gutMDisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

To analyze the relationship between gut microbiota, metabolites and migraine: a two-sample Mendelian randomization study

Background

It has been suggested in several observational studies that migraines are associated with the gut microbiota. It remains unclear, however, how the gut microbiota and migraines are causally related.

Methods

We performed a bidirectional two-sample mendelian randomization study. Genome-wide association study (GWAS) summary statistics for the gut microbiota were obtained from the MiBioGen consortium (n = 18,340) and the Dutch Microbiota Project (n = 7,738). Pooled GWAS data for plasma metabolites were obtained from four different human metabolomics studies. GWAS summary data for migraine (cases = 48,975; controls = 450,381) were sourced from the International Headache Genetics Consortium. We used inverse-variance weighting as the primary analysis. Multiple sensitivity analyses were performed to ensure the robustness of the estimated results. We also conducted reverse mendelian randomization when a causal relationship between exposure and migraine was found.

Results

LachnospiraceaeUCG001 (OR = 1.12, 95% CI: 1.05-1.20) was a risk factor for migraine. Blautia (OR = 0.93, 95% CI: 0.88-0.99), Eubacterium (nodatum group; OR = 0.94, 95% CI: 0.90-0.98), and Bacteroides fragilis (OR = 0.97, 95% CI: 0.94-1.00) may have a suggestive association with a lower migraine risk. Functional pathways of methionine synthesis (OR = 0.89, 95% CI: 0.83-0.95) associated with microbiota abundance and plasma hydrocinnamate (OR = 0.85, 95% CI: 0.73-1.00), which are downstream metabolites of Blautia and Bacteroides fragilis, respectively, may also be associated with lower migraine risk. No causal association between migraine and the gut microbiota or metabolites was found in reverse mendelian randomization analysis. Both significant horizontal pleiotropy and significant heterogeneity were not clearly identified.

Conclusion

This Mendelian randomization analysis showed that LachnospiraceaeUCG001 was associated with an increased risk of migraine, while some bacteria in the gut microbiota may reduce migraine risk. These findings provide a reference for a deeper comprehension of the role of the gut-brain axis in migraine as well as possible targets for treatment interventions.

Promising Intestinal Microbiota Associated with Clinical Characteristics of COPD Through Integrated Bioinformatics Analysis

Introduction

Chronic obstructive pulmonary disease (COPD), an incurable chronic respiratory disease, has become a major public health problem. The relationship between the composition of intestinal microbiota and the important clinical factors affecting COPD remains unclear. This study aimed to identify specific intestinal microbiota with high clinical diagnostic value for COPD.

Methods

The fecal microbiota of patients with COPD and healthy individuals were analyzed by 16S rDNA sequencing. Random forest classification was performed to analyze the different intestinal microbiota. Spearman correlation was conducted to analyze the correlation between different intestinal microbiota and clinical characteristics. A microbiota-disease network diagram was constructed using the gut MDisorder database to identify the possible pathogenesis of intestinal microorganisms affecting COPD, screen for potential treatment, and guide future research.

Results

No significant difference in biodiversity was shown between the two groups but significant differences in microbial community structure. Fifteen genera of bacteria with large abundance differences were identified, including Bacteroides, Prevotella, Lachnospira, and Parabacteroides. Among them, the relative abundance of Lachnospira and Coprococcus was negatively related to the smoking index and positively related to lung function results. By contrast, the relative abundance of Parabacteroides was positively correlated with the smoking index and negatively correlated with lung function findings. Random forest classification showed that Lachnospira was the genus most capable of distinguishing between patients with COPD and healthy individuals suggesting it may be a potential biomarker of COPD. A Lachnospira disease network diagram suggested that Lachnospira decreased in some diseases, such as asthma, diabetes mellitus, and coronavirus disease 2019 (COVID-19), and increased in other diseases, such as irritable bowel syndrome, hypertension, and bovine lichen.

Conclusion

The dominant intestinal microbiota with significant differences is related to the clinical characteristics of COPD, and the Lachnospira has the potential value to identify COPD.

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.

Progress on network modeling and analysis of gut microecology: a review

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

An Inflammatory Bowel Diseases Integrated Resources Portal (IBDIRP)

IBD, including ulcerative colitis and Crohn's disease, is a chronic and debilitating gastrointestinal disorder that affects millions of people worldwide. Research on IBD has generated massive amounts of data, including literature, metagenomics, metabolomics, bioresources and databases. We aim to create an IBD Integrated Resources Portal (IBDIRP) that provides the most comprehensive resources for IBD. An integrated platform was developed that provides information on different aspects of IBD research resources, such as single-nucleotide polymorphisms (SNPs), genes, transcriptome, microbiota, metabolomics, single cells and other resources. Valuable and comprehensive IBD-related data were collected from PubMed, Google, GMrepo, gutMega, gutMDisorder, Single Cell Portal and other sources. Then, the data were systematically sorted, and these resources were manually curated. We systematically sorted and cataloged more than 320 unique risk SNPs associated with IBD in the SNP section. We presented over 289 IBD-related genes based on the database collection in the gene section. We also obtained 153 manually curated IBD transcriptomics data, including 12 388 samples, on the Gene Expression Omnibus database. The sorted IBD-related microbiota data from three primary microbiome databases (GMrepo, gutMega and gutMDisorder) were available for download. We selected 23 149 IBD-related taxonomic records from these databases. Additionally, we collected 24 IBD metabolomics studies with 2896 participants in the metabolomics section. We introduced two interactive single-cell data plug-in units that provided data visualization based on cells and genes. Finally, we listed 18 significant IBD web resources, such as the official European Crohn's and Colitis Organisation and International Organization for the Study of IBD websites, IBD scoring tools, IBD genetic and multi-omics resources, IBD biobanks and other useful research resources. The IBDIRP website is the first integrated resource for global IBD researchers. This portal will help researchers by providing comprehensive knowledge and enabling them to reinforce the multidimensional impression of IBD. The IBDIRP website is accessible via www.ibdirp.com Database URL: www.ibdirp.com.

The distribution profiles of tetracycline resistance genes in rice: Comparisons using four genotypes

The potential transmission of antibiotic resistance genes (ARGs) from the rhizosphere to plants and humans poses a significant concern. This study aims to investigate the distribution of tetracycline resistance genes (TRGs) in rice using four genotypes and identify the primary source of TRGs in grains. Quantitative polymerase chain reaction (qPCR) was employed to determine the abundance of seven TRGs and intI1 in four rice varieties and three partitions during the jointing and heading stages, respectively. The analysis of the bacterial community was conducted to elucidate the underlying mechanism of the profiles of TRGs. It was observed that tetZ was predominantly present in the rhizosphere and endoroot, whereas tetX became dominant in grains. The relative abundances of TRGs and intI1 exhibited significant variations across both the variety and partition. However, no significant differences were observed in grains, where the abundances of TRGs were several orders of magnitude lower compared to those in the rhizosphere. Nevertheless, the potential risk of the dissemination of TRGs to humans, particularly those carried by potential pathogens in grains, warrants attention. The increased likelihood of TRGs accumulation in the rhizosphere and endoroot of hybrid rice varieties, as opposed to japonica varieties, may be attributed to the heightened metabolic activities of their roots. The significant associations observed between intI1 and TRGs, coupled with the substantial alterations in potential hosts for intI1 across various treatments, indicate that intI1-mediated horizontal gene transfer plays a role in the diverse range of bacterial hosts for TRGs. The study also revealed that rhizosphere bacteria during the jointing stage serve as the primary contributors of TRGs in grains through the endoroot junction. The findings indicate that Japonica rice varieties exhibit superior control over TRGs compared to hybrid varieties, emphasizing the need for early interventions throughout the entire growth period of rice.

The African Human Microbiome Portal: a public web portal of curated metagenomic metadata

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL:  https://microbiome.h3abionet.org/.

NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network

The prediction of potential microbe-drug associations is of great value for drug research and development, especially, methods, based on deep learning, have been achieved significant improvement in bio-medicine. In this manuscript, we proposed a novel computational model named NMGMDA based on the nuclear norm minimization and graph attention network to infer latent microbe-drug associations. Firstly, we created a heterogeneous microbe-drug network in NMGMDA by fusing the drug and microbe similarities with the established drug-microbe associations. After this, by using GAT and NNM to calculate the predict scores. Lastly, we created a fivefold cross validation framework to assess the new model NMGMDA's progressiveness. According to the simulation results, NMGMDA outperforms some of the most advanced methods, with a reliable AUC of 0.9946 on both MDAD and aBioflm databases. Furthermore, case studies on Ciprofloxacin, Moxifoxacin, HIV-1 and Mycobacterium tuberculosis were carried out in order to assess the effectiveness of NMGMDA even more. The experimental results demonstrated that, following the removal of known correlations from the database, 16 and 14 medications as well as 19 and 17 microbes in the top 20 predictions were validated by pertinent literature. This demonstrates the potential of our new model, NMGMDA, to reach acceptable prediction performance.

Lactobacillus johnsonii N6.2 phospholipids induce immature-like dendritic cells with a migratory-regulatory-like transcriptional signature

Shifts in the gut microbiota composition, called dysbiosis, have been directly associated with acute and chronic diseases. However, the underlying biological systems connecting gut dysbiosis to systemic inflammatory pathologies are not well understood. Phospholipids (PLs) act as precursors of both, bioactive inflammatory and resolving mediators. Their dysregulation is associated with chronic diseases including cancer. Gut microbial-derived lipids are structurally unique and capable of modulating host's immunity. Lactobacillus johnsonii N6.2 is a Gram-positive gut symbiont with probiotic characteristics. L. johnsonii N6.2 reduces the incidence of autoimmunity in animal models of Type 1 Diabetes and improves general wellness in healthy volunteers by promoting, in part, local and systemic anti-inflammatory responses. By utilizing bioassay-guided fractionation methods with bone marrow-derived dendritic cells (BMDCs), we report here that L. johnsonii N6.2 purified lipids induce a transcriptional signature that resembles that of migratory (mig) DCs. RNAseq-based analysis showed that BMDCs stimulated with L. johnsonii N6.2 total lipids upregulate maturation-mig related genes Cd86, Cd40, Ccr7, Icam1 along with immunoregulatory genes including Itgb8, Nfkbiz, Jag1, Adora2a, IL2ra, Arg1, and Cd274. Quantitative reverse transcription (qRT)-PCR analysis indicated that PLs are the bioactive lipids triggering the BMDCs response. Antibody-blocking of surface Toll-like receptor (TLR)2 resulted in boosted PL-mediated upregulation of pro-inflammatory Il6. Chemical inhibition of the IKKα kinase from the non-canonical NF-κB pathway specifically restricted upregulation of Il6 and Tnf. Phenotypically, PL-stimulated BMDCs displayed an immature like-phenotype with significantly increased surface ICAM-1. This study provides insight into the immunoregulatory capacity of Gram-positive, gut microbial-derived phospholipids on innate immune responses.

The gut microbiome dysbiosis and regulation by fecal microbiota transplantation: umbrella review

Background

Gut microbiome dysbiosis has been implicated in various gastrointestinal and extra-gastrointestinal diseases, but evidence on the efficacy and safety of fecal microbiota transplantation (FMT) for therapeutic indications remains unclear.

Methods

The gutMDisorder database was used to summarize the associations between gut microbiome dysbiosis and diseases. We performed an umbrella review of published meta-analyses to determine the evidence synthesis on the efficacy and safety of FMT in treating various diseases. Our study was registered in PROSPERO (CRD42022301226).

Results

Gut microbiome dysbiosis was associated with 117 gastrointestinal and extra-gastrointestinal. Colorectal cancer was associated with 92 dysbiosis. Dysbiosis involving Firmicutes (phylum) was associated with 34 diseases. We identified 62 published meta-analyses of FMT. FMT was found to be effective for 13 diseases, with a 95.56% cure rate (95% CI: 93.88-97.05%) for recurrent Chloridoids difficile infection (rCDI). Evidence was high quality for rCDI and moderate to high quality for ulcerative colitis and Crohn's disease but low to very low quality for other diseases.

Conclusion

Gut microbiome dysbiosis may be implicated in numerous diseases. Substantial evidence suggests FMT improves clinical outcomes for certain indications, but evidence quality varies greatly depending on the specific indication, route of administration, frequency of instillation, fecal preparation, and donor type. This variability should inform clinical, policy, and implementation decisions regarding FMT.

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.

Gut Phageome-An Insight into the Role and Impact of Gut Microbiome and Their Correlation with Mammal Health and Diseases

The gut microbiota, including bacteria, archaea, fungi, and viruses, compose a diverse mammalian gut environment and are highly associated with host health. Bacteriophages, the viruses that infect bacteria, are the primary members of the gastrointestinal virome, known as the phageome. However, our knowledge regarding the gut phageome remains poorly understood. In this review, the critical role of the gut phageome and its correlation with mammalian health were summarized. First, an overall profile of phages across the gastrointestinal tract and their dynamic roles in shaping the surrounding microorganisms was elucidated. Further, the impacts of the gut phageome on gastrointestinal fitness and the bacterial community were highlighted, together with the influence of diets on the gut phageome composition. Additionally, new reports on the role of the gut phageome in the association of mammalian health and diseases were reviewed. Finally, a comprehensive update regarding the advanced phage benchwork and contributions of phage-based therapy to prevent/treat mammalian diseases was provided. This study provides insights into the role and impact of the gut phagenome in gut environments closely related to mammal health and diseases. The findings provoke the potential applications of phage-based diagnosis and therapy in clinical and agricultural fields. Future research is needed to uncover the underlying mechanism of phage-bacterial interactions in gut environments and explore the maintenance of mammalian health via phage-regulated gut microbiota.

Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar's test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar's test results found statistically significant differences between different Machine Learning approaches.

A display and analysis platform for gut microbiomes of minority people and phenotypic data in China

The minority people panmicrobial community database (MPPCD website: http://mppmcdb.cloudna.cn/ ) is the first microbe-disease association database of Chinese ethnic minorities. To research the relationships between intestinal microbes and diseases/health in the ethnic minorities, we collected the microbes of the Han people for comparison. Based on the data, such as age, among the different ethnic groups of the different regions of Sichuan Province, MPPCD not only provided the gut microbial composition but also presented the relative abundance value at the phylum, class, order, family and genus levels in different groups. In addition, differential analysis was performed in different microbes in the two different groups, which contributed to exploring the difference in intestinal microbe structures between the two groups. Meanwhile, a series of related factors, including age, sex, body mass index, ethnicity, physical condition, and living altitude, were included in the MPPCD, with special focus on living altitude. To date, this is the first intestinal microbe database to introduce altitude features. In conclusion, we hope that MPPCD will serve as a fundamental research support for the relationship between human gut microbes and host health and disease, especially in ethnic minorities.

GMMAD: a comprehensive database of human gut microbial metabolite associations with diseases

BACKGROUND

The natural products, metabolites, of gut microbes are crucial effect factors on diseases. Comprehensive identification and annotation of relationships among disease, metabolites, and microbes can provide efficient and targeted solutions towards understanding the mechanism of complex disease and development of new markers and drugs.

RESULTS

We developed Gut Microbial Metabolite Association with Disease (GMMAD), a manually curated database of associations among human diseases, gut microbes, and metabolites of gut microbes. Here, this initial release (i) contains 3,836 disease-microbe associations and 879,263 microbe-metabolite associations, which were extracted from literatures and available resources and then experienced our manual curation; (ii) defines an association strength score and a confidence score. With these two scores, GMMAD predicted 220,690 disease-metabolite associations, where the metabolites all belong to the gut microbes. We think that the positive effective (with both scores higher than suggested thresholds) associations will help identify disease marker and understand the pathogenic mechanism from the sense of gut microbes. The negative effective associations would be taken as biomarkers and have the potential as drug candidates. Literature proofs supported our proposal with experimental consistence; (iii) provides a user-friendly web interface that allows users to browse, search, and download information on associations among diseases, metabolites, and microbes. The resource is freely available at http://guolab.whu.edu.cn/GMMAD .

CONCLUSIONS

As the online-available unique resource for gut microbial metabolite-disease associations, GMMAD is helpful for researchers to explore mechanisms of disease- metabolite-microbe and screen the drug and marker candidates for different diseases.

Gut Microbiota and Respiratory Infections: Insights from Mendelian Randomization

The role of the gut microbiota in modulating the risk of respiratory infections has garnered increasing attention. However, conventional clinical trials have faced challenges in establishing the precise relationship between the two. In this study, we conducted a Mendelian randomization analysis with single nucleotide polymorphisms employed as instrumental variables to assess the causal links between the gut microbiota and respiratory infections. Two categories of bacteria, family Lactobacillaceae and genus Family XIII AD3011, were causally associated with the occurrence of upper respiratory tract infections (URTIs). Four categories of gut microbiota existed that were causally associated with lower respiratory tract infections (LRTIs), with order Bacillales and genus Paraprevotella showing a positive association and genus Alistipes and genus Ruminococcaceae UCG009 showing a negative association. The metabolites and metabolic pathways only played a role in the development of LRTIs, with the metabolite deoxycholine acting negatively and menaquinol 8 biosynthesis acting positively. The identification of specific bacterial populations, metabolites, and pathways may provide new clues for mechanism research concerning therapeutic interventions for respiratory infections. Future research should focus on elucidating the potential mechanisms regulating the gut microbiota and developing effective strategies to reduce the incidence of respiratory infections. These findings have the potential to significantly improve global respiratory health.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Achieving pan-microbiome biological insights via the dbBact knowledge base

16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact - a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes.

Leveraging pre-trained language models for mining microbiome-disease relationships

BACKGROUND

The growing recognition of the microbiome's impact on human health and well-being has prompted extensive research into discovering the links between microbiome dysbiosis and disease (healthy) states. However, this valuable information is scattered in unstructured form within biomedical literature. The structured extraction and qualification of microbe-disease interactions are important. In parallel, recent advancements in deep-learning-based natural language processing algorithms have revolutionized language-related tasks such as ours. This study aims to leverage state-of-the-art deep-learning language models to extract microbe-disease relationships from biomedical literature.

RESULTS

In this study, we first evaluate multiple pre-trained large language models within a zero-shot or few-shot learning context. In this setting, the models performed poorly out of the box, emphasizing the need for domain-specific fine-tuning of these language models. Subsequently, we fine-tune multiple language models (specifically, GPT-3, BioGPT, BioMedLM, BERT, BioMegatron, PubMedBERT, BioClinicalBERT, and BioLinkBERT) using labeled training data and evaluate their performance. Our experimental results demonstrate the state-of-the-art performance of these fine-tuned models ( specifically GPT-3, BioMedLM, and BioLinkBERT), achieving an average F1 score, precision, and recall of over [Formula: see text] compared to the previous best of  0.74.

CONCLUSION

Overall, this study establishes that pre-trained language models excel as transfer learners when fine-tuned with domain and problem-specific data, enabling them to achieve state-of-the-art results even with limited training data for extracting microbiome-disease interactions from scientific publications.

Potential mechanism of pyrotinib-induced diarrhea was explored by gut microbiome and ileum metabolomics

Pyrotinib is a novel epidermal growth factor receptor/human epidermal growth factor receptor-2 (HER2) tyrosine kinase inhibitor that exhibited clinical efficacy in patients with HER2-positive breast cancer and HER2-mutant/amplified lung cancer. However, severe diarrhea adverse responses preclude its practical use. At present, the mechanism of pyrotinib-induced diarrhea is unknown and needs further study. First, to develop a suitable and reproducible animal model, we compared the effects of different doses of pyrotinib (20, 40, 60 and 80 mg/kg) in Wistar rats. Second, we used this model to examine the intestinal toxicity of pyrotinib. Finally, the mechanism underlying pyrotinib-induced diarrhea was fully studied using gut microbiome and host intestinal tissue metabolomics profiling. Reproducible diarrhea occurred in rats when they were given an 80 mg/kg daily dose of pyrotinib. Using the pyrotinib-induced model, we observed that Lachnospiraceae and Acidaminococcaceae decreased in the pyrotinib groups, whereas Enterobacteriaceae, Helicobacteraceae and Clostridiaceae increased at the family level by 16S rRNA gene sequence. Multiple bioinformatics methods revealed that glycocholic acid, ursodeoxycholic acid and cyclic AMP increased in the pyrotinib groups, whereas kynurenic acid decreased, which may be related to the pathogenesis of pyrotinib-induced diarrhea. Additionally, pyrotinib-induced diarrhea may be associated with a number of metabolic changes mediated by the gut microbiome, such as Primary bile acid biosynthesis. We reported the establishment of a reproducible pyrotinib-induced animal model for the first time. Furthermore, we concluded from this experiment that gut microbiome imbalance and changes in related metabolites are significant contributors to pyrotinib-induced diarrhea.

Gut microbiota crosstalk mechanisms are key in pulmonary hypertension: The involvement of melatonin is instrumental too

The microbiota refers to a plethora of microorganisms with a gene pool of approximately three million, which inhabits the human gastrointestinal tract or gut. The latter, not only promotes the transport of nutrients, ions, and fluids from the lumen to the internal environment but is linked with the development of diseases including coronary artery disease, heart failure, and lung diseases. The exact mechanism of how the microbiota achieves crosstalk between itself and distant organs/tissues is not clear, but factors released to other organs may play a role, like inflammatory and genetic factors, and now we highlight melatonin as a novel mediator of the gut-lung crosstalk. Melatonin is present in high concentrations in the gut and the lung and has recently been linked to the pathogenesis of pulmonary hypertension (PH). In this comprehensive review of the literature, we suggest that melatonin is an important link between the gut microbiota and the development of PH (where suppressed melatonin-crosstalk between the gut and lungs could promote the development of PH). More studies are needed to investigate the link between the gut microbiota, melatonin and PH. Studies could also investigate whether microbiota genes play a role in the epigenetic aspects of PH. This is relevant because, for example, dysbiosis (caused by epigenetic factors) could reduce melatonin signaling between the gut and lungs, reduce subcellular melatonin concentrations in the gut/lungs, or reduce melatonin serum levels secondary to epigenetic factors. This area of research is largely unexplored and further studies are warranted.

SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM

Introduction

Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious.

Methods

Here, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine.

Results

The proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation.

Conclusion

We anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.

Effects of microbial-derived biotics (meta/pharma/post-biotics) on the modulation of gut microbiome and metabolome; general aspects and emerging trends

Potential effects of metabiotics (probiotics effector molecules or signaling factors), pharmabiotics (pro-functional metabolites produced by gut microbiota (GMB)) and postbiotics (multifunctional metabolites and structural compounds of food-grade microorganisms) on GMB have been rarely reviewed. These multifunctional components have several promising capabilities for prevention, alleviation and treatment of some diseases or disorders. Correlations between these essential biotics and GMB are also very interesting and important in human health and nutrition. Furthermore, these natural bioactives are involved in modulation of the immune function, control of metabolic dysbiosis and regulation of the signaling pathways. This review discusses the potential of meta/pharma/post-biotics as new classes of pharmaceutical agents and their effective mechanisms associated with GMB-host cell to cell communications with therapeutic benefits which are important in balance and the integrity of the host microbiome. In addition, cutting-edge findings about bioinformatics /metabolomics analyses related to GMB and these essential biotics are reviewed.

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.

Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning

Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.

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/.

Parkinson's Disease, It Takes Guts: The Correlation between Intestinal Microbiome and Cytokine Network with Neurodegeneration

Parkinson's disease is a progressive neurodegenerative disorder with motor, physical and behavioral symptoms that can have a profound impact on the patient's quality of life. Most cases are idiopathic, and the exact mechanism of the disease's cause is unknown. The current hypothesis focuses on the gut-brain axis and states that gut microbiota dysbiosis can trigger inflammation and advances the development of Parkinson's disease. This systematic review presents the current knowledge of gut microbiota analysis and inflammation based on selected studies on Parkinson's patients and experimental animal models. Changes in gut microbiota correlate with Parkinson's disease, but only a few studies have considered inflammatory modulators as important triggers of the disease. Nevertheless, it is evident that proinflammatory cytokines and chemokines are induced in the gut, the circulation, and the brain before the development of the disease's neurological symptoms and exacerbate the disease. Increased levels of tumor necrosis factor, interleukin-1β, interleukin-6, interleukin-17A and interferon-γ can correlate with altered gut microbiota. Instead, treatment of gut dysbiosis is accompanied by reduced levels of inflammatory mediators in specific tissues, such as the colon, brain and serum and/or cerebrospinal fluid. Deciphering the role of the immune responses and the mechanisms of the PD-associated gut microbiota will assist the interpretation of the pathogenesis of Parkinson's and will elucidate appropriate therapeutic strategies.

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations.

Availability

The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.

Quorum sensing-based interactions among drugs, microbes, and diseases

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

MADET: a Manually Curated Knowledge Base for Microbiomic Effects on Efficacy and Toxicity of Anticancer Treatments

A plethora of studies have reported the associations between microbiota and multiple diseases, leading to the development of at least four databases to demonstrate microbiota-disease associations, i.e., gutMDisorder, mBodyMap, Gmrepo, and Amadis. Moreover, gut microbiota mediates drug efficacy and toxicity, whereas a comprehensive database to elucidate the microbiota-drug associations is lacking. Here, we report an open-access knowledge base, MADET (Microbiomics of Anticancer Drug Efficacy and Toxicity), which harbors 483 manually annotated microbiota-drug associations from 26 studies. MADET provides user-friendly functions allowing users to freely browse, search, and download data conveniently from the database. Users can customize their search filters in MADET using different types of keywords, including bacterial name (e.g., Akkermansia muciniphila), anticancer treatment (e.g., anti-PD-1 therapy), and cancer type (e.g., lung cancer) with different types of experimental evidence of microbiota-drug association and causation. We have also enabled user submission to further enrich the data documented in MADET. The MADET database is freely available at https://www.madet.info. We anticipate that MADET will serve as a useful resource for a better understanding of microbiota-drug associations and facilitate the future development of novel biomarkers and live biotherapeutic products for anticancer therapies. IMPORTANCE Human microbiota plays an important role in mediating drug efficacy and toxicity in anticancer treatment. In this work, we developed a comprehensive online database, which documents over 480 microbiota-drug associations manually curated from 26 research articles. Users can conveniently browse, search, and download the data from the database. Search filters can be customized using different types of keywords, including bacterial name (e.g., Akkermansia muciniphila), anticancer treatment (e.g., anti-PD-1 therapy), and cancer type (e.g., lung cancer), with different types of experimental evidence of microbiota-drug association. We anticipate that this database will serve as a convenient platform for facilitating research on microbiota-drug associations, including the development of novel biomarkers for predicting drug outcomes as well as novel live biotherapeutic products for improving the outcomes of anticancer drugs.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion

MOTIVATION

Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed.

RESULTS

In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

gutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

microbioTA: an atlas of the microbiome in multiple disease tissues of Homo sapiens and Mus musculus

microbioTA (http://bio-annotation.cn/microbiota) was constructed to provide a comprehensive, user-friendly resource for the application of microbiome data from diseased tissues, helping users improve their general knowledge and deep understanding of tissue-derived microbes. Various microbes have been found to colonize cancer tissues and play important roles in cancer diagnoses and outcomes, with many studies focusing on developing better cancer-related microbiome data. However, there are currently no independent, comprehensive open resources cataloguing cancer-related microbiome data, which limits the exploration of the relationship between these microbes and cancer progression. Given this, we propose a new strategy to re-align the existing next-generation sequencing data to facilitate the mining of hidden sequence data describing the microbiome to maximize available resources. To this end, we collected 417 publicly available datasets from 25 human and 14 mouse tissues from the Gene Expression Omnibus database and use these to develop a novel pipeline to re-align microbiome sequences facilitating in-depth analyses designed to reveal the microbial profile of various cancer tissues and their healthy controls. microbioTA is a user-friendly online platform which allows users to browse, search, visualize, and download microbial abundance data from various tissues along with corresponding analysis results, aimimg at providing a reference for cancer-related microbiome research.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Lung Cancer Stage Prediction Using Multi-Omics Data

Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.

Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm

Single-nucleotide polymorphism (SNP) involves the replacement of a single nucleotide in a deoxyribonucleic acid (DNA) sequence and is often linked to the development of specific diseases. Although current genotyping methods can tag SNP loci within biological samples to provide accurate genetic information for a disease associated, they have limited prediction accuracy. Furthermore, they are complex to perform and may result in the prediction of an excessive number of tag SNP loci, which may not always be associated with the disease. Therefore in this manuscript, we aimed to evaluate the impact of a newly optimized fuzzy clustering and binary particle swarm optimization algorithm (FCBPSO) on the accuracy and running time of informative SNP selection. Fuzzy clustering and FCBPSO were first applied to identify the equivalence relation and the candidate tag SNP set to reduce the redundancy between loci. The FCBPSO algorithm was then optimized and used to obtain the final tag SNP set. The prediction performance and running time of the newly developed model were compared with other traditional methods, including NMC, SPSO, and MCMR. The prediction accuracy of the FCBPSO algorithm was always higher than that of the other algorithms especially as the number of tag SNPs increased. However, when the number of tag SNPs was low, the prediction accuracy of FCBPSO was slightly lower than that of MCMR (add prediction accuracy values for each algorithm). However, the running time of the FCBPSO algorithm was always lower than that of MCMR. FCBPSO not only reduced the size and dimension of the optimization problem but also simplified the training of the prediction model. This improved the prediction accuracy of the model and reduced the running time when compared with other traditional methods.

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.

Effect of daily co-exposure to inulin and chlorpyrifos on selected microbiota endpoints in the SHIME® model

The intestinal microbiota has a key role in human health via the interaction with the somatic and immune cells in the digestive tract environment. Food, through matrix effect, nutrient and non-nutrient molecules, is a key regulator of microbiota diversity. As a food contaminant, the pesticide chlorpyrifos (CPF) has an effect on the composition of the intestinal microbiota and induces perturbation of microbiota. Prebiotics (and notably inulin) are known for their ability to promote an equilibrium of the microbiota that favours saccharolytic bacteria. The SHIME® dynamic in vitro model of the human intestine was exposed to CPF and inulin concomitantly for 30 days, in order to assess variations in both the bacterial populations and their metabolites. Various analyses of the microbiota (notably temporal temperature gradient gel electrophoresis) revealed a protective effect of the prebiotic through inhibition of the enterobacterial (E. coli) population. Bifidobacteria were only temporarily inhibited at D15 and recovered at D30. Although other potentially beneficial populations (lactobacilli) were not greatly modified, their activity and that of the saccharolytic bacteria in general were highlighted by an increase in levels of short-chain fatty acids and more specifically butyrate. Given the known role of host-microbiota communication, CPF's impact on the body's homeostasis remains to be determined.

Large-scale microbiome data integration enables robust biomarker identification

The close association between gut microbiota dysbiosis and human diseases is being increasingly recognized. However, contradictory results are frequently reported, as confounding effects exist. The lack of unbiased data integration methods is also impeding the discovery of disease-associated microbial biomarkers from different cohorts. Here we propose an algorithm, NetMoss, for assessing shifts of microbial network modules to identify robust biomarkers associated with various diseases. Compared to previous approaches, the NetMoss method shows better performance in removing batch effects. Through comprehensive evaluations on both simulated and real datasets, we demonstrate that NetMoss has great advantages in the identification of disease-related biomarkers. Based on analysis of pandisease microbiota studies, there is a high prevalence of multidisease-related bacteria in global populations. We believe that large-scale data integration will help in understanding the role of the microbiome from a more comprehensive perspective and that accurate biomarker identification will greatly promote microbiome-based medical diagnosis.

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/

Distance-based support vector machine to predict DNA N6-methyladenine modification

Background:

DNA N6-methyladenine plays an important role in the restriction-modification system to isolate invasion from adventive DNA. The shortcomings of the high time-consumption and high costs of experimental methods have been exposed, and some computational methods have emerged. The support vector machine theory has received extensive attention in the bioinformatics field due to its solid theoretical foundation and many good characteristics.

Objective:

General machine learning methods include an important step of extracting features. The research has omitted this step and replaced with easy-to-obtain sequence distances matrix to obtain better results

Method:

First sequence alignment technology was used to achieve the similarity matrix. Then a novel transformation turned the similarity matrix into a distance matrix. Next, the similarity-distance matrix is made positive semi-definite so that it can be used in the kernel matrix. Finally, the LIBSVM software was applied to solve the support vector machine.

Results:

The five-fold cross-validation of this model on rice and mouse data has achieved excellent accuracy rates of 92.04% and 96.51%, respectively. This shows that the DB-SVM method has obvious advantages compared with traditional machine learning methods. Meanwhile this model achieved 0.943,0.982 and 0.818 accuracy,0.944, 0.982, and 0.838 Matthews correlation coefficient and 0.942, 0.982 and 0.840 F1 scores for the rice, M. musculus and cross-species genome datasets, respectively.

Conclusion:

These outcomes show that this model outperforms the iIM-CNN and csDMA in the prediction of DNA 6mA modification, which are the lastest research on DNA 6mA.

Review of bioinformatics in Azheimer's Disease Research

Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods.

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure

Chronic heart failure (CHF) is the final outcome of almost all forms of cardiovascular diseases, remaining the main cause of mortality worldwide. Accumulating evidence is focused on the roles of gut microbial community in cardiovascular disease, but few studies have unveiled the alterations and further directions of gut microbiota in severe CHF patients. Aimed to investigate this deficiency, fecal samples from 29 CHF patients diagnosed with NYHA Class III-IV and 30 healthy controls were collected and then analyzed using bacterial 16S rRNA gene sequencing. As a result, there were many significant differences between the two groups. Firstly, the phylum Firmicutes was found to be remarkably decreased in severe CHF patients, and the phylum Proteobacteria was the second most abundant phyla in severe CHF patients instead of phylum Bacteroides strangely. Secondly, the α diversity indices such as chao1, PD-whole-tree and Shannon indices were significantly decreased in the severe CHF versus the control group, as well as the notable difference in β-diversity between the two groups. Thirdly, our result revealed a remarkable decrease in the abundance of the short-chain fatty acids (SCFA)-producing bacteria including genera Ruminococcaceae UCG-004, Ruminococcaceae UCG-002, Lachnospiraceae FCS020 group, Dialister and the increased abundance of the genera in Enterococcus and Enterococcaceae with an increased production of lactic acid. Finally, the alternation of the gut microbiota was presumably associated with the function including Cell cycle control, cell division, chromosome partitioning, Amino acid transport and metabolism and Carbohydrate transport and metabolism through SCFA pathway. Our findings provide the direction and theoretical knowledge for the regulation of gut flora in the treatment of severe CHF.

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women's health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers. Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data. Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively). Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

ADDAGMA: A Database for Domestic Animal Gut Microbiome Atlas

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We curated all publicly available high-throughput sequencing data on gut microbiomes for four domestic animal species.

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We compiled data for multiple levels of microbial taxa and classified the associated animal phenotypes in detail.

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Exhibiting the dynamic changes of animal gut microbes under different conditions.

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We developed a user-friendly website for browsing, searching, and displaying dynamic changes in animal gut microbes under different conditions.

Animal gut microbiomes play important roles in the health, diseases, and production of animal hosts. The volume of animal gut metagenomic data, including both 16S amplicon and metagenomic sequencing data, has been increasing exponentially in recent years, making it increasingly difficult for researchers to query, retrieve, and reanalyze experimental data and explore new hypotheses. We designed a database called the domestic animal gut microbiome atlas (ADDAGMA) to house all publicly available, high-throughput sequencing data for the gut microbiome in domestic animals. ADDAGMA enhances the availability and accessibility of the rapidly growing body of metagenomic data. We annotated microbial and metadata from four domestic animals (cattle, horse, pig, and chicken) from 356 published papers to construct a comprehensive database that is equipped with browse and search functions, enabling users to make customized, complicated, biologically relevant queries. Users can quickly and accurately obtain experimental information on sample types, conditions, and sequencing platforms, and experimental results including microbial relative abundances, microbial taxon-associated host phenotype, and P-values for gut microbes of interest. The current version of ADDAGMA includes 290,422 quantification events (changes in abundance) for 3215 microbial taxa associated with 48 phenotypes. ADDAGMA presently covers gut microbiota sequencing data from pig, cattle, horse, and chicken, but will be expanded to include other domestic animals. ADDAGMA is freely available at (http://addagma.omicsbio.info/).