Strain identification and quantitative analysis in microbial communities

Exploring omics approaches in probiotics: Contemporary developments and prospective pathways

The application of omics technologies in combination with bench investigations has brought about a significant transformation in the field of probiotics, enabling a thorough investigation of the basic elements contributing to the probiotic activity. Genomics studies have decoded the complete set of genes of probiotic organisms, shedding light on beneficial traits and mechanisms of probiotic action. Transcriptomics analyses focus on gene expression patterns and investigate probiotic adaptation and functionality. Proteomic studies have revealed the intricate connections between proteins in probiotic cells and their relationship with the host environment. Metabolomic profiling has provided a comprehensive perspective on the metabolic pathways related to probiotic metabolism and the production of bioactive substances. The ongoing development of omics technology presents exciting opportunities for probiotic research, as it allows for a deeper exploration of probiotic-host interactions and the creation of advanced and tailored probiotics that offer specific health advantages. A comprehensive analysis of recent progress in genomics, transcriptomics, proteomics, and metabolomics related to probiotics is presented in this review.

Early detection and population dynamics of Listeria monocytogenes in naturally contaminated drains from a meat processing plant

Listeria monocytogenes, a significant foodborne pathogen, often contaminates ready-to-eat foods through cross-contamination in food processing environments, and floor drains represent one of the most common sites of persistence. Subtyping of L. monocytogenes from food processing plants for the purpose of source tracking is usually performed on a single colony obtained after selective enrichment. This study investigates the temporal variation and population dynamics of L. monocytogenes in drains, focusing on the diversity of L. monocytogenes and the impact of the resident microbiota. Six different drains in a meat processing plant were each sampled four times over a period of 8 weeks and subjected to two-step selective enrichment in Half Fraser and Full Fraser broths. The clonal complexes (CCs) of at least 20 individual L. monocytogenes isolates from each positive sample (460 isolates in total) were determined using either the GenoListeria Multiplex qPCR assay or whole genome sequencing (WGS). The microbiota in drains and enrichment cultures was analyzed by 16S rRNA gene amplicon sequencing and metagenomic or quasimetagenomic sequencing. L. monocytogenes was detected in the majority of samples and four different CCs were identified - CC9, CC11 (ST451), CC121 and CC8 - with up to three CCs in the same sample and with different CCs dominating in different drains. The same clones of CC9, CC11, and CC121 had persisted in the facility for 3-5 years. The composition of the drain microbiota remained relatively stable over time, with Pseudomonas, Acinetobacter, Janthinobacterium, Chryseobacterium, Staphylococcus, and Sphingomonas as the most commonly identified genera. There were no apparent differences in the microbial genera present in L. monocytogenes positive and negative drains or samples. The study highlights the use of techniques such as qPCR and quasimetagenomics for monitoring and controlling the risk of L. monocytogenes contamination in processing environments.

Human microbiome acquisition and transmission

As humans, we host personal microbiomes intricately connected to our biology and health. Far from being isolated entities, our microbiomes are dynamically shaped by microbial exchange with the surroundings, in lifelong microbiome acquisition and transmission processes. In this Review, we explore recent studies on how our microbiomes are transmitted, beginning at birth and during interactions with other humans and the environment. We also describe the key methodological aspects of transmission inference, based on the uniqueness of the building blocks of the microbiome - single microbial strains. A better understanding of human microbiome transmission will have implications for studies of microbial host regulation, of microbiome-associated diseases, and for effective microbiome-targeting strategies. Besides exchanging strains with other humans, there is also preliminary evidence we acquire microorganisms from animals and food, and thus a complete understanding of microbiome acquisition and transmission can only be attained by adopting a One Health perspective.

Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique

Bacteriophages (phages) are increasingly viewed as a promising alternative for the treatment of antibiotic-resistant bacterial infections. However, the diversity of host ranges complicates the identification of target phages. Existing computational tools often fail to accurately identify phages across different bacterial species. In this study, we present GE-PHI, a machine-learning-based model for predicting phage-host interactions (PHIs) by integrating knowledge graph embedding algorithm with a large-scale protein language model. First, a phage-host heterogeneous association network (PHAN) was constructed that incorporated phage-phage and host-host similarity networks. Then, the multi-relational Poincaré graph embedding (MuRP) was used to extract topological patterns. Additionally, we employed the ESM-2 protein language model to capture evolutionary information from phage tail proteins and host-receptor-binding proteins. GE-PHI achieved a cross-validation area under the curve (AUC) of up to 0.9453 in silico and maintains this performance in case studies. This study provides insights into machine-learning-guided phage therapeutics and diagnostics in microbial engineering.

Quantifying Metagenomic Strain Associations from Microbiomes with Anpan

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpan's methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Matching excellence: Oxford Nanopore Technologies' rise to parity with Pacific Biosciences in genome reconstruction of non-model bacterium with high G+C content

The reconstruction of complete bacterial genomes is essential for microbial research, offering insights into genetic content, ontology and regulation. While Pacific Biosciences (PacBio) provides high-quality genomes, its cost remains a limitation. Oxford Nanopore Technologies (ONT) offers long reads at a lower cost, yet its error rate raises scepticism. Recent ONT advancements, such as new Flow cells (R10.4.1), chemistry (V14) and duplex mode, improve data quality. Our study compares ONT with PacBio and Illumina, including hybrid data. We used Propionibacterium freudenreichii, a bacterium with a genome known for being difficult to reconstruct. By combining data from ONT's Native Barcoding and a custom-developed BARSEQ method, we achieved high-quality, near-perfect genome assemblies. Our findings demonstrate, for the first time, that the combination of nanopore-only long-native with shorter PCR DNA reads (~3 kb) results in high-quality genome reconstruction, comparable to hybrid data assembly from two sequencing platforms. This endorses ONT as a cost-effective, stand-alone strategy for bacterial genome reconstruction. Additionally, we compared methylated motif detection between PacBio and ONT R10.4.1 data, showing that results comparable to PacBio are achievable using ONT, especially when utilizing the advanced Nanomotif tool.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Impact of a Purified Microbiome Therapeutic on Abundance of Antimicrobial Resistance Genes in Patients With Recurrent Clostridioides difficile Infection

BACKGROUND

The gastrointestinal microbiota is an important line of defense against colonization with antimicrobial resistant (AR) bacteria. In this post hoc analysis of the phase 3 ECOSPOR III trial, we assessed impact of a microbiota-based oral therapeutic (fecal microbiota spores, live; VOWST Oral Spores [VOS], formerly SER-109]; Seres Therapeutics) compared with placebo, on AR gene (ARG) abundance in patients with recurrent Clostridioides difficile infection (rCDI).

METHODS

Adults with rCDI were randomized to receive VOS or placebo orally for 3 days following standard-of-care antibiotics. ARG and taxonomic profiles were generated using whole metagenomic sequencing of stool at baseline and weeks 1, 2, 8, and 24 posttreatment.

RESULTS

Baseline (n = 151) and serial posttreatment stool samples collected through 24 weeks (total N = 472) from 182 patients (59.9% female; mean age: 65.5 years) in ECOSPOR III as well as 68 stool samples obtained at a single time point from a healthy cohort were analyzed. Baseline ARG abundance was similar between arms and significantly elevated versus the healthy cohort. By week 1, there was a greater decline in ARG abundance in VOS versus placebo (P = .003) in association with marked decline of Proteobacteria and repletion of spore-forming Firmicutes, as compared with baseline. We observed abundance of Proteobacteria and non-spore-forming Firmicutes were associated with ARG abundance, while spore-forming Firmicutes abundance was negatively associated.

CONCLUSIONS

This proof-of-concept analysis suggests that microbiome remodeling with Firmicutes spores may be a potential novel approach to reduce ARG colonization in the gastrointestinal tract.

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

Probiotic and postbiotic analytical methods: a perspective of available enumeration techniques

Probiotics are the largest non-herbal/traditional dietary supplements category worldwide. To be effective, a probiotic strain must be delivered viable at an adequate dose proven to deliver a health benefit. The objective of this article is to provide an overview of the various technologies available for probiotic enumeration, including a general description of each technology, their advantages and limitations, and their potential for the future of the probiotics industry. The current "gold standard" for analytical quantification of probiotics in the probiotic industry is the Plate Count method (PC). PC measures the bacterial cell's ability to proliferate into detectable colonies, thus PC relies on cultivability as a measure of viability. Although viability has widely been measured by cultivability, there has been agreement that the definition of viability is not limited to cultivability. For example, bacterial cells may exist in a state known as viable but not culturable (VBNC) where the cells lose cultivability but can maintain some of the characteristics of viable cells as well as probiotic properties. This led to questioning the association between viability and cultivability and the accuracy of PC in enumerating all the viable cells in probiotic products. PC has always been an estimate of the number of viable cells and not a true cell count. Additionally, newer probiotic categories such as Next Generation Probiotics (NGPs) are difficult to culture in routine laboratories as NGPs are often strict anaerobes with extreme sensitivity to atmospheric oxygen. Thus, accurate quantification using culture-based techniques will be complicated. Another emerging category of biotics is postbiotics, which are inanimate microorganisms, also often referred to as tyndallized or heat-killed bacteria. Obviously, culture dependent methods are not suitable for these products, and alternative methods are needed for their quantification. Different methodologies provide a more complete picture of a heterogeneous bacterial population versus PC focusing exclusively on the eventual multiplication of the cells. Alternative culture-independent techniques including real-time PCR, digital PCR and flow cytometry are discussed. These methods can measure viability beyond cultivability (i.e., by measuring cellular enzymatic activity, membrane integrity or membrane potential), and depending on how they are designed they can achieve strain-specific enumeration.

Maast: genotyping thousands of microbial strains efficiently

Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.

Identifying species-specific k-mers for fast and accurate metagenotyping with Maast and GT-Pro

Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a)1 and Shi et al. (2022b).2.

Eco-friendly bioremediation of pollutants from contaminated sewage wastewater using special reference bacterial strain of Bacillus cereus SDN1 and their genotoxicological assessment in Allium cepa

The present study aimed to assess the Bacillus cereus SDN1 native bacterium's ability to clean up contaminated or polluted water. The isolated bacterium was identified by its morphological and biochemical characteristics, which were then confirmed at the genus level. Furthermore, the isolated B. cereus (NCBI accession No: MW828583) was identified genomically by PCR amplifying 16 s rDNA using a universal primer. The phylogenetic analysis of the rDNA sequence was analyzed to determine the taxonomic and evolutionary profile of the isolate of the previously identified Bacillus sp. Besides, B. cereus and the bacterial consortium were treated using sewage wastewater. After 15 days of treatment, the following pollutants or chemicals were reduced: total hardness particles removal varied from 63.33 % to 67.55 %, calcium removal varied from 90 % to 93.33 %, and total nitrate decreased range from 37.77 % to 22.22 %, respectively. Electrical conductivity ranged from 1809 mS/cm to 2500 mS/cm, and pH values ranged from 6.5 to 8.95. The outcome of in-situ remediation results suggested that B. cereus has a noticeable remediation efficiency to the suspended particles. A root tip test was also used to investigate the genotoxicity of treated and untreated sewage-contaminated waters on onion (Allium cepa) root cells. The highest chromosomal aberrations and mitotic inhibition were found in roots exposed to contaminated sewage water, and their results displayed chromosome abnormalities, including disorganized, sticky chain, disturbed metaphase, chromosomal displacement in anaphase, abnormal telophase, spindle disturbances, and binucleate cells observed in A. cepa exposed to untreated contaminated water. The study can thus be applied as a biomarker to detect the genotoxic impacts of sewage water pollution on biota. Furthermore, based on an identified bacterial consortium, this work offers a low-cost and eco-favorable method for treating household effluents.

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

Statistical and Computational Methods for Microbial Strain Analysis

Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced "too many" recombination events and can be successfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and computational methods have recently been developed to track the strains in samples based on shotgun metagenomics data either based on reference genome sequences or Metagenome-assembled genomes (MAGs). In this paper, we review some recent statistical methods for strain identifications based on frequency counts at a set of single nucleotide variants (SNVs) within a set of single-copy marker genes. These methods differ in terms of whether reference genome sequences are needed, how SNVs are called, what methods of deconvolution are used and whether the methods can be applied to multiple samples. We conclude our review with areas that require further research.

Genotyping Microbial Communities with MIDAS2: From Metagenomic Reads to Allele Tables

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.