Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome.

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids.

Metagenomic next-generation sequencing (mNGS) enables comprehensive pathogen detection and has become increasingly popular in clinical diagnosis. The distinct pathogenic traits between strains require mNGS to achieve a strain-level resolution, but an equivocal concept of 'strain' as well as the low pathogen loads in most clinical specimens hinders such strain awareness. Here we introduce a metagenomic intra-species typing (MIST) tool (https://github.com/pandafengye/MIST), which hierarchically organizes reference genomes based on average nucleotide identity (ANI) and performs maximum likelihood estimation to infer the strain-level compositional abundance. In silico analysis using synthetic datasets showed that MIST accurately predicted the strain composition at a 99.9% average nucleotide identity (ANI) resolution with a merely 0.001× sequencing depth. When applying MIST on 359 culture-positive and 359 culture-negative real-world specimens of infected body fluids, we found the presence of multiple-strain reached considerable frequencies (30.39%-93.22%), which were otherwise underestimated by current diagnostic techniques due to their limited resolution. Several high-risk clones were identified to be prevalent across samples, including Acinetobacter baumannii sequence type (ST)208/ST195, Staphylococcus aureus ST22/ST398 and Klebsiella pneumoniae ST11/ST15, indicating potential outbreak events occurring in the clinical settings. Interestingly, contaminations caused by the engineered Escherichia coli strain K-12 and BL21 throughout the mNGS datasets were also identified by MIST instead of the statistical decontamination approach. Our study systemically characterized the infected body fluids at the strain level for the first time. Extension of mNGS testing to the strain level can greatly benefit clinical diagnosis of bacterial infections, including the identification of multi-strain infection, decontamination and infection control surveillance.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome.

BACKGROUND: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. RESULTS: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. CONCLUSIONS: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

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.

Strain specificity in fusobacterial co-aggregation with colorectal cancer-relevant species.

OBJECTIVES: The purpose of the present study was to characterize co-aggregation interactions between isolates of Fusobacterium nucleatum subsp. animalis and other colorectal cancer (CRC)-relevant species. METHODS: Co-aggregation interactions were assessed by comparing optical density values following 2-h stationary strain co-incubations to strain optical density values when incubated alone. Co-aggregation was characterized between strains from a previously isolated, CRC biopsy-derived community and F. nucleatum subsp. animalis, a species linked to CRC and known to be highly aggregative. Interactions were also investigated between the fusobacterial isolates and strains sourced from alternate human gastrointestinal samples whose closest species match aligned with species in the CRC biopsy-derived community. RESULTS: Co-aggregation interactions were observed to be strain-specific, varying between both F. nucleatum subsp. animalis strains and different strains of the same co-aggregation partner species. F. nucleatum subsp. animalis strains were observed to co-aggregate strongly with several taxa linked to CRC: Campylobacter concisus, Gemella spp., Hungatella hathewayi, and Parvimonas micra. CONCLUSIONS: Co-aggregation interactions suggest the ability to encourage the formation of biofilms, and colonic biofilms, in turn, have been linked to promotion and/or progression of CRC. Co-aggregation between F. nucleatum subsp. animalis and CRC-linked species such as C. concisus, Gemella spp., H. hathewayi, and P. micra may contribute to both biofilm formation along CRC lesions and to disease progression.

Gut Microbiota Perturbation in IgA Deficiency Is Influenced by IgA-Autoantibody Status.

BACKGROUND & AIMS: IgA exerts its primary function at mucosal surfaces, where it binds microbial antigens to regulate bacterial growth and epithelial attachment. One third of individuals with IgA deficiency (IgAD) suffers from recurrent mucosal infections, possibly related to an altered microbiota. We aimed to delineate the impact of IgAD and the IgA-autoantibody status on the composition and functional capacity of the gut microbiota. METHODS: We performed a paired, lifestyle-balanced analysis of the effect of IgA on the gut microbiota composition and functionality based on fecal samples from individuals with IgAD and IgA-sufficient household members (n = 100), involving quantitative shotgun metagenomics, species-centric functional annotation of gut bacteria, and strain-level analyses. We supplemented the data set with 32 individuals with IgAD and examined the influence of IgA-autoantibody status on the composition and functionality of the gut microbiota. RESULTS: The gut microbiota of individuals with IgAD exhibited decreased richness and diversity and was enriched for bacterial species encoding pathogen-related functions including multidrug and antimicrobial peptide resistance, virulence factors, and type III and VI secretion systems. These functional changes were largely attributed to Escherichia coli but were independent of E coli strain variations and most prominent in individuals with IgAD with IgA-specific autoreactive antibodies. CONCLUSIONS: The microbiota of individuals with IgAD is enriched for species holding increased proinflammatory potential, thereby potentially decreasing the resistance to gut barrier-perturbing events. This phenotype is especially pronounced in individuals with IgAD with IgA-specific autoreactive antibodies, thus warranting a screening for IgA-specific autoreactive antibodies in IgAD to identify patients with IgAD with increased risk for gastrointestinal implications.

Understanding Responses of Soil Microbiome to the Nitrogen and Phosphorus Addition in Metasequoia glyptostroboides Plantations of Different Ages.

Nitrogen (N) and phosphorus (P) have significant effects on soil microbial community diversity, composition, and function. Also, trees of different life stages have different fertilization requirements. In this study, we designed three N additions and three P levels (5 years of experimental treatment) at two Metasequoia glyptostroboides plantations of different ages (young, 6 years old; middle mature, 24 years old) to understand how different addition levels of N and P affect the soil microbiome. Here, the N fertilization of M. glyptostroboides plantation land (5 years of experimental treatment) significantly enriched microbes (e.g., Lysobacter, Luteimonas, and Rhodanobacter) involved in nitrification, denitrification, and P-starvation response regulation, which might further lead to the decreasing in alpha diversity (especially in 6YMP soil). The P addition could impact the genes involved in inorganic P-solubilization and organic P-mineralization by increasing soil AP and TP. Moreover, the functional differences in the soil microbiomes were identified between the 6YMP and 24YMP soil. This study provides valuable information that improves our understanding on the effects of N and P input on the belowground soil microbial community and functional characteristics in plantations of different stand ages.

QuantTB - a method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data.

BACKGROUND: Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis. Here we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, and a list of drugs for which resistance-conferring mutations (or heteroresistance) have been predicted within the sample. RESULTS: We show that QuantTB has a high degree of resolution and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1× coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of 50 paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach. CONCLUSION: QuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients - even in low-coverage (1×) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples.

Corrosion-Induced Mass Loss Measurement under Strain Conditions through Gr/AgNW-Based, Fe-C Coated LPFG Sensors.

In this study, graphene/silver nanowire (Gr/AgNW)-based, Fe-C coated long period fiber gratings (LPFG) sensors were tested up to 72 hours in 3.5 w.t% NaCl solution for corrosion-induced mass loss measurement under four strain levels: 0, 500, 1000 and 1500 µÎµ. The crack and interfacial bonding behaviors of laminate Fe-C and Gr/AgNW layer structures were characterized using Scanning Electron Microscopy (SEM) and electrical resistance measurement. Both optical transmission spectra and electrical impedance spectroscopy (EIS) data were simultaneously measured from each sensor. Under increasing strains, transverse cracks appeared first and were followed by longitudinal cracks on the laminate layer structures. The spacing of transverse cracks and the length of longitudinal cracks were determined by the bond strength at the weak Fe-C and Gr/AgNW interface. During corrosion tests, the shift in resonant wavelength of the Fe-C coated LPFG sensors resulted from the effects of the Fe-C layer thinning and the NaCl solution penetration through cracks on the evanescent field surrounding the LPFG sensors. Compared with the zero-strained sensor, the strain-induced cracks on the laminate layer structures initially increased and then decreased the shift in resonant wavelength in two main stages of the Fe-C corrosion process. In each corrosion stage, the Fe-C mass loss was linearly related to the shift in resonant wavelength under zero strain and with the applied strain taken into account in general cases. The general correlation equation was validated at 700 and 1200 µÎµ to a maximum error of 2.5% in comparison with 46.5% from the zero-strain correlation equation.

Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study.

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.

Multilocus sequence typing of Carnobacterium maltaromaticum strains associated with fish disease and dairy products.

AIMS: Carnobacterium maltaromaticum is a lactic acid bacterium of technological interest in the field of dairy ripening and food bioprotection and is generally recognized as safe in the United States. As it is associated with fish infections, the European Food Safety Agency did not include this species in the qualified presumption safety list of micro-organisms. This implies that the risk assessment for the species has to be performed at the strain level. METHODS AND RESULTS: Multilocus sequence typing (MLST) is a tool that (i) potentially allows to discriminate strains isolated from diseased fish from apathogenic strains and (ii) to assess the genetic relatedness between both groups of strains. In this study, we characterized by MLST 21 C. maltaromaticum strains including 16 strains isolated from diseased fish and 5 apathogenic dairy strains isolated from cheese. The resulting population structure was investigated by integrating these new data to the previously published population structure (available at http://pubmlst.org), which represents an overall of 71 strains. CONCLUSIONS: This analysis revealed that none of the strains isolated from diseased fish is assigned to a clonal complex containing cheese isolates, and that 11 strains exhibit singleton genotypes suggesting that the population of diseased fish isolates is not clonal. SIGNIFICANCE AND IMPACT OF THE STUDY: This study thus provides a population structure of C. maltaromaticum that could serve in the future as a reference that could contribute to the risk assessment of C. maltaromaticum strains intended to be used in the food chain.

Strain-level typing and identification of bacteria - a novel approach for SERS active plasmonic nanostructures.

One of the potential applications of surface-enhanced Raman spectroscopy (SERS) is the detection of biological compounds and microorganisms. Here we demonstrate that SERS coupled with principal component analysis (PCA) serves as a perfect method for determining the taxonomic affiliation of bacteria at the strain level. We demonstrate for the first time that it is possible to distinguish different genoserogroups within a single species, Listeria monocytogenes, which is one of the most virulent foodborne pathogens and in some cases contact with which may be fatal. We also postulate that it is possible to detect additional proteins in the L. monocytogenes cell envelope, which provide resistance to benzalkonium chloride and cadmium. A better understanding of this infectious agent could help in selecting the appropriate pharmaceutical product for enhanced treatment. Graphical abstract ᅟ.

The structured diversity of specialized gut symbionts of the New World army ants.

Symbiotic bacteria play important roles in the biology of their arthropod hosts. Yet the microbiota of many diverse and influential groups remain understudied, resulting in a paucity of information on the fidelities and histories of these associations. Motivated by prior findings from a smaller scale, 16S rRNA-based study, we conducted a broad phylogenetic and geographic survey of microbial communities in the ecologically dominant New World army ants (Formicidae: Dorylinae). Amplicon sequencing of the 16S rRNA gene across 28 species spanning the five New World genera showed that the microbial communities of army ants consist of very few common and abundant bacterial species. The two most abundant microbes, referred to as Unclassified Firmicutes and Unclassified Entomoplasmatales, appear to be specialized army ant associates that dominate microbial communities in the gut lumen of three host genera, Eciton, Labidus and Nomamyrmex. Both are present in other army ant genera, including those from the Old World, suggesting that army ant symbioses date back to the Cretaceous. Extensive sequencing of bacterial protein-coding genes revealed multiple strains of these symbionts coexisting within colonies, but seldom within the same individual ant. Bacterial strains formed multiple host species-specific lineages on phylogenies, which often grouped strains from distant geographic locations. These patterns deviate from those seen in other social insects and raise intriguing questions about the influence of army ant colony swarm-founding and within-colony genetic diversity on strain coexistence, and the effects of hosting a diverse suite of symbiont strains on colony ecology.

HuMiChip2 for strain level identification and functional profiling of human microbiomes.

With the massive data generated by the Human Microbiome Project, how to transform such data into useful information and knowledge remains challenging. Here, with currently available sequencing information (reference genomes and metagenomes), we have developed a comprehensive microarray, HuMiChip2, for strain-level identification and functional characterization of human microbiomes. HuMiChip2 was composed of 29,467 strain-specific probes targeting 2063 microbial strains/species and 133,924 sequence- and group-specific probes targeting 157 key functional gene families involved in various metabolic pathways and host-microbiome interaction processes. Computational evaluation of strain-specific probes suggested that they were not only specific to mock communities of sequenced microorganisms and metagenomes from different human body sites but also to non-sequenced microbial strains. Experimental evaluation of strain-specific probes using single strains/species and mock communities suggested a high specificity of these probes with their corresponding targets. Application of HuMiChip2 to human gut microbiome samples showed the patient microbiomes of alcoholic liver cirrhosis significantly (p < 0.05) shifted their functional structure from the healthy individuals, and the relative abundance of 21 gene families significantly (p < 0.1) differed between the liver cirrhosis patients and healthy individuals. At the strain level, five Bacteroides strains were significantly (p < 0.1) and more frequently detected in liver cirrhosis patients. These results suggest that the developed HuMiChip2 is a useful microbial ecological microarray for both strain-level identification and functional profiling of human microbiomes.

An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data.

With the development of sequencing technology and analytic tools, studying within-species variations enhances the understanding of microbial biological processes. Nevertheless, most existing methods designed for strain-level analysis lack the capability to concurrently assess both strain proportions and genome-wide single nucleotide variants (SNVs) across longitudinal metagenomic samples. In this study, we introduce LongStrain, an integrated pipeline for the analysis of large-scale metagenomic data from individuals with longitudinal or repeated samples. In LongStrain, we first utilize two efficient tools, Kraken2 and Bowtie2, for the taxonomic classification and alignment of sequencing reads, respectively. Subsequently, we propose to jointly model strain proportions and shared haplotypes across samples within individuals. This approach specifically targets tracking a primary strain and a secondary strain for each subject, providing their respective proportions and SNVs as output. With extensive simulation studies of a microbial community and single species, our results demonstrate that LongStrain is superior to two genotyping methods and two deconvolution methods across a majority of scenarios. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young study and a gastric intestinal metaplasia microbiome study. In summary, the proposed analytic pipeline demonstrates marked statistical efficiency over the same type of methods and has great potential in understanding the genomic variants and dynamic changes at strain level. LongStrain and its tutorial are freely available online at https://github.com/BoyanZhou/LongStrain. IMPORTANCE: The advancement in DNA-sequencing technology has enabled the high-resolution identification of microorganisms in microbial communities. Since different microbial strains within species may contain extreme phenotypic variability (e.g., nutrition metabolism, antibiotic resistance, and pathogen virulence), investigating within-species variations holds great scientific promise in understanding the underlying mechanism of microbial biological processes. To fully utilize the shared genomic variants across longitudinal metagenomics samples collected in microbiome studies, we develop an integrated analytic pipeline (LongStrain) for longitudinal metagenomics data. It concurrently leverages the information on proportions of mapped reads for individual strains and genome-wide SNVs to enhance the efficiency and accuracy of strain identification. Our method helps to understand strains' dynamic changes and their association with genome-wide variants. Given the fast-growing longitudinal studies of microbial communities, LongStrain which streamlines analyses of large-scale raw sequencing data should be of great value in microbiome research communities.

Strain-level multidrug-resistant pathogenic bacteria in urban wastewater treatment plants: Transmission, source tracking and evolution.

Wastewater treatment plants (WWTPs) serve as reservoirs for various pathogens and play a pivotal role in safeguarding environmental safety and public health by mitigating pathogen release. Pathogenic bacteria, known for their potential to cause fatal infections, present a significant and emerging threat to global health and remain poorly understood regarding their origins and transmission in the environment. Using metagenomic approaches, we identified a total of 299 pathogens from three full-scale WWTPs. We comprehensively elucidated the occurrence, dissemination, and source tracking of the pathogens across the WWTPs, addressing deficiencies in traditional detection strategies. While indicator pathogens in current wastewater treatment systems such as Escherichia coli are effectively removed, specific drug-resistant pathogens, including Pseudomonas aeruginosa, Pseudomonas putida, and Aeromonas caviae, persist throughout the treatment process, challenging complete eradication efforts. The anoxic section plays a predominant role in controlling abundance but significantly contributes to downstream pathogen diversity. Additionally, evolution throughout the treatment process enhances pathogen diversity, except for upstream transmission, such as A. caviae str. WP8-S18-ESBL-04 and P. aeruginosa PAO1. Our findings highlight the necessity of expanding current biomonitoring indicators for wastewater treatment to optimize treatment strategies and mitigate the potential health risks posed by emerging pathogens. By addressing these research priorities, we can effectively mitigate risks and safeguard environmental safety and public health.

Advances in the isolation, cultivation, and identification of gut microbes.

The gut microbiome is closely associated with human health and the development of diseases. Isolating, characterizing, and identifying gut microbes are crucial for research on the gut microbiome and essential for advancing our understanding and utilization of it. Although culture-independent approaches have been developed, a pure culture is required for in-depth analysis of disease mechanisms and the development of biotherapy strategies. Currently, microbiome research faces the challenge of expanding the existing database of culturable gut microbiota and rapidly isolating target microorganisms. This review examines the advancements in gut microbe isolation and cultivation techniques, such as culturomics, droplet microfluidics, phenotypic and genomics selection, and membrane diffusion. Furthermore, we evaluate the progress made in technology for identifying gut microbes considering both non-targeted and targeted strategies. The focus of future research in gut microbial culturomics is expected to be on high-throughput, automation, and integration. Advancements in this field may facilitate strain-level investigation into the mechanisms underlying diseases related to gut microbiota.

Identification of Escherichia coli strains using MALDI-TOF MS combined with long short-term memory neural networks.

The current study aims to develop a new technique for the precise identification of Escherichia coli strains, utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with a long short-term memory (LSTM) neural network. A total of 48 Escherichia coli strains were isolated and cultured on tryptic soy agar medium for 24 hours for the generation of MALDI-TOF MS spectra. Eight hundred MALDI-TOF MS spectra were obtained per strain, resulting in a database of 38,400 spectra. Fifty percent of the data was utilized for LSTM neural network training, with fine-tuned parameters for strain-level identification. The other half served as the test set to assess model performance. Traditional PCA dimension reduction of MALDI-TOF MS spectra indicated 47 out of 48 strains to be unclassifiable. In contrast, the LSTM neural network demonstrated remarkable efficacy. After 20 training epochs, the model achieved a loss value of 0.0524, an accuracy of 0.999, a precision of 0.985, and a recall of 0.982. When tested on the unseen data, the model attained an overall accuracy of 92.24%. The integration of MALDI-TOF MS and LSTM neural network markedly enhances the identification of Escherichia coli strains. This innovative approach offers an effective and accurate tool for MALDI-TOF MS-based strain-level identification, thus expanding the analytical capabilities of microbial diagnostics.

StrainIQ: A Novel n-Gram-Based Method for Taxonomic Profiling of Human Microbiota at the Strain Level.

The emergence of next-generation sequencing (NGS) technology has greatly influenced microbiome research and led to the development of novel bioinformatics tools to deeply analyze metagenomics datasets. Identifying strain-level variations in microbial communities is important to understanding the onset and progression of diseases, host-pathogen interrelationships, and drug resistance, in addition to designing new therapeutic regimens. In this study, we developed a novel tool called StrainIQ (strain identification and quantification) based on a new n-gram-based (series of n number of adjacent nucleotides in the DNA sequence) algorithm for predicting and quantifying strain-level taxa from whole-genome metagenomic sequencing data. We thoroughly evaluated our method using simulated and mock metagenomic datasets and compared its performance with existing methods. On average, it showed 85.8% sensitivity and 78.2% specificity on simulated datasets. It also showed higher specificity and sensitivity using n-gram models built from reduced reference genomes and on models with lower coverage sequencing data. It outperforms alternative approaches in genus- and strain-level prediction and strain abundance estimation. Overall, the results show that StrainIQ achieves high accuracy by implementing customized model-building and is an efficient tool for site-specific microbial community profiling.

Strain-Level Dynamics Reveal Regulatory Roles in Atopic Eczema by Gut Bacterial Phages.

The vast population of bacterial phages or viruses (virome) plays pivotal roles in the ecology of human microbial flora and health conditions. Obstacles, including poor viral sequence inference, strain-sensitive virus-host relationship, and the high diversity among individuals, hinder the in-depth understanding of the human virome. We conducted longitudinal studies of the virome based on constructing a high-quality personal reference metagenome (PRM). By applying long-read sequencing for representative samples, we could build a PRM of high continuity that allows accurate annotation and abundance estimation of viruses and bacterial species in all samples of the same individual by aligning short sequencing reads to the PRM. We applied this approach to a series of fecal samples collected for 6 months from a 2-year-old boy who had experienced a 2-month flare-up of atopic eczema (dermatitis) in this period. We identified 31 viral strains in the patient's gut microbiota and deciphered their strain-level relationship to their bacterial hosts. Among them, a lytic crAssphage developed into a dozen substrains and coordinated downregulation in the catabolism of aromatic amino acids (AAAs) in their host bacteria which govern the production of immune-active AAA derivates. The metabolic alterations confirmed based on metabolomic assays cooccurred with symptom remission. Our PRM-based analysis provides an easy approach for deciphering the dynamics of the strain-level human gut virome in the context of entire microbiota. Close temporal correlations among virome alteration, microbial metabolism, and disease remission suggest a potential mechanism for how bacterial phages in microbiota are intimately related to human health. IMPORTANCE The vast populations of viruses or bacteriophages in human gut flora remain mysterious. However, poor annotation and abundance estimation remain obstacles to strain-level analysis and clarification of their roles in microbiome ecology and metabolism associated with human health and diseases. We demonstrate that a personal reference metagenome (PRM)-based approach provides strain-level resolution for analyzing the gut microbiota-associated virome. When applying such an approach to longitudinal samples collected from a 2-year-old boy who has experienced a 2-month flare-up of atopic eczema, we observed thriving substrains of a lytic crAssphage, showing temporal correlation with downregulated catabolism of aromatic amino acids, lower production of immune-active metabolites, and remission of the disease. The PRM-based approach is practical and powerful for strain-centric analysis of the human gut virome, and the underlying mechanism of how strain-level virome dynamics affect disease deserves further investigation.