Skin microbe-dependent TSLP-ILC2 priming axis in early life is co-opted in allergic inflammation.

Although early life colonization of commensal microbes contributes to long-lasting immune imprinting in host tissues, little is known regarding the pathophysiological consequences of postnatal microbial tuning of cutaneous immunity. Here, we show that postnatal exposure to specific skin commensal Staphylococcus lentus (S. lentus) promotes the extent of atopic dermatitis (AD)-like inflammation in adults through priming of group 2 innate lymphoid cells (ILC2s). Early postnatal skin is dynamically populated by discrete subset of primed ILC2s driven by microbiota-dependent induction of thymic stromal lymphopoietin (TSLP) in keratinocytes. Specifically, the indole-3-aldehyde-producing tryptophan metabolic pathway, shared across Staphylococcus species, is involved in TSLP-mediated ILC2 priming. Furthermore, we demonstrate a critical contribution of the early postnatal S. lentus-TSLP-ILC2 priming axis in facilitating AD-like inflammation that is not replicated by later microbial exposure. Thus, our findings highlight the fundamental role of time-dependent neonatal microbial-skin crosstalk in shaping the threshold of innate type 2 immunity co-opted in adulthood.

Antibiotic Sensitivity and Nasal Microbiome in Patients with Acute Bacterial Rhinosinusitis.

OBJECTIVES: Acute rhinosinusitis (ARS) is a common upper respiratory tract infection that is mostly of viral origin. However, little is known about the nasal microbiome profile at presentation and the changes caused by antibiotics in acute bacterial rhinosinusitis (ABRS). METHODS: This was a prospective single-center study. Overall, 43 ARS patients were screened and were assessed with the symptom questionnaires, nasal endoscopy, and Water's view. Five healthy subjects were recruited as controls. Middle meatal mucus samples were obtained using a cotton swab (for bacterial culture and antimicrobial susceptibility testing) and the suction technique (for 16S rRNA sequencing). After 1 week of antibiotic use (amoxicillin with clavulanic acid), we enrolled 13 patients with ABRS with positive isolates and middle meatal samples for 16S rRNA sequencing were obtained again. RESULTS: Overall, we demonstrated a significantly lower abundance of the Lactobacillaceae family in ABRS patients than in healthy controls. Resistant ABRS had different characteristics of middle meatal microbiomes when compared to sensitive ABRS as follows: (1) lower proportion of lactic acid bacteria, (2) increased pathogens such as Rhodococcus sp., Massila sp., Acinetobacter sp., and H. influenza, and (3) increased beta diversity. However, no remarkable changes were observed in the middle meatal microbiome after antibiotic use. CONCLUSION: We showed the roles of Lactobacillaceae in ABRS, and Acinetobacter and Massilia in case of amoxicillin resistance. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1081-1088, 2024.

Comparative analyses of the faecal resistome against ß-lactam and quinolone antibiotics in humans and livestock using metagenomic sequencing.

To assess the prevalence and abundance of antibiotic resistance genes in human and livestock gut microbiomes, 87 humans (healthy individuals and patients with Clostridioides difficile infection (CDI)) and 108 livestock (swine, cattle, and chickens) were enrolled. Gut microbiomes and fluoroquinolone-resistant Escherichia coli isolates were sequenced, and mobile genetic elements adjacent to the ß-lactamase (bla) and transferable quinolone resistance (qnr) genes were compared using metagenomic contigs. Each group of humans and livestock exhibited distinctive microbiota and resistome compositions in the gut. Concerning the resistome of bla and qnr, the prevalence rates between chickens and patients with CDI were the most similar (R2 = 0.46); blaTEM, blaOXA, blaCTX-M, and qnrS were highly prevalent in both groups. According to genomic and phylogenetic analyses, blaCTX-M and blaOXA expressed lineage specificity to either humans or livestock, while qnrS and blaTEM displayed a shared lineage between humans and livestock. A qnrS1 mobilome comprising five genes, including two recombinases, a transposase, and a plasmid gene, is commonly found in human and chicken gut microbiomes. Humans and chickens showed the most similar gut resistomes to ß-lactams and quinolones. QnrS and blaTEM displayed especially strong co-occurrence between the guts of humans and livestock.

Identification of Micrococcin P2-Derivatives as Antibiotic Candidates against Two Gram-Positive Pathogens.

Thiopeptides exhibit potent antimicrobial activity against Gram-positive pathogens by inhibiting bacterial protein synthesis. Micrococcins are among the structurally simpler thiopeptides, but they have not been exploited in detail. This research involved a computational simulation of micrococcin P2 (MP2) docking in parallel with the structure-activity relationship (SAR) studied. The incorporation of particular nitrogen heterocycles in the side chain of MP2 enhances the antimicrobial activity. Micrococcin analogues 6c and 6d thus proved to be more effective against impetigo and C. difficile infection (CDI), respectively, as compared to current first-line treatments. Compound 6c also showed a shorter treatment period than that of a first-line treatment for impetigo. This may be attributed to its ability to downregulate pro-inflammatory cytokines. Compound 6d had no observed recurrence for C. difficile and exerted a minimal impact on the beneficial gut microbiome. Their pharmacokinetic properties and low toxicity profile make these compounds ideal candidates for the treatment of impetigo and CDI and validate their involvement in preclinical development.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes.

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Discovery and Characterization of Polymyxin-Resistance Genes pmrE and pmrF from Sediment and Seawater Microbiome.

Polymyxins are the last-line antibiotics used to treat Gram-negative pathogens. Thus, the discovery and biochemical characterization of the resistance genes against polymyxins are urgently needed for diagnosis, treatment, and novel antibiotic design. Herein, we report novel polymyxin-resistance genes identified from sediment and seawater microbiome. Despite their low sequence identity against the known pmrE and pmrF, they show in vitro activities in UDP-glucose oxidation and l-Ara4N transfer to undecaprenyl phosphate, respectively, which occur as the part of lipid A modification that leads to polymyxin resistance. The expression of pmrE and pmrF also showed substantially high MICs in the presence of vanadate ions, indicating that they constitute polymyxin resistomes. IMPORTANCE Polymyxins are one of the last-resort antibiotics. Polymyxin resistance is a severe threat to combat multidrug-resistant pathogens. Thus, up-to-date identification and understanding of the related genes are crucial. Herein, we performed structure-guided sequence and activity analysis of five putative polymyxin-resistant metagenomes. Despite relatively low sequence identity to the previously reported polymyxin-resistance genes, at least four out of five discovered genes show reactivity essential for lipid A modification and polymyxin resistance, constituting antibiotic resistomes.

Eosinophil-derived interferon-γ drives transmembrane protein 119-induced new bone formation in chronic rhinosinusitis with nasal polyps.

BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory sinonasal disease characterized by eosinophilic infiltration and new bone formation. These changes indicate the severity and prognosis of CRSwNP and may be closely linked to each other. METHODS: We performed RNA sequencing to screen specific osteogenic molecules and validated transmembrane protein 119 (TMEM119) expression by quantitative polymerase chain reaction (qPCR) and immunohistochemistry analyses. TMEM119 knockdown was performed to observe the downregulation of bone mineralization. We validated the bone-forming activity of interferon-γ (IFN-γ) and its signaling pathways in cultured primary sinus bone cells. Cellular sources of IFN-γ were identified using immunohistochemistry and immunofluorescence analyses. Interleukin-4-eosinophil-IFN-γ axis and the effect of dupilumab were investigated in Eol-1 cells. RESULTS: We observed elevated IFN-γ levels and eosinophils in the nasal fluid and predominantly eosinophil-derived IFN-γ in the sinus mucosa of patients with CRSwNP. TMEM119 expression and bone-forming activities were increased in the osteitic and primary sinus bone cells of CRSwNP. IFN-γ treatment enhanced bone mineralization and TMEM119 expression via signal transducer and activator of transcription 1 (STAT1) signaling. Moreover, TMEM119 knockdown inhibited sinus bone cell mineralization and dupilumab attenuated IFN-γ secretion by IL4-stimulated Eol-1 cells. CONCLUSION: Eosinophil-derived IFN-γ promotes the bone-forming activities of sinus bone cells via the STAT1-TMEM119 signaling pathway. Interleukin-4-eosinophil-IFN-γ axis may be crucial for TMEM119-mediated new bone formation in CRSwNP.

Metagenomic characterization of sphingomyelinase C in the microbiome of humans and environments.

Bacterial sphingomyelinases (SMases) hydrolyze sphingomyelin and play an important role in membrane dynamics and the host immune system. While the number of sequenced genomes and metagenomes is increasing, a limited number of experimentally validated SMases have been reported, and the genomic diversity of SMases needs to be elucidated extensively. This study investigated the sequence and structural characteristics of SMases in bacterial genomes and metagenomes. Using previously identified SMases, such as the ß-toxin of Staphylococcus aureus, we identified 276 putative SMases and 15 metagenomic SMases by a sequence homology search. Among the predicted metagenomic SMases, six non-redundant metagenomic SMases (M-SMase1-6) were selected for further analysis. The predicted SMases were confirmed to contain highly conserved residues in the central metal-binding site; however, the edge metal-binding site showed high diversity according to the taxon. In addition, protein structure modeling of metagenomic SMases confirmed structural conservation of the central metal-binding site and variance of the edge metal-binding site. From the activity assay on M-SMase2 and M-SMase5, we found that they displayed sphingomyelinase activity compared to Bacillus cereus SMase. This study elucidates a comprehensive genomic characterization of SMases and provides insight into the sequence-structure-activity relationship.

Different maturation of gut microbiome in Korean children.

Introduction: Gut microbiome plays a crucial role in maintaining human health and is influenced by food intake, age, and other factors. Methods: In this study based in Korea, we examined the bacterial taxonomic composition of the gut microbiota in infants (≤ 1 year), toddlers (1-<4 years), and school-aged children (4-13 years) and compared them with those of healthy adults to investigate the microbiota changes in early life and their association with the resistome. We used whole metagenome sequences obtained by Illumina HiSeq sequencing and clinical information of 53 healthy children, and sequence data of 61 adults from our previous study. Results: Our results indicate that the bacterial proportion of the gut in the population ranging from infants to adults forms three clusters: the Ruminococcus-Eubacterium (G1), Bifidobacterium-Escherichia (G2), and Bacteroides-Faecalibacterium (G3) groups. The gut microbiota of infants and toddlers (100% of infants and 85% of toddlers) constituted mostly of G2 and G3 groups, whereas 90% of adults showed G1-type gut microbiota. School-aged children showed a transitional gut microbiota composition of both infants and adults (31%, 38%, and 31% in G1, G2, and G3, respectively). Notably, the three clusters of microbiota showed significantly different patterns of bacterial diversity (p < 0.001): G2 showed the lowest Shannon index, followed by G3 and G1 (1.41, 2.08, and 2.48, respectively; median Shannon index). When combined with the adult group, alpha diversity showed a positive correlation with age (R2 = 0.3). Furthermore, clustering the composition of antibiotic resistance genes (ARG) identified two clusters (A1 and A2), and most of G1 (95%) and G3 (80%) belonged to A1. However, G2 showed the least diversity and the highest abundance of ARGs. Nine ARG families showed a significant difference among age groups; three tetracycline resistance genes, tet32, tetO, and tetW, showed a positive correlation, and six other genes, ampC, TEM, ileS, bacA, pmr transferase, and cepA, showed a negative correlation with age. Discussion: In conclusion, our results highlighted that a delayed persistence of the Bifidobacterium-dominant enterotype with a lower bacterial diversity was observed in Korean children up to 13 years of age, which suggests a different maturation process with a delayed maturation time.

Deciphering Resistome in Patients With Chronic Obstructive Pulmonary Diseases and Clostridioides difficile Infections.

Antibiotics alter the gut microbiome and cause dysbiosis leading to antibiotic-resistant organisms. Different patterns of antibiotic administration cause a difference in bacterial composition and resistome in the human gut. We comprehensively investigated the association between the distribution of antibiotic resistance genes (ARGs), bacterial composition, and antibiotic treatments in patients with chronic obstructive pulmonary diseases (COPD) and Clostridioides difficile infections (CDI) who had chronic or acute intermittent use of antibiotics and compared them with healthy individuals. We analyzed the gut microbiomes of 61 healthy individuals, 16 patients with COPD, and 26 patients with CDI. The COPD patients were antibiotic-free before stool collection for a median of 40 days (Q1: 9.5; Q3: 60 days), while the CDI patients were antibiotic-free for 0 days (Q1: 0; Q3: 0.3). The intra-group beta diversity measured by the median Bray-Curtis index was the lowest for the healthy individuals (0.55), followed by the COPD (0.69) and CDI groups (0.72). The inter-group beta diversity was the highest among the healthy and CDI groups (median index = 0.89). The abundance of ARGs measured by the number of reads per kilobase per million reads (RPKM) was 684.2; 1,215.2; and 2,025.1 for the healthy, COPD, and CDI groups. It was negatively correlated with the alpha diversity of bacterial composition. For the prevalent ARG classes, healthy individuals had the lowest diversity and abundance of aminoglycoside, ß-lactam, and macrolide-lincosamide-streptogramin (MLS) resistance genes, followed by the COPD and CDI groups. The abundances of Enterococcus and Escherichia species were positively correlated with ARG abundance and the days of antibiotic treatment, while Bifidobacterium and Ruminococcus showed negative correlations for the same. In addition, we analyzed the mobilome patterns of aminoglycoside and ß-lactam resistance gene carriers using metagenomic sequencing data. In conclusion, the ARGs were significantly enhanced in the CDI and COPD groups than in healthy individuals. In particular, aminoglycoside and ß-lactam resistance genes were more abundant in the CDI and COPD groups, but the dominant mobile genetic elements that enable the transfer of such genes showed similar prevalence patterns among the groups.

ViBE: a hierarchical BERT model to identify eukaryotic viruses using metagenome sequencing data.

Viruses are ubiquitous in humans and various environments and continually mutate themselves. Identifying viruses in an environment without cultivation is challenging; however, promoting the screening of novel viruses and expanding the knowledge of viral space is essential. Homology-based methods that identify viruses using known viral genomes rely on sequence alignments, making it difficult to capture remote homologs of the known viruses. To accurately capture viral signals from metagenomic samples, models are needed to understand the patterns encoded in the viral genomes. In this study, we developed a hierarchical BERT model named ViBE to detect eukaryotic viruses from metagenome sequencing data and classify them at the order level. We pre-trained ViBE using read-like sequences generated from the virus reference genomes and derived three fine-tuned models that classify paired-end reads to orders for eukaryotic deoxyribonucleic acid viruses and eukaryotic ribonucleic acid viruses. ViBE achieved higher recall than state-of-the-art alignment-based methods while maintaining comparable precision. ViBE outperformed state-of-the-art alignment-free methods for all test cases. The performance of ViBE was also verified using real sequencing datasets, including the vaginal virome.

Multimodal deep learning applied to classify healthy and disease states of human microbiome.

Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which could be used in the diagnosis of patients. Despite significant progress in this regard, the accuracy of these tools needs to be improved for applications in diagnostics and therapeutics. MDL4Microbiome, the method developed herein, demonstrated high accuracy in predicting disease status by using various features from metagenome sequences and a multimodal deep learning model. We propose combining three different features, i.e., conventional taxonomic profiles, genome-level relative abundance, and metabolic functional characteristics, to enhance classification accuracy. This deep learning model enabled the construction of a classifier that combines these various modalities encoded in the human microbiome. We achieved accuracies of 0.98, 0.76, 0.84, and 0.97 for predicting patients with inflammatory bowel disease, type 2 diabetes, liver cirrhosis, and colorectal cancer, respectively; these are comparable or higher than classical machine learning methods. A deeper analysis was also performed on the resulting sets of selected features to understand the contribution of their different characteristics. MDL4Microbiome is a classifier with higher or comparable accuracy compared with other machine learning methods, which offers perspectives on feature generation with metagenome sequences in deep learning models and their advantages in the classification of host disease status.

A case study on the distribution of the environmental resistome in Korean shrimp farms.

Hundreds of tons of antibiotics are widely used in aquaculture to prevent microbial infections and promote fish growth. However, the overuse of antibiotics and chemical products can lead to the selection and spreading of antibiotic-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), which are of great concern considering the threat to public health worldwide. Here, in-depth metagenome sequencing was performed to explore the environmental resistome and ARB distribution across farming stages in shrimp farms and examine anthropogenic effects in nearby coastal waters. A genome-centric analysis using a metagenome binning approach allowed us to accurately investigate the distribution of pathogens and ARG hosts in shrimp farms. The diversity of resistomes was higher in shrimp farms than in coastal waters, and the distribution of resistomes was dependent on the farming stage. In particular, the tetracycline resistance gene was found mainly at the early post-larval stage regardless of the farm. The metagenome-assembled genomes of Vibrio spp. were dominant at this stage and harbored tet34, which is known to confer resistance to oxytetracycline. In addition, opportunistic pathogens such as Francisella, Mycoplasma, Photobacterium, and Vibrio were found in abundance in shrimp farms, which had multiple virulence factors. This study highlights the increased resistance diversity and environmental selection of pathogens in shrimp farms. The use of environmental pollutants on farms may cause an increase in resistome diversity/abundance and the transmission of pathogens to the surrounding environment, which may pose future risks to public health and aquatic organisms.

Association of Microbial Dysbiosis with Gallbladder Diseases Identified by Bile Microbiome Profiling.

BACKGROUND: Cholecystitis is an important risk factor for gallbladder cancer, but the bile microbiome and its association with gallbladder disease has not been investigated fully. We aimed to analyze the bile microbiome in normal conditions, chronic cholecystitis, and gallbladder cancer, and to identify candidate bacteria that play an important role in gallbladder carcinogenesis. METHODS: We performed metagenome sequencing on bile samples of 10 healthy individuals, 10 patients with chronic cholecystitis, and 5 patients with gallbladder cancer, and compared the clinical, radiological, and pathological characteristics of the participants. RESULTS: No significant bacterial signal was identified in the normal bile. The predominant dysbiotic bacteria in both chronic cholecystitis and gallbladder cancer were those belonging to the Enterobacteriaceae family. Klebsiella increased significantly in the order of normal, chronic cholecystitis, and gallbladder cancer. Patients with chronic cholecystitis and dysbiotic microbiome patterns had larger gallstones and showed marked epithelial atypia, which are considered as precancerous conditions. CONCLUSION: We investigated the bile microbiome in normal, chronic cholecystitis, and gallbladder cancer. We suggest possible roles of Enterobacteriaceae, including Klebsiella, in gallbladder carcinogenesis. Our findings reveal a possible link between a dysbiotic bile microbiome and the development of chronic calculous cholecystitis and gallbladder cancer.

Genomic Determinants Encode the Reactivity and Regioselectivity of Flavin-Dependent Halogenases in Bacterial Genomes and Metagenomes.

Halogenases create diverse natural products by utilizing halide ions and are of great interest in the synthesis of potential pharmaceuticals and agrochemicals. An increasing number of halogenases discovered in microorganisms are annotated as flavin-dependent halogenases (FDHs), but their chemical reactivities are markedly different and the genomic contents associated with such functional distinction have not been revealed yet. Even though the reactivity and regioselectivity of FDHs are essential in the halogenation activity, these FDHs are annotated inaccurately in the protein sequence repositories without characterizing their functional activities. We carried out a comprehensive sequence analysis and biochemical characterization of FDHs. Using a probabilistic model that we built in this study, FDHs were discovered from 2,787 bacterial genomes and 17 sediment metagenomes. We analyzed the essential genomic determinants that are responsible for substrate binding and subsequent reactions: four flavin adenine dinucleotide-binding, one halide-binding, and four tryptophan-binding sites. Compared with previous studies, our study utilizes large-scale genomic information to propose a comprehensive set of sequence motifs that are related to the active sites and regioselectivity. We reveal that the genomic patterns and phylogenetic locations of the FDHs determine the enzymatic reactivities, which was experimentally validated in terms of the substrate scope and regioselectivity. A large portion of publicly available FDHs needs to be reevaluated to designate their correct functions. Our genomic models establish comprehensive links among genotypic information, reactivity, and regioselectivity of FDHs, thereby laying an important foundation for future discovery and classification of novel FDHs. IMPORTANCE Halogenases are playing an important role as tailoring enzymes in biosynthetic pathways. Flavin-dependent tryptophan halogenases (Trp-FDHs) are among the enzymes that have broad substrate scope and high selectivity. From bacterial genomes and metagenomes, we found highly diverse halogenase sequences by using a well-trained profile hidden Markov model built from the experimentally validated halogenases. The characterization of genotype, steady-state activity, substrate scope, and regioselectivity has established comprehensive links between the information encoded in the genomic sequence and reactivity of FDHs reported here. By constructing models for accurate and detailed sequence markers, our work should guide future discovery and classification of novel FDHs.

High fecal carriage of blaCTX-M, blaCMY-2, and plasmid-mediated quinolone resistance genes among healthy Korean people in a metagenomic analysis.

To characterize the carriage of antibiotic resistance genes (ARGs) in the gut microbiome of healthy individuals. Fecal carriage of ARGs was investigated in 61 healthy individuals aged 30 to 59 years through whole metagenome sequencing of the gut microbiome and a targeted metagenomic approach. The number of ARGs in the gut microbiome was counted and normalized per million predicted genes (GPM). In the Korean population, the resistome ranged from 49.7 to 292.5 GPM (median 89.7). Based on the abundance of ARGs, the subjects were categorised into high (> 120 GPM), middle (60‒120 GPM), and low (< 60 GPM) ARG groups. Individuals in the high ARG group tended to visit hospitals more often (P = 0.065), particularly for upper respiratory tract infections (P = 0.066), and carried more blaCTX-M (P = 0.008). The targeted metagenome approach for bla and plasmid-mediated quinolone resistance (PMQR) genes revealed a high fecal carriage rate; 23% or 13.1% of the subjects carried blaCTX-M or blaCMY-2, respectively. Regarding PMQR genes, 59% of the subjects carried PMQR, and 83% of them harboured 2‒4 PMQR genes (qnrB 44.3%, qnrS 47.5% etc.). The presence of blaCTX-M correlated with ARG abundance in the gut resistome, whereas PMQR genes were irrelevant to other ARGs (P = 0.176). Fecal carriage of blaCTX-M and PMQR genes was broad and multiplexed among healthy individuals.

Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.

With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .

Application of computational approaches to analyze metagenomic data.

Microorganisms play a vital role in living systems in numerous ways. In the soil or ocean environment, microbes are involved in diverse processes, such as carbon and nitrogen cycle, nutrient recycling, and energy acquisition. The relation between microbial dysbiosis and disease developments has been extensively studied. In particular, microbial communities in the human gut are associated with the pathophysiology of several chronic diseases such as inflammatory bowel disease and diabetes. Therefore, analyzing the distribution of microorganisms and their associations with the environment is a key step in understanding nature. With the advent of next-generation sequencing technology, a vast amount of metagenomic data on unculturable microbes in addition to culturable microbes has been produced. To reconstruct microbial genomes, several assembly algorithms have been developed by incorporating metagenomic features, such as uneven depth. Since it is difficult to reconstruct complete microbial genomes from metagenomic reads, contig binning approaches were suggested to collect contigs that originate from the same genome. To estimate the microbial composition in the environment, various methods have been developed to classify individual reads or contigs and profile bacterial proportions. Since microbial communities affect their hosts and environments through metabolites, metabolic profiles from metagenomic or metatranscriptomic data have been estimated. Here, we provide a comprehensive review of computational methods that can be applied to investigate microbiomes using metagenomic and metatranscriptomic sequencing data. The limitations of metagenomic studies and the key approaches to overcome such problems are discussed.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.

BACKGROUND: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. RESULTS: We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. CONCLUSIONS: Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes.

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1-V2 and V3-V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.