Analysis Of Microbial Genomes Research Articles

Mechanism of improving anaerobic fermentation performance of kitchen waste pretreated by ionizing irradiation-part 1: rice.

Ionizing irradiation, as a new pretreatment method for the anaerobic fermentation of organic pollutants, is featured with fast reaction speed, good treatment effect, no need to add any chemical reagents, and no secondary pollution. This study explores the mechanism of improving anaerobic fermentation performance of rice samples pretreated by cobalt-60 gamma irradiation through the influence on fermentation substrate, acidogenic phase and methanogenic phase. The results reveal that the soluble chemical oxygen demand of the irradiated rice sample at an absorbed dose of 9.6 kGy increases by 12.4 times due to the dissolution of small molecules of fat-soluble organic matter. The yield of biogas in the acidogenic phase increases by 22.2% with a slight increase in hydrogen gas content. The yield of biogas and methane gas content in the methanogenic phase increases by 27.3% and 15%, respectively. Microbial genome analysis, performed with MiSeq high-throughput sequencing and metagenomic methods, suggests the microbial abundance and metabolic functions in the anaerobic fermentation process change significantly as a result of the pretreatment by gamma irradiation.

Emerging applications of artificial intelligence in pathogen genomics

The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.

Characterization of phyllosphere endophytic lactic acid bacteria reveals a potential novel route to enhance silage fermentation quality.

The naturally attached phyllosphere microbiota play a crucial role in plant-derived fermentation, but the structure and function of phyllosphere endophytes remain largely unidentified. Here, we reveal the diversity, specificity, and functionality of phyllosphere endophytes in alfalfa (Medicago sativa L.) through combining typical microbial culture, high-throughput sequencing, and genomic comparative analysis. In comparison to phyllosphere bacteria (PB), the fermentation of alfalfa solely with endophytes (EN) enhances the fermentation characteristics, primarily due to the dominance of specific lactic acid bacteria (LAB) such as Lactiplantibacillus, Weissella, and Pediococcus. The inoculant with selected endophytic LAB strains also enhances the fermentation quality compared to epiphytic LAB treatment. Especially, one key endophytic LAB named Pediococcus pentosaceus EN5 shows enrichment of genes related to the mannose phosphotransferase system (Man-PTS) and carbohydrate-metabolizing enzymes and higher utilization of carbohydrates. Representing phyllosphere, endophytic LAB shows great potential of promoting ensiling and provides a novel direction for developing microbial inoculant.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies.

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

Bovine milk osteopontin improved intestinal health of pregnant rats fed a high-fat diet through improving bile acid metabolism

The main purpose of the current study was to investigate the ameliorative effects of bovine milk osteopontin (bmOPN) on the gut dysfunction of pregnant rats fed a high-fat diet (HFD). Bovine milk osteopontin was supplemented at a dose of 6 mg/kg body weight. Bovine milk osteopontin supplementation during pregnancy reduced colonic inflammation of HFD dams, and it also increased the colonic expression of ZO-1 and claudin-4 of HFD dams. Bovine milk osteopontin significantly enriched the relative abundance of Bacteroidetes, whereas it decreased Proteobacteria, Helicobacteraceae, and Desulfovibrionaceae in feces of HFD dams. The levels of isobutyric acid and pentanoic acid in the HFD + bmOPN group were higher than that of the HFD group. Functional predication analysis of microbial genomes revealed that bmOPN supplementation to HFD pregnancies changed 4 Kyoto Encyclopedia of Genes and Genomes pathways including bile acid biosynthesis. Further, bmOPN enriched hepatic taurochenodeoxycholic acid and tauroursodeoxycholic acid plus taurohyodeoxycholic acid in the gut of HFD maternal rats. Our findings suggested that bmOPN improved the gut health of HFD pregnant rats partially through modulating bile acid biosynthesis.

Ancient Clostridium DNA and variants of tetanus neurotoxins associated with human archaeological remains

The analysis of microbial genomes from human archaeological samples offers a historic snapshot of ancient pathogens and provides insights into the origins of modern infectious diseases. Here, we analyze metagenomic datasets from 38 human archaeological samples and identify bacterial genomic sequences related to modern-day Clostridium tetani, which produces the tetanus neurotoxin (TeNT) and causes the disease tetanus. These genomic assemblies had varying levels of completeness, and a subset of them displayed hallmarks of ancient DNA damage. Phylogenetic analyses revealed known C. tetani clades as well as potentially new Clostridium lineages closely related to C. tetani. The genomic assemblies encode 13 TeNT variants with unique substitution profiles, including a subgroup of TeNT variants found exclusively in ancient samples from South America. We experimentally tested a TeNT variant selected from an ancient Chilean mummy sample and found that it induced tetanus muscle paralysis in mice, with potency comparable to modern TeNT. Thus, our ancient DNA analysis identifies DNA from neurotoxigenic C. tetani in archaeological human samples, and a novel variant of TeNT that can cause disease in mammals.

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

GenoVi, an open-source automated circular genome visualizer for bacteria and archaea.

The increase in microbial sequenced genomes from pure cultures and metagenomic samples reflects the current attainability of whole-genome and shotgun sequencing methods. However, software for genome visualization still lacks automation, integration of different analyses, and customizable options for non-experienced users. In this study, we introduce GenoVi, a Python command-line tool able to create custom circular genome representations for the analysis and visualization of microbial genomes and sequence elements. It is designed to work with complete or draft genomes, featuring customizable options including 25 different built-in color palettes (including 5 color-blind safe palettes), text formatting options, and automatic scaling for complete genomes or sequence elements with more than one replicon/sequence. Using a Genbank format file as the input file or multiple files within a directory, GenoVi (i) visualizes genomic features from the GenBank annotation file, (ii) integrates a Cluster of Orthologs Group (COG) categories analysis using DeepNOG, (iii) automatically scales the visualization of each replicon of complete genomes or multiple sequence elements, (iv) and generates COG histograms, COG frequency heatmaps and output tables including general stats of each replicon or contig processed. GenoVi's potential was assessed by analyzing single and multiple genomes of Bacteria and Archaea. Paraburkholderia genomes were analyzed to obtain a fast classification of replicons in large multipartite genomes. GenoVi works as an easy-to-use command-line tool and provides customizable options to automatically generate genomic maps for scientific publications, educational resources, and outreach activities. GenoVi is freely available and can be downloaded from https://github.com/robotoD/GenoVi.

Analysis of Ancient Microbial DNA.

The development of next-generation sequencing has led to a breakthrough in the analysis of ancient genomes, and the subsequent genomic analyses of ancient human skeletal remains have revolutionized our understanding of human evolution. This research led to the discovery of a new hominin lineage, and demonstrated multiple admixture events with more distantly related archaic human populations such as Neandertals and Denisovans over the last 100,000years. Moreover, it has also yielded novel insights into the evolution of ancient pathogens. The analysis of ancient microbial genomes enables the study of their recent evolution, presently covering the last several millennia. These spectacular results have been obtained despite the degradation of DNA that takes place after the death of the host and increases with time. This cumulative degradation results in very short ancient DNA molecules, low in quantity, and highly prone to contamination by modern DNA molecules, especially from human and animal DNA present in reagents used in downstream biomolecular analyses. Finally, the minute amounts of ancient molecules are further diluted in environmental DNA from the soil microorganisms that colonize bones and teeth. Thus, ancient skeletal remains can share DNA profiles with environmental samples, and the identification of ancient microbial genomes among the more recent, presently poorly characterized, environmental microbiome is particularly challenging. Here, we describe the methods developed and/or in use in our laboratory to produce reliable and reproducible paleogenomic results from ancient skeletal remains that can be used to identify the presence of ancient microbiota.

Analyzing Prokaryotic Transcriptomics in the Light of Genome Data with the MicroScope Platform.

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species occurring in our environment. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. To address this challenge, the MicroScope platform has been developed. It is an integrated Web platform for management, annotation, comparative analysis, and visualization of microbial genomes ( https://mage.genoscope.cns.fr/microscope ). Launched in 2005, the platform has been under continuous development and provides analyzes for complete and ongoing genome projects together with metabolic network reconstruction and transcriptomic experiments allowing users to improve the understanding of gene functions. MicroScope platform is widely used by microbiologists from academia and industry all around the world for collaborative studies and expert annotation. It enables collaborative work in a rich comparative genomic context and improves community-based curation efforts. Here, we describe the protocol to follow for the integration and analysis of transcriptomics data in the Microscope platform. The chapter reviews each key step from the experimental design to the analysis and interpretation of the experiment data and results. The integration of transcriptomics data gives a dynamic view of the genome by allowing the users to improve the understanding of gene functions by interpreting them in the light of regulatory cell processes. Moreover, they can also contribute to the refinement of genome annotation through the discovery of new genes and help to fill metabolic gaps.

The Multi-Omics Analysis Revealed Microbiological Regulation of Rabbit Colon with Diarrhea Fed an Antibiotic-Free Diet.

Simple SummaryAfter being fed an antibiotic-free diet, some rabbits showed typical diarrhea symptoms. In order to explore the reasons, this study used multiple omics analysis. Bacteroidetes and Proteobacteria were significantly upregulated in the colon of diarrhea rabbits, and the ratio of Firmicutes to Bacteroidetes was decreased. The significantly upregulated differential genes were mainly enriched in the IL-17 signaling pathway and were involved in promoting inflammatory response. The different metabolites were mainly enriched in tryptophan metabolism and bile secretion, which affected the anti-inflammatory function. In addition, Bacteroides is positively correlated with 4-Morpholinobenzoic acid and Diacetoxyscirpenol, which is believed to be an important cause of inflammation. The enrichment of Proteobacteria is also related to the high expression of the IL-17 signaling pathway.Diarrhea symptoms appeared after antibiotics were banned from animal feed based on the law of the Chinese government in 2020. The colon and its contents were collected and analyzed from diarrheal and healthy rabbits using three omics analyses. The result of the microbial genomic analysis showed that the abundance of Bacteroidetes and Proteobacteria increased significantly (p-value < 0.01). Transcriptomes analysis showed that differentially expressed genes (DEGs) are abundant in the IL-17 signaling pathway and are highly expressed in the pro-inflammatory pathway. The metabolome analysis investigated differential metabolites (DMs) that were mainly enriched in tryptophan metabolism and bile secretion, which were closely related to the absorption and immune function of the colon. The results of correlation analysis showed that Bacteroidetes was positively correlated with 4-Morpholinobenzoic acid, and 4-Morpholinobenzoic acid could aggravate inflammation through its influence on the bile secretion pathway. The enriched DMs L-Tryptophan in the tryptophan metabolism pathway will lead to the functional disorder of inhibiting inflammation by affecting the protein digestion and absorption pathway. Thus, the colonic epithelial cells were damaged, affecting the function of the colon and leading to diarrhea in rabbits. Therefore, the study provided an idea for feed development and a theoretical basis for maintaining intestinal tract fitness in rabbits.

Complete Genome Sequence Resource for Xanthomonas translucens pv. undulosa MAI5034, a Wheat Pathogen from Uruguay.

Complete Genome Sequence Resource for Xanthomonas translucens pv. undulosa MAI5034, a Wheat Pathogen from Uruguay.

High-Quality Complete Genome Resource of Pectobacterium parvum Isolate FN20211 CausingAerial Stem Rot of Potato.

High-Quality Complete Genome Resource of Pectobacterium parvum Isolate FN20211 CausingAerial Stem Rot of Potato.

A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters.

Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product’s antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.

New Family of Carbohydrate-Binding Modules Defined by a Galactosyl-Binding Protein Module from a Cellvibrio japonicus Endo-Xyloglucanase.

Carbohydrate-binding modules (CBMs) are usually appended to carbohydrate-active enzymes (CAZymes) and serve to potentiate catalytic activity, for example, by increasing substrate affinity. The Gram-negative soil saprophyte Cellvibrio japonicus is a valuable source for CAZyme and CBM discovery and characterization due to its innate ability to degrade a wide array of plant polysaccharides. Bioinformatic analysis of the CJA_2959 gene product from C. japonicus revealed a modular architecture consisting of a fibronectin type III (Fn3) module, a cryptic module of unknown function (X181), and a glycoside hydrolase family 5 subfamily 4 (GH5_4) catalytic module. We previously demonstrated that the last of these, CjGH5F, is an efficient and specific endo-xyloglucanase (M. A. Attia, C. E. Nelson, W. A. Offen, N. Jain, et al., Biotechnol Biofuels 11:45, 2018, https://doi.org/10.1186/s13068-018-1039-6). In the present study, C-terminal fusion of superfolder green fluorescent protein in tandem with the Fn3-X181 modules enabled recombinant production and purification from Escherichia coli. Native affinity gel electrophoresis revealed binding specificity for the terminal galactose-containing plant polysaccharides galactoxyloglucan and galactomannan. Isothermal titration calorimetry further evidenced a preference for galactoxyloglucan polysaccharide over short oligosaccharides comprising the limit-digest products of CjGH5F. Thus, our results identify the X181 module as the defining member of a new CBM family, CBM88. In addition to directly revealing the function of this CBM in the context of xyloglucan metabolism by C. japonicus, this study will guide future bioinformatic and functional analyses across microbial (meta)genomes. IMPORTANCE This study reveals carbohydrate-binding module family 88 (CBM88) as a new family of galactose-binding protein modules, which are found in series with diverse microbial glycoside hydrolases, polysaccharide lyases, and carbohydrate esterases. The definition of CBM88 in the carbohydrate-active enzymes classification (http://www.cazy.org/CBM88.html) will significantly enable future microbial (meta)genome analysis and functional studies.

MetaSanity: an integrated microbial genome evaluation and annotation pipeline.

SummaryAs the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g. marine, built, host associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from 11 existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows that (i) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (ii) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function. Importantly, this software provides built-in optimization for ‘big data’ analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research.Availability and implementationMetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C++. Instructions for its installation and use are available within the GitHub wiki page at https://github.com/cjneely10/MetaSanity/wiki, and additional instructions are available at https://cjneely10.github.io/year-archive/. MetaSanity is optimized for users with limited programing experience.Supplementary information Supplementary data are available at Bioinformatics online.

Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera. Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes. Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications. The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation. Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes. There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question. In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications.

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera. Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes. Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications. The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation. Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes. There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question. In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

Microbial genomic island discovery, visualization and analysis.

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Microbial genome analysis: the COG approach.

For the past 20 years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms; (ii) identification of missing and/or undetected genes in complete microbial genomes; (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems; (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes; (v) comparison of organisms by COG functional categories; and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.