Taxonomic Profiles Research Articles

Occurrence and Distribution of Tetrabromobisphenol A and Diversity of Microbial Community Structure in the Sediments of Mangrove.

The occurrence and distribution characteristics of tetrabromobisphenol A (TBBPA) and its relationship with microbial community diversity in different mangrove sediments need further investigation. The results of this study indicated levels of TBBPA in mangrove sediments from the Zhangjiang Estuary (ZJ), Jiulongjiang Estuary (JLJ), and Quanzhou Bay (QZ) in Southeast China ranging from 1.80 to 20.46, 3.47 to 40.77, and 2.37 to 19.83 ng/g dry weight (dw), respectively. Mangrove sediments from JLJ contained higher levels of TBBPA, possibly due to agricultural pollution. A correlation analysis revealed a significant correlation between total organic carbon (TOC), total nitrogen (TN), and TBBPA distribution in ZJ and JLJ mangrove sediments, but not in QZ mangrove sediments. TOC significantly affected the distribution of TBBPA in mangrove sediments, but pH had no effect. High-throughput 16S rRNA gene sequencing showed that Pseudomonadota dominated the sediment bacteria followed by Chloroflexota, Actinobacteota, Bacillota, Acidobacteriota, Bacteroidota, and Aminicenantes in mangrove sediments. Although the microbial community structure of the ZJ, JLJ, and QZ mangrove sediments was similar, the taxonomic profile of their sensitive responders differed markedly. The genus Anaerolinea was dominant in the mangrove sediments and was responsible for the in situ dissipation of TBBPA. Based on redundancy analysis, there was a correlation between TBBPA, TOC, TN, C/N, pH, and microbial community structure at the genus level. Combining TBBPA, TN, and TOC may induce variations in the microbial community of mangrove sediments.

Metagenomic Sequencing Analysis Identifies Cross-Cohort Gut Microbial Signatures Associated With Age-Related Macular Degeneration.

Alterations in the gut microbiota have been associated with age-related macular degeneration (AMD). However, the dysbiosis shared by different ethnicity and geographic groups, which may associate with the disease pathogenesis, remain underexplored. Here, we characterized dysbiosis of the gut microbiota in patients with AMD from Chinese and Swiss cohorts and identified cross-cohort signatures associated with AMD. Shotgun metagenomic sequencing was performed on fecal samples from 30 patients with AMD and 30 healthy subjects. Published datasets with 138 samples from Swiss patients with AMD and healthy subjects were re-analyzed. Comprehensive taxonomic profiling was conducted by matching to the RefSeq genome database, metagenome-assembled genome (MAG) database, and Gut Virome Database (GVD). Functional profiling was performed by reconstruction of the MetaCyc pathways. The α-diversity of the gut microbiota was decreased in patients with AMD according to taxonomic profiles generated using MAG but not RefSeq database as reference. The Firmicutes/Bacteroidetes ratio was also decreased in patients with AMD. Among AMD-associated bacteria shared between Chinese and Swiss cohorts, Ruminococcus callidus, Lactobacillus gasseri, and Prevotellaceae (f) uSGB 2135 were enriched in patients with AMD, whereas Bacteroidaceae (f) uSGB 1825 was depleted in patients with AMD and was negatively associated with hemorrhage size. Bacteroidaceae was one of the major hosts of phages associated with AMD. Three degradation pathways were reduced in AMD. These results demonstrated that dysbiosis of the gut microbiota was associated with AMD. We identified cross-cohort gut microbial signatures involving bacteria, viruses, and metabolic pathways, which potentially serve as promising targets for the prevention or treatment of AMD.

Gut microbiome profiling of neonates using Nanopore MinION and Illumina MiSeq sequencing.

This study aimed to evaluate the difference in gut microbiomes between preterm and term infants using third-generation long-read sequencing (Oxford Nanopore Technologies, ONT) compared with an established gold standard, Illumina (second-generation short-read sequencing). A total of 69 fecal samples from 51 term (T) and preterm (P) infants were collected at 7 and 28 days of life. Gut colonization profiling was performed by 16S rRNA gene sequencing using ONT. We used Illumina to validate and compare the patterns in 13 neonates. Using bioinformatic analysis, we identified features that differed between P and T. Both T1 and P1 microbiomes were dominated by Firmicutes (Staphylococcus and Enterococcus), whereas sequentially showed dominant transitions to Lactobacillus (p < 0.001) and Streptococcus in T2 (p = 0.001), and pathogenic bacteria (Klebsiella) in P2 (p = 0.001). The abundance of beneficial bacteria (Bifidobacterium and Lactobacillus) increased in T2 (p = 0.026 and p < 0.001, respectively). These assignments were correlated with the abundance at the species-level. Bacterial α-diversity increased in T (p = 0.005) but not in P (p = 0.156), and P2 showed distinct β-diversity clustering than T2 (p = 0.001). The ONT reliably identified pathogenic bacteria at the genus level, and taxonomic profiles were comparable to those identified by Illumina at the genus level. This study shows that ONT and Illumina are highly correlated. P and T had different microbiome profiles, and the α- and β-diversity varied. ONT sequencing has potential for pathogen detection in neonates in clinical settings.

Core microbiota of wheat rhizosphere under Upper Indo-Gangetic plains and their response to soil physicochemical properties.

Wheat is widely cultivated in the Indo-Gangetic plains of India and forms the major staple food in the region. Understanding microbial community structure in wheat rhizosphere along the Indo-Gangetic plain and their association with soil properties can be an important base for developing strategies for microbial formulations. In the present study, an attempt was made to identify the core microbiota of wheat rhizosphere through a culture-independent approach. Rhizospheric soil samples were collected from 20 different sites along the upper Indo-Gangetic plains and their bacterial community composition was analyzed based on sequencing of the V3-V4 region of the 16S rRNA gene. Diversity analysis has shown significant variation in bacterial diversity among the sites. The taxonomic profile identified Proteobacteria, Chloroflexi, Actinobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Planctomycetes, Verrucomicrobia, Firmicutes, and Cyanobacteria as the most dominant phyla in the wheat rhizosphere in the region. Core microbiota analysis revealed 188 taxa as core microbiota of wheat rhizosphere with eight genera recording more than 0.5% relative abundance. The order of most abundant genera in the core microbiota is Roseiflexus> Flavobacterium> Gemmatimonas> Haliangium> Iamia> Flavisolibacter> Ohtaekwangia> Herpetosiphon. Flavobacterium, Thermomonas, Massilia, Unclassified Rhizobiaceae, and Unclassified Crenarchaeota were identified as keystone taxa of the wheat rhizosphere. Correlation studies revealed, pH, organic carbon content, and contents of available nitrogen, phosphorus, and iron as the major factors driving bacterial diversity in the wheat rhizosphere. Redundancy analysis has shown the impact of different soil properties on the relative abundance of different genera of the core microbiota. The results of the present study can be used as a prelude to be developing microbial formulations based on core microbiota.

Non-small cell lung cancer patients treated with Anti-PD1 immunotherapy show distinct microbial signatures and metabolic pathways according to progression-free survival and PD-L1 status

ABSTRACT Due to the high variance in response rates concerning anti-PD1 immunotherapy (IT), there is an unmet need to discover innovative biomarkers to predict immune checkpoint inhibitor (ICI)-efficacy. Our study included 62 Caucasian advanced-stage non-small cell lung cancer (NSCLC) patients treated with anti-PD1 ICI. Gut bacterial signatures were evaluated by metagenomic sequencing and correlated with progression-free survival (PFS), PD-L1 expression and other clinicopathological parameters. We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). We find that alpha-diversity showed no significant difference in any comparison. However, there was a significant difference in beta-diversity between patients with long- (>6 months) vs. short (≤6 months) PFS and between chemotherapy (CHT)-treated vs. CHT-naive cases. Short PFS was associated with increased abundance of Firmicutes (F) and Actinobacteria phyla, whereas elevated abundance of Euryarchaeota was specific for low PD-L1 expression. F/Bacteroides (F/B) ratio was significantly increased in patients with short PFS. Multivariate analysis revealed an association between Alistipes shahii, Alistipes finegoldii, Barnesiella visceriola, and long PFS. In contrast, Streptococcus salivarius, Streptococcus vestibularis, and Bifidobacterium breve were associated with short PFS. Using Random Forest machine learning approach, we find that taxonomic profiles performed superiorly in predicting PFS (AUC = 0.74), while metabolic pathways including Amino Acid Synthesis and Fermentation were better predictors of PD-L1 expression (AUC = 0.87). We conclude that specific metagenomic features of the gut microbiome, including bacterial taxonomy and metabolic pathways might be suggestive of ICI efficacy and PD-L1 expression in NSCLC patients.

Evaluating the prebiotic effect of oligosaccharides on gut microbiome wellness using in vitro fecal fermentation

We previously proposed the Gut Microbiome Wellness Index (GMWI), a predictor of disease presence based on a gut microbiome taxonomic profile. As an application of this index for food science research, we applied GMWI as a quantitative tool for measuring the prebiotic effect of oligosaccharides. Mainly, in an in vitro anaerobic batch fermentation system, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2’-fucosyllactose (2FL), were mixed separately with fecal samples obtained from healthy adult volunteers. To find out how 24 h prebiotic fermentation influenced the GMWI values in their respective microbial communities, changes in species-level relative abundances were analyzed in the five prebiotics groups, as well as in two control groups (no substrate addition at 0 h and for 24 h). The GMWI of fecal microbiomes treated with any of the five prebiotics (IN (0.48 ± 0.06) > FOS (0.47 ± 0.03) > XOS (0.33 ± 0.02) > GOS (0.26 ± 0.02) > 2FL (0.16 ± 0.06)) were positive, which indicates an increase of relative abundances of microbial species previously found to be associated with a healthy, disease-free state. In contrast, the GMWI of samples without substrate addition for 24 h (–0.60 ± 0.05) reflected a non-healthy, disease-harboring microbiome state. Compared to the original prebiotic index (PI) and α-diversity metrics, GMWI provides a more data-driven, evidence-based indexing system for evaluating the prebiotic effect of food components. This study demonstrates how GMWI can be applied as a novel PI in dietary intervention studies, with wider implications for designing personalized diets based on their impact on gut microbiome wellness.

Integrative Metatranscriptomic Analysis Reveals Disease-specific Microbiome-host Interactions in Oral Squamous Cell Carcinoma.

Studies have shown that a distinct microbiome is associated with OSCC, but how the microbiome functions within the tumor interacts with the host cells remains unclear. By simultaneously characterizing the microbial and host transcriptomes in OSCC and control tissues, the study provides novel insights into microbiome-host interactions in OSCC which can be validated in future mechanistic studies.

High-resolution characterization of short-term temporal variability in the taxonomic and resistome composition of wastewater influent.

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study, we characterized taxonomic and resistome differences between single-timepoint-grab and 24 h composites of wastewater influent from a large UK-based wastewater treatment work [WWTW (population equivalent: 223 435)]. We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24 h composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S rRNA gene sequencing was performed. One composite and six grabs from day 1 underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all 3 days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day 1 samples, single grab samples identified a median of six (IQR: 5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median: 0.19; interquartile range: 0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (bla VIM, bla IMP, bla KPC) were intermittently or completely missed by grab sampling but captured by the 24 h composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimization of WBE methods is vital for its development into a robust AMR surveillance approach.

Current Viewpoint on Female Urogenital Microbiome-The Cause or the Consequence?

An increasing amount of evidence implies that native microbiota is a constituent part of a healthy urinary tract (UT), making it an ecosystem on its own. What is still not clear is whether the origin of the urinary microbial community is the indirect consequence of the more abundant gut microbiota or a more distinct separation exists between these two systems. Another area of uncertainty is the existence of a link between the shifts in UT microbial composition and both the onset and persistence of cystitis symptoms. Cystitis is one of the most common reasons for antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of the majority of cystitis cases is a single pathogen overgrowth or a systemic disorder affecting the entire urinary microbiota. There is an increasing trend in studies monitoring changes and dynamics of UT microbiota, but this field of research is still in its infancy. Using NGS and bioinformatics, it is possible to obtain microbiota taxonomic profiles directly from urine samples, which can provide a window into microbial diversity (or the lack of) underlying each patient's cystitis symptoms. However, while microbiota refers to the living collection of microorganisms, an interchangeably used term microbiome referring to the genetic material of the microbiota is more often used in conjunction with sequencing data. It is this vast amount of sequences, which are truly "Big Data", that allow us to create models that describe interactions between different species contributing to an UT ecosystem, when coupled with machine-learning techniques. Although in a simplified predator-prey form these multi-species interaction models have the potential to further validate or disprove current beliefs; whether it is the presence or the absence of particular key players in a UT microbial ecosystem, the exact cause or consequence of the otherwise unknown etiology in the majority of cystitis cases. These insights might prove to be vital in our ongoing struggle against pathogen resistance and offer us new and promising clinical markers.

Methanogenic archaea community in relation to methane production in oil palm planted on tropical peatland

Understanding the microbial processes that regulate methane emissions in the tropical peatlands can facilitate their future management. However, the microbiota of Malaysian tropical peatlands specifically the methanogens (methane producers) and methanotrophs (methane consumers) remains poorly characterised. This study aimed to determine the key players in methane production and to assess their spatial distribution in different oil palm management zones (i.e., avenue, cover crop, frond pile, and weeded circle) across depths namely 0–15 cm, 15–30 cm, and 30–45 cm in an 11-year-old oil palm plantation that was established on the peatland of Sarawak. Based on high-throughput Illumina sequencing targeting the 16S hypervariable V4–V5 regions of archaeal rRNA amplicon, the diversity indices (Shannon and Simpson) and richness (Chao1 and ACE) were highest in the upper peat layers across management zones, and the microbial diversity and composition are influenced by depths. Taxonomic profiling revealed that the domain Archaea was dominated by Halobacteriota, Thermoproteota, and Thermoplasmatota that increased with depth across management zones. Meanwhile, the order Methanomassiliicoccales was predominant, with Methanomethylophilus sp002495325 as the most abundant species in the upper peat layers in the frond pile zone due to a high decomposition rate of organic matter. The key players of methanogenic archaea belonging to Methanomassiliicoccales, Methanosarcinales, Methanomicrobiales, and Methanobacteriales were detected in oil palm peat soils, providing indirect genetic evidence for the biogeochemical processes associated with methane production in the oil palm plantation. Having established the feasibility of utilising high-throughput sequencing to characterise the archaeome of Malaysian oil palm plantation, future works in correlating the relative abundance of methanogens and methanotrophs with methane production will be instrumental in providing valuable insights for prediction and mitigation of methane emissions in oil palm cultivation on peat.

Effect of potentially toxic elements on soil multifunctionality at a lead smelting site

Contaminated soil at smelting sites affects land utilization and environmental regulation, resulting in soil degradation. However, the extent to which potentially toxic elements (PTEs) contribute to site soil degradation and the relationship between soil multifunctionality and microbial diversity in the process remains poorly understood. In this study, we investigated changes in soil multifunctionality and the correlation between soil multifunctionality and microbial diversity under the influence of PTEs. The change in microbial community diversity was closely related to changes in soil multifunctionality caused by PTEs. Microbial diversity, not richness, drives the delivery of ecosystem services in smelting site PTEs-stressed environments. Structural equation modeling identified that soil contamination, microbial taxonomic profile and microbial functional profile could explain 70% of the variance in soil multifunctionality. Furthermore, our findings demonstrate that PTEs limit soil multifunctionality by affecting soil microbial communities and functionality, whilst the positive effect of microorganisms on soil multifunctionality was mainly driven by the fungal diversity and biomass. Finally, specific fungal genera closely related to soil multifunctionality were identified, with saprophytic fungi being particularly important for maintaining multiple soil functions. The results of the study provide potential guidance for the remediation, pollution control practices and mitigation of degraded soils at smelting sites.

Microbiome and Volatile Metabolic Profile of Acetic Acid Fermentation Using Multiple Starters for Traditional Grain Vinegar

Traditional grain vinegar is fermented using multiple acetic acid bacteria (AAB) at various temperatures. A single AAB showed high acid-producing ability at 30 °C with a 5% alcohol concentration and an initial pH adjusted to 4.0. Multiple AAB were similar to a single AAB; however, the optimal initial pH was 3.0. Acid production ability according to the type of AAB was higher in multiple AAB than in single AAB. That is, using multiple AAB helped increase the titratable acidity of traditional grain vinegar. In addition, increasing the titratable acidity and content of volatile flavor compounds was advantageous when two, rather than four, AAB types were mixed and used. The titratable acidity was high at medium temperatures (30 °C); however, volatile flavor compounds increased at low temperatures (20 °C) under multiple AAB. A 16S rDNA-based microbiome taxonomic profiling analysis identified differences in beta diversity due to multiple AAB and fermentation temperatures. In particular, beta diversity analysis revealed a specific pattern when a mixture of Acetobacter ascedens GV–8 and Acetobacter pasteurianus GV–22 was fermented at a low temperature (20 °C). Therefore, we propose the application of multiple AAB with acidic and flavor-producing properties in traditional grain vinegar.

Gut Microbiome and Associated Metabolites Following Bariatric Surgery and Comparison to Healthy Controls.

The gut microbiome plays a significant role in regulating the host's ability to store fat, which impacts the development of obesity. This observational cohort study recruited obese adult men and women scheduled to undergo sleeve gastrectomy and followed up with them 6 months post-surgery to analyse their microbial taxonomic profiles and associated metabolites in comparison to a healthy control group. There were no significant differences in the gut bacterial diversity between the bariatric patients at baseline and at follow-up or between the bariatric patients and the cohort of healthy controls. However, there were differential abundances in specific bacterial groups between the two cohorts. The bariatric patients were observed to have significant enrichment in Granulicatella at baseline and Streptococcus and Actinomyces at follow-up compared to the healthy controls. Several operational taxonomic units assigned to commensal Clostridia were significantly reduced in the stool of bariatric patients both at baseline and follow-up. When compared to a healthy cohort, the plasma levels of the short chain fatty acid acetate were significantly higher in the bariatric surgery group at baseline. This remained significant when adjusted for age and sex (p = 0.013). The levels of soluble CD14 and CD163 were significantly higher (p = 0.0432 and p = 0.0067, respectively) in the bariatric surgery patients compared to the healthy controls at baseline. The present study demonstrated that there are alterations in the abundance of certain bacterial groups in the gut microbiome of obese patients prior to bariatric surgery compared to healthy individuals, which persist post-sleeve gastrectomy.

Effects of monsoons and storms on the structuring and diversity of picoeukaryotic microbial communities in a tropical coastal environment

Picoeukaryotes are key components in marine ecosystems that play crucial roles in food webs and biogeochemical cycles. Despite their significance, many aspects of their community ecology and diversity remain understudied. Here, we investigated the taxonomic and functional diversity of picoeukaryotic communities in response to monsoonal patterns and weather disturbances brought about by storms, characterizing tropical coastal regions. To do this, water samples were collected almost weekly or bi-weekly at a single location in a tropical coastal environment covering the late northeast (NE) and southwest (SW) monsoons. We then performed high-throughput amplicon sequencing of the V4 region of the 18S rRNA gene to generate taxonomic profiles of the communities across time. Clustering based on environmental parameters grouped our samples into months associated with NE monsoon, SW monsoon, and stormy SW monsoon, demonstrating seasonality influenced by monsoons and storms, typically observed in tropical coastal waters. In comparison, clustering based on abundance only grouped the samples into NE and SW monsoon, with most communities during storm period joining the NE monsoon samples. These samples exhibited greater diversity, with smaller taxa such as Syndiniales, Prymnesiophyceae, Picozoa, Cercozoa, Stramenopiles, and Chlorophytes being the most abundant groups present. In contrast, SW monsoon samples have lower diversity but have become generally dominated by large-celled taxa, mostly diatoms. Multivariate and correlation analyses both revealed nitrate as the strongest environmental driver of the picoeukaryotic community structuring. Meanwhile, network analysis grouped the taxa into three modules, more consistent with the clustering based on environmental parameters, implying that although storms may not significantly change the community composition, they may however influence the dominating taxa. Each module was composed of a unique set of co-occurring taxa, highlighting high turnover of picoeukaryotic communities between each season. In addition, our results showed that SW monsoon-associated module had higher interconnectivity than other modules, suggesting that the interactions during this period may be less species-specific, thus, more adaptable than during NE monsoon. However, we observed that extreme fluctuations caused by storms could have possibly allowed for selection of dominant taxa. Shotgun metagenomic sequencing of representative samples from each monsoon period also revealed that differently abundant functional genes, particularly genes associated to nitrogen metabolism, might have also helped in adaptation to the changing nutrient conditions. Our observations provide new insights on the potential trajectory of microbial communities under environmental stresses, which are important in understanding the implications of emerging threats such as coastal eutrophication and climate change.

In Silico Analysis of Changes in Predicted Metabolic Capabilities of Intestinal Microbiota after Fecal Microbial Transplantation for Treatment of Recurrent Clostridioides difficile Infection.

Although highly effective in treating recurrent Clostridioides difficile infection (RCDI), the mechanisms of action of fecal microbial transplantation (FMT) are not fully understood. The aim of this study was to explore microbially derived products or pathways that could contribute to the therapeutic efficacy of FMT. Stool shotgun metagenomic sequencing data from 18 FMT-treated RCDI patients at 4 points in time were used for the taxonomic and functional profiling of their gut microbiome. The abundance of the KEGG orthology (KO) groups was subjected to univariate linear mixed models to assess the significance of the observed differences between 0 (pre-FMT), 1, 4, and 12 weeks after FMT. Of the 59,987 KO groups identified by shotgun metagenomic sequencing, 27 demonstrated a statistically significant change after FMT. These KO groups are involved in many cellular processes, including iron homeostasis, glycerol metabolism, and arginine regulation, all of which have been implicated to play important roles in bacterial growth and virulence in addition to modulating the intestinal microbial composition. Our findings suggest potential changes in key KO groups post-FMT, which may contribute to FMT efficacy beyond the restored microbial composition/diversity and metabolism of bile acids and short-chain fatty acids. Future larger studies that include a fecal metabolomics analysis combined with animal model validation work are required to further elucidate the molecular mechanisms.

Influence of carbon-based cathodes on biofilm composition and electrochemical performance in soil microbial fuel cells

Increasing energy demands and environmental pollution concerns press for sustainable and environmentally friendly technologies. Soil microbial fuel cell (SMFC) technology has great potential for carbon-neutral bioenergy generation and self-powered electrochemical bioremediation. In this study, an in-depth assessment on the effect of several carbon-based cathode materials on the electrochemical performance of SMFCs is provided for the first time. An innovative carbon nanofibers electrode doped with Fe (CNFFe) is used as cathode material in membrane-less SMFCs, and the performance of the resulting device is compared with SMFCs implementing either Pt-doped carbon cloth (PtC), carbon cloth, or graphite felt (GF) as the cathode. Electrochemical analyses are integrated with microbial analyses to assess the impact on both electrogenesis and microbial composition of the anodic and cathodic biofilm. The results show that CNFFe and PtC generate very stable performances, with a peak power density (with respect to the cathode geometric area) of 25.5 and 30.4 mW m−2, respectively. The best electrochemical performance was obtained with GF, with a peak power density of 87.3 mW m−2. Taxonomic profiling of the microbial communities revealed differences between anodic and cathodic communities. The anodes were predominantly enriched with Geobacter and Pseudomonas species, while cathodic communities were dominated by hydrogen-producing and hydrogenotrophic bacteria, indicating H2 cycling as a possible electron transfer mechanism. The presence of nitrate-reducing bacteria, combined with the results of cyclic voltammograms, suggests microbial nitrate reduction occurred on GF cathodes. The results of this study can contribute to the development of effective SMFC design strategies for field implementation.

Association between gut microbiota and anxiety symptoms: A large population-based study examining sex differences

BackgroundThis study aimed to examine the association between anxiety symptoms and gut microbial composition and to infer their functional pathways. MethodsIn total, 605 participants were included in this study. Participants were categorized into anxious and non-anxious groups according to their Beck Anxiety Inventory scores, and their fecal microbiota was profiled using 16S ribosomal RNA gene sequencing. The microbial diversity and taxonomic profiles of the participants with anxiety symptoms were analyzed using generalized linear models. The function of the gut microbiota was inferred by comparing 16S rRNA data between the anxious and non-anxious groups. ResultsThe gut microbiome of the anxious group showed lower alpha diversity than that of the non-anxious group, and there were prominent differences in the community structure of the gut microbiota between the two groups. Male participants with anxiety had lower relative abundances of the family Oscillospiraceae, fibrolytic bacteria including those of the family Monoglobaceae, and short-chain fatty acid-producing bacteria such as those of the genus Lachnospiraceae_NK4A136 than those without anxiety symptoms. Female participants with anxiety symptoms had a lower relative abundance of the genus Prevotella than those without anxiety symptoms. LimitationThe direction of causality between anxiety symptoms and the gut microbiota was unclear owing to the cross-sectional design of the study. ConclusionOur results elucidate the association between anxiety symptoms and gut microbiota and provide insights for developing interventions to treat anxiety symptoms.

The selection of software and database for metagenomics sequence analysis impacts the outcome of microbial profiling and pathogen detection.

Shotgun metagenomic sequencing analysis is widely used for microbial profiling of biological specimens and pathogen detection. However, very little is known about the technical biases caused by the choice of analysis software and databases on the biological specimen. In this study, we evaluated different direct read shotgun metagenomics taxonomic profiling software to characterize the microbial compositions of simulated mice gut microbiome samples and of biological samples collected from wild rodents across multiple taxonomic levels. Using ten of the most widely used metagenomics software and four different databases, we demonstrated that obtaining an accurate species-level microbial profile using the current direct read metagenomics profiling software is still a challenging task. We also showed that the discrepancies in results when different databases and software were used could lead to significant variations in the distinct microbial taxa classified, in the characterizations of the microbial communities, and in the differentially abundant taxa identified. Differences in database contents and read profiling algorithms are the main contributors for these discrepancies. The inclusion of host genomes and of genomes of the interested taxa in the databases is important for increasing the accuracy of profiling. Our analysis also showed that software included in this study differed in their ability to detect the presence of Leptospira, a major zoonotic pathogen of one health importance, especially at the species level resolution. We concluded that using different databases and software combinations can result in confounding biological conclusions in microbial profiling. Our study warrants that software and database selection must be based on the purpose of the study.

GutAlive® enables DNA-based microbiome analysis without disrupting the original composition and diversity.

A precise fecal microbiome analysis requires normalized methods for microbiome sampling, transport and manipulation in order to obtain a representative snapshot of the microbial community. GutAlive® is the unique stool collection kit that generates an anaerobic atmosphere enabling oxygen sensitive bacteria to survive, maintaining the original microbiome composition and diversity. Five stool samples from different donors were collected using two different sampling devices, GutAlive® and Zymo DNA/RNA Shield®, and processed at four different time points. Shotgun metagenomics was used to evaluate the influence of the device and the processing timing on the microbial populations to unravel the potential fluctuations on the composition and diversity of the fecal microbiome and the metabolic pathways profiling. Additionally, RT-qPCR was used to quantify bacterial cell viability for downstream applications of microbiota samples beyond metagenomics. Our results show that GutAlive® enables bacterial cell viability overtime preserving DNA integrity, obtaining high-quantity and high-quality DNA to perform microbiome analysis using shotgun metagenomics. Based on the taxonomic profiling, metabolic pathways analysis, phylogeny and metagenome-assembled genomes, GutAlive® displayed greater performance without significant variability over time, showcasing the stabilization of the microbiome preserving the original composition and diversity. Indeed, this DNA stabilization is enabled with the preservation of bacterial viability on an anaerobic environment inside of the sampling device, without the addition of any reagents that interact directly with sample. All the above makes GutAlive® an user-friendly kit for self-collection of biological samples, suitable for microbiome analysis, diagnostics, fecal microbiota transplant and bacterial isolation, maintaining the stability and bacterial viability over time, preserving the original composition and diversity of the microbiome.

Land use modification causes slow, but predictable, change in soil microbial community composition and functional potential

BackgroundBacterial communities are critical to ecosystem functioning and sensitive to their surrounding physiochemical environment. However, the impact of land use change on microbial communities remains understudied. We used 16S rRNA gene amplicon sequencing and shotgun metagenomics to assess soil microbial communities' taxonomic and functional responses to land use change. We compared data from long-term grassland, exotic forest and horticulture reference sites to data from sites that transitioned from (i) Grassland to exotic forest or horticulture and from (ii) Exotic forest to grassland.ResultsCommunity taxonomic and functional profiles of the transitional sites significantly differed from those within reference sites representing both their historic and current land uses (P < 0.001). The bacterial communities in sites that transitioned more recently were compositionally more similar to those representing their historic land uses. In contrast, the composition of communities from sites exposed to older conversion events had shifted towards the compositions at reference sites representing their current land use.ConclusionsOur study indicates that microbial communities respond in a somewhat predictable way after a land use conversion event by shifting from communities reflecting their former land use towards those reflecting their current land use. Our findings help us to better understand the legacy effects of land use change on soil microbial communities and implications for their role in soil health and ecosystem functioning. Understanding the responsiveness of microbial communities to environmental disturbances will aid us in incorporating biotic variables into soil health monitoring techniques in the future.