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Advanced Science (Weinheim,... Sep 2023The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for...
The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well-established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi-center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types.
Topics: Humans; Animals; Mice; Antineoplastic Agents; Metagenome; Peptides; Neoplasms; Cell Line
PubMed: 37382183
DOI: 10.1002/advs.202300107 -
Microbiology Spectrum Aug 2023Microbial secondary metabolites play crucial roles in microbial competition, communication, resource acquisition, antibiotic production, and a variety of other...
Microbial secondary metabolites play crucial roles in microbial competition, communication, resource acquisition, antibiotic production, and a variety of other biotechnological processes. The retrieval of full-length BGC (biosynthetic gene cluster) sequences from uncultivated bacteria is difficult due to the technical constraints of short-read sequencing, making it impossible to determine BGC diversity. Using long-read sequencing and genome mining, 339 mainly full-length BGCs were recovered in this study, illuminating the wide range of BGCs from uncultivated lineages discovered in seawater from Aoshan Bay, Yellow Sea, China. Many extremely diverse BGCs were discovered in bacterial phyla such as , , , and as well as the previously uncultured archaeal phylum " Thermoplasmatota." The data from metatranscriptomics showed that 30.1% of secondary metabolic genes were being expressed, and they also revealed the expression pattern of BGC core biosynthetic genes and tailoring enzymes. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional expression of BGCs in environmental processes. Genome mining of metagenomic data has become the preferred method for the bioprospecting of novel compounds by cataloguing secondary metabolite potential. However, the accurate detection of BGCs requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until recently with new long-read technologies. We used high-quality metagenome-assembled genomes generated from long-read data to determine the biosynthetic potential of microbes found in the surface water of the Yellow Sea. We recovered 339 highly diverse and mostly full-length BGCs from largely uncultured and underexplored bacterial and archaeal phyla. Additionally, we present long-read metagenomic sequencing combined with metatranscriptomic analysis as a potential method for gaining access to the largely underutilized genetic reservoir of specialized metabolite gene clusters in the majority of microbes that are not cultured. The combination of long-read metagenomic and metatranscriptomic analyses is significant because it can more accurately assess the mechanisms of microbial adaptation to the environment through BGC expression based on metatranscriptomic data.
Topics: Metagenomics; Bacteria; Metagenome; Archaea; Bacteroidetes
PubMed: 37409950
DOI: 10.1128/spectrum.01501-23 -
Frontiers in Cellular and Infection... 2023There is no direct evidence of gut microbiota disturbance in children with gastroesophageal reflux disease (GERD). This study aimed to provide direct evidence and a...
BACKGROUND
There is no direct evidence of gut microbiota disturbance in children with gastroesophageal reflux disease (GERD). This study aimed to provide direct evidence and a comprehensive understanding of gut microbiota disturbance in children with GERD through combined metagenomic and metabolomic analysis.
METHODS
30 children with GERD and 30 healthy controls (HCs) were continuously enrolled, and the demographic and clinical characteristics of the subjects were collected. First, 16S rRNA sequencing was used to evaluate differences in the gut microbiota between children with GERD and HC group, and 10 children with GERD and 10 children in the HC group were selected for metagenomic analysis. Nontargeted metabolomic analysis was performed using liquid chromatography/mass spectrometry (LC/MS), and metagenomic and metabolomic data were analyzed together.
RESULTS
There were significant differences in the gut microbiota diversity and composition between children with GERD and HCs. The dominant bacteria in children with GERD were Proteobacteria and Bacteroidota. At the species level, the top three core bacterial groups were , and . The main differential pathways were identified to be related to energy, amino acid, vitamin, carbohydrate and lipid metabolism. LC/MS detected 288 different metabolites in the positive and negative ion modes between children with GERD and HCs, which were mainly involved in arachidonic acid (AA), tyrosine, glutathione and caffeine metabolism.
CONCLUSION
This study provides new evidence of the pathogenesis of GERD. There are significant differences in the gut microbiota, metabolites and metabolic pathways between HCs and children with GERD, and the differences in metabolites are related to specific changes in bacterial abundance. In the future, GERD may be treated by targeting specific bacteria related to AA metabolism.
Topics: Humans; Child; Gastrointestinal Microbiome; RNA, Ribosomal, 16S; Metabolomics; Bacteria; Metagenomics; Gastroesophageal Reflux
PubMed: 37900308
DOI: 10.3389/fcimb.2023.1267192 -
Cell Feb 2024Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear...
Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.
Topics: Humans; Bacteria; Bacteroidetes; Feces; Metagenome; Plasmids; Gastrointestinal Tract
PubMed: 38428395
DOI: 10.1016/j.cell.2024.01.039 -
The Journal of Clinical Investigation Feb 2024Next-generation sequencing (NGS) applications for the diagnostics of infectious diseases has demonstrated great potential with three distinct approaches: whole-genome...
Next-generation sequencing (NGS) applications for the diagnostics of infectious diseases has demonstrated great potential with three distinct approaches: whole-genome sequencing (WGS), targeted NGS (tNGS), and metagenomic NGS (mNGS, also known as clinical metagenomics). These approaches provide several advantages over traditional microbiologic methods, though challenges still exist.
Topics: High-Throughput Nucleotide Sequencing; Metagenomics; Whole Genome Sequencing; Sensitivity and Specificity
PubMed: 38357923
DOI: 10.1172/JCI178003 -
EBioMedicine Jun 2024Non-high-density lipoprotein cholesterol (non-HDL-c) was a strong risk factor for incident cardiovascular diseases and proved to be a better target of lipid-lowering...
BACKGROUND
Non-high-density lipoprotein cholesterol (non-HDL-c) was a strong risk factor for incident cardiovascular diseases and proved to be a better target of lipid-lowering therapies. Recently, gut microbiota has been implicated in the regulation of host metabolism. However, its causal role in the variation of non-HDL-c remains unclear.
METHODS
Microbial species and metabolic capacities were assessed with fecal metagenomics, and their associations with non-HDL-c were evaluated by Spearman correlation, followed by LASSO and linear regression adjusted for established cardiovascular risk factors. Moreover, integrative analysis with plasma metabolomics were performed to determine the key molecules linking microbial metabolism and variation of non-HDL-c. Furthermore, bi-directional mendelian randomization analysis was performed to determine the potential causal associations of selected species and metabolites with non-HDL-c.
FINDINGS
Decreased Eubacterium rectale but increased Clostridium sp CAG_299 were causally linked to a higher level of non-HDL-c. A total of 16 microbial capacities were found to be independently associated with non-HDL-c after correcting for age, sex, demographics, lifestyles and comorbidities, with the strongest association observed for tricarboxylic acid (TCA) cycle. Furthermore, decreased 3-indolepropionic acid and N-methyltryptamine, resulting from suppressed capacities for microbial reductive TCA cycle, functioned as major microbial effectors to the elevation of circulating non-HDL-c.
INTERPRETATION
Overall, our findings provided insight into the causal effects of gut microbes on non-HDL-c and uncovered a novel link between non-HDL-c and microbial metabolism, highlighting the possibility of regulating non-HDL-c by microbiota-modifying interventions.
FUNDING
A full list of funding bodies can be found in the Sources of funding section.
Topics: Gastrointestinal Microbiome; Humans; Female; Male; Middle Aged; Metabolomics; Metagenomics; Feces; Aged; Biomarkers; Risk Factors; Mendelian Randomization Analysis; Metagenome; Cholesterol; Metabolome; Cardiovascular Diseases
PubMed: 38728837
DOI: 10.1016/j.ebiom.2024.105150 -
Frontiers in Cellular and Infection... 2023Urinary tract infections (UTIs) remain a diagnostic challenge and often promote antibiotic overuse. Despite urine culture being the gold standard for UTI diagnosis, some...
BACKGROUND
Urinary tract infections (UTIs) remain a diagnostic challenge and often promote antibiotic overuse. Despite urine culture being the gold standard for UTI diagnosis, some uropathogens may lead to false-negative or inconclusive results. Although PCR testing is fast and highly sensitive, its diagnostic yield is limited to targeted microorganisms. Metagenomic next-generation sequencing (mNGS) is a hypothesis-free approach with potential of deciphering the urobiome. However, clinically relevant information is often buried in the enormous amount of sequencing data.
METHODS
Precision metagenomics (PM) is a hybridization capture-based method with potential of enhanced discovery power and better diagnostic yield without diluting clinically relevant information. We collected 47 urine samples of clinically suspected UTI and in parallel tested each sample by microbial culture, PCR, and PM; then, we comparatively analyzed the results. Next, we phenotypically classified the cumulative microbial population using the Explify® data analysis platform for potential pathogenicity.
RESULTS
Results revealed 100% positive predictive agreement (PPA) with culture results, which identified only 13 different microorganisms, compared to 19 and 62 organisms identified by PCR and PM, respectively. All identified organisms were classified into phenotypic groups (0-3) with increasing pathogenic potential and clinical relevance. This PM can simultaneously quantify and phenotypically classify the organisms readily through bioinformatic platforms like Explify®, essentially providing dissected and quantitative results for timely and accurate empiric UTI treatment.
CONCLUSION
PM offers potential for building effective diagnostic models beyond usual care testing in complex UTI diseases. Future studies should assess the impact of PM-guided UTI management on clinical outcomes.
Topics: Humans; Metagenomics; Urinary Tract Infections; Anti-Bacterial Agents; Computational Biology; High-Throughput Nucleotide Sequencing
PubMed: 37469596
DOI: 10.3389/fcimb.2023.1221289 -
Scientific Data Oct 2023Biofloc technology is increasingly recognised as a sustainable aquaculture method. In this technique, bioflocs are generated as microbial aggregates that play pivotal...
Biofloc technology is increasingly recognised as a sustainable aquaculture method. In this technique, bioflocs are generated as microbial aggregates that play pivotal roles in assimilating toxic nitrogenous substances, thereby ensuring high water quality. Despite the crucial roles of the floc-associated bacterial (FAB) community in pathogen control and animal health, earlier microbiota studies have primarily relied on the metataxonomic approaches. Here, we employed shotgun sequencing on eight biofloc metagenomes from a commercial aquaculture system. This resulted in the generation of 106.6 Gbp, and the reconstruction of 444 metagenome-assembled genomes (MAGs). Among the recovered MAGs, 230 were high-quality (≥90% completeness, ≤5% contamination), and 214 were medium-quality (≥50% completeness, ≤10% contamination). Phylogenetic analysis unveiled Rhodobacteraceae as dominant members of the FAB community. The reported metagenomes and MAGs are crucial for elucidating the roles of diverse microorganisms and their functional genes in key processes such as nitrification, denitrification, and remineralization. This study will contribute to scientific understanding of phylogenetic diversity and metabolic capabilities of microbial taxa in aquaculture environments.
Topics: Animals; Aquaculture; Bacteria; Metagenome; Metagenomics; Microbiota; Phylogeny
PubMed: 37848477
DOI: 10.1038/s41597-023-02622-0 -
MSystems Aug 2023With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of...
With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.
Topics: Humans; Diabetes Mellitus, Type 2; Microbiota; Metagenome; Gastrointestinal Microbiome; Phenotype
PubMed: 37404032
DOI: 10.1128/msystems.00531-23 -
Nucleic Acids Research Jan 2024Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or...
Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.
Topics: Databases, Factual; Metagenome; Metagenomics; Microbiota
PubMed: 37897342
DOI: 10.1093/nar/gkad943