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Introduction: The effect of co-inoculation with plant growth-promoting bacteria on the microbiome of soybean roots was investigated in a field experiment. Soybean plants were inoculated with Bacillus subtilis , Bacillus aryabhattai , Streptomyces sp., and Saccharopolyspora spinosa and compared to a control treatment that received mineral fertilization. Methods: The yield parameters and endophytic microbiome of soybean roots were evaluated. Results: No significant differences in yield were observed among the treatments, suggesting that microbial inoculation can serve as an alternative to mineral fertilization without compromising productivity. Among the most abundant genera, there was a high prevalence of members of the phylum Proteobacteria (21 of the top 25 genera). Overall, the genera of these phyla represented 88.61% of the samples on average. There were also genera in the phyla Bacteroidetes (2/25), Actinobacteria (1/25), and Firmicutes (1/25). The massive presence of Bradyrhizobium , which represented 71.22% of the sequences at the genus level, was remarkable. Bradyrhizobium was the most abundant genus in all samples, except for Saccharopolyspora spinosa (ST treatment), whose abundance was only 12.66%. Co-occurrence network analysis revealed changes in the microbial community structure and genera considered as hubs. Discussion: These findings demonstrate the potential of co-inoculation with plant growth-promoting bacteria to modulate the root microbiome and enhance the colonization of B. japonicum , which may contribute to improving the efficiency of this symbiont in promoting plant growth. Further research is required to elucidate the mechanisms underlying these interactions and their implications for soybean productivity. [ABSTRACT FROM AUTHOR]

Chemical exposure in the environment can adversely affect the biodiversity of living organisms, particularly when persistent chemicals accumulate over time and disrupt the balance of microbial populations. In this study, we examined how chemical contaminants influence microorganisms in sediment and overlaying water samples collected from the Kinnickinnic, Milwaukee, and Menomonee Rivers near Milwaukee, Wisconsin, USA. We characterized these samples using shotgun metagenomic sequencing to assess microbiome diversity and employed chemical analyses to quantify more than 200 compounds spanning 16 broad classes, including pesticides, industrial products, personal care products, and pharmaceuticals. Integrative and differential comparative analyses of the combined datasets revealed that microbial density, approximated by adjusted total sequence reads, declined with increasing total chemical concentrations. Protozoan, metazoan, and fungal populations were negatively correlated with higher chemical concentrations, whereas certain bacterial (particularly Proteobacteria) and archaeal populations showed positive correlations. As expected, sediment samples exhibited higher concentrations and a wider dynamic range of chemicals compared to water samples. Varying levels of chemical contamination appeared to shape the distribution of microbial taxa, with some bacterial, metazoan, and protozoan populations present only at certain sites or in specific sample types (sediment versus water). These findings suggest that microbial diversity may be linked to both the type and concentration of chemicals present. Additionally, this study demonstrates the potential roles of multiple microbial kingdoms in degrading environmental pollutants, emphasizing the metabolic versatility of bacteria and archaea in processing complex contaminants such as polyaromatic hydrocarbons and bisphenols. Through functional and resistance gene profiling, we observed that multi-kingdom microbial consortia—including bacteria, fungi, and protozoa—can contribute to bioremediation strategies and help restore ecological balance in contaminated ecosystems. This approach may also serve as a valuable proxy for assessing the types and levels of chemical pollutants, as well as their effects on biodiversity. [ABSTRACT FROM AUTHOR]

Purpose: This is the first study to attempt microbiome diversity using metagenomic full-length 16S rRNA from respiratory specimens suspected of chronic pulmonary TB patients. Materials and methods: A 33 patients with suspected chronic pulmonary TB were included. Sputum specimens were cultured to detect mycobacterium sp. and extracted using QiAmp DNA mini kit modification and 16S rRNA metagenomic sequencing by nanopore grid ion sequencer. Microbiome analysis was performed using Pavian and Krona tools. Results: 9 patients were diagnosed with TB based on GeneXpert MTB/RIF assay, and 3 patients were detected with NTM pulmonary infection.The common genera identified from TB culture positive patients were streptococcus sp., prevotella sp., and veilonella sp. However, less was detected in two NTM infection patients. Metagenomic analysis revealed community bacteria species, including mycobacterium tuberculosis and NTM species, with the lowest number of unique reads. The abundance of streptococcus sp. were less than 30% in 4 patient with comorbid diabetes mellitus. Conclusions: Metagenomic targeted 16SrRNA full-length sequencing in the clinical respiratory specimen can provide diagnostic insight beyond standard microbiologic cultures and detailed profiling of microbial communities at the species level. [ABSTRACT FROM AUTHOR]

Introduction: The contribution of gut microbiota to health and disease is now well accepted and under continuous investigation. It is the most representative in the body (about 70% of the total) with over 400 different species of microorganisms living there. It is exclusive to each individual and therefore represents a real biological imprint, capable of distinguishing humans from one another. Gut microbiota works synergically with the host, promoting health by performing protective, metabolic and structural functions. Thus, when a modification of the personal normal gut microbiota composition occurs, a predisposition to develop different diseases may increase. Probiotics are functional food that may modulate gut microbiota stimulating protective bacterial properties and inhibiting detrimental activity. We have previously reported a beneficial effect from the administration of an enriched nutraceutical composition containing probiotics in reducing irritable bowel syndrome-related symptoms and improving metabolic profile by lowering triglycerides. However, if these effects are attributed to gut microbiota remain unknown. Objective: This analysis aims at evaluating microbiota modulation in subjects before and after Triobiotix® administration using a metagenomic approach Methods: Triobiotix® is an enriched nutraceutical composition containing maltodextrin, hyaluroic acid and mineralized seaweed extract Lithothamnion Calcareum plus a mix of five different lactic bacterial species. A total of 46 subjects with availability of sample test before and after treatment were included in the present analysis selected from 265 subjects. A fecal sample was collected before and up to 90 days after the administration of Triobiotix®. Feces were analyzed by Wellmicro® gut test that uses the next-generation sequencing of the 16S rRNA gene on Illumina MiSeq. Additionally, for 171 subjects a 90-day clinical evaluation was available. Results: Treatment with this enriched nutraceutical for up to 3 months resulted in a positive modulation of the main indices of improved gut microbiota, such as biodiversity and dysbiosis. Improvement on metabolic functions, such as glycemic and lipid metabolisms, as well as on immunological and anti-inflammatory activities have been described, thus indicating that the observed clinical benefits associated with the administration of this nutraceutical formulation might be also attributed to a gut microbiota modulation. Improvement in main IBS-related symptoms and bloating was maintained up to 90 days as well as microbiota diversity. Conclusions: Our analysis suggests that the gut microbiota of subjects taking Triobiotix® rapidly returns to a high diversity with reduced dysbiosis after treatment with associated improvement of main physiological functions linked to brain, heart and liver. [ABSTRACT FROM AUTHOR]

IntroductionThe effect of co-inoculation with plant growth-promoting bacteria on the microbiome of soybean roots was investigated in a field experiment. Soybean plants were inoculated with Bacillus subtilis, Bacillus aryabhattai, Streptomyces sp., and Saccharopolyspora spinosa and compared to a control treatment that received mineral fertilization.MethodsThe yield parameters and endophytic microbiome of soybean roots were evaluated.ResultsNo significant differences in yield were observed among the treatments, suggesting that microbial inoculation can serve as an alternative to mineral fertilization without compromising productivity. Among the most abundant genera, there was a high prevalence of members of the phylum Proteobacteria (21 of the top 25 genera). Overall, the genera of these phyla represented 88.61% of the samples on average. There were also genera in the phyla Bacteroidetes (2/25), Actinobacteria (1/25), and Firmicutes (1/25). The massive presence of Bradyrhizobium, which represented 71.22% of the sequences at the genus level, was remarkable. Bradyrhizobium was the most abundant genus in all samples, except for Saccharopolyspora spinosa (ST treatment), whose abundance was only 12.66%. Co-occurrence network analysis revealed changes in the microbial community structure and genera considered as hubs.DiscussionThese findings demonstrate the potential of co-inoculation with plant growth-promoting bacteria to modulate the root microbiome and enhance the colonization of B. japonicum, which may contribute to improving the efficiency of this symbiont in promoting plant growth. Further research is required to elucidate the mechanisms underlying these interactions and their implications for soybean productivity.

Aging clocks are predictive models of biological age derived from age-related changes, such as epigenetic changes, blood biomarkers, and, more recently, the microbiome. Gut and skin microbiota regulate more than barrier and immune function. Recent studies have shown that human microbiomes may predict aging. In this narrative review, we aim to discuss how the gut and skin microbiomes influence aging clocks as well as clarify the distinction between chronological and biological age. A literature search was performed on PubMed/MEDLINE databases with the following keywords: "skin microbiome" OR "gut microbiome" AND "aging clock" OR "epigenetic". Gut and skin microbiomes may be utilized to create aging clocks based on taxonomy, biodiversity, and functionality. The top contributing microbiota or metabolic pathways in these aging clocks may influence aging clock predictions and biological age. Furthermore, gut and skin microbiota may directly and indirectly influence aging clocks through the regulation of clock genes and the production of metabolites that serve as substrates or enzymatic regulators. Microbiome-based aging clock models may have therapeutic potential. However, more research is needed to advance our understanding of the role of microbiota in aging clocks. [ABSTRACT FROM AUTHOR]

In a large cohort of 1,772 participants from the Hispanic Community Health Study/Study of Latinos with overlapping 16SV4 rRNA gene (bacterial amplicon), ITS1 (fungal amplicon), and shotgun sequencing data, we demonstrate that 16SV4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. In contrast, for fungal taxa, we did not observe meaningful agreements between shotgun and ITS1 amplicon results. Finally, we show that amplicon and shotgun data can be harmonized and pooled to yield larger microbiome datasets with excellent agreement (

Chemical exposure in the environment can adversely affect the biodiversity of living organisms, particularly when persistent chemicals accumulate over time and disrupt the balance of microbial populations. In this study, we examined how chemical contaminants influence microorganisms in sediment and overlaying water samples collected from the Kinnickinnic, Milwaukee, and Menomonee Rivers near Milwaukee, Wisconsin, USA. We characterized these samples using shotgun metagenomic sequencing to assess microbiome diversity and employed chemical analyses to quantify more than 200 compounds spanning 16 broad classes, including pesticides, industrial products, personal care products, and pharmaceuticals. Integrative and differential comparative analyses of the combined datasets revealed that microbial density, approximated by adjusted total sequence reads, declined with increasing total chemical concentrations. Protozoan, metazoan, and fungal populations were negatively correlated with higher chemical concentrations, whereas certain bacterial (particularly Proteobacteria) and archaeal populations showed positive correlations. As expected, sediment samples exhibited higher concentrations and a wider dynamic range of chemicals compared to water samples. Varying levels of chemical contamination appeared to shape the distribution of microbial taxa, with some bacterial, metazoan, and protozoan populations present only at certain sites or in specific sample types (sediment versus water). These findings suggest that microbial diversity may be linked to both the type and concentration of chemicals present. Additionally, this study demonstrates the potential roles of multiple microbial kingdoms in degrading environmental pollutants, emphasizing the metabolic versatility of bacteria and archaea in processing complex contaminants such as polyaromatic hydrocarbons and bisphenols. Through functional and resistance gene profiling, we observed that multi-kingdom microbial consortia—including bacteria, fungi, and protozoa—can contribute to bioremediation strategies and help restore ecological balance in contaminated ecosystems. This approach may also serve as a valuable proxy for assessing the types and levels of chemical pollutants, as well as their effects on biodiversity.

Black soldier fly larvae have been proven to reduce greenhouse gas emissions in the treatment of organic waste. However, the microbial mechanisms involved have not been fully understood. The current study mainly examined the dynamic changes of carbon and nitrogen, greenhouse gas emissions, the succession of microbial community structure, and changes in functional gene abundance in organic waste under larvae treatment and non-aeration composting. Thirty percent carbon and 55% nitrogen in the organic waste supplied were stored in larvae biomass. Compared to the non-aeration composting, the larvae bioreactor reduced the proportion of carbon and nitrogen converted into greenhouse gases (CO2, CH4, and N2O decreased by 62%, 87%, and 95%, respectively). 16S rRNA sequencing analysis indicated that the larvae bioreactor increased the relative abundance of Methanophaga, Marinobacter, and Campylobacter during the bioprocess, enhancing the consumption of CH4 and N2O. The metagenomic data showed that the intervention of larvae reduced the ratio of (nirK + nirS + nor)/nosZ in the residues, thereby reducing the emission of N2O. Larvae also increased the functional gene abundance of nirA, nirB, nirD, and nrfA in the residues, making nitrite more inclined to be reduced to ammonia instead of N2O. The larvae bioreactor mitigated greenhouse gas emissions by redistributing carbon and nitrogen and remodeling microbiomes during waste bioconversion, giving related enterprises a relative advantage in carbon trading. [ABSTRACT FROM AUTHOR]

IntroductionResearch has illustrated the presence of a diverse range of microbiota in human milk. The composition of the milk microbiome varies across different stages of lactation, emphasizing the need to consider the lactation stage when studying its composition. Additionally, the transfer of both milk and skin microbiota during breastfeeding is crucial for understanding their collective impact on infant health and development. Further exploration of the complete breastfeeding microbiome is necessary to unravel the role these organisms play in infant development. We aim to longitudinally assess the bacterial breastfeeding microbiome across stages of lactation. This includes all the bacteria that infants are exposed to during breastfeeding, such as bacteria found within human milk and any bacteria found on the breast and nipple.MethodsForty-six human milk samples were collected from 15 women at 1, 4, 7, and 10 months postpartum. Metagenomic analysis of the bacterial microbiome for these samples was performed by CosmosID (Rockville, MD) via deep sequencing.ResultsStaphylococcus epidermidis and Propionibacteriaceae species are the most abundant bacterial species from these samples. Samples collected at 10 months showed higher abundances of Proteobacteria, Streptococcaceae, Lactobacillales, Streptococcus, and Neisseria mucosa compared to other timepoints. Alpha diversity varied greatly between participants but did not change significantly over time.DiscussionAs the bacterial breastfeeding microbiome continues to be studied, bacterial contributions could be used to predict and reduce health risks, optimize infant outcomes, and design effective management strategies, such as altering the maternal flora, to mitigate adverse health concerns.

Monitoring the prevalence of antimicrobial resistance genes (ARGs) is vital for addressing the global crisis of antibiotic-resistant bacterial infections. Despite its importance, the characterization of ARGs and microbiome structures, as well as the identification of indicators for routine ARG monitoring in pig farms, are still lacking, particularly concerning variations in antimicrobial exposure in different countries or regions. Here, metagenomics and random forest machine learning were used to elucidate the ARG profiles, microbiome structures, and ARG contamination indicators in pig manure under different antimicrobial pressures between China and Europe. Results showed that Chinese pigs exposed to high-level antimicrobials exhibited higher total and plasmid-mediated ARG abundances compared to those in European pigs (P<0.05). ANT(6)-Ib, APH(3')-IIIa, and tet(40) were identified as shared core ARGs between the two pig populations. Furthermore, the core ARGs identified in pig populations were correlated with those found in human populations within the same geographical regions. Lactobacillus and Prevotella were identified as the dominant genera in the core microbiomes of Chinese and European pigs, respectively. Forty ARG markers and 43 biomarkers were able to differentiate between the Chinese and European pig manure samples with accuracies of 100% and 98.7%, respectively. Indicators for assessing ARG contamination in Chinese and European pigs also achieved high accuracy (r=0.72–0.88). Escherichia flexneri in both Chinese and European pig populations carried between 21 and 37 ARGs. The results of this study emphasize the importance of global collaboration in reducing antimicrobial resistance risk and provide validated indicators for evaluating the risk of ARG contamination in pig farms. [ABSTRACT FROM AUTHOR]

The various forms of interactions that microorganisms engage in within fermented foods result in distinct metabolic product patterns. Fermentation products often feature both yeasts and bacteria, each possessing characteristics that can enhance the overall quality of the food, thus benefiting consumers. Kefir, a fermented milk originating from grains containing a unique and intricate blend of bacteria and yeasts living in a symbiotic relationship, is a valuable model for studying the evolution of the interactions between yeasts and bacteria. Targeted metagenomics was applied to investigate the microbiome of a batch of traditional Romanian kefir and further examine the growth and metabolic properties of the dominant yeast and bacterial strains isolated from this batch. In contrast to yeast, which is either unaffected or harmed by the presence of bacteria, our study revealed that a specific strain of Lactobacillus (L. rhamnosus) derived from the kefir batch benefited from the presence of Saccharomyces cerevisiae. The analysis of short-chain fatty acids (SCFAs) produced by in vitro cultures of these two chosen strains indicated significant changes in SCFA levels compared to single cultures. The dynamic interactions described in this and other studies on kefir emphasize the importance of a more profound comprehension of the ecological mechanisms governing interactions between yeast, bacterial, and mammalian cells. [ABSTRACT FROM AUTHOR]

Subgingival microbiome dysbiosis promotes the development of periodontitis, an irreversible chronic inflammatory disease associated with metabolic diseases. However, studies regarding the effects of a hyperglycemic microenvironment on host–microbiome interactions and host inflammatory response during periodontitis are still scarce. Here, we investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and transcriptome of a gingival coculture model stimulated with dysbiotic subgingival microbiomes. HGF-1 cells overlaid with U937 macrophage-like cells were stimulated with subgingival microbiomes collected from four healthy donors and four patients with periodontitis. Pro-inflammatory cytokines and matrix metalloproteinases were measured while the coculture RNA was submitted to a microarray analysis. Subgingival microbiomes were submitted to 16s rRNA gene sequencing. Data were analyzed using an advanced multi-omics bioinformatic data integration model. Our results show that the genes krt76, krt27, pnma5, mansc4, rab41, thoc6, tm6sf2, and znf506 as well as the pro-inflammatory cytokines IL-1β, GM-CSF, FGF2, IL-10, the metalloproteinases MMP3 and MMP8, and bacteria from the ASV 105, ASV 211, ASV 299, Prevotella, Campylobacter and Fretibacterium genera are key intercorrelated variables contributing to periodontitis-induced inflammatory response in a hyperglycemic microenvironment. In conclusion, our multi-omics integration analysis unveiled the complex interrelationships involved in the regulation of periodontal inflammation in response to a hyperglycemic microenvironment. [ABSTRACT FROM AUTHOR]

This study aimed to explore the bacteria present in the digestive tracts of wild and cultivated Indonesian shortfin eel during the elver phase. The eel has high export potential due to its vitamin and micronutrient content, but slow growth and vulnerability to collapse in farm conditions hinder its cultivation. The microbiota in the eel's digestive tract is crucial for its health, particularly during the elver phase. This study used Next Generation Sequencing to analyze the community structure and diversity of bacteria in the eels' digestive tracts, focusing on the V3–V4 regions of the 16S rRNA gene. Mothur software was used for data analysis and PAST v.3.26 was used to calculate alpha diversity. The results showed that Proteobacteria (64.18%) and Firmicutes (33.55%) were the predominant phyla in the digestive tract of cultivated eels, while Bacteroidetes (54.16%), Firmicutes (14.71%), and Fusobacteria (10.56%) were predominant in wild eels. The most prevalent genera in cultivated and wild elver were Plesiomonas and Cetobacterium, respectively. The microbiota in the digestive tract of cultivated eels was diverse despite uneven distribution. The KEGG database analysis revealed that the primary function of the microbiome was to facilitate the eel's absorption of nutrients by contributing significantly to the metabolism of carbohydrates and amino acids. This study's findings can aid in assessing eel health and improving eel farming conditions. [ABSTRACT FROM AUTHOR]

The contamination of environmental sully with antibiotics is regarded as a major problem today and predictable to attain more recognition in near future. However, human intervention resulting in antibiotic consumption is being enhancing all around the world. Our review of literature revealed the role of microbiome in sully and how antibiotic resistant genes raised. The structure of antibiotics basically influenced by natural components such as biotic and abiotic push which shifts based on different soils. Therefore, management of microbiome in soil and their expression studies were distinctively revealed. The assessment of antibiotic resistance genes with help of next generation sequencing provided a clear comprehension on genome and transcriptome of the bacterial genes. Thus, interaction of microbiome with soil can also be well understood. The current findings in our study will guide every researcher to follow logical protocol in analyzing microbiota composition is covered as well and also to understand its metagenomic and sequenced with next-generation sequencer which helps to comprehend the diverse micro-flora present in soil and its operation. Finally, later progresses in bioinformatics computer program, flow of work, and applications for analyzing metagenomic information are put in a nutshell.

High rates of antibiotic prescription in residential aged care are likely to promote enteric carriage of antibiotic-resistant pathogens and increase the risk of antibiotic treatment failure. Despite their importance, relationships between antibiotic exposures and patterns of enteric resistance carriage in this population remain poorly understood. We conducted a cross-sectional metagenomic cohort analysis of stool samples from residents of five long-term aged-care facilities in South Australia. Taxonomic composition was determined, and enteric carriage of antibiotic resistance genes (ARGs) was identified and quantified against the Comprehensive Antibiotic Resistance Database. Both the detection and abundance of stool taxa and ARGs were related to antibiotic exposures up to 12 months prior. Factors associated with the abundance of ARGs of high clinical concern were identified. Stool samples were provided by 164 participants (median age: 88 years, IQR 81–93; 72% female). Sixty-one percent (n = 100) of participants were prescribed antibiotics at least once in the prior 12 months (median prescriptions: 4, range: 1–52), most commonly a penicillin (n = 55, 33.5%), cephalosporin (n = 53, 32.3%), diaminopyrimidine (trimethoprim) (n = 36, 22%), or tetracycline (doxycycline) (n = 21, 12.8%). More than 1100 unique ARGs, conferring resistance to 38 antibiotic classes, were identified, including 20 ARGs of high clinical concern. Multivariate logistic regression showed doxycycline exposure to be the greatest risk factor for high ARG abundance (adjusted odds ratio [aOR]=14.8, q<0.001) and a significant contributor to inter-class selection, particularly for ARGs relating to penicillins (aOR=3.1, q=0.0004) and cephalosporins (aOR=3.4, q=0.003). High enteric ARG abundance was associated with the number of separate antibiotic exposures (aOR: 6.4, q<0.001), exposures within the prior 30 days (aOR: 4.6, q=0.008) and prior 30–100 days (aOR: 2.6, q=0.008), high duration of antibiotic exposure (aOR: 7.9, q<0.001), and exposure to 3 or more antibiotic classes (aOR: 7.4, q<0.001). Carriage of one or more ARGs of high clinical concern was identified in 99% of participants (n = 162, median: 3, IQR: 2–4), involving 11 ARGs conferring resistance to aminoglycosides, four to beta-lactams, one to glycopeptides, three to fluoroquinolones, and one to oxazolidinones. Carriage of ARGs of high clinical concern was positively associated with exposure to doxycycline (aminoglycoside, fluoroquinolone, and oxazolidinone ARGs) and trimethoprim (fluoroquinolone and beta-lactam ARGs). Analysis of doxycycline impact on microbiota composition suggested that observed resistome changes arose principally through direct ARG selection, rather than through the antibiotic depletion of sensitive bacterial populations. The gut microbiome of aged care residents is a major reservoir of antibiotic resistance. As a critical antibiotic in medical practice, a comprehensive understanding of the impact of doxycycline exposure on the gut resistome is paramount for informed antibiotic use, particularly in an evolving landscape of prophylactic applications. Near-universal asymptomatic carriage of clinically critical resistance determinants is highly concerning and reinforces the urgent need for improved management of antibiotic use in long-term aged care. [ABSTRACT FROM AUTHOR]

Antibiotic resistance genes (ARGs) in the environment pose a threat to human and animal health. Dairy cows are important livestock in China; however, a comprehensive understanding of antibiotic resistance in their production environment has not been well clarified. In this study, we used metagenomic methods to analyze the resistomes, microbiomes, and potential ARG bacterial hosts in typical dairy farm environments (including feces, wastewater, and soil). The ARGs resistant to tetracyclines, MLS, ß-lactams, aminoglycoside, and multidrug was dominant in the dairy farm ecosystem. The abundance and diversity of total ARGs in dairy feces and wastewater were significantly higher than in soil (P < 0.05). The same environmental samples from different dairy have similar resistomes and microbiomes. A high detection rate of tet(X) in wastewater and feces (100% and 71.4%, respectively), high abundance (range from 5.74 to 68.99 copies/Gb), and the finding of tet(X5) challenged the clinical application of the last antibiotics resort of tigecycline. Network analysis identified Bacteroides as the dominant genus in feces and wastewater, which harbored the greatest abundance of their respective total ARG coverage and shared ARGs. These results improved our understanding of ARG profiles and their bacterial hosts in dairy farm environments and provided a basis for further surveillance. [ABSTRACT FROM AUTHOR]

Thermal hot springs are present throughout the world and constitute a unique habitat for microbial diversity. The current investigation is conducted to study the bacterial diversity of thermophilic microorganisms in thermal hot springs of the Indian Himalayan Region (IHR). As of today, 110 geothermal hot springs have been explored for microbial diversity. In this study, we observed that the growth of thermophilic bacteria isolated from thermal hot springs of IHR ranges between 40 and 100 °C, and pH of 3.5–8 have been reported in the literature. The major bacterial species reported from the thermal hot springs of IHR are Bacillus spp., Geobacillus spp., Paenibacillus spp., Pseudomonas spp., Anoxybacillus, Paenibacillus, Brevibacillus, Aneurinibacillus, Thermus aquaticus, Aquimonas, Flavobacterium, etc. Furthermore, bacterial isolates from thermal hot springs of IHR have been reported to produce various enzymes and metabolites such as amylase, β-galactosidase, cellulase, nitrate reductase, acetoin, caffeine degradation enzymes, lipase, urease, and laccase. Metagenomic study and the entire genomic shotgun project have established the impact of physicochemical parameters (temperature and pH) on developing the microbiome. We have discussed the discoveries of microbiological data on the hot springs of IHR until the end of year 2021. As a whole, the microbiome adapts themselves as successful inhabitants to extreme environmental conditions and also serves as a diverse resource for potential applications in health, food, and environment. [ABSTRACT FROM AUTHOR]

Abstract Acid resistance is critical for the survival of bacteria in the dental caries oral micro-environment. However, there are few acid-resistant genes of microbiomes obtained through traditional molecular biology experimental techniques. This study aims to try macrogenomics technologies to efficiently identify acid-resistant genes in oral microbes of patients with dental caries. Total DNA was extracted from oral microbiota obtained from thirty dental caries patients and subjected to high-throughput sequencing. This data was used to build a metagenomic library, which was compared to the sequences of two Streptococcus mutant known acid-resistant genes, danK and uvrA, using a BLAST search. A total of 19 and 35 unknown gene sequences showed similarities with S. mutans uvrA and dnaK in the metagenomic library, respectively. Two unknown genes, mo-dnaK and mo-uvrA, were selected for primer design and bioinformatic analysis based on their sequences. Bioinformatics analysis predicted them encoding of a human heat-shock protein (HSP) 70 and an ATP-dependent DNA repair enzyme, respectively, closely related with the acid resistance mechanism. After cloning, these genes were transferred into competent Escherichia coli for acid resistance experiments. E. coli transformed with both genes demonstrated acid resistance, while the survival rate of E. coli transformed with mo-uvrA was significantly higher in an acidic environment (pH = 3). Through this experiment we found that identify unknown acid-resistant genes in oral microbes of patients with caries by establishing a metagenomic library is very efficient. Our results provide an insight into the mechanisms and pathogenesis of dental caries for their treatment without affecting oral probiotics.

Abstract Background Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. Results In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). Conclusion With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of "temporal alignment" – an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies. [ABSTRACT FROM AUTHOR]

A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of “temporal alignment” – an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies.

Rhesus macaques (Macaca mulatta) are the most widely distributed species of Old World monkey and are frequently used as animal models to study human health and disease. Their gastrointestinal microbial community likely plays a major role in their physiology, ecology and evolution. Herein, we compared the fecal microbiome and antibiotic resistance genes in 15 free-ranging and 81 zoo-captive rhesus macaques sampled from two zoos in China, using both 16S amplicon sequencing and whole genome shotgun DNA sequencing approaches. Our data revealed similar levels of microbial diversity/richness among the three groups, although the composition of each group differed significantly and were particularly marked between the two zoo-captive and one wild groups. Zoo-captive animals also demonstrated a greater abundance and diversity of antibiotic genes. Through whole genome shotgun sequencing we also identified a mammalian (simian) associated adenovirus. Overall, this study provides a comprehensive analysis of resistomes and microbiomes in zoo-captive and free-ranging monkeys, revealing that semi-captive wildlife might harbor a higher diversity of antimicrobial resistant genes.

The contamination of environmental sully with antibiotics is regarded as a major problem today and predictable to attain more recognition in near future. However, human intervention resulting in antibiotic consumption is being enhancing all around the world. Our review of literature revealed the role of microbiome in sully and how antibiotic resistant genes raised. The structure of antibiotics basically influenced by natural components such as biotic and abiotic push which shifts based on different soils. Therefore, management of microbiome in soil and their expression studies were distinctively revealed. The assessment of antibiotic resistance genes with help of next generation sequencing provided a clear comprehension on genome and transcriptome of the bacterial genes. Thus, interaction of microbiome with soil can also be well understood. The current findings in our study will guide every researcher to follow logical protocol in analyzing microbiota composition is covered as well and also to understand its metagenomic and sequenced with next-generation sequencer which helps to comprehend the diverse micro-flora present in soil and its operation. Finally, later progresses in bioinformatics computer program, flow of work, and applications for analyzing metagenomic information are put in a nutshell. [ABSTRACT FROM AUTHOR]

Background: While there have been a lot of studies on the termite gut microbiota, there has been very little research directly on the cellulose-degrading microbiota in termites or their soil environment. This study addresses this problem by profiling cellulose-degrading bacteria and archaea in the selective cellulose cultures of two samples of the eastern subterranean termite ( Reticulitermes flavipes) and one soil sample collected at the same location as one of the termite samples. Methods: All the cultures were examined for cell concentration and remaining cellulose after the culture was completed. The 16S rRNA pyrotag sequencing method was used to identify the prokaryotic microbiota for the three cultures and one termite colony without culture. The MOTHUR, SSU-ALIGN, RDPTools, phyloseq, and other R packages were used for sequence and statistical analyses. Results: Biochemical analyses of the cultures suggested high efficiency of cellulose degradation. Comparative analyses between the cultured and uncultured termite gut microbiota revealed a significant difference. Proteobacteria and Firmicutes were found to be the two most abundant phyla of cellulose-degrading bacteria from the three cultures, but different classes within each phylum dominated the different samples. Shared and sample-specific cellulose-degrading taxa, including a core set of taxa across all the cultures, were identified. Conclusions: Our study demonstrates the importance of using selective cellulose culture to study the cellulose-degrading microbial community. It also revealed that the cellulose-degrading microbiota in the eastern subterranean termite is significantly influenced by the microbiota in the surrounding soil environment. Biochemical analyses showed that the microbial communities enriched from all the selective cultures were efficient in degrading cellulose, and a core set of bacteria have been identified as targets for further functional analyses.

Recent studies have identified the critical role of microbiota in the pathophysiology of autoimmune liver diseases (AILDs), including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC). Metagenomic studies reveal significant decrease of gut bacterial diversity in AILDs. Although profiles of metagenomic vary widely, Veillonella is commonly enriched in AIH, PBC, and PSC. Apart from gut microbiome, the oral and bile microbiome seem to be associated with these diseases as well. The functional analysis of metagenomics suggests that metabolic pathways changed in the gut microbiome of the patients. Microbial metabolites, including short-chain fatty acids (SCFAs) and microbial bile acid metabolites, have been shown to modulate innate immunity, adaptive immunity, and inflammation. Taken together, the evidence of host–microbiome interactions and in-depth mechanistic studies needs further accumulation, which will offer more possibilities to clarify the mechanisms of AILDs and provide potential molecular targets for the prevention and treatment in the future.

Recent studies have identified the critical role of microbiota in the pathophysiology of autoimmune liver diseases (AILDs), including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC). Metagenomic studies reveal significant decrease of gut bacterial diversity in AILDs. Although profiles of metagenomic vary widely, Veillonella is commonly enriched in AIH, PBC, and PSC. Apart from gut microbiome, the oral and bile microbiome seem to be associated with these diseases as well. The functional analysis of metagenomics suggests that metabolic pathways changed in the gut microbiome of the patients. Microbial metabolites, including short-chain fatty acids (SCFAs) and microbial bile acid metabolites, have been shown to modulate innate immunity, adaptive immunity, and inflammation. Taken together, the evidence of host–microbiome interactions and in-depth mechanistic studies needs further accumulation, which will offer more possibilities to clarify the mechanisms of AILDs and provide potential molecular targets for the prevention and treatment in the future. [ABSTRACT FROM AUTHOR]

Abstract Background In the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data. Results In this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim. Conclusion metaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence (“1”) or absence (“0”) of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn’s disease cohorts.

During commercial saturation diving, divers live and work under hyperbaric and hyperoxic conditions. The myriads of bacteria that live in and on the human body must adjust to the resultant hyperbaric stress. In this study, we examined the shifts in bacterial content in the oral cavity of saturation divers, using a metagenomic approach to determine the diversity in the composition of bacterial phyla and genera in saliva from 23 male divers before, during, and immediately after 4 weeks of commercial heliox saturation diving to a working depth of circa 200 m. We found that the bacterial diversity fell during saturation, and there was a change in bacterial composition; with a decrease at the phylum level of obligate anaerobe Fusobacteria, and an increase of the relative abundance of Actinobacteria and Proteobacteria. At the genus level, Fusobacterium, Leptotrichia, Oribacterium, and Veillonella decreased, whereas Neisseria and Rothia increased. However, at the end of the decompression, both the diversity and composition of the microbiota returned to pre-dive values. The results indicate that the hyperoxic conditions during saturation may suppress the activity of anaerobes, leaving a niche for other bacteria to fill. The transient nature of the change could imply that hyperbaric heliox saturation has no lasting effect on the oral microbiota, but it is unknown whether or how a shift in oral bacterial diversity and abundance during saturation might impact the divers’ health or well-being.

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence ("1") or absence ("0") of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn's disease cohorts. [ABSTRACT FROM AUTHOR]

During commercial saturation diving, divers live and work under hyperbaric and hyperoxic conditions. The myriads of bacteria that live in and on the human body must adjust to the resultant hyperbaric stress. In this study, we examined the shifts in bacterial content in the oral cavity of saturation divers, using a metagenomic approach to determine the diversity in the composition of bacterial phyla and genera in saliva from 23 male divers before, during, and immediately after 4 weeks of commercial heliox saturation diving to a working depth of circa 200 m. We found that the bacterial diversity fell during saturation, and there was a change in bacterial composition; with a decrease at the phylum level of obligate anaerobe Fusobacteria , and an increase of the relative abundance of Actinobacteria and Proteobacteria. At the genus level, Fusobacterium , Leptotrichia , Oribacterium , and Veillonella decreased, whereas Neisseria and Rothia increased. However, at the end of the decompression, both the diversity and composition of the microbiota returned to pre-dive values. The results indicate that the hyperoxic conditions during saturation may suppress the activity of anaerobes, leaving a niche for other bacteria to fill. The transient nature of the change could imply that hyperbaric heliox saturation has no lasting effect on the oral microbiota, but it is unknown whether or how a shift in oral bacterial diversity and abundance during saturation might impact the divers' health or well-being. [ABSTRACT FROM AUTHOR]

Identification of the offending organism and appropriate antimicrobial therapy are crucial for treating empyema. Diagnosis of empyema is largely obscured by the conventional bacterial cultivation and PCR process that has relatively low sensitivity, leading to limited understanding of the etiopathogenesis, microbiology, and role of antibiotics in the pleural cavity. To expand our understanding of its pathophysiology, we have carried out a metagenomic snapshot of the pleural effusion from 45 empyema patients by Illumina sequencing platform to assess its taxonomic, and antibiotic resistome structure. Our results showed that the variation of microbiota in the pleural effusion is generally stratified, not continuous. There are two distinct microbiome clusters observed in the forty-five samples: HA-SA type and LA-SA type. The categorization is mostly driven by species composition: HA-SA type is marked by Staphylococcus aureus as the core species, with other enriched 6 bacteria and 3 fungi, forming a low diversity and highly stable microbial community; whereas the LA-SA type has a more diverse microbial community with a distinct set of bacterial species that are assumed to be the oral origin. The microbial community does not shape the dominant antibiotic resistance classes which were common in the two types, while the increase of microbial diversity was correlated with the increase in antibiotic resistance genes. The existence of well-balanced microbial symbiotic states might respond differently to pathogen colonization and drug intake. This study provides a deeper understanding of the pathobiology of pleural empyema and suggests that potential resistance genes may hinder the antimicrobial therapy of empyema.

Identification of the offending organism and appropriate antimicrobial therapy are crucial for treating empyema. Diagnosis of empyema is largely obscured by the conventional bacterial cultivation and PCR process that has relatively low sensitivity, leading to limited understanding of the etiopathogenesis, microbiology, and role of antibiotics in the pleural cavity. To expand our understanding of its pathophysiology, we have carried out a metagenomic snapshot of the pleural effusion from 45 empyema patients by Illumina sequencing platform to assess its taxonomic, and antibiotic resistome structure. Our results showed that the variation of microbiota in the pleural effusion is generally stratified, not continuous. There are two distinct microbiome clusters observed in the forty-five samples: HA-SA type and LA-SA type. The categorization is mostly driven by species composition: HA-SA type is marked by Staphylococcus aureus as the core species, with other enriched 6 bacteria and 3 fungi, forming a low diversity and highly stable microbial community; whereas the LA-SA type has a more diverse microbial community with a distinct set of bacterial species that are assumed to be the oral origin. The microbial community does not shape the dominant antibiotic resistance classes which were common in the two types, while the increase of microbial diversity was correlated with the increase in antibiotic resistance genes. The existence of well-balanced microbial symbiotic states might respond differently to pathogen colonization and drug intake. This study provides a deeper understanding of the pathobiology of pleural empyema and suggests that potential resistance genes may hinder the antimicrobial therapy of empyema. [ABSTRACT FROM AUTHOR]

Acid resistance is critical for the survival of bacteria in the dental caries oral micro-environment. However, there are few acid-resistant genes of microbiomes obtained through traditional molecular biology experimental techniques. This study aims to try macrogenomics technologies to efficiently identify acid-resistant genes in oral microbes of patients with dental caries. Total DNA was extracted from oral microbiota obtained from thirty dental caries patients and subjected to high-throughput sequencing. This data was used to build a metagenomic library, which was compared to the sequences of two Streptococcus mutant known acid-resistant genes, danK and uvrA, using a BLAST search. A total of 19 and 35 unknown gene sequences showed similarities with S. mutans uvrA and dnaK in the metagenomic library, respectively. Two unknown genes, mo-dnaK and mo-uvrA, were selected for primer design and bioinformatic analysis based on their sequences. Bioinformatics analysis predicted them encoding of a human heat-shock protein (HSP) 70 and an ATP-dependent DNA repair enzyme, respectively, closely related with the acid resistance mechanism. After cloning, these genes were transferred into competent Escherichia coli for acid resistance experiments. E. coli transformed with both genes demonstrated acid resistance, while the survival rate of E. coli transformed with mo-uvrA was significantly higher in an acidic environment (pH = 3). Through this experiment we found that identify unknown acid-resistant genes in oral microbes of patients with caries by establishing a metagenomic library is very efficient. Our results provide an insight into the mechanisms and pathogenesis of dental caries for their treatment without affecting oral probiotics. [ABSTRACT FROM AUTHOR]

Background: Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. Results: In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). Conclusion: With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics. [ABSTRACT FROM AUTHOR]

Research in the human gut microbiome has bloomed with advances in next generation sequencing (NGS) and other high-throughput molecular profiling technologies. This has enabled the generation of multi-omics datasets which holds promises for big data–enabled knowledge acquisition in the form of understanding the normal physiological and pathological involvement of gut microbiomes. Ample evidence suggests that distinct microbial compositions in the human gut are associated with different diseases. However, the biological mechanisms underlying these associations are often unclear. There is a need to move beyond statistical associations to discover how changes in the gut microbiota mechanistically affect host physiology and disease development. This review summarises state-of-the-art big data and systems biology approaches for mechanism discovery. [ABSTRACT FROM AUTHOR]

Investigation of the microbial community in the female reproductive tract with the use of sequencing techniques has revealed that endometrial samples obtained through a transvaginal catheter are dominated by Lactobacillus species. Dysbiotic changes in the endometrial microbiota may be associated with implantation failure or early spontaneous abortion in patients who undergo assisted reproductive technology treatment. Whether or not there is an endometrial microbiota in early pregnancy is unknown. Herein we describe, the human endometrial microbiota in a patient who subsequently had an 8th week spontaneous clinical miscarriage with euploid embryos in the next cycle and, for the first time, during a successful pregnancy in which the endometrial fluid was sampled at 4 weeks of gestation. The microbial profile found on the endometrial sample before the spontaneous abortion had higher bacterial diversity and lower Lactobacillus abundance than the endometrial fluid from the healthy pregnancy. Functional metagenomics detected different Lactobacillus species between the 2 samples. Lactobacillus crispatus was present in the endometrium before the spontaneous abortion, as were other bacteria involved in dysbiosis, which had an unstable functional pattern characterized by transposases and insertion elements. Lactobacillus iners was the most prevalent microbe found in the endometrium during early pregnancy; its presence was associated with defense mechanisms and basal functions. These novel observations prompt future investigations to understand the potential implications of microbiology on healthy and pathologic human pregnancy. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

The microbiome of initial soils formed at the heaps and bottoms of surface sediment quarries in the surroundings of Yakutsk City (Eastern Siberia, Russia) has been characterized for the first time. In the initial Entisols, we detected Alphaproteobacteria (represented mainly by the family Rizobiales), Gammaproteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes (mostly Chitinophagales), Deltaproteobacteria, and Chloroflexi. The lower soil horizons had a more homogenous species diversity taxonomy that was dominated by Gammaproteobacteria. The morphologically different horizons did not differ microbiologically. This is caused by the limited soil development and relatively slow rate of revegetation of the spoil banks and heaps of the quarries under the severe climatic conditions of the Eastern part of Russian Arctic zone. Based on our findings, we propose that the soil microbiomes in such recently abandoned quarries are characterized by low diversity, which is a characteristic feature of the polar soils surrounding Yakutsk. Data obtained can be used for elaboration of reclamation strategies with taking into account the information about key microbial drivers of soil processes. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

Lactic acid bacteria (LAB) dominate human vaginal microbiota and inhibit pathogen proliferation. In other mammals, LAB do not dominate vaginal microbiota, however shifts of dominant microorganisms occur during ovarian cycle. The study objectives were to characterize equine vaginal microbiota in mares by culture-dependent and independent methods and to describe its variation in estrus and diestrus. Vaginal swabs from 8 healthy adult Arabian mares were obtained in estrus and diestrus. For culture-dependent processing, bacteria were isolated on Columbia blood agar (BA) and Man Rogosa Sharpe (MRS) agar. LAB comprised only 2% of total bacterial isolates and were not related to ovarian phases. For culture-independent processing, V3/V4 variable regions of the 16S ribosomal RNA gene were amplified and sequenced using Illumina Miseq. The diversity and composition of the vaginal microbiota did not change during the estrous cycle. Core equine vaginal microbiome consisted of Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria at the phylum level. At the genus level it was defined by Porphyromonas, Campylobacter, Arcanobacterium, Corynebacterium, Streptococcus, Fusobacterium, uncultured Kiritimatiaellae and Akkermansia. Lactobacillus comprised only 0.18% of the taxonomic composition in estrus and 0.37% in diestrus. No differences in the relative abundance of the most abundant phylum or genera were observed between estrus and diestrus samples.