The bacterial diversity and composition of water yam (Dioscorea alata L. cv. A-19), which can grow without chemical fertilization, have recently been characterized with no significant differences compared with the use of chemical fertilization. However, the diversity and community structure of bacteria associated with the white Guinea yam (Dioscorea rotundata), the most cultivated and economically important yam in West Africa, have not yet been investigated. This study characterized the bacterial diversity and composition associated with bulk soil, rhizosphere, and plant roots in six white Guinea yam genotypes (S004, S020, S032, S042, S058, and S074) in field experiments in Ibadan, Nigeria under N-based chemical fertilizer application. The largest diversity of bacteria was found in the bulk soil, followed by the rhizosphere and roots. Based on the alpha diversity analysis, the bacterial diversity in both S020 and S042 increased with fertilizer application among the bulk soil samples. S058 grown under no-fertilizer conditions had the highest bacterial diversity among the rhizosphere samples. Beta diversity analysis highlighted the significant difference in the composition of bacteria associated with the genotypes and fertilizer treatments, and S032 had a unique bacterial composition compared to the other genotypes. The dominant phylum across all sample types was Proteobacteria. Actinobacteriota was the dominant phylum among bulk soil samples. At the genus level, Bacillus was the most abundant bacterial genus across both the control and treated samples. Pseudomonas was predominant across all rhizosphere samples. Chryseobacterium, Sphingobium, Delftia and Klebsiella associated with the rhizosphere were shown the altered relative abundance between the control and treated samples depending on genotypes. A genus related to symbiotic nitrogen-fixing bacteria, the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade, showed higher relative abundance among all root samples, indicating that it is a core bacterial genus. Furthermore, the field application of chemical fertilizer had a significant impact on the relative abundances of two genera related to symbiotic nitrogen-fixers, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade and Bradyrhizobium in the rhizosphere and root. These results suggest that N-based chemical fertilizers and plant genotypes would influence the compositional arrangement of associated bacterial communities, including symbiotic nitrogen-fixing bacteria.

The performance of sequence variant resolution analytic tools for metabarcoding has not yet been adequately benchmarked for high-diversity environmental samples. We therefore evaluated the sequence variant tools DADA2, Deblur, Swarm, and UNOISE, using high-diversity seafloor samples, resulting in comparisons of 1800 sequence variant tables. The evaluation was based on 30 sediment grab samples, for which 3 replica samples were collected. Each replica sample was extracted using 5 common DNA extraction kits, resulting in 450 DNA extracts which were 16S rRNA gene sequenced (V3-V4), using Illumina. Assessments included variation across replica samples, extraction kits, and denoising methods, in addition to applying prior knowledge about alpha diversity correlations toward the cosmopolitan marine archaeon Nitrosopumilus with high diversity and the sulfide oxidizing Sulfurovum with low diversity. DADA2 displayed the highest variance between replicates (Manhattan distance 1.14), while Swarm showed the lowest variance (Manhattan distance 0.93). For the analysis based on prior biological knowledge, UNOISE displayed the highest alpha diversity (Simpson\'s D) correlation toward Nitrosopumilus (Spearman rho = 0.85), while DADA2 showed the lowest (Spearman rho = 0.10). Deblur completely eliminated Nitrosopumilus from the dataset. For Sulfurovum, on the other hand, all the methods showed comparable results. In conclusion, our evaluations show that Swarm and UNOISE performed better than DADA2 and Deblur for high-diversity seafloor samples.

BACKGROUND: Dinoflagellates play critical roles in the functioning of marine ecosystems but also may pose a hazard to human and ecosystem health by causing harmful algal blooms (HABs). The Coral Sea is a biodiversity hotspot, but its dinoflagellate assemblages in pelagic waters have not been studied by modern sequencing methods. We used metabarcoding of the 18 S rRNA V4 amplicon to assess the diversity and structure of dinoflagellate assemblages throughout the water column to a depth of 150 m at three stations in the Western Coral Sea. Additionally, at one station we compared metabarcoding with morphological methods to optimise identification and detection of dinoflagellates.
RESULTS: Stratification of dinoflagellate assemblages was evident in depth-specific relative abundances of taxonomic groups; the greatest difference was between the 5-30 m assemblages and the 130-150 m assemblages. The relative abundance of Dinophyceae (photosynthetic and heterotrophic) decreased with increasing depth, whereas that of Syndiniales (parasitic) increased with increasing depth. The composition of major taxonomic groups was similar among stations. Taxonomic richness and diversity of amplicon sequence variants (ASVs) were similar among depths and stations; however, the abundance of dominant taxa was highest within 0-30 m, and the abundance of rare taxa was highest within 130-150 m, indicating adaptations to specific depth strata. The number of unclassified ASVs at the family and species levels was very high, particularly for Syndinian representatives.
CONCLUSIONS: Dinoflagellate assemblages in open water of the Coral Sea are highly diverse and taxonomically stratified by depth; patterns of relative abundance along the depth gradient reflect environmental factors and ecological processes. Metabarcoding detects more species richness than does traditional microscopical methods of sample analysis, yet the methods are complementary, with morphological analysis revealing additional richness. The large number of unclassified dinoflagellate-ASVs indicates a need for improved taxonomic reference databases and suggests presence of dinoflagellate-crypto and-morphospecies.

Horseflies from the Tabanidae family play a significant role in traditional Chinese medicine to treat various health conditions, including coronary heart disease, stroke, headaches, liver cirrhosis, psoriasis, and hepatic carcinoma. There are 27 species of Tabaninae (Tabanidae) used as medicine, and they showed high morphological similarities with those for which medicinal properties have not been reported. Nonetheless, there have been reports suggesting that medicinal crude drugs sometimes contain irrelevant or false species, impacting the drug\'s efficacy. In this current study, we collected 14 batches, totaling 13,528 individuals, from various provinces in China. Instead of \"classic\" DNA barcoding strategy, we employed a high-throughput metabarcoding approach to assess the biological composition of crude drug mixtures derived from horseflies. Our analysis identified 40 Amplicon Sequence Variants (ASVs) with similarity percentages ranging from 92% to 100% with 12 previously reported species. Species delimitation methods revealed the presence of 11 Molecular Operational Taxonomic Units (MOTUs), with ten belonging to the Tabanus genus and one to Hybomitra. Tabanus sp6 displayed the highest relative abundance, and its ASVs showed close resemblance to Tabanus pleski. Our investigations revealed that the medicinal batches were biologically composed of 6 to 12 species. Some batches contained ASVs that closely resembled species previously associated with false Tabanus species. In conclusion, our findings offer valuable insights into the biological composition of crude drugs derived from horseflies and have the potential to enhance the quality of these traditional medicines.

Coffea arabica L. and Coffea canephora L. are coffee species most consumed and marketed in the world. The coffee crop requires a large amount of nitrogen, which shows the importance of knowledge of the population of nitrogen-fixing bacteria (NFB) from the rhizosphere of these crops. These microorganisms may help the reduction of nitrogen fertilizing. However, there is no production of NFB inoculum in the coffee. Therefore, our objective was to evaluate the diversity of potential nitrogen-fixing bacteria (PNFB) in the rhizosphere of C. arabica and C. canephora. The microbial DNA of the soil was extracted, amplified through PCR, and sequenced at the Illumina Miseq. platform. The PNFB prediction was performed using the program PICRUSt2. Three hundred and thirty-seven amplicon sequence variants (ASVs) were identified as PNFB in two coffee species. Xanthobacteraceae, Rhizobium multhospitiium, Rhizobium mesosinicum, and Bradyrhizobium sp. were detected in all samples and main components of the core microbiota of the coffee plant rhizosphere. Some ASVs are exclusive from one of the coffee farms, showing that the coffee specie cultivated may influence the PNFB communities. However, edaphoclimatic factors and soil chemical attributes can also influence the distribution of ASVs in coffee soil. In the C. canephora, the PNFB diversity was influenced by the altitude and the soil chemical attributes, while the altitude and the phosphorus content influenced the PNFB population in C. arabica. Our results are important to the understanding of the PNFB dynamic in coffee soil and for the agricultural inputs bioprospecting to coffee.

This study delves into the impact of various pretreatment methods on the inoculum in dark fermentation trials, specifically exploring thermal shock at different temperatures (60, 80, and 100 °C) and durations (15, 30, and 60 min), as well as acid shock at pH 5.5. Initial acidification of the substrate/inoculum mixture facilitates H2 generation, making acid shock an effective pretreatment option. However, it is also observed that combining thermal and acid pretreatments boosts H2 production synergistically. The synergy between thermal and acid pretreatments results in a significant improvement, increasing the overall hydrogen production efficiency by more than 9% compared to assays involving acidification alone. This highlights the considerable potential for optimizing pretreatment strategies. Furthermore, the study sheds light on the critical role of inoculum characteristics in the process, with diverse hydrogen-generating bacteria significantly influencing outcomes. The established equivalent performance of HCl and H2SO4 in inoculum pretreatment demonstrates the versatility of these acids in shaping the microbial community and influencing hydrogen production. The analysis of glucose conversion data highlights a prevalence of butyric acid in all trials, irrespective of the pretreatment method, emphasizing the dominance of the butyrate pathway in hydrogen generation. Additionally, an examination of the microbial community offers valuable insights into the intricate relationships between temperature, pH, and microbial diversity. Bacteroidota established its dominance among the bacterial populations, with a relative abundance exceeding 20-25% in the raw inoculum, and this dominance further increased following the treatment. Thermal and acid pretreatments result in significant shifts in dominant microbial communities, with some non-dominant phyla like Cloacimonadota and Spirochaetota becoming more prominent. These shifts in microbial diversity underscore the sensitivity of microbial communities to environmental conditions and pretreatment methods, further highlighting the importance of understanding their dynamics in dark fermentation processes.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

The gut microbiome of bees is vital for the health of their hosts. Given the ecosystem functions performed by bees, and the declines faced by many species, it is important to improve our understanding of the amount of natural variation in the gut microbiome, the level of sharing of bacteria among co-occurring species (including between native and non-native species), and how gut communities respond to infections. We conducted 16S rRNA metabarcoding to discern the level of microbiome similarity between honey bees (Apis mellifera, N = 49) and bumble bees (Bombus spp., N = 66) in a suburban-rural landscape. We identified a total of 233 amplicon sequence variants (ASVs) and found simple gut microbiomes dominated by bacterial taxa belonging to Gilliamella, Snodgrassella, and Lactobacillus. The average number of ASVs per species ranged from 4.00-15.00 (8.79 ± 3.84, mean ± SD). Amplicon sequence variant of one bacterial species, G. apicola (ASV 1), was widely shared across honey bees and bumble bees. However, we detected another ASV of G. apicola that was either exclusive to honey bees, or represented an intra-genomic 16S rRNA haplotype variant in honey bees. Other than ASV 1, honey bees and bumble bees rarely share gut bacteria, even ones likely derived from outside environments (e.g., Rhizobium spp., Fructobacillus spp.). Honey bee bacterial microbiomes exhibited higher alpha diversity but lower beta and gamma diversities than those of bumble bees, likely a result of the former possessing larger, perennial hives. Finally, we identified pathogenic or symbiotic bacteria (G. apicola, Acinetobacter sp. and Pluralibacter sp.) that associate with Trypanosome and/or Vairimorpha infections in bees. Such insights help to determine bees\' susceptibility to infections should gut microbiomes become disrupted by chemical pollutants and contribute to our understanding of what constitutes a state of dysbiosis.

In this study, the coast of Lebanon was analyzed for the dynamic changes in sediment microbial communities in response to a major petroleum oil spill and tar contamination that occurred in the summer of 2021. Spatio-temporal variations in the microbial structure along the shores of Lebanon were assessed in comparison to baseline microbial structure determined in 2017. Microbial community structure and diversity were determined using Illumina MiSeq technology and DADA2 pipeline. The results show a significant diversity of microbial populations along the Lebanese shore, and a significant change in the sediment microbial structure within four years. Namely, Woeseia, Blastopirellula, and Muriicola were identified in sediment samples collected in year 2017, while a higher microbial diversity was observed in 2021 with Woeseia, Halogranum, Bacillus, and Vibrio prevailing in beach sediments. In addition, the results demonstrate a significant correlation between certain hydrocarbon degraders, such as Marinobacter and Vibrio, and measured hydrocarbon concentrations.