Amadis: A Comprehensive Database for Association Between Microbiota and Disease

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.

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.

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.

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes

Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

Fucosyltransferases Regulated by Fusobacterium Nucleatum and Act as Novel Biomarkers in Colon Adenocarcinoma

Purpose

Colon adenocarcinoma (COAD) is one of the leading causes of cancer-associated mortality worldwide. Fucosyltransferases (FUTs) are associated with numerous cancers. We aimed to investigate the functions of FUTs in COAD.

Patients and Methods

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the expression and clinical relevance of FUTs in COAD. Real Time Quantitative PCR (RT-qPCR), Western blot, immunohistochemistry and ELISA were used to detect the relative RNA and protein expression levels. Colitis-associated cancer mice treated with Fusobacterium nucleatum were used to illustrate the effects of Fusobacterium nucleatum on FUTs and COAD. Luciferase reporting assay was used to investigate the binding of miRNA to mRNA.

Results

TCGA and GEO datasets showed abnormal expression of FUTs in COAD at transcript level. RT-qPCR, Western blot and immunohistochemistry showed increased expression of FUT1, POFUT1 and POFUT2 in COAD. COAD patients with a high expression of FUT1, FUT11, FUT13 (POFUT2) had a worse prognosis, while patients with a high expression of FUT2, FUT3, FUT6 had a better prognosis. FUT1 and POFUT2 could independently predict the prognosis of COAD patients. Functional analysis by CancerSEA database showed that FUT3, FUT6, FUT8, FUT12 (POFUT1) and FUT13 are associated with differentiation, apoptosis, invasion, quiescence, and hypoxia. FUTs are associated with the tumor microenvironment of COAD. FUT1 regulated by miR-939-3p inhibit the expression of MUC2. Fusobacterium nucleatum may affect the expression of FUTs by affecting their transcription factors and miRNA levels. Moreover, Fusobacterium nucleatum promotes COAD progression through the miR-939-3p/FUT1/MUC2 axis.

Conclusion

Fucosyltransferases play an important role and may be the mediator of Fusobacterium nucleatum promoting COAD progression.

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.

Large scale text mining for deriving useful insights: A case study focused on microbiome

Text mining has been shown to be an auxiliary but key driver for modeling, data harmonization, and interpretation in bio-medicine. Scientific literature holds a wealth of information and embodies cumulative knowledge and remains the core basis on which mechanistic pathways, molecular databases, and models are built and refined. Text mining provides the necessary tools to automatically harness the potential of text. In this study, we show the potential of large-scale text mining for deriving novel insights, with a focus on the growing field of microbiome. We first collected the complete set of abstracts relevant to the microbiome from PubMed and used our text mining and intelligence platform Taxila for analysis. We drive the usefulness of text mining using two case studies. First, we analyze the geographical distribution of research and study locations for the field of microbiome by extracting geo mentions from text. Using this analysis, we were able to draw useful insights on the state of research in microbiome w. r.t geographical distributions and economic drivers. Next, to understand the relationships between diseases, microbiome, and food which are central to the field, we construct semantic relationship networks between these different concepts central to the field of microbiome. We show how such networks can be useful to derive useful insight with no prior knowledge encoded.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Pan-Cancer Analysis of Prognostic and Immune Infiltrates for CXCs

Simple Summary

CXCs are important genes that regulate inflammation and tumor metastasis. While there are many studies with a focus on individual CXCs, few present a pan-cancer analysis of the whole CXC family. Our results indicate that CXCs are a potential therapeutic target in a variety of tumors and a potential prognostic marker that could improve the survival of cancer patients and the accuracy of prognosis. Meanwhile, we found that CXCs may be involved in diseases caused by intestinal flora.

Abstract

Background: CXCs are important genes that regulate inflammation and tumor metastasis. However, the expression level, prognosis value, and immune infiltration of CXCs in cancers are not clear. Methods: Multiple online datasets were used to analyze the expression, prognosis, and immune regulation of CXCs in this study. Network analysis of the Amadis database and GEO dataset was used to analyze the regulation of intestinal flora on the expression of CXCs. A mouse model was used to verify the fact that intestinal bacterial dysregulation can affect the expression of CXCs. Results: In the three cancers, multiple datasets verified the fact that the mRNA expression of this family was significantly different; the mRNA levels of CXCL3, 8, 9, 10, 14, and 17 were significantly correlated with the prognosis of three cancers. CXCs were correlated with six types of immuno-infiltrating cells in three cancers. Immunohistochemistry of clinical samples confirmed that the expression of CXCL8 and 10 was higher in three cancer tissues. Animal experiments have shown that intestinal flora dysregulation can affect CXCL8 and 10 expressions. Conclusion: Our results further elucidate the function of CXCs in cancers and provide new insights into the prognosis and immune infiltration of breast, colon, and pancreatic cancers, and they suggest that intestinal flora may influence disease progression through CXCs.