Illumina MiSeq 16S rRNA Gene Library Preparation for Poultry Processing Microbiome Analyses.

The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, *a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety.This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.

Atractylodes macrocephala polysaccharides shield a D-galactose-induced aging model via gut microbiota modulation.

This study explored the effect of a heteropolysaccharide (RAMP) on aging model mice and the importance of changes in the gut microbiota mediated by RAMP for the first time. The findings revealed that RAMP exerted protective effects on cognitive decline and oxidative stress in mice subjected to D-gal-induced aging, potentially by regulating the intestinal flora, according to the results of the Morris water maze test; brain and immune organ indices; hematoxylin and eosin-stained cerebral cortex images; transmission electron microscopy analysis of cortical neurons; and biochemical index measurements. In addition, 16S rRNA sequencing revealed notable changes in the abundance of Acidobacteriota, Anaerovoracaceae, and GCA-900066575 in the mouse model, all of which were abrogated by RAMP. These findings confirm that RAMP regulates the composition of mouse intestinal microorganisms. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional analyses linked these changes to 27 metabolic pathways, including those of the nervous system. Furthermore, metabolomics analysis revealed four RAMP-regulated metabolites related to lipid metabolism (2-dodecylbenzenesulfonic acid, N-undecylbenzenesulfonic acid, aspartyl-isoleucine, and 1-palmitoyl-2-(5-oxo-valeroyl)-sn-glycero-3-phosphate), suggesting that the mechanism potentially associated with lipid metabolism regulation. This study provides novel insights into the antiaging mechanisms of RAMP, suggesting its potential use in antiaging treatments.

Microbial source tracking and identification of fecal contamination patterns in saltwater estuaries between base- and storm-flow events using dual genome-based approaches.

Fecal contamination from natural and anthropogenic sources poses significant threats to saltwater estuaries, particularly after storms or heavy rainfall. Monitoring fecal contamination is essential for protecting these vulnerable ecosystems having important ecological and economic values. In this study, we investigated the abundance, sources, and potential causes of fecal contamination at three marine and seven freshwater stations across Vaughn Bay (WA, USA), a shellfish growing district, during base- and storm-flow events. Additionally, we evaluated the performance of fecal indicator bacteria (FIB) quantification, optical brightener assessment, and qPCR analysis for fecal contamination quantification. We compared the effectiveness of qPCR-based microbial source tracking (MST), which targeted a broad range of hosts including, such as humans, birds, cows, horses, ruminants, dogs, and pigs, with sequencing-based MST in identifying fecal contamination sources. Both MST analysis approaches identified birds and humans as the primary sources of fecal contamination. For marine water stations, freshwater creeks VBU001, VBU002, and VB047, along with drain VB007, were identified as the main sources of human-derived fecal contamination in Vaughn Bay, based on Kendall's tau analysis (τ: 0.58-0.97). This information indicates that the septic systems in the catchment areas of these creeks and drains require further investigation to achieve effective fecal contamination control. Optical brightener, FIB enumeration and qPCR quantification results were generally higher during storm-flow events, although they showed poor correlation with each other (Pearson r < 0.40), likely due to physiological and phylogenetic differences among the target organisms of these methods. However, the sequencing-based method faces challenges in precise quantitative identification of differences in fecal contamination between base- and storm-flow events. Due to its high-throughput and cost-effectiveness, we recommend using sequencing-based analysis for large-scale identification of the primary sources of fecal contamination in water environments, followed by targeted qPCR quantification of MST markers for more precise assessments.

Diversity of microbial, biocontrol agents and nematode abundance on a susceptible Prunus rootstock under a Meloidogyne root gradient infection.

Root-knot nematodes (RKNs) of the genus Meloidogyne are one of the most damaging genera to cultivated woody plants with a worldwide distribution. The knowledge of the soil and rhizosphere microbiota of almonds infested with Meloidogyne could help to establish new sustainable and efficient management strategies. However, the soil microbiota interaction in deciduous woody plants infected with RKNs is scarcely studied. This research was carried out in six commercial almond groves located in southern Spain and infested with different levels of Meloidogyne spp. within each grove. Several parameters were measured: nematode assemblages, levels and biocontrol agents in Meloidogyne's eggs, levels of specific biocontrol agents in rhizoplane and soil, levels of bacteria and fungi in rhizoplane and soil, fungal and bacterial communities by high-throughput sequencing of internal transcribed spacer (ITS), and 16S rRNA gene in soil and rhizosphere of the susceptible almond hybrid rootstock GF-677 infested with Meloidogyne spp. The studied almond groves showed soil degradation by nematode assemblies and fungi:bacterial ratio. Fungal parasites of Meloidogyne eggs were found in 56.25% of the samples. However, the percentage of parasitized eggs by fungi ranged from 1% to 8%. Three fungal species were isolated from Meloidogyne eggs, specifically Pochonia chlamydosporia, Purpureocillium lilacinum, and Trichoderma asperellum. The diversity and composition of the microbial communities were more affected by the sample type (soil vs rhizosphere) and by the geographical location of the samples than by the Meloidogyne density, which could be explained by the vigorous hybrid rootstock GF-677 and a possible dilution effect. However, the saprotrophic function in the functional guilds of the fungal ASV was increased in the highly infected roots vs the low infected roots. These results indicate that the presence of biocontrol agents in almond fields and the development of new management strategies could increase their populations to control partially RKN infection levels.

Optimization of a DNA extraction protocol for improving bacterial and fungal classification based on Nanopore sequencing.

Ribosomal RNA gene amplicon sequencing is commonly used to evaluate microbiome profiles in health and disease and document the impact of interventional treatments. Nanopore sequencing is attractive since it can provide greater classification at the species level. However, optimized protocols to target marker genes for bacterial and fungal profiling are needed. To achieve an increased taxonomic resolution, we developed extraction and full-length amplicon PCR-based approaches using Nanopore sequencing. Three lysis conditions were applied to a mock microbial community, including known bacterial and fungal species: ZymoBIOMICS lysis buffer (ML) alone, incorporating bead-beating (MLB) or bead-beating plus MetaPolyzyme enzymatic treatment (MLBE). In profiling of bacteria in comparison to reference data, MLB had more statistically different bacterial phyla and genera than the other two conditions. In fungal profiling, MLB had a significant increase of Ascomycota and a decline of Basidiomycota, subsequently failing to detect Malassezia and Cryptococcus. Also, a principal coordinates analysis plot by the Bray-Curtis metric showed a significant difference among groups for bacterial (P=0.033) and fungal (P=0.012) profiles, highlighting the importance of understanding the biases present in pretreatment. Overall, microbial profiling and diversity analysis revealed that ML and MLBE are more similar than MLB for both bacteria and fungi; therefore, using this specific pipeline, bead-beating is not recommended for whole gene amplicon sequencing. However, ML alone was suggested as an optimal approach considering DNA yield, taxonomic classification, reagent cost and hands-on time. This could be an initial proof-of-concept study for simultaneous human bacterial and fungal microbiome studies.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Responses of soil enzymes, bacterial communities and soil nitrification to the pre-emergence herbicide pyroxasulfone.

Pyroxasulfone is a relatively new herbicide that is sprayed on soils to control grassy weeds and some broadleaf weeds during the cultivation of agronomic crops. However, information regarding its environmental risks to soil ecosystems is currently limited. Here, the response of soil characteristics and soil bacterial communities to pyroxasulfone exposure were evaluated. The rate of pyroxasulfone degradation in soil decreased with increasing herbicide concentration, and its half-life at doses of 0.12 (the recommended field rate), 1.2 and 12 mg kg-1 was estimated to be 15.75 d, 39.46 d and 78.08 d, respectively. Soil pH markedly increased after pyroxasulfone treatment. Pyroxasulfone significantly inhibited urease activity but had a small effect on soil sucrase activity. In the late stages of degradation, the abundance of bacteria clearly decreased in soils treated with pyroxasulfone at doses of 1.2 and 12 mg kg-1. Compared with the control group, a distinct decrease in bacterial network complexity was observed at a pyroxasulfone dose of 0.12 mg kg-1, while the opposite phenomenon was observed at a pyroxasulfone dose of 12 mg kg-1. The copy numbers of the AOA amoA and AOB amoA genes exposed to 10- and 100-fold the recommended rates of pyroxasulfone were significantly lower than those in soils without pyroxasulfone residue at 25 and 60 days after treatment. In summary, pyroxasulfone at the recommended rate had a slight effect on soil enzymes, the bacterial community and soil nitrification; however, the potential adverse impacts of pyroxasulfone at higher concentrations on these soil factors deserve further attention.

A Rare Case of Persistent Bacteremia Caused by Asaia spp. in an Infant.

Asaia spp. has recently been reported to cause opportunistic infections in humans and is becoming an emerging hospital pathogen. To our knowledge, this is the first report on Asaia spp. in Malaysia. bacteremia in an infant. A girl with underlying Hirschsprung's disease, who was on parenteral feeding via a central venous catheter, developed persistent multidrug-resistant Gram-negative bacteremia. Routine automated identification methods failed to identify the organism, which was later identified by 16S ribosomal RNA sequencing. Bacterial clearance was achieved after the removal of the catheter and initiation of IV amikacin. This case highlights the role of molecular identification and the clinical importance of Asaia spp. in causing infections in humans, especially in patients with indwelling devices.

Beach wracks microbiome and its putative function in plastic polluted Mediterranean marine ecosystem.

The coasts of the world's oceans and seas accumulate various types of floating debris, commonly known as beach wracks, including organic seaweeds, seagrass, and ubiquitous anthropogenic waste, mainly plastic. Beach wrack microbiome (MB), surviving in the form of a biofilm, ensures decomposition and remineralization of wracks, but can also serve as a vector of potential pathogens in the environment. Through the interdisciplinary approach and comprehensive sampling design that includes geological analysis of the sediment, plastic debris composition analysis (ATR-FTIR) and application of 16S rRNA gene metabarcoding of beach wrack MBs, this study aims to describe MB in relation to beach exposure, sediment type and plastic pollution. Major contributors in beach wrack MB were Proteobacteria, Bacteroidetes, Actinobacteria, Planctomycetes, Verrucomicrobia and Firmicutes and there was significant dissimilarity between sample groups with Vibrio, Cobetia and Planococcus shaping the Exposed beach sample group and Cyclobacteriaceae and Flavobacterium shaping the Sheltered beach sample group. Our results suggest plastisphere MB is mostly shaped by beach exposure, type of seagrass, sediment type and probably beach naturalness with heavy influence of seawater MB and shows no significant dissimilarity between MBs from a variety of microplastics (MP). Putative functional analysis of MB detected plastic degradation and potential human pathogen bacteria in both beach wrack and seawater MB. The research provides the next crucial step in beach wrack MP accumulation research, MB composition and functional investigation with focus on beach exposure as an important variable.

Prokaryotic communities profiling of Indonesian hot springs using long-read Oxford Nanopore sequencing.

OBJECTIVES: Indonesia's location at the convergence of multiple tectonic plates results in a unique geomorphological feature with abundant hot springs. This study pioneers the metagenomic exploration of Indonesian hot springs, harbouring unique life forms despite high temperatures. The microbial community of hot springs is taxonomically versatile and biotechnologically valuable. 16s rRNA amplicon sequencing of the metagenome is a viable option for the microbiome investigation. This study utilized Oxford Nanopore's long-read 16 S rRNA sequencing for enhanced species identification, improved detection of rare members, and a more detailed community composition profile. DATA DESCRIPTION: Water samples were taken from three hot springs of the Bali, Indonesia (i) Angseri, 8.362503 S, 115.133452 E; (ii) Banjar, 8.210270 S, 114.967063 E; and (iii) Batur, 8.228806 S, 115.404829 E. BioLit Genomic DNA Extraction Kit (SRL, Mumbai, India) was used to isolate DNA from water samples. The quantity and quality of the DNA were determined using a NanoDrop™ spectrophotometer and a Qubit fluorometer (Thermo Fisher Scientific, USA). The library was created using Oxford Nanopore Technology kits, and the sequencing was done using Oxford Nanopore's GridION platform. All sequencing data was obtained in FASTQ files and filtered using NanoFilt software. This dataset is valuable for searching novel bacteria diversity and their existence.

Differences in the human gut microbiota with varying depressive symptom severity scores.

Depression is a prevalent mental health disorder, and its incidence has increased further because of the coronavirus disease 2019 (COVID-19) pandemic. The gut microbiome has been suggested as a potential target for mental health treatment because of the bidirectional communication system between the brain and gastrointestinal tract, known as the gut-brain axis. We aimed to investigate the relationship between the human gut microbiome and depression screening by analyzing the abundance and types of microbiomes among individuals living in Japan, where mental health awareness and support may differ from those in other countries owing to cultural factors. We used a data-driven approach to evaluate the gut microbiome of participants who underwent commercial gut microbiota testing services and completed a questionnaire survey that included a test for scoring depressive tendencies. Our data analysis results indicated that no significant differences in gut microbiome composition were found among the groups based on their depression screening scores. However, the results also indicated the potential existence of a few differentially abundant bacterial taxa. Specifically, the detected bacterial changes in abundance suggest that the Bifidobacteriaceae, Streptococcaceae, and Veillonellaceae families are candidates for differentially abundant bacteria. Our findings should contribute to the growing body of research on the relationship between gut microbiome and mental health, highlighting the potential of microbiome-based interventions for depression treatment. The limitations of this study include the lack of clear medical information on the participants' diagnoses. Future research could benefit from a larger sample size and more detailed clinical information.

High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform.

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.

Unveiling the gut microbiota blueprint of schizophrenia: a multilevel omics approach.

Background: Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Methods: Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and ß-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software. Results: The findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and ß-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Conclusion: Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia.

Improvement in Microbiota Recovery Using Cas-9 Digestion of Manuka Plastid and Mitochondrial DNA.

Understanding host-microbe interactions in planta is an expanding area of research. Amplicon sequencing of the 16S rRNA gene is a powerful and common method to study bacterial communities associated with plants. However, the co-amplification of mitochondrial and plastid 16S rRNA genes by universal primers impairs the sensitivity and performance of 16S rRNA sequencing. In 2020, a new method, Cas-16S-seq, was reported in the literature to remove host contamination for profiling the microbiota in rice, a well-studied domestic plant, by engineering RNA-programmable Cas9 nuclease in 16S rRNA sequencing. For the first time, we tested the efficiency and applicability of the Cas-16S-seq method on foliage, flowers, and seed of a non-domesticated wild plant for which there is limited genomic information, Leptospermum scoparium (manuka). Our study demonstrated the efficiency of the Cas-16S-seq method for L. scoparium in removing host contamination in V4-16S amplicons. An increase of 46% in bacterial sequences was found using six guide RNAs (gRNAs), three gRNAs targeting the mitochondrial sequence, and three gRNAs targeting the chloroplast sequence of L. scoparium in the same reaction. An increase of 72% in bacterial sequences was obtained by targeting the mitochondrial and chloroplast sequences of L. scoparium in the same sample at two different steps of the library preparation (DNA and 1st step PCR). The number of OTUs (operational taxonomic units) retrieved from soil samples was consistent when using the different methods (Cas-16S-seq and 16S-seq) indicating that the Cas-16S-seq implemented for L. scoparium did not introduce bias to microbiota profiling. Our findings provide a valuable tool for future studies investigating the bacterial microbiota of L. scoparium in addition to evaluating an important tool in the plant microbiota research on other non-domesticated wild species.

An investigative study on Yersinia enterocolitica in animals, humans and dried milk in New Valley Governorate, Egypt.

BACKGROUND: Yersiniosis is one of the most significant intestinal disorders caused by Yersinia enterocolitica and affects both humans and animals. This study aimed to investigate the prevalence of Y. enterocolitica in New Valley Governorate, Egypt in animals, humans, fresh milk and dried milk. Additionally, this study analyzed the presence of virulence genes, including ail and Yst in tested isolates and conducted a phylogenetic analysis to determine the genetic similarity between human, and animal Y. enterocolitica isolates. Finally, the antimicrobial resistance patterns of the isolates were examined. RESULTS: Among the 982 samples examined, the prevalence of Y. enterocolitica based on ISO10273-2017 was 11.7% in animal samples including 12.8% of animal faeces, and 10.4% in milk samples. Moreover, the prevalence of Y. enterocolitica was 13.2% in human stool, and 9.5% in dried milk samples. The molecular characterization of the six randomly selected isolates showed that the 16S rRNA, ail and Yst genes were found in 50, 33.3 and 100% of the examined Y. enterocolitica isolates, respectively. Phylogenetic analysis of animal and human isolates based on the 16S rRNA gene revealed a high degree of similarity between the isolates. All the tested animal and human Y. enterocolitica isolates (100%) were resistant to ampicillin and cefotaxime, but highly sensitive to norfloxacin. CONCLUSIONS: The high prevalence of Y. enterocolitica in animal and human samples with high degrees of genetic similarity poses a threat to public and animal health. Animal faeces, milk and milk powder represent the main sources of Y. enterocolitica infection in humans. Additionally, high levels of antibiotic resistance of Y. enterocolitica can cause public health hazards by leading to the failure of disease prevention and treatment programs in humans and animals.

Unveiling the microbial diversity across the northern Ninety East Ridge in the Indian Ocean.

Prokaryotes play a crucial role in marine ecosystem health and drive biogeochemical processes. The northern Ninety East Ridge (NER) of the Indian Ocean, a pivotal yet understudied area for these cycles, has been the focus of our study. We employed high-throughput 16S rRNA gene sequencing to analyze 35 water samples from five stations along the ridge, categorized into three depth- and dissolved oxygen-level-based groups. Our approach uncovered a clear stratification of microbial communities, with key bioindicators such as Prochlorococcus MIT9313, Sva0996 marine group, and Candidatus Actinomarina in the upper layer; Ketobacter, Pseudophaeobacter, Nitrospina, and SAR324 clade in the middle layer; and Methylobacterium-Methylorubrum, Sphingomonas, Sphingobium, and Erythrobacter in the deep layer. Methylobacterium-Methylorubrum emerged as the most abundant bacterial genus, while Nitrosopumilaceae predominated among archaeal communities. The spatial and depth-wise distribution patterns revealed that Ketobacter was unique to the northern NER, whereas Methylobacterium-Methylorubrum, UBA10353, SAR324 clade, SAR406, Sva0996_marine_group, Candidatus Actinomarina were ubiquitous across various marine regions, exhibiting niche differentiation at the OTU level. Environmental factors, especially dissolved oxygen (DO), silicate, nitrate, and salinity, significantly influence community structure. These findings not only reveal the novelty and adaptability of the microbial ecosystem in the northern NER but also contribute to the broader understanding of marine microbial diversity and its response to environmental heterogeneity.

Dataset on bacterial diversity using 16S metagenome analysis in fermented rice beer from two breweries and grape wine of Mizoram, Northeast India.

The microbial diversity of fermented rice beer and grape wine in Mizoram was explored using 16S metagenome analysis. The collected samples were marked as C1 and B1 for fermented rice beer and D1 for grape wine. Next-generation sequencing of the 16S rRNA (V3-V4 region) was performed using the Illumina NovoSeq 6000 platform. Operational taxonomic units (OTUs) were identified with QIIME2, and statistical analyses were performed using R packages. The metagenome of the three samples comprised 464,826 raw reads that represented 116,206,500 base pairs and were clustered into 336 OTUs. The phylum Firmicutes was predominant in C1 (55 %), B1 (53 %) and D1 (52 %), respectively and biosysnthesis, pyruvate fermentation to be abundant functions. By applying 16S metagenome analysis, this data provide insights in to the complex community of bacteria involved in the fermentation process and their potential roles and interactions.

Amplicon sequencing with internal standards yields accurate picocyanobacteria cell abundances as validated with flow cytometry.

To understand ecosystem state and function, marine microbial ecologists seek measurements of organismal abundance and diversity at high taxonomic resolution. Conventional flow cytometry accurately estimates microbial cell abundance but only discerns broad groups with distinct optical properties. While amplicon sequencing resolves more comprehensive diversity within microbiomes, it typically only provides relative organismal abundances within samples, not absolute abundance changes. Internal genomic standards offer a solution for absolute amplicon-based measures. Here, we spiked genomic standards into plankton samples from surface seawater, gathered at 46-km intervals along a cruise transect spanning the southern California Current System and the oligotrophic North Pacific Subtropical Gyre. This enabled evaluation of the absolute volumetric gene copy abundances of 16S rRNA amplicon sequence variants (amplified with 515Y-926R universal primers, quantitatively validated with mock communities) and cell abundances of picocyanobacteria with known genomic 16S copy numbers. Comparison of amplicon-derived cell abundances of Prochlorococcus and Synechococcus with flow cytometry data from nearby locations yielded nearly identical results (slope = 1.01; Pearson's r = 0.9942). Our findings show that this amplicon sequencing protocol combined with genomic internal standards accurately measures absolute cell counts of marine picocyanobacteria in complex field samples. By extension, we expect this approach to reasonably estimate volumetric gene copies for other amplified taxa in these samples.

The impact of carvacrol on the larval gut bacterial structure and function of Lymantria dispar.

Introduction: The gut bacteria of insects play an important role in regulating their metabolism, immune system and metabolizing pesticides. Our previous results indicate that carvacrol has certain gastric toxic activity on Lymantria dispar larvae and affects their detoxification metabolism at the mRNA level. However, the impact of carvacrol on the gut bacteria of L. dispar larvae has been unclear. Methods: In this study, the 16S rRNA sequencing technology was used to sequence and analyze the gut bacteria of the larvae which were exposed with sublethal concentration (0.297 mg/mL) and median lethal concentration (1.120 mg/mL), respectively. Results: A total of 10 phyla, 16 classes, 47 orders, 72 families, 103 genera, and 135 species were obtained by using a 97% similarity cutoff level. The dominant bacterial phyla in the gut of the L. dispar larvae are Firmicutes and Proteobacteria. The treatment with carvacrol can significantly affect the structure of gut bacteria in the larvae of the L. dispar. At both doses, carvacrol can shift the dominant gut bacteria of the larvae from Proteobacteria to Firmicutes. At the genus level, two doses of carvacrol can significantly enhance the relative abundance of probiotic Lactobacillus in the gut of L. dispar larvae (p ≤ 0.01). Additionally, significant differences were observed among the five bacterial genera Burkholderia-Caballeronia-Paraburkholderia, Anoxybacillus, Pelomonas, Mesorhizobium (p ≤ 0.05). The analysis of α-diversity and ß-diversity indicates that the treatment with carvacrol at two doses significantly affect the bacterial richness and diversity in the larvae. However, the results of functional classification prediction (PICRUSt) indicate that carvacrol significantly down-regulate 7 functions, including Energy metabolism, Cell growth and death, and up-regulate 2 functions, including Carbohydrate metabolism and Membrane transport. The network analysis indicates that the correlation between gut bacteria also has been changed. In addition, the insecticidal activity results of carvacrol against L. dispar larvae with gut bacteria elimination showed that gut bacteria can reduce the insecticidal activity of carvacrol against L. dispar larvae. Discussion: This study provides a theoretical foundation for understanding the role of gut bacteria in detoxifying plant toxins and conferring pesticide resistance.

rRNA operon improves species-level classification of bacteria and microbial community analysis compared to 16S rRNA.

Precise identification of species is fundamental in microbial genomics and is crucial for understanding the microbial communities. While the 16S rRNA gene, particularly its V3-V4 regions, has been extensively employed for microbial identification, however has limitations in achieving species-level resolution. Advancements in long-read sequencing technologies have highlighted the rRNA operon as a more accurate marker for microbial classification and analysis than the 16S rRNA gene. This study aims to compare the accuracy of species classification and microbial community analysis using the rRNA operon versus the 16S rRNA gene. We evaluated the species classification accuracy of the rRNA operon,16S rRNA gene, and 16S rRNA V3-V4 regions using a BLAST-based method and a k-mer matching-based method with public data available from NCBI. We further performed simulations to model microbial community analysis. We accessed the performance using each marker in community composition estimation and differential abundance analysis. Our findings demonstrate that the rRNA operon offers an advantage over the 16S rRNA gene and its V3-V4 regions for species-level classification within the genus. When applied to microbial community analysis, the rRNA operon enables a more accurate determination of composition. Using the rRNA operon yielded more reliable results in differential abundance analysis as well. IMPORTANCE: We quantitatively demonstrated that the rRNA operon outperformed the 16S rRNA and its V3-V4 regions in accuracy for both individual species identification and species-level microbial community analysis. Our findings can provide guidelines for selecting appropriate markers in the field of microbial research.