects of human genetics. We utilized a genome-wide approach to identify the associations between genetic variants and multiple VBTs and performed enrichment analyses. The human genetics during pregnancy may be involved in multiple pathways. The results may disclose innate functional factors involved in shaping the vaginal bacteriome during pregnancy and provide insight into the establishment of specific strategies for prevention and clinical treatment of pregnancy complications.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

High fat diet, gut microbiome and gastrointestinal cancer

Gastrointestinal cancer is currently one of the main causes of cancer death, with a large number of cases and a wide range of lesioned sites. A high fat diet, as a public health problem, has been shown to be correlated with various digestive system diseases and tumors, and can accelerate the occurrence of cancer due to inflammation and altered metabolism. The gut microbiome has been the focus of research in recent years, and associated with cell damage or tumor immune microenvironment changes via direct or extra-intestinal effects; this may facilitate the occurrence and development of gastrointestinal tumors. Based on research showing that both a high fat diet and gut microbes can promote the occurrence of gastrointestinal tumors, and that a high fat diet imbalances intestinal microbes, we propose that a high fat diet drives gastrointestinal tumors by changing the composition of intestinal microbes.

The miRNA: a small but powerful RNA for COVID-19

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

Genome-Wide Identification and Analysis of the Methylation of lncRNAs and Prognostic Implications in the Glioma

Glioma is characterized by rapid cell proliferation and extensive infiltration among brain tissues, but the molecular pathology has been still poorly understood. Previous studies found that DNA methylation modifications play a key role in contributing to the pathogenesis of glioma. On the other hand, long noncoding RNAs (lncRNAs) has been discovered to be associated with some key tumorigenic processes of glioma. Moreover, genomic methylation can influence expression and functions of lncRNAs, which contributes to the pathogenesis of many complex diseases. However, to date, no systematic study has been performed to detect the methylation of lncRNAs and its influences in glioma on a genome-wide scale. Here, we selected the methylation data, clinical information, expression of lncRNAs, and DNA methylation regulatory proteins of 537 glioma patients from TCGA and TANRIC databases. Then, we performed a differential analysis of lncRNA expression and methylated regions between low-grade glioma (LGG) and glioblastoma multiform (GBM) subjects, respectively. Next, we further identified and verified potential key lncRNAs contributing the pathogenesis of glioma involved in methylation modifications by an annotation and correlation analysis, respectively. In total, 18 such lncRNAs were identified, and 7 of them have been demonstrated to be functionally linked to the pathogenesis of glioma by previous studies. Finally, by the univariate Cox regression, LASSO regression, clinical correlation, and survival analysis, we found that all these 18 lncRNAs are high-risk factors for clinical prognosis of glioma. In summary, this study provided a strategy to explore the influence of lncRNA methylation on glioma, and our findings will be benefit to improve understanding of its pathogenesis.

The 2021 Nucleic Acids Research database issue and the online molecular biology database collection

The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.

Genome-Wide Analysis of LysM-Containing Gene Family in Wheat: Structural and Phylogenetic Analysis during Development and Defense

The lysin motif (LysM) family comprise a number of defense proteins that play important roles in plant immunity. The LysM family includes LysM-containing receptor-like proteins (LYP) and LysM-containing receptor-like kinase (LYK). LysM generally recognizes the chitin and peptidoglycan derived from bacteria and fungi. Approximately 4000 proteins with the lysin motif (Pfam PF01476) are found in prokaryotes and eukaryotes. Our study identified 57 LysM genes and 60 LysM proteins in wheat and renamed these genes and proteins based on chromosome distribution. According to the phylogenetic and gene structure of intron-exon distribution analysis, the 60 LysM proteins were classified into seven groups. Gene duplication events had occurred among the LysM family members during the evolution process, resulting in an increase in the LysM gene family. Synteny analysis suggested the characteristics of evolution of the LysM family in wheat and other species. Systematic analysis of these species provided a foundation of LysM genes in crop defense. A comprehensive analysis of the expression and cis-elements of LysM gene family members suggested that they play an essential role in defending against plant pathogens. The present study provides an overview of the LysM family in the wheat genome as well as information on systematic, phylogenetic, gene duplication, and intron-exon distribution analyses that will be helpful for future functional analysis of this important protein family, especially in Gramineae species.