Microbial Community Profiles Research Articles

Periodontitis Exacerbates Colorectal Cancer by Altering Gut Microbiota-Derived Metabolomics in Mice.

The correlation between periodontitis and colorectal cancer (CRC) has drawn widespread attention. However, how periodontitis affects CRC progression remains unclear. C57BL/6 mice were used to establish experimental periodontitis and CRC model. Histological alterations of periodontium and colon were observed by hematoxylin and eosin staining. Micro-computed tomography (micro-CT) was applied to evaluate alveolar bone loss (ABL). Tumor growth was detected by immunofluorescence. Gut bacteria were analyzed using 16S rRNA sequencing. Gas chromatography-mass spectrometry (GC-MS) was performed to observe the alterations of gut microbial metabolites. The detection of associated pathways was carried out using quantitative real-time PCR (qRT-PCR). Experimental periodontitis significantly induced increases in tumor number in mice with CRC. Double immunofluorescence for Ki67 and β-catenin, as well as Cyclin D1 and β-catenin, indicated that experimental periodontitis observably promoted tumor growth. 16S rRNA sequencing and untargeted metabolomics analysis displayed that experimental periodontitis altered gut microbial community and metabolite profiles in CRC mice. Notably, we found that experimental periodontitis dramatically increased the level of three oncometabolites (serotonin, adenosine, and spermine) in mice with CRC. Alterations of gut microbial community and metabolites might be relevant in experimental periodontitis deteriorating CRC.

Exploring animal food microbiomes and resistomes via 16S rRNA gene amplicon sequencing and shotgun metagenomics.

As a diverse and complex food matrix, the animal food microbiota and repertoire of antimicrobial resistance (AMR) genes remain to be better understood. In this study, 16S rRNA gene amplicon sequencing and shotgun metagenomics were applied to three types of animal food samples (cattle feed, dry dog food, and poultry feed). ZymoBIOMICS mock microbial community was used for workflow optimization including DNA extraction kits and bead-beating conditions. The four DNA extraction kits (AllPrep PowerViral DNA/RNA Kit, DNeasy Blood & Tissue Kit, DNeasy PowerSoil Kit, and ZymoBIOMICS DNA Miniprep Kit) were compared in animal food as well as the use of peptide nucleic acid blockers for 16S rRNA gene amplicon sequencing. Distinct microbial community profiles were generated, which varied by animal food type and DNA extraction kit. Employing peptide nucleic acid blockers prior to 16S rRNA gene amplicon sequencing was comparable with post-sequencing in silico filtering at removing chloroplast and mitochondrial sequences. There was a good agreement between 16S rRNA gene amplicon sequencing and shotgun metagenomics on community profiles in animal feed data sets; however, they differed in taxonomic resolution, with the latter superior at resolving at the species level. Although the overall prevalence of AMR genes was low, resistome analysis of animal feed data sets by shotgun metagenomics revealed 10 AMR gene/protein families, including beta-lactamases, erythromycin/lincomycin/pristinamycin/tylosin, fosfomycin, phenicol, and quinolone. Future expansion of microbiome and resistome profiling in animal food will help better understand the bacterial and AMR gene diversity in these commodities and help guide pathogen control and AMR prevention efforts.IMPORTANCEWith the growing interest and application of metagenomics in understanding the structure/composition and function of diverse microbial communities along the One Health continuum, this study represents one of the first attempts to employ these advanced sequencing technologies to characterize the microbiota and AMR genes in animal food. We unraveled the effects of DNA extraction kits on sample analysis by 16S rRNA gene amplicon sequencing and showed similar efficacies of two strategies at removing chloroplast and mitochondrial reads. The in-depth analysis using shotgun metagenomics shed light on the community compositions and the presence of an array of AMR genes in animal food. This exploration of microbiomes and resistomes in representative animal food samples by both sequencing approaches laid important groundwork for future metagenomic investigations to gain a better understanding of the baseline/core microbiomes and associated AMR functions in these diverse commodities and help guide pathogen control and AMR prevention efforts.

Long-term microbial functional responses in soil contaminated with biofuel/fossil fuel blends triggered by different bioremediation treatments.

The use of biofuel blends with fossil fuels is widespread globally, raising concerns over novel contamination types in environments impacted by these mixtures. This study investigates the microbial functional in soils contaminated by biofuel and fossil fuel blends and subjected to various bioremediation treatments. Using metagenomic analysis, it was compared hydrocarbon degradation functional profiles across areas polluted with ethanol/gasoline and biodiesel/diesel blends. Results indicate that long-term natural attenuation areas exhibited distinct functional profiles compared to actively bioremediated areas. However, same hydrocarbon degradation genes were enriched across all areas, highlighting functional redundancy despite taxonomic variation in hydrocarbon-degrading microbes. Finally, several of the keystone species found were hydrocarbon degraders, such as members of the families Clostridiaceae and Comamonadaceae, representing potential targets for biostimulation in future remediation efforts. This long-term, field-scale study uniquely focuses on the functional profiles of microbial communities, offering new insights into the bioremediation of complex biofuel/fossil fuel contaminants in situ.

The effect of probiotics, prebiotics and synbiotics on gut microbial community profile in overweight and obese Latin American and Caribbean populations: a systematic review of human trials

The effect of probiotics, prebiotics and synbiotics on gut microbial community profile in overweight and obese Latin American and Caribbean populations: a systematic review of human trials

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery.

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures.

Topical Mupirocin Treatment Reduces Interferon and Myeloid Signatures in Cutaneous Lupus Erythematous Lesions Through Targeting of Staphyloccal Species.

Cutaneous lupus erythematosus (CLE) is an inflammatory skin manifestation of systemic lupus erythematosus. Type I interferons (IFNs) promote inflammatory responses and are elevated in CLE lesions. We recently reported that CLE lesions are frequently colonized with Staphylococcus aureus. Here, we follow up via a proof-of-concept study to investigate whether type I IFN and inflammatory gene signatures in CLE lesions can be modulated with mupirocin, a topical antibiotic treatment against S aureus-mediated skin infections. Participants with active CLE lesions (n = 12) were recruited and randomized into a week of topical treatment with either 2% mupirocin or petroleum jelly vehicle. Paired samples were collected before and after seven days of treatment to assess microbial lesional skin responses. Microbial samples from nares and lesional skin were used to determine baseline and posttreatment Staphylococcus abundance and microbial community profiles by 16S ribosomal RNA gene sequencing. Inflammatory responses were evaluated by bulk RNA sequencing of lesional skin biopsies. We identified 173 differentially expressed genes in CLE lesions after topical mupirocin treatment. Decreased lesional Staphylococcus burden correlated with decreased IFN pathway signaling and inflammatory gene expression and barrier dysfunction. Interestingly, mupirocin treatment lowered skin monocyte levels, and this mupirocin-associated depletion of monocytes correlated with decreased inflammatory gene expression. Mupirocin treatment decreased lesional Staphylococcus, and this correlated with decreased IFN signaling and inflammatory gene expression. This study suggests a topical antibiotic could be employed to decrease lupus skin inflammation and type I IFN responses by reducing Staphylococcus colonization.

Influence of environmental factors on microbial community and flavor profile of dajiang of Liaoning

Influence of environmental factors on microbial community and flavor profile of dajiang of Liaoning

Industrial hemp (Cannabis sativa L.) adapts to cadmium stress by reshaping rhizosphere fungal community

Increasing evidence indicates that plants under environmental stress can actively seek the help of microbes (‘cry-for-help’ hypothesis). However, empirical evidence underlying this strategy is limited under metal-stress conditions. Here, we employed integrated microbial community profiling in cadmium (Cd) polluted soil and culture-based methods to investigate the three-way interactions between the industrial hemp (Cannabis Sativa L.), rhizospheric microbes, and Cd stress. Results from the pot and three cycles of the successful hemp planting experiments showed that Cd stress significantly affected the composition of rhizosphere fungi in industrial hemp and induced enrichment of the fungal operational taxonomic unit (OTU)3 (Cladosporium). A representative of OTU3 (strain DM-2) was successfully isolated. In a hydroponic experiment, inoculation of DM-2 significantly increased the shoot length (by 25.84 %) and fresh weight (by 92.66 %) of hemp seedlings when compared to the absence of DM-2 under the Cd stress. The findings indicate that DM-2 inoculation could effectively alleviate the Cd stress in hemp seedlings. Metabolomic analysis of spent media with or without DM-2 revealed the association of DM-2 with the transformation of root exudates to melatonin, which may be a key chemical in plant-microbe interactions against abiotic stresses. The findings will inform efforts to manipulate the root microbiome to enhance plant growth in polluted environments.

Evaluation of DNA Extraction Methods for Microbial Community Profiling in Deadwood Decomposition.

As technologies advance alongside metabarcoding and metagenomic resources, particularly for larger fungal genomes, DNA extraction methods must be optimized to meet higher thresholds, especially from complex environmental substrates. This study focused on extracting fungal genomic compounds from woody substrates, a challenge due to the embedment of endophytic and saprotrophic fungi within wood cells, the physical recalcitrance of wood, the adsorption of nucleic acids to wood polymers, and the release of downstream inhibitors. Hypothesizing that cetyltrimethylammonium bromide would be the best option, we compared prominent methods by extracting and sequencing microbial DNA from sound and decayed birch (Betula papyrifera) and pine (Pinus resinosa). DNA quantities varied significantly depending on extraction methods and decay stage. The quality of DNA, in terms of purity and integrity, significantly impacted whether the samples could be amplified and sequenced. However, amplicon sequencing of bacterial and fungal communities revealed no significant extraction bias. This, along with the sequencing effectiveness and cost/time efficiency, indicates that Qiagen is the gold standard for woody substrates. This study increases confidence in published amplicon data sets regardless of the extraction methods, provides a cost-benefit table for making protocol decisions, and offers guidance on fungal DNA extractions from complex organic substrates (sound and decayed wood) that would best suit future metagenomic efforts.

Pseudostellaria heterophylla cultivar mixtures driven changes in rhizosphere metabolites to suppress soil-borne Fusarium disease

Crop diversification contributes to a decrease in soil-borne crop diseases, as well as an increase in agricultural productivity. However, few studies have investigated the changes in the composition of the rhizosphere microbial communities and rhizosphere metabolites, as well as their suppressive effect on soil-borne diseases under different crop cultivar mixture regimes. We carried out a series of experiments to assess changes in the rhizosphere microbial community and metabolites profile under different Pseudostellaria heterophylla cultivar mixture cultivation in consecutive monoculture fields by employing amplicon metagenomics (16S rRNA, ITS, and 18S rRNA) and non-targeted metabolomics. The impact of key metabolites on pathogenic Fusarium oxysporum, crop growth, and soil microorganisms was assessed under controlled conditions. Our study indicated that the cultivar mixtures improved the P. heterophylla performance, increased the fresh root biomass by 81.9–115.4 % and the heterophyllin B content by 35 % compared to the consecutive monoculture, respectively. Cultivar mixtures increased the abundance of beneficial bacteria (Lactobacillus, Pseudomonas, Nitrosospira) and consumer protists, and decreased the abundance of pathogenic fungal genera (Fusarium, Alternaria, Curvularia, Stemphylium, Gibberella). The qPCR results indicated that the cultivar mixtures significantly decreased the abundance of pathogenic F. oxysporum by 64.0–84.3 % compared to the consecutive monoculture treatment. Non-targeted metabolomics analysis showed that the cultivar mixtures significantly altered the soil metabolite profiles, and increased the contents of d-galactose, galactinol, d-sorbitol, glycerol, melibiose, D-fructose and D-tagatose. Subsequently, the key upregulated metabolites (glycerol, d-fructose, gluconic acid, quinic acid, and l-valine), identified through the random forest analysis, significantly inhibited the growth of F. oxysporum. The crucial metabolites in the presence of a pathogen (F. oxysporum) and single metabolite treatment significantly increased the biomass, SOD and CAT activity and decreased the POD and MAD activity of P. heterophylla compared to FOP (F. oxysporum treatment). Furthermore, the crucial metabolites under pathogen treatment significantly lowered the abundance of total fungi and F. oxysporum and increased the abundance of Pseudomonas spp. compared to FOP. Therefore, our study was able to emphasize the efficacy of using cultivar mixtures to combat soil-borne Fusarium disease through the modulation of rhizosphere metabolites.

Aqueous copper geochemistry shapes the sediment microbial resistome in a recovering stream.

Aqueous metals are pervasive contaminants associated with historical mining. We produced and examined 16 metagenomes from a contaminated creek to investigate how anthropogenic metal contamination shapes the functional profiles of microbial communities. We then incorporated the metagenomic profiles and concurrently collected geochemical context into a multivariate model to examine correlations between stream geochemistry and microbial functional potential. Integrating the metagenomes with full geochemical profiles emphasised that even low metalloid concentrations shaped microbial functionality, seasonal shifts in copper bioavailability and arsenic exposure correlated with genetic variation, and copper resistomes were spatiotemporally distinct. This study provides new insights into microbial metabolic potential and microbe-metal(loid) interactions.

Transplantation of fecal microbiota from low to high residual feed intake chickens: Impacts on RFI, microbial community and metabolites profiles

Improving feed efficiency is vital to bolster profitability and sustainability in poultry production. Although several studies have established links between gut microbiota and feed efficiency, the direct effects remain unclear. In this study, two distinct lines of Huiyang bearded chickens, exhibiting significant differences in residual feed intake (RFI), were developed after 15 generations of selective breeding. Fecal microbiota transplantation (FMT) from low RFI (LRFI) chickens to high RFI (HRFI) chickens resulted in a reduction trend in RFI, decreasing from 5.65 to 4.49 in the HRFI recipient chickens (HFMT). Microbiota composition and functional profiles in LRFI and HFMT chickens formed a distinct cluster compared to HRFI chickens. Using 16S rDNA sequencing and RandomForest analysis, Slackia, Peptococcus, Blautia, and Dorea were identified as key microbial markers associated with feed efficiency. Additionally, untargeted metabolomics identified common differential metabolites between HFMT and LRFI vs. HRFI groups. Correlation analysis showed significant correlations between these microbial markers and differential metabolites. These findings provide a foundation for microbiome-based strategies to improve feed efficiency in poultry.

A novel computational approach for the mining of signature pathways using species co-occurrence networks in gut microbiomes

BackgroundAdvances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological insights that pave the way for more accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. However, such attempts have mostly used partial or incomplete data to accurately capture those associations.MethodsThis study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data at various granularity levels.ResultsWhen comparing the IBD group to a control group, we show that certain co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment.ConclusionsThe obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that can be used to diagnose certain health conditions.

Taxonomic and functional profiling of microbial community in municipal solid waste dumpsite.

Understanding the microbial ecology of landfills is crucial for improving waste management strategies and utilizing the potential of these microbial communities for biotechnological applications. This study aimed to conduct a comprehensive taxonomic and functional profiling of the microbial community present in the Addis Ababa municipal solid waste dumpsite using a shotgun metagenomics sequencing approach. The taxonomic analysis of the sample revealed the significant presence of bacteria, with the Actinomycetota (56%), Pseudomonadota (23%), Bacillota (3%), and Chloroflexota (3%) phyla being particularly abundant. The most abundant KEGG categories were carbohydrates metabolism, membrane transport, signal transduction, and amino acid metabolism. The biodegradation and metabolism of xenobiotics, as well as terpenoids and polyketides, were also prevalent. Moreover, the Comprehensive Antibiotic Resistance Database (CARD) identified 52 antibiotic resistance gene (ARG) subtypes belonging to 14 different drug classes, with the highest abundances observed for glycopeptide, phosphonic acid, and multidrug resistance genes. Actinomycetota was the dominant phylum harboring ARGs, followed by Pseudomonadota and Chloroflexota. This study offers valuable insights into the taxonomic and functional diversity of the microbial community in the Addis Ababa municipal solid waste dumpsite. It sheds light on the widespread presence of metabolically versatile microbes, antibiotic resistance genes, mobile genetic elements, and pathogenic bacteria. This understanding can contribute to the creation of efficient waste management strategies and the investigation of possible biotechnological uses for these microbial communities.

<b>Machine Learning in Genomics: Applications in Whole Genome Sequencing, Whole Exome Sequencing, Single-Cell Genomics, and Spatial Transcriptomics</b>

The application of machine learning (ML) to genomics has transformed the process of analyzing and interpreting large-scale, complex datasets, leading to important breakthroughs in our knowledge of biological systems. This review provides a comprehensive overview of ML applications in key genomic areas: Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), single-cell genomics, and spatial transcriptomics. In WGS and WES, ML techniques are employed for variant calling, genome-wide association studies, rare variant analysis, and the prediction of pathogenicity. In single-cell genomics, ML facilitates clustering, trajectory inference, and cell type identification, while in spatial transcriptomics, it aids in deciphering spatial patterns of gene expression and tissue heterogeneity. This review further explores the application of ML in related omics fields, including proteomics, transcriptomics, metagenomics, epigenomics, and microbiome research. These applications encompass protein structure prediction, functional annotation, microbial community profiling, and the analysis of epigenetic modifications. We address the challenges caused by high dimensionality, variability in the data, and the requirement for interpretable machine learning models when dealing with genomic data. Emerging technologies like explainable AI and federated learning are highlighted for their potential to address these challenges. Additionally, the review addresses ethical considerations, data privacy issues, and the necessity for standardized protocols in ML applications. This comprehensive examination underscores the transformative impact of ML in genomics and highlights its potential to drive future innovations in personalized medicine and biological research. Received: 17 August 2024 | Revised: 17 October 2024 | Accepted: 31 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Saheed Adegbola Adeyanju: Conceptualization, Methodology, Validation, Data curation, Writing - original draft, Writing - review & editing, Supervision. Taiwo Temitope Ogunjobi: Methodology, Investigation, Writing - original draft, Writing - review & editing.

Rapid diagnosis of Mycoplasma pneumoniae and prediction of antibiotic resistance by nanopore adaptive sampling

AbstractBackgroundMicrobial infections, particularly in children, require rapid and accurate diagnostics. It is difficult to differentiate pathogens from commensal organisms, and it is impossible to identify antibiotic resistance genes that belong to pathogens with current methods. Third‐generation sequencing provides rapid library preparation and real‐time data acquisition. Nanopore normal sampling (NNS) enables unbiased sequencing of clinical samples without amplification, aiding pathogen identification and antimicrobial resistance gene prediction. However, clinical samples often contain a considerable amount of human DNA, potentially masking pathogen data. Nanopore adaptive sampling (NAS) aims to selectively enrich pathogens, promising improved diagnostics for acute infections and better treatment decisions in clinical practice. This study aimed to determine the utility of NAS in enhancing the real‐time detection of pathogens and predicting AMR in infectious disease outbreaks.MethodsThis study used NAS technology to rapidly and directly detect Mycoplasma pneumoniae infection in bronchoalveolar lavage fluid samples from 28 pediatric patients at Shenzhen Children's Hospital. We assessed the efficacy of NAS compared with that of NNS by evaluating the number of microbial reads and the amount of microbial DNA data. We then compared the accuracy of detecting pathogens between NNS and NAS and between NAS and real‐time polymerase chain reaction assays. Furthermore, we predicted antimicrobial resistance (AMR) and examined AMR genes associated with pathogens.ResultsNAS showed up to a 14.67‐fold increase in the amount of microbial DNA data from patients' samples compared with NNS within the initial 2.5 h of sequencing. Additionally, NAS reduced the amount of host DNA data by up to 6.67‐fold compared with NNS. Unlike TaqMan real‐time polymerase chain reaction assays, NAS technology identified dominant pathogens and provided detailed insight into the abundance of the microbial community. Furthermore, NAS was able to predict AMR profiles of microbial communities and attribute specific AMR traits to individual microbes within the samples.ConclusionThis study shows that NAS advances the clinical diagnosis because it can rapidly detect pathogens directly from patients' samples and provides antimicrobial resistance information for clinical guidance. These abilities further facilitate the application of NAS in personalized treatment, reduce the misuse of broad‐spectrum antibiotics, and promote patients' recovery.

Differentiation complex sputum microbiome in patients suspected TB pulmonary

<b>Purpose: </b>This is the first study to attempt microbiome diversity using metagenomic full-length 16S rRNA from respiratory specimens suspected of chronic pulmonary TB patients.<br /> <b>Materials and methods:</b> A 33 patients with suspected chronic pulmonary TB were included. Sputum specimens were cultured to detect <i>mycobacterium sp.</i> 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.<br /> <b>Results: </b>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 <i>streptococcus sp.</i>, <i>prevotella sp., </i>and <i>veilonella sp. </i>However, less was detected in two NTM infection patients<i>. </i>Metagenomic analysis revealed community bacteria species, including mycobacterium tuberculosis and NTM species, with the lowest number of unique reads. The abundance of <i>streptococcus sp.</i> were less than 30% in 4 patient with comorbid diabetes mellitus.<br /> <b>Conclusions:</b> 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.

Response of Ceratophyllum demersum L. and its epiphytic biofilms to 6PPD and 6PPD-Q exposure: Based on metabolomics and microbial community analysis

The emerging contaminant N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its ozone conversion product N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6PPD-Q) pose a threat to aquatic ecosystems. Aquatic animals and plants exhibit vigorous responses at very low ambient concentrations. However, studies of submerged macrophytes, key producers in aquatic ecosystems, are limited and the full extent of their toxic effects and feedback mechanisms is unknown. To investigate the phytotoxicity of 6PPD and 6PPD-Q, we modeled plant responses to abiotic stress using Ceratophyllum demersum L. (C. demersum) as a representative submerged plant. Our findings indicate that 6PPD and 6PPD-Q disrupt physiological and biochemical processes in C. demersum, encompassing growth inhibition, reduction in photosynthetic pigments, induction of oxidative damage, and metabolic alterations. Moreover, unfavorable modifications to biofilms induced were also discernible supported by confocal laser scanning microscopy (CLSM) images and microbial community profiling. More importantly, we found a robust correlation between differentially expressed metabolites (DEMs) and dominant genera, and 6PPD and 6PPD-Q significantly altered their correlation. Overall, our results imply that even though C. demersum is a resilient submerged macrophyte, the toxic effects of 6PPD and 6PPD-Q cannot be disregarded.

SALINITY-Induced Changes in Diversity, Stability, and Functional Profiles of Microbial Communities in Different Saline Lakes in Arid Areas

Saline lakes, characterized by high salinity and limited nutrient availability, provide an ideal environment for studying extreme halophiles and their biogeochemical processes. The present study examined prokaryotic microbial communities and their ecological functions in lentic sediments (with the salinity gradient and time series) using 16S rRNA amplicon sequencing and a metagenomic approach. Our findings revealed a negative correlation between microbial diversity and salinity. The notable predominance of Archaea in high-salinity lakes signified a considerable alteration in the composition of the microbial community. The results indicate that elevated salinity promotes homogeneous selection pressures, causing substantial alterations in microbial diversity and community structure, and simultaneously hindering interactions among microorganisms. This results in a notable decrease in the complexity of microbial ecological networks, ultimately influencing the overall ecological functional responses of microbial communities such as carbon fixation, sulfur, and nitrogen metabolism. Overall, our findings reveal salinity drives a notable predominance of Archaea, selects for species adapted to extreme conditions, and decreases microbial community complexity within saline lake ecosystems.

The source and dissemination of ARGs in pristine environments: Elucidating the role of migratory birds in the Arctic

Antibiotic resistance genes (ARGs) are a class of emerging contaminants that significantly threaten public health. In this work, the profiles of ARGs and microbial communities in the soil, sediment, migratory bird, and local deer fecal samples collected from the Arctic were characterized using a metagenomic approach. The results retrieved the baseline profiles of ARGs and identified the role of migratory birds in disseminating ARGs in the Arctic. A total of 26 ARG types and 718 subtypes were determined, and 131 core ARGs were identified. All the samples were dominated by multidrug resistance genes, and some genes resistant to antibiotics commonly used in anthropogenic were also detected. Characterization of ARGs in bird fecal samples was significantly distinct from other media, with higher abundance, richness, and unique ARGs detected. Proteobacteria was the most predominant phylum in soil and fecal samples, while Thaumarchaeota was prevalent in sediment samples. Firmicutes harbored the majority of ARGs in all samples. The results of FEAST indicated that migratory birds were crucial allochthonous ARG sources in the Arctic. These significant findings shed light on the global spread of ARGs and should facilitate efforts to map baseline levels of ARGs before the era of antibiotics.