Taxonomic Annotation Research Articles

Red imported fire ant nesting affects the structure of soil microbial community.

The red imported fire ants (RIFA, Solenopsis invicta) have become a well-known invasive species that poses significant ecological and economic threats globally. As of recent times, the geographic scope of its invasion in China is rapidly expanding, thereby aggravating the extent and severity of its detrimental effects. The importance of soil microorganisms for maintaining soil health and ecosystem function has been widely acknowledged. However, the negative impact of RIFAs on soil microbial communities and their functions has not yet been fully understood. In this study, we sequenced the V3-V4 variable region of the bacterial 16S rRNA gene in soil samples collected from three types of RIFA nests to investigate the impact of RIFA invasion on soil microbial diversity and composition. The results of alpha diversity analysis showed that the normal soil without nests of RIFAs exhibited the highest level of diversity, followed by the soil samples from RIFA-invaded nests and abandoned nests. Taxonomy and biological function annotation analyses revealed significant differences in microbial community structure and function among the different samples. Our findings demonstrate that RIFA invasion can significantly alter soil microbial community composition, which could ultimately affect ecosystem function. Therefore, effective management strategies are urgently needed to mitigate the negative impact of invasive species on native ecosystems.

A catalog of bacterial reference genomes from cultivated human oral bacteria

The oral cavity harbors highly diverse communities of microorganisms. However, the number of isolated species and high-quality genomes is limited. Here we present a Cultivated Oral Bacteria Genome Reference (COGR), comprising 1089 high-quality genomes based on large-scale aerobic and anaerobic cultivation of human oral bacteria isolated from dental plaques, tongue, and saliva. COGR covers five phyla and contains 195 species-level clusters of which 95 include 315 genomes representing species with no taxonomic annotation. The oral microbiota differs markedly between individuals, with 111 clusters being person-specific. Genes encoding CAZymes are abundant in the genomes of COGR. Members of the Streptococcus genus make up the largest proportion of COGR and many of these harbor entire pathways for quorum sensing important for biofilm formation. Several clusters containing unknown bacteria are enriched in individuals with rheumatoid arthritis, emphasizing the importance of culture-based isolation for characterizing and exploiting oral bacteria.

Microbial taxonomic and functional shifts in adolescents with type 1 diabetes are associated with clinical and dietary factors.

Evidence indicates a link between the pathogenesis of type 1 diabetes (T1D) and the gut microbiome. However, the regulation of microbial metabolic pathways and the associations of bacterial species with dietary factors in T1D are largely unknown. We investigated whether microbial metagenomic signatures in adolescents with T1D are associated with clinical/dietary factors. Adolescents with T1D (case) and healthy adolescents (control) were recruited, and microbiome profiling in participants' stool samples was performed using shotgun metagenomic sequencing. The bioBakery3 pipeline (Kneaddata, Metaphlan 4 and HUMAnN) was used to assign taxonomy and functional annotations. Clinical (HbA1c) and dietary information (3-day food record) were collected for conducting association analysis using Spearman's correlation. Adolescents with T1D exhibited modest changes in taxonomic composition of gut microbiome. Nineteen microbial metabolic pathways were altered in T1D, including downregulation of biosynthesis of vitamins (B2/flavin, B7/biotin and B9/folate), enzyme cofactors (NAD+ and s-adenosyl methionine) and amino acids (aspartate, asparagine and lysine) with an upregulation in the fermentation pathways. Furthermore, bacterial species associated with dietary and clinical factors differed between healthy adolescents and adolescents with T1D. Supervised models modeling identified taxa predictive of T1D status, and the top features included Coprococcus and Streptococcus. Our study provides new insight into the alteration of microbial and metabolic signatures in adolescents with T1D, suggesting that microbial biosynthesis of vitamins, enzyme cofactors and amino acids may be potentially altered in T1D. Research grants from NIH/NCCIH: R01AT010247 and USDA/NIFA: 2019-67017-29253; and Larry & Gail Miller Family Foundation Assistantship.

The Intestinal Microbiota Composition in Early and Late Stages of Diabetic Kidney Disease.

Many studies have suggested that gut microbiota dysbiosis may be one of the pathogenesis factors of diabetes mellitus (DM), while it is not clear whether it is involved in the development of diabetic kidney diseases (DKD). The objective of this study was to determine bacterial taxa biomarkers during the progression of DKD by investigating bacterial compositional changes in early and late DKD. 16S rRNA gene sequencing was performed on fecal samples, including the diabetes mellitus (DM), DNa (early DKD), and DNb (late DKD) groups. Taxonomic annotation of microbial composition was performed. Samples were sequenced on the Illumina NovaSeq platform. At the genus level, we found counts of Fusobacterium, Parabacteroides, and Ruminococcus_gnavus were significantly elevated both in the DNa group (P = 0.0001, 0.0007, and 0.0174, respectively) and the DNb group (P < 0.0001, 0.0012, and 0.0003, respectively) compared with those in the DM group. Only the level of Agathobacter was significantly decreased in the DNa group than the DM group and in the DNb group than the DNa group. Counts of Prevotella_9, Roseburia were significantly decreased in the DNa group compared with those in the DM group (P = 0.001 and 0.006, respectively) and in the DNb group compared with those in the DM group (P < 0.0001 and 0.003, respectively). Levels of Agathobacter, Prevotella_9, Lachnospira, and Roseburia were positively correlated with an estimated glomerular filtration rate (eGFR), but negatively correlated with microalbuminuria (MAU), 24 h urinary protein quantity (24hUP), and serum creatinine (Scr). Moreover, the areas under the curve (AUCs) of Agathobacter and Fusobacteria were 83.33% and 80.77%, respectively, for the DM and DNa cohorts, respectively. Notably, the largest AUC for DNa and DNb cohorts was also that of Agathobacter at 83.60%. Gut microbiota dysbiosis was found in the early and late stages of DKD, especially in the early stage. Agathobacter may be the most promising intestinal bacteria biomarker that can help distinguish different stages of DKD. IMPORTANCE It is not clear as to whether gut microbiota dysbiosis is involved in the progression of DKD. This study may be the first to explore gut microbiota compositional changes in diabetes, early-DKD, and late DKD. We identify different gut microbial characteristics during different stages of DKD. Gut microbiota dysbiosis is found in the early and late stages of DKD. Agathobacter may be the most promising intestinal bacteria biomarker that can help distinguish different stages of DKD, although further studies are warranted to illustrate these mechanisms.

Metagenomic and metaproteomic insights into the microbiome and the key geobiochemical potentials on the sandstone of rock-hewn Beishiku Temple in Northwest China

Metagenomics and metaproteomics analyses were used to determine the microbial diversity and taxon composition, as well as the biochemical potentials of the microbiome on the sandstone of Beishiku Temple located in Northwest China. Taxonomic annotation of the metagenomic dataset revealed the predominant taxa of the stone microbiome on this cave temple with characteristics of resistance to harsh environmental conditions. Meanwhile, there were also taxa in the microbiome that showed sensitivity to environmental factors. The taxa distribution and the metabolic functional distribution patterns by the metagenome and metaproteome, respectively, showed clear differences. The high abundance of energy metabolism represented in the metaproteome suggested that there were active geomicrobiological cycles of elements within the microbiome. The taxa responsible for reactions in the nitrogen cycle from both metagenome and metaproteome supported a metabolically active nitrogen cycle, and the high activity of Comammox bacteria indicated the strong metabolic activity of ammonia oxidation to nitrate in the outdoor site. The SOX-related taxa involved in the sulfur cycle showed higher activity outdoors than indoors, and on the outdoor ground than at the outdoor cliff, as detected through metaproteomic analysis. The development of petrochemical industry in the vicinity resulting in the deposition of sulfur/oxidized sulfur via atmosphere may stimulate the physiological activity of SOX. Our findings provide metagenomic and metaproteomic evidence for microbially driven geobiochemical cycles that result in the biodeterioration of stone monuments.

Investigating the modulatory effects of Moringa oleifera on the gut microbiota of chicken model through metagenomic approach.

This study aimed to assess the effects of supplementing chicken feed with Moringa oleifera leaf powder, a phytobiotic, on the gastrointestinal microbiota. The objective was to examine the microbial changes induced by the supplementation. A total of 40, one-day-old chickens were fed their basal diet for 42 days and then divided into two groups: SG1 (basal diet) and SG2 (basal diet + 10 g/kg Moringa oleifera leaf powder). Metagenomics analysis was conducted to analyze operational taxonomic units (OTUs), species annotation, and biodiversity. Additionally, 16S rRNA sequencing was performed for molecular characterization of isolated gut bacteria, identified as Enterococcus faecium. The isolated bacteria were tested for essential metabolites, demonstrating antibacterial, antioxidant, and anticancer activities. The analysis revealed variations in the microbial composition between the control group (SG1) and the M. oleifera-treated group (SG2). SG2 showed a 47% increase in Bacteroides and a 30% decrease in Firmicutes, Proteobacteria, Actinobacteria, and Tenericutes compared to SG1. TM7 bacteria were observed exclusively in the M. oleifera-treated group. These findings suggest that Moringa oleifera leaf powder acts as a modulator that enhances chicken gut microbiota, promoting the colonization of beneficial bacteria. PICRUSt analysis supported these findings, showing increased carbohydrate and lipid metabolism in the M.oleifera-treated gut microbiota. This study indicates that supplementing chicken feed with Moringa oleifera leaf powder as a phytobiotic enhances the gut microbiota in chicken models, potentially improving overall health. The observed changes in bacterial composition, increased presence of Bacteroides, and exclusive presence of TM7 bacteria suggest a positive modulation of microbial balance. The essential metabolites from isolated Enterococcus faecium bacteria further support the potential benefits of Moringa oleifera supplementation.

MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.

Microbiome studies have become routine in biomedical, agriculturaland environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, importantbiomarkers, and potentialactivities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis andinteractive visualizations for popular graphical outputs,updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca.

Metataxonomic characterization of the microbial community involved in the production of biogas with microcrystalline cellulose in pilot and laboratory scale

Biogas, produced in anaerobic digestion, is a sustainable alternative for generating energy from agro-industrial and municipal waste. Information from the microbiota active in the process expands the possibilities for technological innovation. In this study, taxonomic annotations, and functional prediction of the microbial community of the inoculum of two processes were carried out: an industrial unit (pilot-scale urban solid waste plant-IU) and a laboratory-scale reactor fed with swine and cattle waste (LS). The biochemical potential of biogas was obtained using tested inoculum with microcrystalline cellulose, obtaining 682 LN/kgVS (LSC-laboratory scale inoculum and microcrystalline cellulose), and 583 LN/kgVS (IUC-industrial unit inoculum and microcrystalline cellulose), which is equivalent to a recovery of 91.5% of total biogas to LSC. The phyla Synergistota and Firmicutes were more abundant in LS/LSC. In the IU/IUC (treatment of restaurant waste and customs seizures), there was a greater microbiological variety and a predominance of the Bacteroidota, Cloacimonadota, Firmicutes and Caldatribacteriota. The genus Methanosaeta predominated in the process, and it was possible to infer the genes (K01895, K00193 and K00625) related to acetoclastic pathway, as well as endoglucanases that are involved in the metabolism of cellulose (LSC). Terpenoids, polyketides, cofactors, and vitamin metabolism were higher in reactors that received different substrates (IU; IUC). The taxonomic and functional differences revealed the importance of determining the microbiota in the analysis of the potential of an inoculum, combined with the use of microcrystalline cellulose, which can provide optimization information in the production of clean energy.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations.

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats.

The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our in silico assessment by a "global view" of 35,889 microbe species across multiple reference databases summarizes the optimal sequencing strategy for 16S short reads. On the other hand, since microbes are unevenly distributed according to their habitats, we also provide the recommended configuration for 16 typical ecosystems based on the Qscores of 157,390 microbiomes in the Microbiome Search Engine (MSE). Detailed data simulation further proves that the 16S amplicons produced with Qscore-suggested parameters exhibit high precision in microbiome profiling, which is close to that of shotgun metagenomes under CAMI metrics. Therefore, by reconsidering the precision of 16S-based microbiome profiling, our work not only enables the high-quality reusability of massive sequence legacy that has already been produced but is also significant for guiding microbiome studies in the future. We have implemented the Qscore as an online service at http://qscore.single-cell.cn to parse the recommended sequencing strategy for specific habitats or expected microbial structures. IMPORTANCE 16S rRNA has long been used as a biomarker to identify distinct microbes from complex communities. However, due to the influence of the amplification region, sequencing type, sequence processing, and reference database, the accuracy of 16S rRNA has not been fully verified on a global range. More importantly, the microbial composition of different habitats varies greatly, and it is necessary to adopt different strategies according to the corresponding target microbes to achieve optimal analytical performance. Here, we developed Qscore, which evaluates the comprehensive performance of 16S amplicons from multiple perspectives, thus providing the best sequencing strategies for common ecological environments by using big data.

Metagenome and metabolome insights into the energy compensation and exogenous toxin degradation of gut microbiota in high-altitude rhesus macaques (Macaca mulatta)

There have been many reports on the genetic mechanism in rhesus macaques (RMs) for environmental adaptation to high altitudes, but the synergistic involvement of gut microbiota in this adaptation remains unclear. Here we performed fecal metagenomic and metabolomic studies on samples from high- and low-altitude populations to assess the synergistic role of gut microbiota in the adaptation of RMs to high-altitude environments. Microbiota taxonomic annotation yielded 7471 microbiota species. There were 37 bacterial species whose abundance was significantly enriched in the high-altitude populations, 16 of which were previously reported to be related to the host’s dietary digestion and energy metabolism. Further functional gene enrichment found a stronger potential for gut microbiota to synthesize energy substrate acetyl-CoA using CO2 and energy substrate pyruvate using oxaloacetate, as well as a stronger potential to transform acetyl-CoA to energy substrate acetate in high-altitude populations. Interestingly, there were no apparent differences between low-altitude and high-altitude populations in terms of genes enriched in the main pathways by which the microbiota consumed the three energy substrates, and none of the three energy substrates were detected in the fecal metabolites. These results strongly suggest that gut microbiota plays an important energy compensatory role that helps RMs to adapt to high-altitude environments. Further functional enrichment after metabolite source analysis indicated the abundance of metabolites related to the degradation of exogenous toxins was also significantly higher in high-altitude populations, which suggested a contributory role of gut microbiota to the degradation of exogenous toxins in wild RMs adapted to high-altitude environments.

Isolation and pan-genome analysis of Enterobacter hormaechei Z129, a ureolytic bacterium, from the rumen of dairy cow.

Urea is an important non-protein nitrogen source for ruminants. In the rumen, ureolytic bacteria play critical roles in urea-nitrogen metabolism, however, a few ureolytic strains have been isolated and genomically sequenced. The purpose of this study was to isolate a novel ureolytic bacterial strain from cattle rumen and characterize its genome and function. The ureolytic bacterium was isolated using an anaerobic medium with urea and phenol red as a screening indicator from the rumen fluid of dairy cattle. The genome of isolates was sequenced, assembled, annotated, and comparatively analyzed. The pan-genome analysis was performed using IPGA and the biochemical activity was also analyzed by test kits. A gram-positive ureolytic strain was isolated. Its genome had a length of 4.52 Mbp and predicted genes of 4223. The 16S rRNA gene and genome GTDB-Tk taxonomic annotation showed that it was a novel strain of Enterobacter hormaechei, and it was named E. hormaechei Z129. The pan-genome analysis showed that Z129 had the highest identity to E. hormaechei ATCC 49162 with a genome average nucleotide identity of 98.69% and possessed 238 unique genes. Strain Z129 was the first E. hormaechei strain isolated from the rumen as we know. The functional annotation of the Z129 genome showed genes related to urea metabolism, including urea transport (urtA-urtE), nickel ion transport (ureJ, tonB, nixA, exbB, exbD, and rcnA), urease activation (ureA-ureG) and ammonia assimilation (gdhA, glnA, glnB, glnE, glnL, glsA, gltB, and gltD) were present. Genes involved in carbohydrate metabolism were also present, including starch hydrolysis (amyE), cellulose hydrolysis (celB and bglX), xylose transport (xylF-xylH) and glycolysis (pgi, pgk, fbaA, eno, pfkA, gap, pyk, gpmL). Biochemical activity analysis showed that Z129 was positive for alkaline phosphatase, leucine arylamidase, acid phosphatase, naphthol-AS-BI-phosphohydrolase, α-glucosidase, β-glucosidase, and pyrrolidone arylaminase, and had the ability to use D-ribose, L-arabinose, and D-lactose. Urea-nitrogen hydrolysis rate of Z129 reached 55.37% at 48 h of incubation. Therefore, the isolated novel ureolytic strain E. hormaechei Z129 had diverse nitrogen and carbon metabolisms, and is a preferred model to study the urea hydrolysis mechanism in the rumen.

Characterization of the Blood Microbiome and Comparison with the Fecal Microbiome in Healthy Dogs and Dogs with Gastrointestinal Disease.

Recent studies have found bacterial DNA in the blood of healthy individuals. To date, most studies on the blood microbiome have focused on human health, but this topic is an expanding research area in animal health as well. This study aims to characterize the blood microbiome of both healthy dogs and those with chronic gastro-enteropathies. For this study, blood and fecal samples were collected from 18 healthy and 19 sick subjects, DNA was extracted through commercial kits, and the V3-V4 regions of the 16S rRNA gene were sequenced on the Illumina platform. The sequences were analyzed for taxonomic annotation and statistical analysis. Alpha and beta diversities of fecal microbiome were significantly different between the two groups of dogs. Principal coordinates analysis revealed that healthy and sick subjects were significantly clustered for both blood and fecal microbiome samples. Moreover, bacterial translocation from the gut to the bloodstream has been suggested because of found shared taxa. Further studies are needed to determine the origin of the blood microbiome and the bacteria viability. The characterization of a blood core microbiome in healthy dogs has potential for use as a diagnostic tool to monitor for the development of gastro-intestinal disease.

Abstract 3047: Association of 16S rRNA fecal microbiome data with colorectal cancer risk: A pooled analysis

Abstract Changes in the fecal microbial composition may have a potential role in colorectal carcinogenesis and could provide insights into an individual’s risk of developing colorectal cancer (CRC). Although the previous studies observed the association between fecal microbiome and CRC risk, the evidence from large-scale population-based studies are lacking and the causality is still yet to be established. To address this gap, we conducted a microbiome-wide association study (MWAS) to identify CRC associated unique microbial markers among Koreans. Three publicly available datasets were downloaded from NCBI Sequence Read Archive under accession numbers PRJNA747730 (70 CRC cases), PRJNA797640 (200 CRC cases), and PRJNA644479 (179 healthy controls). Additionally, 295 CRC patients were recruited from the Center for Colorectal Cancer, National Cancer Center (NCC) hospital, Korea. Total study population comprised with 565 CRC cases and 179 healthy controls. Fecal samples of NCC cases were collected and 16S rRNA gene sequencing was performed. Forward sequence reads from all four datasets were used for quality control and taxonomic annotation using QIIME2 pipeline. Gaussian graphical models were applied to derive a microbial network and microbial communities were detected within the network using R package “linkcomm”. Microbial community-specific scores were calculated for each cluster and the association between community-specific score and CRC risk was observed using unconditional logistic regression models. Microbial communities were evaluated using machine learning models to identify CRC specific bacterial markers. There was a significant divergence of the microbial composition between CRC patients and controls (PERMANOVA p=0.001). Faecalibacterium and Megamonas were enriched in healthy controls whereas Escherichia-Shigella was enriched in CRC cases. Out of 24 communities, community 6: odds ratio (OR=2.68), 95% confidence intervals (CI=1.18-6.09, p-trend=0.006), 7 (OR=4.33, 95% CI=1.86-10.05, p-trend=0.001), and 11 (OR=7.28, 95% CI=3.35-15.81) were positively associated while community 19 (OR=0.34, 95% CI=0.15-0.78, p-trend=0.015) was negatively associated with CRC risk. Random forest showed the best performance to predict CRC risk based on 10 most important microbial communities (AUC=0.97). Community 20 with five genera had highest predictive ability (AUC=0.89) whereas Christensenellaceae_R_7 genus was identified as a unique microbial marker to distinguish CRC patients from healthy controls (AUC=0.85). This MWAS could help to identify Christensenellaceae_R_7 as the unique marker to distinguish CRC patients from healthy controls. Thus, it could work as a promising indicator for CRC diagnosis among Koreans specifically implementing in CRC screening programs. Citation Format: Madhawa Gunathilake, Jeongseon Kim. Association of 16S rRNA fecal microbiome data with colorectal cancer risk: A pooled analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3047.

The root canal microbiome diversity and function. A whole-metagenome shotgun analysis.

To evaluate the root canal microbiome composition and bacterial functional capability in cases of primary and secondary apical periodontitis utilizing whole-metagenome shotgun sequencing. Twenty-two samples from patients with primary root canal infections, and 18 samples obtained from previously treated teeth currently diagnosed with apical periodontitis were analysed with whole-metagenome shotgun sequencing at a depth of 20 M reads. Taxonomic and functional gene annotations were made using MetaPhlAn3 and HUMAnN3 software. The Shannon and Chao1 indices were utilized to measure alpha diversity. Differences in community composition were evaluated utilizing analysis of similarity (ANOSIM) using Bray-Curtis dissimilarities. The Wilcoxon rank sum test was used to compare differences in taxa and functional genes. Microbial community variations within a community were significantly lower in secondary relative to primary infections (alpha diversity p = .001). Community composition was significantly different in primary versus secondary infection (R = .11, p = .005). The predominant taxa observed among samples (>2.5%) were Pseudopropionibacterium propionicum, Prevotella oris, Eubacterium infirmum, Tannerella forsythia, Atopobium rimae, Peptostreptococcus stomatis, Bacteroidetes bacterium oral taxon 272, Parvimonas micra, Olsenella profusa, Streptococcus anginosus, Lactobacillus rhamnosus, Porphyromonas endodontalis, Pseudoramibacter alactolyticus, Fusobacterium nucleatum, Eubacterium brachy and Solobacterium moorei. The Wilcoxon rank test revealed no significant differences in relative abundances of functional genes in both groups. Genes with greater relative abundances (top 25) were associated with genetic, signalling and cellular processes including the iron and peptide/nickel transport system. Numerous genes encoding toxins were identified: exfoliative toxin, haemolysins, thiol-activated cytolysin, phospholipase C, cAMP factor, sialidase, and hyaluronic glucosaminidase. Despite taxonomic differences between primary and secondary apical periodontitis, the functional capability of the microbiomes was similar.

Metagenomic sequencing reveals swine lung microbial communities and metagenome-assembled genomes associated with lung lesions-a pilot study.

Low microbial biomass in the lungs, high host-DNA contamination and sampling difficulty limit the study on lung microbiome. Therefore, little is still known about lung microbial communities and their functions. Here, we perform a preliminary exploratory study to investigate the composition of swine lung microbial community using shotgun metagenomic sequencing and compare the microbial communities between healthy and severe-lesion lungs. We collected ten lavage-fluid samples from swine lungs (five from healthy lungs and five from severe-lesion lungs), and obtained their metagenomes by shotgun metagenomic sequencing. After filtering host genomic DNA contamination (93.5% ± 1.2%) in the lung metagenomic data, we annotated swine lung microbial communities ranging from four domains to 645 species. Compared with previous taxonomic annotation of the same samples by the 16S rRNA gene amplicon sequencing, it annotated the same number of family taxa but more genera and species. We next performed an association analysis between lung microbiome and host lung-lesion phenotype. We found three species (Mycoplasma hyopneumoniae, Ureaplasma diversum, and Mycoplasma hyorhinis) were associated with lung lesions, suggesting they might be the key species causing swine lung lesions. Furthermore, we successfully reconstructed the metagenome-assembled genomes (MAGs) of these three species using metagenomic binning. This pilot study showed us the feasibility and relevant limitations of shotgun metagenomic sequencing for the characterization of swine lung microbiome using lung lavage-fluid samples. The findings provided an enhanced understanding of the swine lung microbiome and its role in maintaining lung health and/or causing lung lesions.

Taxonomic revision of Sapindaceae in Tunisia, with new additions to national flora and the whole north Africa

A taxonomic revision of the family Sapindaceae (order Sapindales) in Tunisia is presented. Field surveys were carried out during the last 10 years, allowing to identify 5 taxa of which 2 (Acer monspessulanum subsp. monspessulanum and A. monspessulanum subsp. martinii) are native, whereas the other 3 (A. negundo, Dodonaea viscosa, and Sapindus saponaria) are alien for the Tunisian flora. A. monspessulanum subsp. martinii, A. negundo, and Dodonaea viscosa are new records at national level, with Dodonaea representing a new genus for the Tunisian flora, whereas Sapindus saponaria is new for the whole North Africa. Information about nomenclature (accepted names, main synonyms, and types), morphology, chromosome number, chorology, occurrence in Tunisia, ecology (phenology and habitat), and taxonomic annotations are provided for each taxon, as well as original photos were prepared. Diagnostic keys at species and infraspecific ranks are also given.

Cobalamin producers and prokaryotic consumers in the Northwest Atlantic.

Cobalamin availability can influence primary productivity and ecological interactions in marine microbial communities. The characterization of cobalamin sources and sinks is a first step in investigating cobalamin dynamics and its impact on productivity. Here, we identify potential cobalamin sources and sinks on the Scotian Shelf and Slope in the Northwest Atlantic Ocean. Functional and taxonomic annotation of bulk metagenomic reads, combined with analysis of genome bins, were used to identify potential cobalamin sources and sinks. Cobalamin synthesis potential was mainly attributed to Rhodobacteraceae, Thaumarchaeota, and cyanobacteria (Synechococcus and Prochlorococcus). Cobalamin remodelling potential was mainly attributed to Alteromonadales, Pseudomonadales, Rhizobiales, Oceanospirilalles, Rhodobacteraceae, and Verrucomicrobia, while potential cobalamin consumers include Flavobacteriaceae, Actinobacteria, Porticoccaceae, Methylophiliaceae, and Thermoplasmatota. These complementary approaches identified taxa with the potential to be involved in cobalamin cycling on the Scotian Shelf and revealed genomic information required for further characterization. The Cob operon of Rhodobacterales bacterium HTCC2255, a strain with known importance in cobalamin cycling, was similar to a major cobalamin producer bin, suggesting that a related strain may represent a critical cobalamin source in this region. These results enable future inquiries that will enhance our understanding of how cobalamin shapes microbial interdependencies and productivity in this region.

Microbiome and Th cytokines association in chronic rhinosinusitis with or without nasal polyp.

Chronic rhinosinusitis (CRS), a common disease in otorhinolaryngology, seriously affects the life quality of patients. The existing therapy has certain limitations, and it is very urgent to deeply explore the pathogenesis and classification of CRS. Microbiome and inflammation are considered the causes of CRS, but the precise roles and the associations between these two factors in the pathogenesis of CRS remain controversial. Secretions were collected from the middle nasal canal, maxillary sinus and ethmoid sinus in CRS patients, then subjected to 16 S rRNA gene sequencing to profile microbiota community. Operational Taxonomic Units clustering and species annotation were adopted to obtain species diversity, prevalence rate and average relative abundance. Comparisons were performed at the level of microbial species and genus between CRS and control using NMDS, Anosim and MetaStat analysis. Th1 cytokines and Th2 cytokines were detected by ELISA. Spearman analysis were adopted to probe into the correlation between Th cytokines and microbial species in CRS. Thirty-seven patients were enrolled, among them 22 with CRS and 15 were controls. The most abundant genera were Corynebacterium and Staphylococcus no matter in CRS patients or control. Corynebacterium propinquum was significant decreased in CRS patients no matter with nasal polyp or not. The abundances of Prevotella birria and Carnobacterium maltaromaticum were significantly different between CRSsNP and CRSwNP group. The levels of cytokines IL-2, TNF-α, IFN-ɣ, IL-4, IL-6, IL-10 were all increased in CRS patients. The cytokines levels were associated with specific microbial species in nasal tissue. The changes of species richness and complexity in nasal microbiome were obvious in CRS patients with nasal polyps or not. The different cytokines levels and microbiome between CRS patients without nasal polyps and patients with nasal polyps suggest heterogeneity in pathogenesis of chronic rhinosinusitis. Distinct microbiota and different cytokines were strongly linked in CRS. NA.

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

BackgroundDiverse communities of microbial eukaryotes in the global ocean provide a variety of essential ecosystem services, from primary production and carbon flow through trophic transfer to cooperation via symbioses. Increasingly, these communities are being understood through the lens of omics tools, which enable high-throughput processing of diverse communities. Metatranscriptomics offers an understanding of near real-time gene expression in microbial eukaryotic communities, providing a window into community metabolic activity.ResultsHere we present a workflow for eukaryotic metatranscriptome assembly, and validate the ability of the pipeline to recapitulate real and manufactured eukaryotic community-level expression data. We also include an open-source tool for simulating environmental metatranscriptomes for testing and validation purposes. We reanalyze previously published metatranscriptomic datasets using our metatranscriptome analysis approach.ConclusionWe determined that a multi-assembler approach improves eukaryotic metatranscriptome assembly based on recapitulated taxonomic and functional annotations from an in-silico mock community. The systematic validation of metatranscriptome assembly and annotation methods provided here is a necessary step to assess the fidelity of our community composition measurements and functional content assignments from eukaryotic metatranscriptomes.