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Background Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of “over classification” is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive. Results Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats. Conclusions IDTAXA’s classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online ( http://DECIPHER.codes ).

Soil microorganisms play an important role in determining nutrient cycling. The integration of fish into rice fields can influence the diversity and structural composition of soil microbial communities. However, regarding the rice–fish co-culture (RF) farming system in Thailand, the study of the diversity and composition of soil microbes is still limited. Here, we aim to compare the microbial diversity, community composition, and functional structure of the bacterial communities between RF and rice monoculture (MC) farming systems and identify the environmental factors shaping bacterial community composition. Bacterial taxonomy was observed using 16s rRNA gene amplicon sequencing, and the functional structures of the bacterial communities were predicted based on their taxonomy and sequences. The results showed that soil organic carbon, total nitrogen (TN), organic matter, available phosphorous, and clay content were significantly higher in RF than in MC. The most dominant taxa across both paddy rice fields belonged to Actinobacteria, Chloroflexi, Proteobacteria, Acidobacteria, and Planctomycetes. The taxa Nitrosporae, Rokubacteria, GAL15, and Elusimicrobia were significantly different between both rice fields. At the genus level, Bacillus, Anaeromyxobacter, and HSB OF53-F07 were the predominant genera in both rice fields. The most abundant genus in MC was Anaeromyxobacter, whereas RF belonged to Bacillus. The community composition in MC was positively correlated with magnesium and sand content, while in RF was positively correlated with pH, TN, and clay content. Nitrogen fixation, aromatic compound degradation, and hydrocarbon degradation were more abundant in RF, while cellulolysis, nitrification, ureolysis, and phototrophy functional groups were more abundant in MC. The enzymes involved in paddy soil ecosystems included phosphatase, β-glucosidase, cellulase, and urease. These results provide novel insights into integrated fish in the paddy field as an efficient agricultural development strategy for enhancing soil microorganisms that increase soil fertility.

Large numbers of microbes can be present in seminal fluid, and there are differences in the semen microbiota between normal and abnormal semen samples. To evaluate the semen microbiota in patients with leukocytospermia, 87 seminal fluid samples, including 33 samples with a normal seminal leukocyte count and 54 samples with leukocytospermia, were obtained for a cross-sectional analysis. Twenty samples with a normal seminal leukocyte count had normal sperm parameters (Control group), and 13 samples with a normal seminal leukocyte count were from asthenozoospermia patients (Ast group). However, 32 samples with leukocytospermia were from asthenozoospermia patients (LA group), and only 22 samples with leukocytospermia had normal sperm parameters (Leu group). The 16S ribosomal RNA (rRNA) gene sequencing method was used to sequence the microbiota in the seminal fluid, and multiple bioinformatics methods were utilized to analyze the data. Finally, the results showed that the worse sperm parameters were observed in the leukocytospermia-related groups. Semen microbiota analysis found that there was increased alpha diversity in the leukocytospermia-related groups. Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes were the primary phyla in the seminal fluid. Two microbiota profiles, namely, Lactobacillus-enriched and Streptococcus-enriched groups, were identified in this study. The majority of the samples in the groups with a normal seminal leukocyte count could be categorized as Lactobacillus-enriched, whereas the majority of the leukocytospermia samples could be categorized as Streptococcus-enriched. Our study indicated that males with leukocytospermia have worse sperm parameters and a different semen microbiota composition compared to males with a normal seminal leukocyte count.

When conducting sequence-based analysis of microbiome samples, it is important to accurately represent the bacterial communities present. The aim of this study was to compare two commercially available DNA isolation and PCR amplification approaches to determine their impact on the taxonomic composition of microbiome samples following 16S rRNA gene sequencing. A well-established 16S rRNA gene profiling approach, which was widely used in the Human Microbiome Project (HMP), was compared with a novel alkaline degenerative technique that utilizes alkaline cell lysis in combination with a degenerate pool of primers for nucleic acid extraction and PCR amplification. When comparing these different approaches for the microbiome profiling of human and mouse fecal samples, we found that the alkaline-based method was able to detect greater taxonomic diversity. An in silico analysis of predicted primer binding against a curated 16S rRNA gene reference database further suggested that this novel approach had the potential to reduce population bias found with traditional methods, thereby offering opportunities for improved microbial community profiling.

While pathogens of the eye have been studied for a very long time, the existence of resident microbes on the surface of healthy eyes has gained interest only recently. It appears that commensal microbes are a normal feature of the healthy eye, whose role and properties are currently the subject of extensive research. This review provides an overview of studies that have used 16s rRNA gene sequencing and whole metagenome shotgun sequencing to characterize microbial communities associated with the healthy ocular surface from kingdom to genus level. Bacteria are the primary colonizers of the healthy ocular surface, with three predominant phyla: , and , regardless of the host, environment, and method used. Refining the microbial classification to the genus level reveals a highly variable distribution from one individual and study to another. Factors accounting for this variability are intriguing - it is currently unknown to what extent this is attributable to the individuals and their environment and how much is artifactual. Clearly, it is technically challenging to accurately describe the microorganisms of the ocular surface because their abundance is relatively low, thus, permitting substantial contaminations. More research is needed, including better experimental standards to prevent biases, and the exploration of the ocular surface microbiome's role in a spectrum of healthy to pathological states. Outcomes from such research include the opportunity for therapeutic interventions targeting the microbiome.

Many bacteria are involved in causing mastitis in dairy cows. Perfect identification of bacteria is crucial for the appropriate choice of drug for treatment. This study aims to find out the various bacteria that cause mastitis through the 16S ribosomal ribonucleic acid ( ) gene. A total of 150 mastitis somatic cell samples were tested with bacterial nested polymerase chain reaction (PCR) universal primers, targeting the gene. The primers had both Gram-positive and Gram-negative bacterial specificities. Inflammatory cytokine interleukin (IL-10), IL-4, and interferon-gamma (IFNγ) expression genes were measured and compared in mastitis-free and mastitis-affected animals. Based on the PCR, 70 (46.7%) samples showed positive results. The expression of the IL-10 gene was significantly higher ( < 0.001) in mastitis-affected cows than noninfected animals. Compared to cows diagnosed with clinical mastitis, the IL-4 and IFNγ genes were expressed more strongly in healthy cows ( > < 0.0001). Mastitis has been linked to both Gram-positive and Gram-negative bacteria. These genes are strong predictors of mastitis in the states analyzed, as evidenced by the differential expression in mastitis and healthy conditions of the IL-4, IL-10, and IFNγ genes. The genes examined here and others will be the subject of additional research.

Background & objectives: Mosquito surveillance is one of the critical functions of local health departments, particularly in the context of outbreaks of severe mosquito-borne viral infections. Unfortunately, some viral and parasitic infections transmitted by mosquitoes, manifests non-specific clinical symptoms which may actually be of rickettsial etiology, including Rickettsia felis infections. This study tested the hypothesis that mosquitoes from southeastern Georgia, USA may be infected with Rickettsia felis and Wolbachia, an endosymbiotic bacterium of the order Rickettsiales. Methods: Specimens of the five most common mosquito species occurring in the region were collected using gravid and light-traps and identified using morphological keys. Mosquitoes were then pooled by species, sex, trap and collection site and their DNA was extracted. Molecular methods were used to confirm mosquito identification, and presence of Wolbachia and R. felis. Results: Wolbachia DNA was detected in 90.8% of the mosquito pools tested, which included 98% pools of Cx. quinquefasciatus Say (Diptera: Culicidae), 95% pools of Ae. albopictus Skuse (Diptera: Culicidae), and 66.7% of pools of Cx. pipiens complex. Samples of An. punctipennis Say (Diptera: Culicidae) and An. crucians Wiedemann (Diptera: Culicidae) were tested negative for Wolbachia DNA. Three genotypes of Wolbachia sp. belonging to Group A (1 type) and Group B (2 types) were identified. DNA of R. felis was not found in any pool of mosquitoes tested. Interpretation & conclusions: This study provides a pilot data on the high presence of Wolbachia in Cx. quinque-fasciatus and Ae. albopictus mosquitoes prevalent in the study region. Whether the high prevalence of Wolbachia and its genetic diversity in mosquitoes affects the mosquitoes' susceptibility to R. felis infection in Georgia will need further evaluation.

Background High-throughput sequencing of the full-length 16S rRNA gene has improved the taxonomic classification of prokaryotes found in natural environments. However, sequencing of shorter regions from the same gene, like the V4-V5 region, can provide more cost-effective high throughput. It is unclear which approach best describes prokaryotic communities from underexplored environments. In this study, we hypothesize that high-throughput full-length 16S rRNA gene sequencing combined with adequate taxonomic databases improves the taxonomic description of prokaryotic communities from underexplored environments in comparison with high-throughput sequencing of a short region of the 16S rRNA gene. Results To test our hypothesis, we compared taxonomic profiles of seawater samples from the Arctic Ocean using: full-length and V4-V5 16S rRNA gene sequencing in combination with either the Genome Taxonomy Database (GTDB) or the Silva taxonomy database. Our results show that all combinations of sequencing strategies and taxonomic databases present similar results at higher taxonomic levels. However, at lower taxonomic levels, namely family, genus, and most notably species level, the full-length approach led to higher proportions of Amplicon Sequence Variants (ASVs) assigned to formally valid taxa. Hence, the best taxonomic description was obtained by the full-length and GTDB combination, which in some cases allowed for the identification of intraspecific diversity of ASVs. Conclusions We conclude that coupling high-throughput full-length 16S rRNA gene sequencing with GTDB improves the description of microbiome profiling at lower taxonomic ranks. The improvements reported here provide more context for scientists to discuss microbial community dynamics within a solid taxonomic framework in environments like the Arctic Ocean with still underrepresented microbiome sequences in public databases.

Antibiotics play an important role in the treatment of infectious diseases. However, the overuse of antibiotics increases the spread of drug-resistant bacteria and causes dysbiosis of the intestinal microbiota. Few studies have addressed the longitudinal effects of antibiotics on the microbiome and host immunity. In this study, the short-term effect of fluoroquinolone (levofloxacin) and β-lactam antibiotics (meropenem, cefoperazone/sulbactam, and aztreonam) on the gut microbiota of mice was evaluated by 16S rRNA gene sequencing. The susceptibility of , and to these antimicrobial agents was assessed using the disc diffusion method. Our results showed that 4-day antibiotic exposure significantly reduced the alpha and beta diversity of gut bacteria and increased serum inflammatory cytokines, and these changes persisted long after antibiotic withdrawal and did not return to pre-treatment levels. Nonetheless, the bacterial community composition tended to return to pre-treatment levels after discontinuing treatment. The tested probiotic strains were resistant to aztreonam but were sensitive to meropenem and cefoperazone/sulbactam. Short-term antibiotic treatment led to significant changes in the intestinal flora with a tendency to recover. The antibiotics had different effects on the intestinal microbial community and probiotic strains. This study provides guidance for the concomitant use of probiotics and antibiotics, and the results emphasize the importance of using broad-spectrum antibiotics responsibly to prevent the long-term disruption of the native microbiota.

The bacterial microbiota inhabiting the endosphere and rhizoplane regulate plant growth. The mutualistic interaction between sweet sorghum and soil bacteria has drawn increasing research attention. Nevertheless, the root-inhabiting bacterial microbiota of sweet sorghum’s perennial analog have rarely been characterized. Here, the root-inhabiting bacterial microbiota of the perennial sweet sorghum cultivar NaPBS778 (N778 simply) and its control TP60 were discovered at the flowering and maturing stages under field growth by high-throughput amplicon sequencing of the 16S rRNA gene via Illumina MiSeq. Nearly all alpha diversity indices of aerial and primary root samples of N778 were not significantly distinct from those of TP60 at the maturing stage, except for the observed species (Sobs) and phylogenetic diversity indices. The beta diversity of aerial and primary root samples showed no significant differences between N778 and its control TP60 at the maturing stage. Moreover, the bacterial microbiota in N778 aerial and primary roots was not only predominated by Proteobacteria, Actinobacteria, and Bacteroidetes at the phylum level but also strikingly distinct from the bacterial microbiota in rhizosphere soil at the genus level. Additionally, the root samples of N778 at the maturing stage were considerably enriched with OTU1262 being a potential cold-adapted bacterium belonging to Pseudarthrobacter, OTU434 plus OTU1304 belonging to Streptomyces and associated with crop nitrogen stress-tolerance, and OTU836 belonging to the family Oxalobacteraceae and potentially promoting crop growth. Our findings suggest that the perennial sweet sorghum cultivar N778 may recruit potentially cold-tolerant, plant growth-promoting, and nitrogen stress-tolerant bacterial taxa into roots at the maturing stage.