Metagenomic Gene Research Articles

Processes and microorganisms driving nitrous oxide production in the Benguela Upwelling System

AbstractUpwelling systems and their associated oxygen deficient zones (ODZs) are hotspots of nitrous oxide (N2O) production in the ocean. The Benguela Upwelling System (BUS) is a highly productive region and an important, yet variable source of N2O to the atmosphere. This study examined underlying processes and microbial key players governing N2O production in the BUS during the austral winter. 15N‐tracer incubation experiments were conducted to track N2O production from NH4+ oxidation and denitrification. N2O production and consumption mechanisms over a longer temporal scale were determined through natural‐abundance isotope analyses. Metagenomics and 16S rRNA gene amplicon sequencing were used to identify potential key prokaryotes driving N2O production. Our results showed that, compared with permanent ODZs, the BUS is characterized by a higher oxidative and a lower reductive N2O production, both of which exhibit substantial spatial variability. N2O production peaked in low‐oxygen (O2) waters, with nearly equal contributions of oxidative and reductive processes, suggesting their co‐occurrence across an O2 concentration range broader than previously thought. However, the observed N2O isotope signatures implied a legacy of recent and extensive N2O reduction to N2. Metagenomic and 16S rRNA gene data identified denitrifiers belonging to Thioglobaceae and the archaeal ammonia‐oxidizers Nitrosopumilaceae among the potential key drivers of N2O production. Our study provides a comprehensive picture of N2O production in the BUS, revealing significant variability in the N‐cycling regime and underlying N2O production mechanisms, and demonstrating the value of combining direct rate measurements with more integrative approaches, such as molecular omics and natural‐abundance stable isotope tracers.

Endophytic Bacterial Communities in Wild Rice (Oryza eichingeri) and Their Effects on Cultivated Rice Growth

Endophytic bacteria play crucial roles in plant pathogen protection and growth. Oryza eichingeri is a unique wild rice species rich with genetic resources. Studies have explored beneficial endophytic bacteria and investigated the synergistic interaction between microbes and wild rice. However, the endophytic bacterial community of Oryza eichingeri and their plant growth-promoting (PGP) abilities characteristics remain largely unknown. Here, endophytic bacteria in the root, stem, and leaf tissues of Oryza eichingeri were characterized using metagenomic Illumina 16S rRNA gene sequencing. Additionally, culturable endophytic bacteria were isolated. The metagenomic analysis showed that, compared to those in other tissue compartments, the endophytic bacterial community in the roots had a more complex structure and enhanced functions, and each compartment had its own specific endophytic bacterial biomarkers. A total of 94 endophytic bacteria were isolated from Oryza eichingeri, among which 80 strains possessed PGP traits including increasing phosphate solubilization, siderophore production, IAA production, and nitrogen fixation. These strains displayed good PGP effects on cultivated rice seedlings, promoting the formation of strong root systems, stimulating biomass accumulation, and increasing root length and plant height. These findings provide insights into the composition of the bacterial endosphere of Oryza eichingeri and potential applications of the dominant PGP bacteria in rice cultivation.

Composition, Seasonal Dynamics and Metabolic Potential of the Rhizosphere Microbiome Associated with Wild White Poplar.

The white poplar (Populus alba) is a dioecious woody plant with significant potential for the phytoremediation of soils. To realize this potential, it is necessary to utilize growth-promoting microorganisms. One potential source of such beneficial microorganisms is the rhizosphere community of wild-growing trees. However, the structure, dynamics, and metabolism of the rhizosphere community of wild-growing white poplar remain poorly understood. To ascertain seasonal dynamics, species diversity, and metabolic potential, we sequenced 16S rRNA genes in metagenomes derived from 165 soil samples collected in spring and autumn from the root surfaces of 102 trees situated in disparate geographical locations. The three most prevalent phyla across all samples are Proteobacteria, Actinobacteriota, and Acidobacteriota. At the order level, the most prevalent orders are Sphingomonadales and Rhizobiales. Accordingly, the families Sphingomonadaceae and Rhizobiaceae were identified as dominant. The rhizospheric microbiome exhibited substantial inter-seasonal variation. Six families, including Caulobacteraceae, Xanthomonadaceae, Chitinophagaceae, Chthoniobacteraceae, Sphingomonadaceae, and Rhizobiaceae, exhibited alterations (spring-to-autumn) across all geographical locations under study. Members of the Rhizobiaceae family, which includes nitrogen-fixing bacteria, can provide poplar with plant-available forms of nitrogen such as nitrate and ammonium. The rhizosphere microbiome may facilitate the conversion of inorganic sulfur into sulfur-containing amino acids, cysteine and methionine, that are bioavailable to plants. Furthermore, the rhizosphere microbiome is capable of synthesizing amino acids, organic acids (including Krebs cycle acids), and some lipids and sugars. Consequently, the rhizosphere community can stimulate poplar growth by providing it with readily available forms of nitrogen and sulfur, as well as building blocks for the synthesis of proteins, nucleic acids, and other macromolecules. Many of these pathways, including nitrogen fixation, were subjected to seasonal changes.

Temporal enrichment of comammox Nitrospira and Ca. Nitrosocosmicus in a coastal plastisphere.

Plastic marine debris is known to harbor a unique microbiome (termed the "plastisphere") that can be important in marine biogeochemical cycles. However, the temporal dynamics in the plastisphere and their implications for marine biogeochemistry remain poorly understood. Here, we characterized the temporal dynamics of nitrifying communities in the plastisphere of plastic ropes exposed to a mangrove intertidal zone. The 39-month colonization experiment revealed that the relative abundances of Nitrospira and Candidatus Nitrosocosmicus representatives increased over time according to 16S rRNA gene amplicon sequencing analysis. The relative abundances of amoA genes in metagenomes implied that comammox Nitrospira were the dominant ammonia oxidizers in the plastisphere, and their dominance increased over time. The relative abundances of two metagenome-assembled genomes of comammox Nitrospira also increased with time and positively correlated with extracellular polymeric substances content of the plastisphere but negatively correlated with NH4+ concentration in seawater, indicating the long-term succession of these two parameters significantly influenced the ammonia-oxidizing community in the coastal plastisphere. At the end of the colonization experiment, the plastisphere exhibited high nitrification activity, leading to the release of N2O (2.52ng N2O N g-1) in a 3-day nitrification experiment. The predicted relative contribution of comammox Nitrospira to N2O production (17.9%) was higher than that of ammonia-oxidizing bacteria (4.8%) but lower than that of ammonia-oxidizing archaea (21.4%). These results provide evidence that from a long-term perspective, some coastal plastispheres will become dominated by comammox Nitrospira and thereby act as hotspots of ammonia oxidation and N2O production.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Correlation between gut microbiota characteristics and non-small cell lung cancer based on macrogenomics sequencing

ObjectiveNon-small cell lung cancer (NSCLC) patients undergoing chemotherapy and immunotherapy experience disturbances in the gut microbiota. This study intends to find out the correlation between gut microbiota and clinical indices before and after radiotherapy for NSCLC.MethodsTen patients with primary NSCLC were screened, and plasma and fecal samples were collected before and after radiotherapy, respectively. Inflammatory indices in plasma were detected. Genomic DNA was extracted from fecal specimens and sequenced on on Illumina HiSeq2000 sequencing platform. Thee sequenced data were subjected to Metagenome assembly, gene prediction, species annotation, and gene function analysis to study and analyze gut microbiota and metabolic functions. The correlation between the diversity of gut microbiota and the clinical indicators of NSCLC patients was evaluated, and the changes of gut microbiota before and after radiotherapy were observed.ResultsThe diversity of gut microbiota in NSCLC patients did not correlate with smoking, pathology, and inflammatory markers. The abundance of phylum (p)_Bacteroidetes increased; p_Firmicutes and p_Bacteroidetes accounted for the highest proportion in NSCLC patients, and the abundance of both was dominantly exchanged after radiotherapy. There was a decrease in genus (g)_Bifidobacterium after radiotherapy in NSCLC patients. There was no significant correlation between the diversity of gut microbiota after radiotherapy and radiotherapy sensitivity, and the structural composition and abundance of gut microbiota remained stable.ConclusionThe diversity of gut microbiota is altered after radiotherapy in NSCLC patients, showing an increase in harmful bacteria and a decrease in beneficial bacteria.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BackgroundCurrent methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms. MethodIn this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests. ResultsWe evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%–0.82 % respectively, and less than 10 % for the MFGC. ConclusionThe MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Occurrence of ceramides in the Acidobacterium Solibacter usitatus: implications for bacterial physiology and sphingolipids in soils

Sphingolipids have long been of interest to the scientific community for their roles in eukaryotic cell structuring and disease pathology. Less is known about the occurrence and function of these diverse compounds in the bacterial domain of life, with most studies on bacterial sphingolipids focused on eukaryotic disease research and host-pathogen or host-symbiont interactions. Thus, bacterial contributions to environmental sphingolipid pools are poorly understood and the function of these lipids outside of pathogenicity remains largely unexplored. This report marks the first instance of sphingolipid production in a member of the phylum Acidobacteria, a globally ubiquitous phylum of soil bacteria. The occurrence of core- and intact-ceramides is reported for the Acidobacterium Solibacter usitatus under various environmentally relevant conditions. Shifts in the production of ceramides across temperature, pH, and oxygen gradients in this organism suggest that these compounds play a role in the physiological adaptation to environmental fluctuations. Additionally, the genetic basis of bacterial ceramide biosynthesis in this species is assessed and used to explore the potential for ceramide biosynthesis across the bacterial domain of life. The extent of the biosynthetic potential for Acidobacteria to produce ceramides coupled to the abundance of their genes in soil metagenomes suggests that soil sphingolipids should not be solely attributed to eukaryotic production.

Green spaces contribute to structural resilience of the gut microbiota in urban mammals

The gut microbiome of wild animals is subject to various environmental influences, including those associated with human-induced alterations to the environment. We investigated how the gut microbiota of a synurbic rodent species, the striped field mouse (Apodemus agrarius), change in cities of varying sizes, seeking the urban microbiota signature for this species. Fecal samples for analysis were collected from animals living in non-urbanized areas and green spaces of different-sized cities (Poland). Metagenomic 16S rRNA gene sequencing and further bioinformatics analyses were conducted. Significant differences in the composition of gut microbiomes among the studied populations were found. However, the observed changes were dependent on local habitat conditions, without strong evidence of a correlation with the size of the urbanized area. The results suggest that ecological detachment from a more natural, non-urban environment does not automatically lead to the development of an “urban microbiome” model in the studied rodent. The exposure to the natural environment in green spaces may serve as a catalyst for microbiome transformations, providing a previously underestimated contribution to the maintenance of native gut microbial communities in urban mammals.

Perturbations in gut microbiota composition in schizophrenia.

Schizophrenia is a severe, complex and long-term psychiatric disorder with unclear etiology. Gut microbes influence the central nervous system via the gut-brain axis. Consequently, investigations of the relationship between gut microbes and schizophrenia are warranted. This study involved 29 patients with schizophrenia and 30 age-matched normal controls. After 16S rRNA gene sequencing and whole-genome shotgun metagenomic sequencing, we analyzed microbial diversity, composition, and function. According to 16S rRNA and metagenomic gene sequencing results, patients with schizophrenia had higher abundances of Clostridium and Megasphaera. Functional analysis showed that sphingolipid, phosphonates and phosphinates, as well as glutamine metabolism were associated with the occurrence and development of schizophrenia. Our data suggest that the gut microbiota exerts an effect on patients with schizophrenia, providing valuable insights into the potential regulation of in the context of this disorder.

Artificial electron snorkels reduce CH4 emissions in paddy soil: Regulation of the electron transfer pathway and microbial community ecology

Climate science has focused on reducing emissions of atmospheric CH4 from paddy soils, as CH4 has stronger infrared absorption characteristics than CO2. Like cable bacteria, artificial electron snorkels (made of solid graphite rods) have been proposed in this study to provide potential remote electron acceptors for soil anaerobic metabolism to compete with methanogenic processes. A systematic investigation was conducted on the modulation of the CH4 emission dynamics by electron snorkels at different densities, as well as the associated metabolism of C/N/Fe in paddy soil. During 124 days of operation, the electron snorkels suppressed CH4 emissions by 7–31% over ten sampling periods, and the high-density setting exhibited stronger performance. The electron snorkels accelerated the early mineralization of soil organic matter, increasing the content of dissolved organic matter by 20–236%. These organics were utilized by microorganisms, where O2, NO3−, and Fe3+ acted as electron acceptors, enhancing soil alternative respiration and producing CO2. Metagenomic and 16S rRNA gene sequencing revealed that microbial abundance and diversity were increased by 4–71% in the soil bottom layer (7 cm–8 cm depth) driven by electron snorkels. Moreover, electron snorkels potentially prevented CH4 escape by either improving soil permeability or electrically stimulating the abundance of methanotrophs (e.g., Methylocystis). The ratio of methanogenic to methane-oxidizing gene abundances was reduced by 22–66% under the electron snorkels. Our results indicate that the applied electron snorkels have significant potential in enriching the soil microbial community involved in methane cycling to mitigate CH4 emissions from paddy fields.

Astragalus mongholicus Bunge and Curcuma aromatica Salisb. modulate gut microbiome and bile acid metabolism to inhibit colon cancer progression.

Alterations in the gut microbiome and bile acid metabolism are known to play a role in the development and progression of colon cancer. Medicinal plants like Astragalus mongholicus Bunge and Curcuma aromatica Salisb. (AC) have shown preferable therapeutic effect on cancer therapy, especially digestive tract tumors like colon cancer. However, the precise mechanisms of AC inhibiting colon cancer, particularly in relation to the gut microbiome and bile acid dynamics, are not fully understood. Our research aimed to investigate the anti-tumor properties of AC in mice with CT26 colon cancer and further investigate its underlying mechanism via intestinal microbiota. The size and pathological changes of solid tumors in colon cancer are used to evaluate the inhibitory effect of AC on colon cancer. Metagenomics and 16s rRNA gene sequencing were employed to clarify the dysbiosis in the gut microbiome of colon cancer and its impact on colon cancer. The levels of bile acids (BAs) in the feces of mice from each group were measured using UPLC-Qtrap-MS/MS. AC effectively suppressed the growth of colon cancer and reduced histological damage. Notably, AC treatment led to changes in the gut microbiome composition, with a decrease in pathogenic species like Citrobacter and Candidatus_Arthromitus, and an increase in beneficial microbial populations including Adlercreutzia, Lachnospiraceae_UCG-001, and Parvibacter. Additionally, AC altered bile acid profiles, resulting in a significant decrease in pro-carcinogenic bile acids such as deoxycholic acid (DCA) and lithocholic acid (LCA), while increasing the concentration of the cancer-inhibitory bile acid, ursodeoxycholic acid (UDCA). Tracking and analyzing the data, AC may mainly upregulate FabG and baiA genes by increasing the relative abundance of Adlercreutzia and Parvibacter bacteria, which promoting the metabolism of pro-carcinogenic LCA. These findings provide strong evidence supporting the role of AC in regulating gut microbiome-mediated bile acid metabolism, which is crucial in impeding the progression of colon cancer.

Multiple approaches in the study of ancient human microbiome

Abstract Research related to the analysis of microbial communities in ancient human samples began two decades ago. Since then, new technologies as well as new bioinformatic tools have been developed for the manipulation of massive data. All this together, with the theoretical knowledge generated from microbial ecology has expanded our understanding of the human microbiome. Despite the challenge of studying ancient samples and their microbiome, some approaches applied to the contemporary human microbiome have been successfully applied to ancient samples. Most studies in ancient samples have focused on taxonomic profiling at the community level with metagenomic or high-throughput 16S rRNA gene amplicon sequencing data. However, other approaches have emerged in order to get deeper understanding of the ancient human microbiome. Therefore, the aim of this review is to discuss all the approaches and methodologies successfully applied to ancient samples and ancient microbiome as well as the information that can be generated using them, in order to understand the evolution of human microbiome interactions throughout history.

Deciphering Vanadium Speciation in Smelting Ash and Adaptive Responses of Soil Microorganisms.

Abundant smelting ash is discharged during pyrometallurgical vanadium (V) production. However, its associated V speciation and resultant ecological impact have remained elusive. In this study, V speciation in smelting ash and its influence on the metabolism of soil microorganisms were investigated. Smelting ashes from V smelters contained abundant V (19.6-115.9 mg/g). V(V) was the dominant species for soluble V, while solid V primarily existed in bioavailable forms. Previously unrevealed V nanoparticles (V-NPs) were prevalently detected, with a peak concentration of 1.3 × 1013 particles/g, a minimal size of 136.0 ± 0.6 nm, and primary constituents comprising FeVO4, VO2, and V2O5. Incubation experiments implied that smelting ash reshaped the soil microbial community. Metagenomic binning, gene transcription, and component quantification revealed that Microbacterium sp. and Tabrizicola sp. secreted extracellular polymeric substances through epsB and yhxB gene regulation for V-NPs aggregation to alleviate toxicity under aerobic operations. The V K-edge X-ray absorption near-edge structure (XANES) spectra suggested that VO2 NPs were oxidized to V2O5 NPs. In the anaerobic case, Comamonas sp. and Achromobacter sp. reduced V(V) to V(IV) for detoxification regulated by the napA gene. This study provides a deep understanding of the V speciation in smelting ash and microbial responses, inspiring promising bioremediation strategies to reduce its negative environmental impacts.

Mining and rational design of psychrophilic catalases using metagenomics and deep learning models.

A complete catalase-encoding gene, designated soiCat1, was obtained from soil samples via metagenomic sequencing, assembly, and gene prediction. soiCat1 showed 73% identity to a catalase-encoding gene of Mucilaginibacter rubeus strain P1, and the amino acid sequence of soiCAT1 showed 99% similarity to the catalase of a psychrophilic bacterium, Pedobacter cryoconitis. soiCAT1 was identified as a psychrophilic enzyme due to the low optimum temperature predicted by the deep learning model Preoptem, which was subsequently validated through analysis of enzymatic properties. Experimental results showed that soiCAT1 has a very narrow range of optimum temperature, with maximal specific activity occurring at the lowest test temperature (4°C) and decreasing with increasing reaction temperature from 4 to 50°C. To rationally design soiCAT1 with an improved temperature range, soiCAT1 was engineered through site-directed mutagenesis based on molecular evolution data analyzed through position-specific amino acid possibility calculation. Compared with the wild type, one mutant, soiCAT1S205K, exhibited an extended range of optimum temperature ranging from 4 to 20°C. The strategies used in this study may shed light on the mining of genes of interest and rational design of desirable proteins. KEY POINTS: • Numerous putative catalases were mined from soil samples via metagenomics. • A complete sequence encoding a psychrophilic catalase was obtained. • A mutant psychrophilic catalase with an extended range of optimum temperature was engineered through site-directed mutagenesis.

Arsenic bioremediation in mining wastewater by controllable genetically modified bacteria with biochar

Arsenic (As) is a highly toxic chemical element associated with cardiovascular diseases and diabetes. Using genetically modified microorganisms (GMOs) for arsenic detoxification or removal is taking center stage in recent years. However, the bio-safety issue of GMOs remains an obstacle. In this study, we proposed a bioremediation process to efficiently adsorb arsenic from mining wastewater by combining a controllable GMO with biochar. One metagenomic gene element (ArsR) with high arsenic adsorption efficiency was newly identified from mining wastewater environment. Then the gene was chromosomally integrated into strain Pseudomonas putida KT2440, which is a widely used strain for environmental bioremediation. The growth-regulation genetic circuit coordinating suicide gene expression was deployed onto the genome of strain KT2440. This non-auxotrophic, antibiotic-free and self-controlled programmable circuit was fine-tuned with highly sensitive perceptibility towards arsenic levels below wastewater discharge standard (< 0.50 mg/L), which made the system applicable. A biochar-microorganism coupled recovery strategy was further implemented in mining wastewater treatment, leading to increased arsenic removal capacities, which is ready for use under a circumstance up to 20 mg/L of total arsenic pollution. No more than 1.32 × 10−9 escapee of GMOs was observed in the process, which meets the US NIH guidelines for GMOs release (<10−8). Thus, this study demonstrated the feasibility for arsenic bioremediation by GMOs in industrial wastewater.

Bacterial diversity in honey bee environment: Embu County, Kenya

In Kenya, small-scale farmers are increasingly venturing into honeybee keeping supplementing their income. However, cases of honeybee populations decline characterized by colony losses, hive absconding and migrating swarms have been documented, resulting in a decline in the number of colonized hives thus raising concerns about their sustainability. Honeybee and colony fitness is dependent on bacterial symbioses, and their disruption leads to disease susceptibility. To assess bacteria associated with honeybees in Kenya, we collected honeycombs, honey, adult worker bees and frame scraping samples from different agro-ecological zones within Embu County. To determine the bacterial composition we characterized the hive microbiota using the targeted metagenomic culture-independent 16S rRNA gene sequencing. Honeybee-associated bacteria community in the hive materials was dominated by Lactobacillus, Leuconostoc, Fructobacillus, Bacillus, Gilliamella, Frischella, Enterobacter, Bombella and Serratia across the sampling environment. In the sample types, adult worker bees lacked the Lactococcus genus but had the other phylotypes consistently similar to those in the larvae samples. Genus Saccharibacter however was absent in honeycomb and larvae samples. Genus Lactococcus was present in all the sample types except in the adult worker bee samples. The significant observation was the presence of genera Serratia and Enterobacter, opportunistic environmental bacteria. High levels of these bacteria in adult and larva samples may present a potential disruption of the microbial community and increase disease susceptibility. Honey production declines as a result of the weakened colony. Regular monitoring will be ideal in maintaining the colony health and thus respond to the growing global market and achieving the African union's Agenda 2063 on agricultural productivity and production.

Fecal metabolomics combined with metagenomics sequencing to analyze the antidepressant mechanism of Yueju Wan

BackgroundYueju Wan (YJW), defined in Danxi's Mastery of Medicine, has Qi-regulating and Qi-promoting effects. YJW has frequently been applied in the clinic for the treatment of depression. Substantial evidence has shown that depression is related to metabolic abnormalities of the gut microbiota, and traditional Chinese medicine (TCM) can treat depression by adjusting gut microbiota metabolism. The antidepressant effect of YJW is well established, but thus far, whether its mechanism of action is achieved by regulating the intestinal flora has not been elucidated. MethodsIn this study, chronic unpredictable mild stress (CUMS) along with isolated feeding created a rat depression model, and YJW was administered for intervention. Rats were put through behavioral tests to determine their level of depression, and ELISA was utilized for measuring the level of monoamine neurotransmitters (MNTs) in the hippocampus. Metagenomic gene sequencing analysis was used to study the effect of depression on the intestinal flora in rats and the regulatory mechanism of YJW on the intestinal flora. Furthermore, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was utilized for fecal metabolomics studies to further reveal the antidepressant mechanism of YJW. The antidepressant mechanism of YJW was explored and further verified by Western blot analysis. ResultsDifferent doses of YJW improved the depressive state of rats and raised the levels of MNTs in the hippocampus. The results of metagenomic sequencing indicated that the YJW recovered the structure and diversity of the intestinal flora in depressed rats. Metabolomics revealed sustained changes in 21 metabolites after the treatment of YJW, suggesting that YJW can play an antidepressant role by improving abnormal metabolic pathways. The results of correlation analysis suggested that YJW might mediate Eubacterium, Oscillibacter, Roseburia, Romboutsia and Bacterium to regulate purine metabolism, tryptophan metabolism, primary bile acid biosynthesis, and glutamate metabolism and exert antidepressant effects. Western blot analysis showed that YJW reduced the content of IL-1β in the hippocampus, inhibited the activation of the NLRP3 inflammasome in the hippocampus of rats, and increased the content of ZO-1 in the colon of rats. ConclusionYJW can alleviate depressive symptoms in depressed rats, and its mechanism is connected to improving intestinal flora and regulating body metabolism.

Metagenomic insights into the microbial cooperative networks of a benz(a)anthracene-7,12-dione degrading community from a creosote-contaminated soil

Genotoxicity of PAH-contaminated soils can eventually increase after bioremediation due to the formation and accumulation of polar transformation products, mainly oxygenated PAHs (oxy-PAHs). Biodegradation of oxy-PAHs has been described in soils, but information on the microorganisms and mechanisms involved is still scarce. Benz(a)anthracene-7,12-dione (BaAQ), a transformation product from benz(a)anthracene frequently detected in soils, presents higher genotoxic potential than its parent PAH. Here, using sand-in-liquid microcosms we identified a specialized BaAQ-degrading subpopulation in a PAH-contaminated soil. A BaAQ-degrading microbial consortium was obtained by enrichment in sand-in-liquid cultures with BaAQ as sole carbon source, and its metagenomic analysis identified members of Sphingobium, Stenotrophomonas, Pusillimonas, Olivibacter, Pseudomonas, Achromobacter, and Hyphomicrobiales as major components. The integration of data from metabolomic and metagenomic functional gene analyses of the consortium revealed that the BaAQ metabolic pathway was initiated by Baeyer-Villiger monooxygenases (BVMOs). The presence of plasmid pANTQ-1 in the metagenomic sequences, identified in a previous multi-omic characterization of a 9,10-anthraquinone-degrading isolate recovered from the same soil, suggested the occurrence of a horizontal gene transfer event. Further metagenomic analysis of the BaAQ-degrading consortium also provided insights into the potential roles and interactions within the consortium members. Several potential auxotrophies were detected, indicating that relevant nutritional interdependencies and syntrophic associations were taking place within the community members, not only to provide suitable carbon and energy sources, but also to supply essential nutrients and cofactors. Our work confirms the essential role that BVMO may play as a detoxification mechanism to mitigate the risk posed by oxy-PAH formation during bioremediation of contaminated soils.

Shotgun metagenomics captures more microbial diversity than targeted 16S rRNA gene sequencing for field specimens and preserved museum specimens.

The use of museum specimens for research in microbial evolutionary ecology remains an under-utilized investigative dimension with important potential. Despite this potential, there remain barriers in methodology and analysis to the wide-spread adoption of museum specimens for such studies. Here, we hypothesized that there would be significant differences in taxonomic prediction and related diversity among sample type (museum or fresh) and sequencing strategy (medium-depth shotgun metagenomic or 16S rRNA gene). We found dramatically higher predicted diversity from shotgun metagenomics when compared to 16S rRNA gene sequencing in museum and fresh samples, with this differential being larger in museum specimens. Broadly confirming these hypotheses, the highest diversity found in fresh samples was with shotgun sequencing using the Rep200 reference inclusive of viruses and microeukaryotes, followed by the WoL reference database. In museum-specimens, community diversity metrics also differed significantly between sequencing strategies, with the alpha-diversity ACE differential being significantly greater than the same comparisons made for fresh specimens. Beta diversity results were more variable, with significance dependent on reference databases used. Taken together, these findings demonstrate important differences in diversity results and prompt important considerations for future experiments and downstream analyses aiming to incorporate microbiome datasets from museum specimens.