The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses

Correlation between Bacterial Cell Density and Abundance of Antibiotic Resistance on Milking Machine Surfaces Assessed by Cultivation and Direct qPCR Methods

The relative abundance of antibiotic-resistant bacteria and antibiotic-resistance genes was surveyed for different parts of a milking machine. A cultivation approach based on swab samples showed a highly diverse microbiota, harboring resistances against cloxacillin, ampicillin, penicillin, and tetracycline. This approach demonstrated a substantial cloxacillin resistance of numerous taxa within milking machine microbiota coming along with regular use of cloxacillin for dry-off therapy of dairy cows. For the less abundant tetracycline-resistant bacteria we found a positive correlation between microbial cell density and relative abundance of tetracycline-resistant microorganisms (R2 = 0.73). This indicated an accelerated dispersion of resistant cells for sampling locations with high cell density. However, the direct quantification of the tetM gene from the swap samples by qPCR showed the reverse relation to bacterial density if normalized against the abundance of 16S rRNA genes (R2 = 0.88). The abundance of 16S rRNA genes was analyzed by qPCR combined with a propidium monoazide treatment, which eliminates 16S rRNA gene signals in negative controls.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

West-Siberian Chernozem: How Vegetation and Tillage Shape Its Bacteriobiome

Managing soil biodiversity using reduced tillage is a popular approach, yet soil bacteriobiomes in the agroecosystems of Siberia has been scarcely studied, especially as they are related to tillage. We studied bacteriobiomes in Chernozem under natural steppe vegetation and cropped for wheat using conventional or no tillage in a long-term field trial in the Novosibirsk region, Russia, by using the sequence diversity of the V3/V4 region of 16S rRNA genes. Actinobacteria, Acidobacteria, and Proteobacteria summarily accounted for 80% of the total number of sequences, with Actinobacteria alone averaging 51%. The vegetation (natural vs. crop) and tillage (ploughed vs. no-till) affected the bacterial relative abundance at all taxonomic levels and many taxa, e.g., hundreds of OTUs. However, such changes did not translate into α-biodiversity changes, i.e., observed and potential OTUs' richness, Shannon, and Simpson, excepting the slightly higher evenness and equitability in the top 0-5 cm of the undisturbed soil. As for the β-biodiversity, substituting conventional ploughing with no tillage and maintaining the latter for 12 years notably shifted the soil bacteriobiome closer to the one in the undisturbed soil. This study, presenting the first inventory of soil bacteriobiomes under different tillage in the south of West Siberia, underscores the need to investigate the seasonality and longevity aspects of tillage, especially as they are related to crop production.

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Feed Regime Slightly Modifies the Bacterial but Not the Fungal Communities in the Intestinal Mucosal Microbiota of Cobia Fish (Rachycentron canadum)

The bacterial community of the intestinal microbiota influences many host functions, and similar effects have been recently reported for the fungal community (mycobiota). Cobia is a tropical fish that has been studied for its potential in marine aquaculture. However, the study of its bacterial community has been underreported and the mycobiota has not been investigated. We analyzed the gut bacterial and fungal profile present in the intestinal mucosa of reared adult cobias fed two diets (frozen fish pieces (FFPs) and formulated feed (FF)) for 4 months by sequencing the 16S rRNA (V3-V4) and internal transcribed spacer-2 (ITS2) regions using Illumina NovaSeq 6000. No significant differences in the alpha diversity of the bacterial community were observed, which was dominated by the phyla Proteobacteria (~96%) and Firmicutes (~1%). Cobia fed FF showed higher abundance of 10 genera, mainly UCG-002 (Family Oscillospiraceae) and Faecalibacterium, compared to cobia fed FFPs, which showed higher abundance of 7 genera, mainly Methylobacterium-Methylorubrum and Cutibacterium. The inferred bacterial functions were related to metabolism, environmental information processing and cellular processes; and no differences were found between diets. In mycobiota, no differences were observed in the diversity and composition of cobia fed the two diets. The mycobiota was dominated by the phyla Ascomycota (~88%) and Basidiomycota (~11%). This is the first study to describe the gut bacterial and fungal communities in cobia reared under captive conditions and fed on different diets and to identify the genus Ascobulus as a new member of the core fish mycobiota.

Hyperarid soil microbial community response to simulated rainfall

The exceptionally long and protracted aridity in the Atacama Desert (AD), Chile, provides an extreme, terrestrial ecosystem that is ideal for studying microbial community dynamics under hyperarid conditions. Our aim was to characterize the temporal response of hyperarid soil AD microbial communities to ex situ simulated rainfall (5% g water/g dry soil for 4 weeks) without nutrient amendment. We conducted replicated microcosm experiments with surface soils from two previously well-characterized AD hyperarid locations near Yungay at 1242 and 1609 masl (YUN1242 and YUN1609) with distinct microbial community compositions and average soil relative humidity levels of 21 and 17%, respectively. The bacterial and archaeal response to soil wetting was evaluated by 16S rRNA gene qPCR, and amplicon sequencing. Initial YUN1242 bacterial and archaeal 16S rRNA gene copy numbers were significantly higher than for YUN1609. Over the next 4 weeks, qPCR results showed significant increases in viable bacterial abundance, whereas archaeal abundance decreased. Both communities were dominated by 10 prokaryotic phyla (Actinobacteriota, Proteobacteria, Chloroflexota, Gemmatimonadota, Firmicutes, Bacteroidota, Planctomycetota, Nitrospirota, Cyanobacteriota, and Crenarchaeota) but there were significant site differences in the relative abundances of Gemmatimonadota and Chloroflexota, and specific actinobacterial orders. The response to simulated rainfall was distinct for the two communities. The actinobacterial taxa in the YUN1242 community showed rapid changes while the same taxa in the YUN1609 community remained relatively stable until day 30. Analysis of inferred function of the YUN1242 microbiome response implied an increase in the relative abundance of known spore-forming taxa with the capacity for mixotrophy at the expense of more oligotrophic taxa, whereas the YUN1609 community retained a stable profile of oligotrophic, facultative chemolithoautotrophic and mixotrophic taxa. These results indicate that bacterial communities in extreme hyperarid soils have the capacity for growth in response to simulated rainfall; however, historic variations in long-term hyperaridity exposure produce communities with distinct putative metabolic capacities.

Challenges and insights in the exploration of the low abundance human ocular surface microbiome

Purpose

The low microbial abundance on the ocular surface results in challenges in the characterization of its microbiome. The purpose of this study was to reveal factors introducing bias in the pipeline from sample collection to data analysis of low-abundant microbiomes.

Methods

Lower conjunctiva and lower lid swabs were collected from six participants using either standard cotton or flocked nylon swabs. Microbial DNA was isolated with two different kits (with or without prior host DNA depletion and mechanical lysis), followed by whole-metagenome shotgun sequencing with a high sequencing depth set at 60 million reads per sample. The relative microbial compositions were generated using the two different tools MetaPhlan3 and Kraken2.

Results

The total amount of extracted DNA was increased by using nylon flocked swabs on the lower conjunctiva. In total, 269 microbial species were detected. The most abundant bacterial phyla were Actinobacteria, Firmicutes and Proteobacteria. Depending on the DNA extraction kit and tool used for profiling, the microbial composition and the relative abundance of viruses varied.

Conclusion

The microbial composition on the ocular surface is not dependent on the swab type, but on the DNA extraction method and profiling tool. These factors have to be considered in further studies about the ocular surface microbiome and other sparsely colonized microbiomes in order to improve data reproducibility. Understanding challenges and biases in the characterization of the ocular surface microbiome may set the basis for microbiome-altering interventions for treatment of ocular surface associated diseases.

Concatenated 16S rRNA sequence analysis improves bacterial taxonomy

Background: Microscopic, biochemical, molecular, and computer-based approaches are extensively used to identify and classify bacterial populations. Advances in DNA sequencing and bioinformatics workflows have facilitated sophisticated genome-based methods for microbial taxonomy although sequencing of the 16S rRNA gene is widely employed to identify and classify bacterial communities as a cost-effective and single-gene approach. However, the 16S rRNA sequence-based species identification accuracy is limited because of the occurrence of multiple copies of the 16S rRNA gene and higher sequence identity between closely related species. The availability of the genomes of several bacterial species provided an opportunity to develop comprehensive species-specific 16S rRNA reference libraries. Methods: Sequences of the 16S rRNA genes were retrieved from the whole genomes available in the Genome databases. With defined criteria, four 16S rRNA gene copy variants were concatenated to develop a species-specific reference library. The sequence similarity search was performed with a web-based BLAST program, and MEGA software was used to construct the phylogenetic tree. Results: Using this approach, species-specific 16S rRNA gene libraries were developed for four closely related  Streptococcus species ( S. gordonii,  S. mitis,  S. oralis, and  S. pneumoniae). Sequence similarity and phylogenetic analysis using concatenated 16S rRNA copies yielded better resolution than single gene copy approaches. Conclusions: The approach is very effective in classifying genetically closely related bacterial species and may reduce misclassification of bacterial species and genome assemblies.

Differentiation and Identification of Endophytic Bacteria from Populus Based on Mass Fingerprints and Gene Sequences

Plant endophytic bacteria play important roles in plants' growth and resistance to stress. It is important to characterize endophytic bacteria to be able to understand their benefits. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has become a powerful technique for bacterial identification due to its high throughput and simple procedures. In this study, the endophytic bacteria separated from Populus (the leaves, roots and stems of Populus tomentosa Carrière; stems of Populus nigra Linn. var. nigra; and stems of Populus canadensis Moench) were identified and classified based on MALDI-TOF MS data and 16S rRNA gene sequencing. The sampling and preparation of bacteria were optimized to obtain meaningful protein mass fingerprints. The composite correlation index (CCI) values of the inter-genera and inter-species protein mass fingerprints demonstrated sufficient differences between the strains. In the CCI value matrix for ten species in the same genus, all the CCI values were less than 0.5. Among the species, 95.6% of all the CCI values were less than 0.5. After data processing, the classification capacity of the protein mass fingerprints was verified using inter-specific and inter-generic PCoA. To compare different methods' potential for differentiation and phylogenetic analysis, a dendrogram of the MS profiles and a phylogenetic tree based on the 16S rRNA gene sequences were constructed using 61 endophytic bacteria found in Populus. The clustering and grouping results show that the phylogenetic analysis based on MALDI-TOF MS is similar to that based on 16S rRNA gene sequencing. This study provides a valuable reference for differentiating and identifying endophytic bacteria according to their protein mass fingerprints.

RibDif2: expanding amplicon analysis to full genomes

Motivation

As previously described, amplicon analysis of the bacterial 16S gene has several limitations owing to fundamental characteristics of both the 16S gene and technological restrictions. Previously, RibDif was introduced to help quantify these limitations by detailed analysis of a given genera and the 16S gene profile of its members, notably multiplicity and divergence of 16S alleles within genomes as well as shared alleles between species. Apart from using amplicon analysis for only the 16S gene, amplicons derived from genus-specific genes or even functional genes are increasingly being utilized. Moreover, long-read technologies are progressively being used to sequence longer amplicons, and since these inherently contain more information, they may likely alleviate the issues proposed in RibDif.

Results

Taking these phenomena into account, we here propose RibDif2. RibDif2 retains the 16S-optimized functionality of the original RibDif but can now run any set of primers on any part of the genome in any set of organisms, be it prokaryote, eukaryote, or archaea. We demonstrate this new functionality by showing full species resolution of Pseudoalteromonas using complete rRNA-operon amplicons, as well as selection of optimally discriminatory primers for Staphylococcus and Pseudomonas. Moreover, we show a potential bias toward terrestrial bacteria relative to marine ones for primers amplifying biosynthetic gene clusters and lastly suggest optimal primers to differentiate the members of the insect genus Drosophila. We believe that RibDif2 will facilitate the work of all scientists using amplicon sequencing, especially in the era of long-read sequencing.

Availability and implementation

Ribdif2 is freely available at https://github.com/Rob-murphys/ribdif.

Multiyear analysis uncovers coordinated seasonality in stocks and composition of the planktonic food web in the Baltic Sea proper

The planktonic realm from bacteria to zooplankton provides the baseline for pelagic aquatic food webs. However, multiple trophic levels are seldomly included in time series studies, hampering a holistic understanding of the influence of seasonal dynamics and species interactions on food web structure and biogeochemical cycles. Here, we investigated plankton community composition, focusing on bacterio-, phyto- and large mesozooplankton, and how biotic and abiotic factors correlate at the Linnaeus Microbial Observatory (LMO) station in the Baltic Sea from 2011 to 2018. Plankton communities structures showed pronounced dynamic shifts with recurring patterns. Summarizing the parts of the planktonic microbial food web studied here to total carbon, a picture emerges with phytoplankton consistently contributing > 39% while bacterio- and large mesozooplankton contributed ~ 30% and ~ 7%, respectively, during summer. Cyanophyceae, Actinobacteria, Bacteroidetes, and Proteobacteria were important groups among the prokaryotes. Importantly, Dinophyceae, and not Bacillariophyceae, dominated the autotrophic spring bloom whereas Litostomatea (ciliates) and Appendicularia contributed significantly to the consumer entities together with the more traditionally observed mesozooplankton, Copepoda and Cladocera. Our findings of seasonality in both plankton composition and carbon stocks emphasize the importance of time series analyses of food web structure for characterizing the regulation of biogeochemical cycles and appropriately constraining ecosystem models.

A Two-Step Single Plex PCR Method for Evaluating Key Colonic Microbiota Markers in Young Mexicans with Autism Spectrum Disorders: Protocol and Pilot Epidemiological Application

Many neurological disorders have a distinctive colonic microbiome (CM) signature. Particularly, children with autism spectrum disorders (ASD) exhibit a very dissimilar CM when compared to neurotypical (NT) ones, mostly at the species level. Thus far, knowledge on this matter comes from high-throughput (yet very expensive and time-consuming) analytical platforms, such as massive high-throughput sequencing of bacterial 16S rRNA. Here, pure (260/280 nm, ~1.85) stool DNA samples (200 ng.µL^-1) from 48 participants [39 ASD, 9 NT; 3-13 y] were used to amplify four candidate differential CM markers [Bacteroides fragilis (BF), Faecalibacterium prausnitzii (FP), Desulfovibrio vulgaris (DV), Akkermansia muciniphila (AM)], using micro-organism-specific oligonucleotide primers [265 bp (BF), 198 bp (FP), 196 bp (DV), 327 bp (AM)] and a standardized two-step [low (step 1: °Tm-5 °C) to high (stage 2: °Tm-0 °C) astringent annealing] PCR protocol (2S-PCR). The method was sensitive enough to differentiate all CM biomarkers in the studied stool donors [↑ abundance: NT (BF, FP, AM), ASD (DV)], and phylogenetic analysis confirmed the primers' specificity.

The predictive power of phylogeny on growth rates in soil bacterial communities

Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Evaluating cpn60 for high-resolution profiling of the mammalian skin microbiome and detection of phylosymbiosis

Despite being the most widely used phylogenetic marker for amplicon-based profiling of microbial communities, limited phylogenetic resolution of the 16S rRNA gene limits its use for studies of host-microbe co-evolution. In contrast, the cpn60 gene is a universal phylogenetic marker with greater sequence variation capable of species-level resolution. This research compared mammalian skin microbial profiles generated from cpn60 and 16S rRNA gene sequencing approaches, testing for patterns of phylosymbiosis that suggest co-evolutionary host-microbe associations. An ~560 bp fragment of the cpn60 gene was amplified with universal primers and subjected to high-throughput sequencing. Taxonomic classification of cpn60 sequences was completed using a naïve-Bayesian QIIME2 classifier created for this project, trained with an NCBI-supplemented curated cpn60 database (cpnDB_nr). The cpn60 dataset was then compared to published 16S rRNA gene amplicon data. Beta diversity comparisons of microbial community profiles generated with cpn60 and 16S rRNA gene amplicons were not significantly different, based on Procrustes analysis of Bray-Curtis and UniFrac distances. Despite similar relationships among skin microbial profiles, improved phylogenetic resolution provided by the cpn60 gene sequencing permitted observations of phylosymbiosis between microbial community profiles and their mammalian hosts that were not previously observed with 16S rRNA gene profiles. Subsequent investigation of Staphylococcaceae taxa using the cpn60 gene showed increased phylogenetic resolution compared the 16S rRNA gene profiles, revealing potential co-evolutionary host-microbe associations. Overall, our results demonstrate that 16S rRNA and cpn60 marker genes generate comparable microbial community composition patterns while cpn60 better facilitates analyses, such as phylosymbiosis, that require increased phylogenetic resolution.

Combining 16S Sequencing and qPCR Quantification Reveals Staphylococcus aureus Driven Bacterial Overgrowth in the Skin of Severe Atopic Dermatitis Patients

Atopic dermatitis (AD) is an inflammatory skin disease with a microbiome dysbiosis towards a high relative abundance of Staphylococcus aureus. However, information is missing on the actual bacterial load on AD skin, which may affect the cell number driven release of pathogenic factors. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene). Skin swabs were sampled cross-sectionally (n = 135 AD patients; n = 20 healthy) and longitudinally (n = 6 AD patients; n = 6 healthy). NGS and qPCR yielded highly inter-correlated S. aureus relative abundances and S. aureus cell numbers. Additionally, intra-individual differences between body sides, skin status, and consecutive timepoints were also observed. Interestingly, a significantly higher total bacterial load, in addition to higher S. aureus relative abundance and cell numbers, was observed in AD patients in both lesional and non-lesional skin, as compared to healthy controls. Moreover, in the lesional skin of AD patients, higher S. aureus cell numbers significantly correlated with the higher total bacterial load. Furthermore, significantly more severe AD patients presented with higher S. aureus cell number and total bacterial load compared to patients with mild or moderate AD. Our results indicate that severe AD patients exhibit S. aureus driven increased bacterial skin colonization. Overall, bacterial quantification gives important insights in addition to microbiome composition by sequencing.

Tomato Plant Microbiota under Conventional and Organic Fertilization Regimes in a Soilless Culture System

Tomato is the main vegetable cultivated under soilless culture systems (SCSs); production of organic tomato under SCSs has increased due to consumer demands for healthier and environmentally friendly vegetables. However, organic tomato production under SCSs has been associated with low crop performance and fruit quality defects. These agricultural deficiencies could be linked to alterations in tomato plant microbiota; nonetheless, this issue has not been sufficiently addressed. Thus, the main goal of the present study was to characterize the rhizosphere and phyllosphere of tomato plants cultivated under conventional and organic SCSs. To accomplish this goal, tomato plants grown in commercial greenhouses under conventional or organic SCSs were tested at 8, 26, and 44 weeks after seedling transplantation. Substrate (n = 24), root (n = 24), and fruit (n = 24) composite samples were subjected to DNA extraction and high-throughput 16S rRNA gene sequencing. The present study revealed that the tomato core microbiota was predominantly constituted by Proteobacteria, Actinobacteria, and Firmicutes. Remarkably, six bacterial families, Bacillaceae, Microbacteriaceae, Nocardioidaceae, Pseudomonadaceae, Rhodobacteraceae, and Sphingomonadaceae, were shared among all substrate, rhizosphere, and fruit samples. Importantly, it was shown that plants under organic SCSs undergo a dysbiosis characterized by significant changes in the relative abundance of Bradyrhizobiaceae, Caulobacteraceae, Chitinophagaceae, Enterobacteriaceae, Erythrobacteraceae, Flavobacteriaceae, Nocardioidaceae, Rhodobacteraceae, and Streptomycetaceae. These results suggest that microbial alterations in substrates, roots, and fruits could be potential factors in contributing to the crop performance and fruit quality deficiencies observed in organic SCSs.

The effect of metabarcoding 18S rRNA region choice on diversity of microeukaryotes including phytoplankton

Metabarcoding using high throughput sequencing of amplicons of the 18S rRNA gene is one of the widely used methods for assessing the diversity of microeukaryotes in various ecosystems. We investigated the effectiveness of the V4 and V8-V9 regions of the 18S rRNA gene by comparing the results of metabarcoding microeukaryotic communities using the DADA2 (ASV), USEARCH-UNOISE3 (ZOTU), and USEARCH-UPARSE (OTU with 97% similarity) algorithms. Both regions showed similar levels of genetic variability and taxa identification accuracy. Richness for DADA2 datasets of both regions was lower than for UNOISE3 and UPARSE datasets, which is due to more accurate error correction in amplicons. Microeukaryotic communities (autotrophs and heterotrophs) structure identified using both regions showed a significant relationship with phytoplankton (autotrophs) communities structure based on microscopy in a seasonal freshwater sample series. The strongest relationship was found between the phytoplankton species and V8-V9 ASVs produced by DADA2.

A novel Gardnerella, Prevotella, and Lactobacillus standard that improves accuracy in quantifying bacterial burden in vaginal microbial communities

Bacterial vaginosis (BV) is the most common vaginal dysbiosis. In this condition, a polymicrobial biofilm develops on vaginal epithelial cells. Accurately quantifying the bacterial burden of the BV biofilm is necessary to further our understanding of BV pathogenesis. Historically, the standard for calculating total bacterial burden of the BV biofilm has been based on quantifying Escherichia coli 16S rRNA gene copy number. However, E. coli is improper for measuring the bacterial burden of this unique micro-environment. Here, we propose a novel qPCR standard to quantify bacterial burden in vaginal microbial communities, from an optimal state to a mature BV biofilm. These standards consist of different combinations of vaginal bacteria including three common BV-associated bacteria (BVAB) Gardnerella spp. (G), Prevotella spp. (P), and Fannyhessea spp. (F) and commensal Lactobacillus spp. (L) using the 16S rRNA gene (G:P:F:L, G:P:F, G:P:L and 1G:9L). We compared these standards to the traditional E. coli (E) reference standard using known quantities of mock vaginal communities and 16 vaginal samples from women. The E standard significantly underestimated the copy numbers of the mock communities, and this underestimation was significantly greater at lower copy numbers of these communities. The G:P:L standard was the most accurate across all mock communities and when compared to other mixed vaginal standards. Mixed vaginal standards were further validated with vaginal samples. This new G:P:L standard can be used in BV pathogenesis research to enhance reproducibility and reliability in quantitative measurements of BVAB, spanning from the optimal to non-optimal (including BV) vaginal microbiota.

Variable Region Sequences Influence 16S rRNA Performance

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and Shigella species can be distinguished by a multi-allelic 16S rRNA variable region single nucleotide polymorphism (SNP). To evaluate the performance of 16S rRNAs with altered variable regions, we developed an in vivo system that measures the acceptance and distribution of variant 16S rRNAs into a large pool of natural versions supporting normal translation and growth. We found that 16S rRNAs containing evolutionarily disparate variable regions were underpopulated both in ribosomes and in active translation pools, even for an SNP. Overall, this study revealed that variable region sequences can substantially influence the performance of 16S rRNAs and that this biological constraint can be leveraged to justify refining taxonomic assignments of variable region sequence data. IMPORTANCE This study reevaluates the notion that 16S rRNA gene variable region sequences are uninformative for intra-genus classification and that single nucleotide variations within them have no consequence to strains that bear them. We demonstrated that the performance of 16S rRNAs in Escherichia coli can be negatively impacted by sequence changes in variable regions, even for single nucleotide changes that are native to closely related Escherichia and Shigella species; thus, biological performance is likely constraining the evolution of variable regions in bacteria. Further, the native nucleotide variations we tested occur in all strains of their respective species and across their multiple 16S rRNA gene copies, suggesting that these species evolved beyond what would be discerned from a consensus sequence comparison. Therefore, this work also reveals that the multiple 16S rRNA gene alleles found in most bacteria can provide more informative phylogenetic and taxonomic detail than a single reference allele.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

16S rRNA gene copy number (16S GCN) varies among bacterial species and this variation introduces potential biases to microbial diversity analyses using 16S rRNA read counts. To correct the biases, methods have been developed to predict 16S GCN. A recent study suggests that the prediction uncertainty can be so great that copy number correction is not justified in practice. Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms other methods in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, NMDS, PERMANOVA and random-forest test.

Differences in the composition of the bacterial element of the urinary tract microbiome in patients undergoing dialysis and patients after kidney transplantation

Introduction

The development of molecular biology methods and their application in microbial research allowed the detection of many new pathogens that cause urinary tract infections (UTIs). Despite the advances of using new research techniques, the etiopathogenesis of UTIs, especially in patients undergoing dialysis and patients after kidney transplantation, is still not fully understood.

Methods

This study aimed to characterize and compare the composition of the bacterial element of the urinary tract microbiome between the groups of patients undergoing dialysis (n = 50) and patients after kidney transplantation (n = 50), with positive or negative urine culture, compared to healthy individuals (n = 50).

Results

Asymptomatic bacteriuria was observed in 30% of the urine cultures of patients undergoing dialysis and patients after kidney transplantation, with Escherichia coli as the most dominant microorganism (73%) detected with the use of classical microbiology techniques. However, differences in the bacterial composition of the urine samples between the evaluated patient groups were demonstrated using the amplicon sequencing. Finegoldia, Leptotrichia, and Corynebacterium were found to be discriminative bacteria genera in patients after dialysis and kidney transplantation compared to the control group. In addition, in all of urine samples, including those without bacteriuria in classical urine culture, many types of bacteria have been identified using 16S rRNA sequencing.

Discussion

The revealed microbial characteristics may form the basis in searching for new diagnostic markers in treatment of patients undergoing dialysis and patients after kidney transplantation.

Conditions Necessary for the Transfer of Antimicrobial Resistance in Poultry Litter

Animal manures contain a large and diverse reservoir of antimicrobial resistance (AMR) genes that could potentially spillover into the general population through transfer of AMR to antibiotic-susceptible pathogens. The ability of poultry litter microbiota to transmit AMR was examined in this study. Abundance of phenotypic AMR was assessed for litter microbiota to the antibiotics: ampicillin (Ap; 25 μg/mL), chloramphenicol (Cm; 25 μg/mL), streptomycin (Sm; 100 μg/mL), and tetracycline (Tc; 25 μg/mL). qPCR was used to estimate gene load of streptomycin-resistance and sulfonamide-resistance genes aadA1 and sul1, respectively, in the poultry litter community. AMR gene load was determined relative to total bacterial abundance using 16S rRNA qPCR. Poultry litter contained 10⁸ CFU/g, with Gram-negative enterics representing a minor population (<10⁴ CFU/g). There was high abundance of resistance to Sm (10⁶ to 10⁷ CFU/g) and Tc (10⁶ to 10⁷ CFU/g) and a sizeable antimicrobial-resistance gene load in regards to gene copies per bacterial genome (aadA1: 0.0001-0.0060 and sul1: 0.0355-0.2455). While plasmid transfer was observed from Escherichia coli R100, as an F-plasmid donor control, to the Salmonella recipient in vitro, no AMR Salmonella were detected in a poultry litter microcosm with the inclusion of E. coli R100. Confirmatory experiments showed that isolated poultry litter bacteria were not interfering with plasmid transfer in filter matings. As no R100 transfer was observed at 25 °C, conjugative plasmid pRSA was chosen for its high plasmid transfer frequency (10^-4 to 10^-5) at 25 °C. While E. coli strain background influenced the persistence of pRSA in poultry litter, no plasmid transfer to Salmonella was ever observed. Although poultry litter microbiota contains a significant AMR gene load, potential to transmit resistance is low under conditions commonly used to assess plasmid conjugation.

Impact of Wood Age on Termite Microbial Assemblages

The decomposition of wood and detritus is challenging to most macroscopic organisms due to the recalcitrant nature of lignocellulose. Moreover, woody plants often protect themselves by synthesizing toxic or nocent compounds which infuse their tissues. Termites are essential wood decomposers in warmer terrestrial ecosystems and, as such, they have to cope with high concentrations of plant toxins in wood. In this paper, we evaluated the influence of wood age on the gut microbial (bacterial and fungal) communities associated with the termites Reticulitermes flavipes (Rhinotermitidae) (Kollar, 1837) and Microcerotermes biroi (Termitidae) (Desneux, 1905). We confirmed that the secondary metabolite concentration decreased with wood age. We identified a core microbial consortium maintained in the gut of R. flavipes and M. biroi and found that its diversity and composition were not altered by the wood age. Therefore, the concentration of secondary metabolites had no effect on the termite gut microbiome. We also found that both termite feeding activities and wood age affect the wood microbiome. Whether the increasing relative abundance of microbes with termite activities is beneficial to the termites is unknown and remains to be investigated. IMPORTANCE Termites can feed on wood thanks to their association with their gut microbes. However, the current understanding of termites as holobiont is limited. To our knowledge, no studies comprehensively reveal the influence of wood age on the termite-associated microbial assemblage. The wood of many tree species contains high concentrations of plant toxins that can vary with their age and may influence microbes. Here, we studied the impact of Norway spruce wood of varying ages and terpene concentrations on the microbial communities associated with the termites Reticulitermes flavipes (Rhinotermitidae) and Microcerotermes biroi (Termitidae). We performed a bacterial 16S rRNA and fungal ITS2 metabarcoding study to reveal the microbial communities associated with R. flavipes and M. biroi and their impact on shaping the wood microbiome. We noted that a stable core microbiome in the termites was unaltered by the feeding substrate, while termite activities influenced the wood microbiome, suggesting that plant secondary metabolites have negligible effects on the termite gut microbiome. Hence, our study shed new insights into the termite-associated microbial assemblage under the influence of varying amounts of terpene content in wood and provides a groundwork for future investigations for developing symbiont-mediated termite control measures.

Microbial Diversity Biased Estimation Caused by Intragenomic Heterogeneity and Interspecific Conservation of 16S rRNA Genes

The 16S rRNA gene has been extensively used as a molecular marker to explore evolutionary relationships and profile microbial composition throughout various environments. Despite its convenience and prevalence, limitations are inevitable. Variable copy numbers, intragenomic heterogeneity, and low taxonomic resolution have caused biases in estimating microbial diversity. Here, analysis of 24,248 complete prokaryotic genomes indicated that the 16S rRNA gene copy number ranged from 1 to 37 in bacteria and 1 to 5 in archaea, and intragenomic heterogeneity was observed in 60% of prokaryotic genomes, most of which were below 1%. The overestimation of microbial diversity caused by intragenomic variation and the underestimation introduced by interspecific conservation were calculated when using full-length or partial 16S rRNA genes. Results showed that, at the 100% threshold, microbial diversity could be overestimated by as much as 156.5% when using the full-length gene. The V4 to V5 region-based analyses introduced the lowest overestimation rate (4.4%) but exhibited slightly lower species resolution than other variable regions under the 97% threshold. For different variable regions, appropriate thresholds rather than the canonical value 97% were proposed for minimizing the risk of splitting a single genome into multiple clusters and lumping together different species into the same cluster. This study has not only updated the 16S rRNA gene copy number and intragenomic variation information for the currently available prokaryotic genomes, but also elucidated the biases in estimating prokaryotic diversity with quantitative data, providing references for choosing amplified regions and clustering thresholds in microbial community surveys. IMPORTANCE Microbial diversity is typically analyzed using marker gene-based methods, of which 16S rRNA gene sequencing is the most widely used approach. However, obtaining an accurate estimation of microbial diversity remains a challenge, due to the intragenomic variation and low taxonomic resolution of 16S rRNA genes. Comprehensive examination of the bias in estimating such prokaryotic diversity using 16S rRNA genes within ever-increasing prokaryotic genomes highlights the importance of the choice of sequencing regions and clustering thresholds based on the specific research objectives.

Isotopic Signatures of Carbon Transfer in a Proterozoic Analogue Microbial Mat

Modern microbial mats are potential analogues for Proterozoic ecosystems, yet only a few studies have characterized mats under low-oxygen conditions that are relevant to Proterozoic environments. Here, we use protein-stable isotope fingerprinting (P-SIF) to determine the protein carbon isotope (δ13C) values of autotrophic, heterotrophic, and mixotrophic organisms in a benthic microbial mat from the low-oxygen Middle Island Sinkhole, Lake Huron, USA (MIS). We also measure the δ13C values of the sugar moieties of exopolysaccharides (EPS) within the mat to explore the relationships between cyanobacterial exudates and heterotrophic anabolic carbon uptake. Our results show that Cyanobacteria (autotrophs) are 13C-depleted, relative to sulfate-reducing bacteria (heterotrophs), and 13C-enriched, relative to sulfur oxidizing bacteria (autotrophs or mixotrophs). We also find that the pentose moieties of EPS are systematically enriched in 13C, relative to the hexose moieties of EPS. We hypothesize that these isotopic patterns reflect cyanobacterial metabolic pathways, particularly phosphoketolase, that are relatively more active in low-oxygen mat environments, rather than oxygenated mat environments. This results in isotopically more heterogeneous C sources in low-oxygen mats. While this might partially explain the isotopic variability observed in Proterozoic mat facies, further work is necessary to systematically characterize the isotopic fractionations that are associated with the synthesis of cyanobacterial exudates. IMPORTANCE The δ13C compositions of heterotrophic microorganisms are dictated by the δ13C compositions of their organic carbon sources. In both modern and ancient photosynthetic microbial mats, photosynthetic exudates are the most likely source of organic carbon for heterotrophs. We measured the δ13C values of autotrophic, heterotrophic, and mixotrophic bacteria as well as the δ13C value of the most abundant photosynthetic exudate (exopolysaccharide) in a modern analogue for a Proterozoic environment. Given these data, future studies will be better equipped to estimate the most likely carbon source for heterotrophs in both modern environments as well as in Proterozoic environments preserved in the rock record.

Disentangling hindgut metabolism in the American cockroach through single-cell genomics and metatranscriptomics

Omnivorous cockroaches host a complex hindgut microbiota comprised of insect-specific lineages related to those found in mammalian omnivores. Many of these organisms have few cultured representatives, thereby limiting our ability to infer the functional capabilities of these microbes. Here we present a unique reference set of 96 high-quality single cell-amplified genomes (SAGs) from bacterial and archaeal cockroach gut symbionts. We additionally generated cockroach hindgut metagenomic and metatranscriptomic sequence libraries and mapped them to our SAGs. By combining these datasets, we are able to perform an in-depth phylogenetic and functional analysis to evaluate the abundance and activities of the taxa in vivo. Recovered lineages include key genera within Bacteroidota, including polysaccharide-degrading taxa from the genera Bacteroides, Dysgonomonas, and Parabacteroides, as well as a group of unclassified insect-associated Bacteroidales. We also recovered a phylogenetically diverse set of Firmicutes exhibiting a wide range of metabolic capabilities, including-but not limited to-polysaccharide and polypeptide degradation. Other functional groups exhibiting high relative activity in the metatranscriptomic dataset include multiple putative sulfate reducers belonging to families in the Desulfobacterota phylum and two groups of methanogenic archaea. Together, this work provides a valuable reference set with new insights into the functional specializations of insect gut symbionts and frames future studies of cockroach hindgut metabolism.

Age-Related Subgingival Colonization of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Parvimonas micra-A Pragmatic Microbiological Retrospective Report

The aim of this study was to compare data about the prevalence and proportions of the bacterial species Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Parvimonas micra in periodontitis pocket samples collected from young, <35 years, and old, >35-year-old patients, YP and OP, respectively. The results from the analyses of a total of 3447 subgingival plaque samples analyzed for clinical diagnosis purposes by cultivation regarding the proportions of these species were collected from a database and elucidated. The prevalence of A. actinomycetemcomitans was found to be more than twice as high (OR = 2.96, 95% CI; 2.50-3.50) in samples from the younger (42.2%) than the older group (20.4%) (p < 0.001). The prevalence of P. micra was significantly lower in samples from the younger age group (OR = 0.43, 95%) (p < 0.001), whereas P. gingivalis was similarly distributed (OR = 0.78, 95%) in the two age groups (p = 0.006). A similar pattern was noticed for A. actinomycetemcomitans and P. gingivalis when high proportions (>50%) of the samples of these bacterial species were elucidated. In contrast, the proportion of samples containing >50% with P. micra was lower compared with the two other bacterial species. Furthermore, it was noted that the proportion of samples from old patients containing A. actinomycetemcomitans in combination with P. micra was almost three times higher than in samples when P. micra was replaced by P. gingivalis. In conclusion, A.actinomycetemcomitans showed an increased presence and proportion in samples from young patients compared with the old patients, while P. gingivalis was similarly distributed in the two age groups. P. micra showed an increased presence and proportion in samples from old patients compared with the young patients.

Simultaneous Analysis of Bacterial and Fungal Communities in Oral Samples from Intubated Patients in Intensive Care Unit

Intubated patients in intensive care units (ICUs) too frequently contract ventilator-associated pneumonia or Candida infections. Oropharyngeal microbes are believed to play an important etiologic role. This study was undertaken to determine whether next-generation sequencing (NGS) can be used to simultaneously analyze bacterial and fungal communities. Buccal samples were collected from intubated ICU patients. Primers targeting the V1-V2 region of bacterial 16S rRNA and the internal transcribed spacer 2 (ITS2) region of fungal 18S rRNA were used. V1-V2, ITS2, or mixed V1-V2/ITS2 primers were used to prepare an NGS library. Bacterial and fungal relative abundances were comparable for V1-V2, ITS2, or mixed V1-V2/ITS2 primers, respectively. A standard microbial community was used to adjust the relative abundances to theoretical abundance, and NGS and RT-PCR-adjusted relative abundances showed a high correlation. Using mixed V1-V2/ITS2 primers, bacterial and fungal abundances were simultaneously determined. The constructed microbiome network revealed novel interkingdom and intrakingdom interactions, and the simultaneous detection of bacterial and fungal communities using mixed V1-V2/ITS2 primers enabled analysis across two kingdoms. This study provides a novel approach to simultaneously determining bacterial and fungal communities using mixed V1-V2/ITS2 primers.

Seasonal and spatial variations of antibiotic resistance genes and bacterial biodiversity in biofilms covering the equipment at successive stages of drinking water purification

The presence of ARGs (antibiotic resistance genes) in the aquatic environment is a serious threat to public health especially in environmental biofilms as natural reservoirs of ARGs in water treatment plants (WTP). It has been shown that the treatment technology and source of water have a significant impact on the abundance and type of genes determining antibiotic resistance. The following indicator genes were proposed that should absolutely be controlled in environmental biofilms: intl1, sul2, sul1, tetA, blaOXA, and blaTEM. In both studied WTPs, the highest number of copies was determined for the intI1 gene. Among the tested ARGs, the highest values were obtained for genes sul1 and tetA. The qPCR analysis also revealed that the amounts of determined ARGs decreased in the following order: sulphonamides>carbapenems >tetracyclines > β-lactams >macrolides. The dominant bacterial types in all analysed samples were Proteobacteria and Bacteroidetes. Both ARGs and bacterial biodiversity was determined rather by sampling site (spatial variation) than seasonality. The obtained results show that biofilms are reservoirs of ARGs. This may affect the microbiological quality of water entering the water system. It is therefore necessary to include their analysis in the classical studies of water quality.

Universal gut microbial relationships in the gut microbiome of wild baboons

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

Microcosm-omics centric investigation reveals elevated bacterial degradation of imidacloprid

Imidacloprid, a broad-spectrum insecticide, is widely used against aphids and other sucking insects. As a result, its toxic effect is becoming apparent in non-targeted organisms. In-situ bioremediation of residual insecticide from the environment utilizing efficient microbes would be helpful in reducing its load. In the present work, in-depth genomics, proteomics, bioinformatics, and metabolomics analyses were employed to reveal the potential of Sphingobacterium sp. InxBP1 for in-situ degradation of imidacloprid. The microcosm study revealed ∼79% degradation with first-order kinetics (k = 0.0726 day^-1). Genes capable of mediating oxidative degradation of imidacloprid and subsequent decarboxylation of intermediates were identified in the bacterial genome. Proteome analysis demonstrated significant overexpression of the enzymes coded by these genes. Bioinformatic analysis revealed significant affinity and binding of the identified enzymes for their respective substrates (the degradation pathway intermediates). The nitronate monooxygenase (K7A41 01745), amidohydrolase (K7A41 03835 and K7A41 07535), FAD-dependent monooxygenase (K7A41 12,275), and ABC transporter enzymes (K7A41 05325, and K7A41 05605) were found to be effective in facilitating the transport and intracellular degradation of imidacloprid. The metabolomic study identified the pathway intermediates and validated the proposed mechanism and functional role of the identified enzymes in degradation. Thus, the present investigation provides an efficient imidacloprid degrading bacterial species as evidenced by its genetic attributes which can be utilized or further improved to develop technologies for in-situ remediation.

Social Interaction is Unnecessary for Hindgut Microbiome Transmission in Honey Bees: The Effect of Diet and Social Exposure on Tissue-Specific Microbiome Assembly

Honey bees are a model for host-microbial interactions with experimental designs evolving towards conventionalized worker bees. Research on gut microbiome transmission and assembly has examined only a fraction of factors associated with the colony and hive environment. Here, we studied the effects of diet and social isolation on tissue-specific bacterial and fungal colonization of the midgut and two key hindgut regions. We found that both treatment factors significantly influenced early hindgut colonization explaining similar proportions of microbiome variation. In agreement with previous work, social interaction with older workers was unnecessary for core hindgut bacterial transmission. Exposure to natural eclosion and fresh stored pollen resulted in gut bacterial communities that were taxonomically and structurally equivalent to those produced in the natural colony setting. Stressed diets of no pollen or autoclaved pollen in social isolation resulted in decreased fungal abundance and bacterial diversity, and atypical microbiome structure and tissue-specific variation of functionally important core bacteria. Without exposure to the active hive environment, the abundance and strain diversity of keystone ileum species Gilliamella apicola was markedly reduced. These changes were associated with significantly larger ileum microbiotas suggesting that extended exposure to the active hive environment plays an antibiotic role in hindgut microbiome establishment. We conclude that core hindgut microbiome transmission is facultative horizontal with 5 of 6 core hindgut species readily acquired from the built hive structure and natural diet. Our findings contribute novel insights into factors influencing assembly and maintenance of honey bee gut microbiota and facilitate future experimental designs.

Mediterranean Sea heatwaves jeopardize greater amberjack's (Seriola dumerili) aquaculture productivity through impacts on the fish microbiota

Climate change is dramatically increasing the frequency and severity of marine heatwaves (MHWs) in the Mediterranean basin, strongly affecting marine food production systems. However, how it will shape the ecology of aquaculture systems, and the cascading effects on productivity, is still a major knowledge gap. The present work aims to increase our understanding of future impacts, caused by raising water temperatures, on the interaction between water and fish microbiotas, and consequential effects upon fish growth. Thus, the bacterial communities present in the water tanks, and mucosal tissues (skin, gills and gut), of greater amberjack farmed in recirculatory aquaculture systems (RAS), at three different temperatures (24, 29 and 33 °C), were characterized in a longitudinal study. The greater amberjack (Seriola dumerili) is a teleost species with high potential for EU aquaculture diversification due to its fast growth, excellent flesh quality and global market. We show that higher water temperatures disrupt the greater amberjack's microbiota. Our results demonstrate the causal mediation exerted by this bacterial community shifts on the reduction of fish growth. The abundance of members of the Pseudoalteromonas is positively correlated with fish performance, whereas members of the Psychrobacter, Chryseomicrobium, Paracoccus and Enterovibrio are suggested as biomarkers for dysbiosis, at higher water temperatures. Hence, opening new evidence-based avenues for the development of targeted microbiota-based biotechnological tools, designed to increase the resilience and adaptation to climate change of the Mediterranean aquaculture industry.

Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities

Recent advances in new molecular biology methods and next-generation sequencing (NGS) technologies have revolutionized metabarcoding studies investigating complex microbial communities from various environments. The inevitable first step in sample preparation is DNA extraction which introduces its own set of biases and considerations. In this study, we assessed the influence of five DNA extraction methods [B1: phenol/chloroform/isoamyl extraction, B2 and B3: isopropanol and ethanol precipitations, respectively-both modifications of B1, K1: DNeasy PowerWater Kit (QIAGEN), K2: modified DNeasy PowerWater Kit (QIAGEN) and direct PCR approach (P) that completely circumvents this step on community composition and DNA yield of mock and marine sample communities from the Adriatic Sea]. B1-B3 methods generally produced higher DNA yields and more similar microbial communities, but with higher interindividual variability. Each method demonstrated significant differences in a specific community structure, where rare taxa seem to play a crucial role. There was not one superior method closest to the theoretically expected mock community composition, they all demonstrated skewed ratios, but in a similar way which might be attributed to other factors, such as primer bias or 16S rRNA gene count for specific taxa. Direct PCR represents an interesting approach when high throughput in sample processing is required. We emphasize the importance of making a cautious decision about the choice of the extraction method or direct PCR approach, but even more importantly its consistent application throughout the study.

Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights

Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome of wildlife are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. Here we evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the faecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional vs. high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective laboratory methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome, and demonstrate that the latter recapitulates biological patterns first described in a published amplicon data set. Unlike for amplicon data, we further demonstrate how shallow shotgun metagenomic data provide useful insights regarding microbiome functional potential which support previously hypothesized diet effects in this study system.

Molecular Detection and Identification of Plant-Associated Lactiplantibacillus plantarum

Lactiplantibacillus plantarum is a lactic acid bacterium often isolated from a wide variety of niches. Its ubiquity can be explained by a large, flexible genome that helps it adapt to different habitats. The consequence of this is great strain diversity, which may make their identification difficult. Accordingly, this review provides an overview of molecular techniques, both culture-dependent, and culture-independent, currently used to detect and identify L. plantarum. Some of the techniques described can also be applied to the analysis of other lactic acid bacteria.

Wheat genome architecture influences interactions with phytobeneficial microbial functional groups in the rhizosphere

Wheat has undergone a complex evolutionary history, which led to allopolyploidization and the hexaploid bread wheat Triticum aestivum. However, the significance of wheat genomic architecture for beneficial plant-microbe interactions is poorly understood, especially from a functional standpoint. In this study, we tested the hypothesis that wheat genomic architecture was an overriding factor determining root recruitment of microorganisms with particular plant-beneficial traits. We chose five wheat species representing genomic profiles AA (Triticum urartu), BB {SS} (Aegilops speltoides), DD (Aegilops tauschii), AABB (Triticum dicoccon) and AABBDD (Triticum aestivum) and assessed by quantitative polymerase chain reaction their ability to interact with free-nitrogen fixers, 1-aminocyclopropane-1-carboxylate deaminase producers, 2,4-diacetylphloroglucinol producers and auxin producers via the phenylpyruvate decarboxylase pathway, in combination with Illumina MiSeq metabarcoding analysis of N fixers (and of the total bacterial community). We found that the abundance of the microbial functional groups could fluctuate according to wheat genomic profile, as did the total bacterial abundance. N fixer diversity and total bacterial diversity were also influenced significantly by wheat genomic profile. Often, rather similar results were obtained for genomes DD (Ae. tauschii) and AABBDD (T. aestivum), pointing for the first time that the D genome could be particularly important for wheat-bacteria interactions.

Specific labeling and identification of bacteria based on concentration-dependent carbon dot staining combined with hyperspectral imaging

Concentration-dependent carbon dot (CD) fluorescence was developed and utilized alongside hyperspectral microscopy as a specific labeling and identification technique for bacteria. Staining revealed that the CD concentration within cells depended on the characteristic intracellular environment of the species. Therefore, based on the concentration dependence of the CD fluorescence, different bacterial species were specifically labeled. Hyperspectral microscopy captured subtle fluorescence variations to identify bacteria. Method validation using Bacillus subtilis and Bacillus licheniformis succeeded with an identification accuracy of 99%. As a simple, rapid method for labeling and identifying bacterial species in mixtures, this technique has excellent potential for bacterial community studies.

Habitats Within the Plant Root Differ in Bacterial Network Topology and Taxonomic Assortativity

The root microbiome is composed of distinct epiphytic (rhizosphere) and endophytic (endosphere) habitats. Differences in abiotic and biotic factors drive differences in microbial community diversity and composition between these habitats, though how they shape the interactions among community members is unknown. Here, we coupled a large-scale characterization of the rhizosphere and endosphere bacterial communities of 30 plant species across two watering treatments with co-occurrence network analysis to understand how root habitats and soil moisture shape root bacterial network properties. We used a novel bootstrapping procedure and null network modeling to overcome some of the limitations associated with microbial co-occurrence network construction and analysis. Endosphere networks had elevated node betweenness centrality versus the rhizosphere, indicating greater overall connectivity among core bacterial members of the root endosphere. Taxonomic assortativity was higher in the endosphere, whereby positive co-occurrence was more likely between bacteria within the same phylum while negative co-occurrence was more likely between bacterial taxa from different phyla. This taxonomic assortativity could be driven by positive and negative interactions among members of the same or different phylum, respectively, or by similar niche preferences associated with phylum rank among root inhabiting bacteria across plant host species. In contrast to the large differences between root habitats, drought had limited effects on network properties but did result in a higher proportion of shared co-occurrences between rhizosphere and endosphere networks. Our study points to fundamentally different ecological processes shaping bacterial co-occurrence across root habitats. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Incipiently social carpenter bees (Xylocopa) host distinctive gut bacterial communities and display geographical structure as revealed by full-length PacBio 16S rRNA sequencing

The gut microbiota of bees affects nutrition, immunity and host fitness, yet the roles of diet, sociality and geographical variation in determining microbiome structure, including variant-level diversity and relatedness, remain poorly understood. Here, we use full-length 16S rRNA amplicon sequencing to compare the crop and gut microbiomes of two incipiently social carpenter bee species, Xylocopa sonorina and Xylocopa tabaniformis, from multiple geographical sites within each species' range. We found that Xylocopa species share a set of core taxa consisting of Bombilactobacillus, Bombiscardovia and Lactobacillus, found in >95% of all individual bees sampled, and Gilliamella and Apibacter were also detected in the gut of both species with high frequency. The crop bacterial community of X. sonorina comprised nearly entirely Apilactobacillus with occasionally abundant nectar bacteria. Despite sharing core taxa, Xylocopa species' microbiomes were distinguished by multiple bacterial lineages, including species-specific variants of core taxa. The use of long-read amplicons revealed otherwise cryptic species and population-level differentiation in core microbiome members, which was masked when a shorter fragment of the 16S rRNA (V4) was considered. Of the core taxa, Bombilactobacillus and Bombiscardovia exhibited differentiation in amplicon sequence variants among bee populations, but this was lacking in Lactobacillus, suggesting that some bacterial genera in the gut may be structured by different processes. We conclude that these Xylocopa species host a distinctive microbiome, similar to that of previously characterized social corbiculate apids, which suggests that further investigation to understand the evolution of the bee microbiome and its drivers is warranted.

Competitive Reactions during Ethanol Chain Elongation Were Temporarily Suppressed by Increasing Hydrogen Partial Pressure through Methanogenesis Inhibition

Organic waste streams can be converted into high-value platform chemicals such as medium-chain carboxylic acids (MCCAs) using mixed microbial communities via chain elongation. However, the heterogeneity of waste streams and the use of complex microbial communities can lead to undesirable reactions, thus decreasing process efficiency. We explored suppressing excessive ethanol oxidation to acetate (EEO) by increasing the hydrogen partial pressure (PH2) through hydrogenotrophic methanogenesis inhibition by periodically adding 2-bromoethanesulfonate (2-BES) to an MCCA-producing bioreactor to reach 10 mM of 2-BES upon addition. The bioreactor was fed with pretreated food waste and brewery waste containing high concentrations of short-chain carboxylic acids and ethanol, respectively. While 2-BES addition initially reduced EEO, some methanogens (Methanobrevibacter spp.) persisted and resistant populations were selected over time. Besides changing the methanogenic community structure, adding 2-BES also changed the bacterial community structure due to its impact on PH2. While we demonstrated that PH2 could be manipulated using 2-BES to control EEO, methods that do not require the addition of a chemical inhibitor should be explored to maintain optimum PH2 for long-term suppression of EEO.

Implementation of helicase-dependent amplification with SYBR Green I for prompt naked-eye detection of bacterial contaminants in platelet products

Platelet transfusions may lead to more significant risks of infection and septic transfusion reactions that can be fatal to the recipient. Platelet products should be screened to limit or detect bacterial contamination before application to patients to minimise any adverse reactions. This study aimed to develop a helicase-dependent amplification (HDA) technique targeting a universal highly conserved bacterial gene, 16S rRNA, in combination with naked-eye detection using SYBR Green I (HDA/SYBR Green I) to detect bacterial contamination in platelet products. Thirty positive samples were obtained from spiked platelet products by five transfusion-relevant bacterial strains and were screened for bacterial contamination by HDA/SYBR Green I. HDA/SYBR Green I showed an enhanced yield of bacterial contaminant detection when performed with medium to late shelf life, Day 2 of storage or later platelet products (98.67% sensitivity and 100% specificity compared to the BACT/ALERT culture system). The limit of detection of HDA/SYBR Green I was 1 ng, and there was no cross-reaction with other organisms that could likely contaminate platelet products. The developed HDA/SYBR Green I assay is rapid and simplistic and only requires an easy-to-find heat box, available in general blood bank laboratories, for the amplification step. This technique is suitable for further development as an alternative method to detect bacterial contamination in platelet products in the near future.

Comparison of genomic and transcriptional microbiome analysis in gastric cancer patients and healthy individuals

BACKGROUND

Helicobacter pylori and the stomach microbiome play a crucial role in gastric carcinogenesis, and detailed characterization of the microbiome is necessary for a better understanding of the pathophysiology of the disease. There are two common modalities for microbiome analysis: DNA (16S rRNA gene) and RNA (16S rRNA transcript) sequencing. The implications from the use of one or another sequencing approach on the characterization and comparability of the mucosal microbiome in gastric cancer (GC) are poorly studied.

AIM

To characterize the microbiota of GC using 16S rRNA gene and its transcript and determine difference in the bacterial composition.

METHODS

In this study, 316 DNA and RNA samples extracted from 105 individual stomach biopsies were included. The study cohort consisted of 29 healthy control individuals and 76 patients with GC. Gastric tissue biopsy samples were collected from damaged mucosa and healthy mucosa at least 5 cm from the tumor tissue. From the controls, healthy stomach mucosa biopsies were collected. From all biopsies RNA and DNA were extracted. RNA was reverse transcribed into cDNA. V1-V2 region of bacterial 16S rRNA gene from all samples were amplified and sequenced on an Illumina MiSeq platform. Bray-Curtis algorithm was used to construct sample-similarity matrices abundances of taxonomic ranks in each sample type. For significant differences between groups permutational multivariate analysis of variance and Mann-Whitney test followed by false-discovery rate test were used.

RESULTS

Microbial analysis revealed that only a portion of phylotypes (18%-30%) overlapped between microbial profiles obtained from DNA and RNA samples. Detailed analysis revealed differences between GC and controls depending on the chosen modality, identifying 17 genera at the DNA level and 27 genera at the RNA level. Ten of those bacteria were found to be different from the control group at both levels. The key taxa showed congruent results in various tests used; however, differences in 7 bacteria taxa were found uniquely only at the DNA level, and 17 uniquely only at the RNA level. Furthermore, RNA sequencing was more sensitive for detecting differences in bacterial richness, as well as differences in the relative abundance of Reyranella and Sediminibacterium according to the type of GC. In each study group (control, tumor, and tumor adjacent) were found differences between DNA and RNA bacterial profiles.

CONCLUSION

Comprehensive microbial study provides evidence for the effect of choice of sequencing modality on the microbiota profile, as well as on the identified differences between case and control.

Molecular Accounting and Profiling of Human Respiratory Microbial Communities: Toward Precision Medicine by Targeting the Respiratory Microbiome for Disease Diagnosis and Treatment

The wide diversity of microbiota at the genera and species levels across sites and individuals is related to various causes and the observed differences between individuals. Efforts are underway to further understand and characterize the human-associated microbiota and its microbiome. Using 16S rDNA as a genetic marker for bacterial identification improved the detection and profiling of qualitative and quantitative changes within a bacterial population. In this light, this review provides a comprehensive overview of the basic concepts and clinical applications of the respiratory microbiome, alongside an in-depth explanation of the molecular targets and the potential relationship between the respiratory microbiome and respiratory disease pathogenesis. The paucity of robust evidence supporting the correlation between the respiratory microbiome and disease pathogenesis is currently the main challenge for not considering the microbiome as a novel druggable target for therapeutic intervention. Therefore, further studies are needed, especially prospective studies, to identify other drivers of microbiome diversity and to better understand the changes in the lung microbiome along with the potential association with disease and medications. Thus, finding a therapeutic target and unfolding its clinical significance would be crucial.

Impact of 16S rRNA Gene Redundancy and Primer Pair Selection on the Quantification and Classification of Oral Microbiota in Next-Generation Sequencing

This study aimed to evaluate the number of 16S rRNA genes in the complete genomes of the bacterial and archaeal species inhabiting the human mouth and to assess how the use of different primer pairs would affect the detection and classification of redundant amplicons and matching amplicons (MAs) from different taxa. A total of 518 oral-bacterial and 191 oral-archaeal complete genomes were downloaded from the NCBI database, and their complete 16S rRNA genes were extracted. The numbers of genes and variants per genome were calculated. Next, 39 primer pairs were used to search for matches in the genomes and obtain amplicons. For each primer, we calculated the number of gene amplicons, variants, genomes, and species detected and the percentage of coverage at the species level with no MAs (SC-NMA). The results showed that 94.09% of oral bacteria and 52.59% of oral archaea had more than one intragenomic 16S rRNA gene. From 1.29% to 46.70% of bacterial species and from 4.65% to 38.89% of archaea detected by the primers had MAs. The best primers were the following (SC-NMA; region; position for Escherichia coli [GenBank version no. J01859.1]): KP_F048-OP_R030 for bacteria (93.55%; V3 to V7; 342 to 1079), KP_F018-KP_R063 for archaea (89.63%; V3 to V9; undefined to 1506), and OP_F114-OP_R121 for both domains (92.52%; V3 to V9; 340 to 1405). In addition to 16S rRNA gene redundancy, the presence of MAs must be controlled to ensure an accurate interpretation of microbial diversity data. The SC-NMA is a more useful parameter than the conventional coverage percentage for selecting the best primer pairs. The pairs used the most in the oral microbiome literature were not among the best performers. IMPORTANCE Hundreds of publications have studied the oral microbiome through 16S rRNA gene sequencing. However, none have assessed the number of 16S rRNA genes in the genomes of oral microbes, or how the use of primer pairs targeting different regions affects the detection of MAs from different taxa. Here, we found that almost all oral bacteria and more than half of oral archaea have more than one intragenomic 16S rRNA gene. The performance of the primer pairs in not detecting MAs increases as the length of the amplicon augments. As none of those most employed in the oral literature were among the best performers, we selected a series of primers to detect bacteria and/or archaea based on their percentage of species detected without MAs. The intragenomic 16S rRNA gene redundancy and the presence of MAs between distinct taxa need to be considered to ensure an accurate interpretation of microbial diversity data.

Implications of a short carbon pulse on biofilm formation on mica schist in microcosms with deep crystalline bedrock groundwater

Microbial life in the deep subsurface occupies rock surfaces as attached communities and biofilms. Previously, epilithic Fennoscandian deep subsurface bacterial communities were shown to host genetic potential, especially for heterotrophy and sulfur cycling. Acetate, methane, and methanol link multiple biogeochemical pathways and thus represent an important carbon and energy source for microorganisms in the deep subsurface. In this study, we examined further how a short pulse of low-molecular-weight carbon compounds impacts the formation and structure of sessile microbial communities on mica schist surfaces over an incubation period of ∼3.5 years in microcosms containing deep subsurface groundwater from the depth of 500 m, from Outokumpu, Finland. The marker gene copy counts in the water and rock phases were estimated with qPCR, which showed that bacteria dominated the mica schist communities with a relatively high proportion of epilithic sulfate-reducing bacteria in all microcosms. The dominant bacterial phyla in the microcosms were Proteobacteria, Firmicutes, and Actinobacteria, whereas most fungal genera belonged to Ascomycota and Basidiomycota. Dissimilarities between planktic and sessile rock surface microbial communities were observed, and the supplied carbon substrates led to variations in the bacterial community composition.

Potential of biological sulphur recovery from thiosulphate by haloalkaliphilic Thioalkalivibrio denitrificans

The aim of this study was to investigate the potential for elemental sulphur recovery from sulphurous solutions under aerobic and anoxic conditions by haloalkalophilic Thioalkalivibrio denitrificans at 0.8-19.6 g S₂O₃^2--S L^-1 and 0.2-0.58 g NO₂ L^-1, respectively. The experiments were conducted as batch assays with haloalkaline (pH 10 and ≥ 14 g Na⁺ L^-1) thiosulphate solution. Aerobically, the highest biotransformation rate of thiosulphate obtained was 0.03 h^-1 at 8.5 g L S₂O₃^2--S. Based on Monod model, the maximum substrate utilisation rate (q_m) was 0.024 h^-1 with half saturation constant (K_s) 0.42 g S₂O₃^2--S L^-1 at initial [S₂O₃^2--S] of 14 g L^-1. S⁰ accumulated at [S₂O₃^2--S] ≥ 1.5 g L^-1 (10% yield at initial 9.5 g S₂O₃^2--S L^-1) and the highest S⁰ yield estimated with the model was 61% with initial [S₂O₃^2--S] of 16.5 g L^-1. Anoxically, the maximum nitrite removal rate based on Monod modelling was 0.011 h^-1 with K_s= 0.84 g NO₂^- L^-1. Aerobically and anoxically the maximum specific growth rates (µ_m) were 0.046 and 0.022 h^-1, respectively. In summary, high-rate aerobic biotransformation kinetics of thiosulphate were demonstrated, whereas the rates were slower and no S⁰ accumulated under anoxic conditions. Thus, future developments of biotechnical applications for the recovery of S⁰ from haloalkaline streams from the process industry should focus on aerobic treatment.HighlightsHaloalkaline S₂O₃^2- biotransformations kinetics by Thioalkalivibrio denitrificansAerobic thiosulphate-S bioconversion up to 0.024 h^-1 with K_s = 0.42 g S₂O₃^2--S L^-110% S⁰ yield with initial 9.5 g S₂O₃^2--S L^-1 in aerobic conditionAnoxic NO₂ removal up to 0.01 h^-1 with K_s = 0.84 g NO₂^- L^-1.

Multi-omics eco-surveillance of bacterial community function in legacy contaminated estuary sediments

Complex legacy contamination is a major issue for many estuaries, with toxicity affecting change in bacterial communities, and their provision of associated goods and services. Sequencing surveys of bacterial community composition provide inferred function; however, additional insights may be generated by measurement of realised metabolic phenotypes. We apply multi-omics (genomics, lipidomics, and metabolomics), with traditional sediment quality analyses, to characterise sediment-associated bacterial communities in an estuary subject to legacy metal contamination (Zn, Hg, As, Cd, Cu and Pb). Analyses of bacterial composition and inferred function (genomics) are coupled with measurements of realised bacterial phenotype (metabolomics and lipidomics) at multiple industrialised and reference sites. At sites with the highest sediment metal concentrations (NTB), we also observed increased abundances of hydrocarbon and sulphuric acid metabolites, indicating additional sediment contamination. Bacterial phyla across sampled sites were dominated by Proteobacteria and Desulfobacteria. NTB sites were enriched with metabolically versatile, cooperative and biofilm forming phyla including, Zixibacteria, Spirochaetota, SAR324 clade, Proteobacteria, Latescibacterota, Desulfobacterota, Deferrisomtota and Acidobateriota; with inferred functions characterised by sulphur metabolism, pathways associated with the degradation of complex organic molecules, and fermentation. Reference sites were characterised by enhanced vitamin biosynthesis, cell wall, cofactor and carbohydrate biosynthesis, and CO₂ fixation. Measured metabolic phenotypes at NTB sites supported predicted functions, with most consistent change observed to naphthalene and aminobenzoate degradation pathways and carbohydrate metabolism (galactose, amino and nucleotide sugar). Change in NTB metabolite profiles was most highly correlated with sediment Hg concentrations, indicative of toxic exposure and potential for Hg methylation. Lipid profiles generated further insight into potential functional (hydroxy fatty acids) and community level change (ceramide phosphoethanolamines, unsaturated glycerides). Multi-omics outputs provided insights into bacterial community functions, modes of contaminant toxicity and expressed mechanisms of adaptation, necessary to better inform management decisions and predictive models in increasingly human-influenced environments.