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.

Analysis of the Nasal Microbiota in Healthy and Diseased Pigs.

Massive sequencing of a fragment of 16S rRNA gene allows the characterization of bacterial communities in different body sites: the microbiota. Nasal microbiota can be analyzed by DNA extraction from nasal swabs, amplification of the specific fragment of interest, and posterior sequencing. The raw sequences obtained need to go through a computational process to check their quality and then assign the taxonomy. Here, we will describe the complete process from sampling to get the microbial diversity of nasal microbiota in health and disease.

Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers.

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and ß-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while ß analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology; however, additional studies are warranted to explore its functional significance.

Climate change and plant rhizosphere microbiomes: an experiential course-embedded research project.

The current and ongoing challenges brought on by climate change will require future scientists who have hands-on experience using advanced molecular techniques, can work with large data sets, and can make correlations between metadata and microbial diversity. A course-embedded research project can prepare students to answer complex research questions that might help plants adapt to climate change. The project described herein uses plants as a host to study the impact of climate change-induced drought on host-microbe interactions through next-generation DNA sequencing and analysis using a command-line program. Specifically, the project studies the impact of simulated drought on the rhizosphere microbiome of Fast Plants rapid cycling Brassica rapa using inexpensive greenhouse supplies and 16S rRNA V3/V4 Illumina sequencing. Data analysis is performed with the freely accessible Python-based microbiome bioinformatics platform QIIME 2.

Unveiling the microbial diversity of biofilms on titanium surfaces in full-scale water-cooling plants using metagenomics approach.

Microbial colonization on the titanium condenser material (TCM) used in the cooling system leads to biofouling and corrosion and influences the water supply. The primary investigation of the titanium condenser was infrequently studied on characterizing biofilm-forming bacterial communities. Different treatment methods like electropotential charge, ultrasonication, and copper coating of titanium condenser material may influence the microbial population over the surface of the titanium condensers. The present study aimed to catalog the primary colonizers and the effect of different treatment methods on the microbial community. CFU (1.7 × 109 CFU/mL) and ATP count (< 5000 × 10-7 relative luminescence units) showed a minimal microbial population in copper-coated surface biofilm as compared with the other treatments. Live and dead cell result also showed consistency with colony count. The biofilm sample on the copper-coated surface showed an increased dead cell count and decreased live cells. In the metagenomic approach, the microbiome coverage was 10.06 Mb in samples derived from copper-coated TCM than in other treated samples (electropotential charge-17.94 Mb; ultrasonication-20.01 Mb), including control (10.18 Mb). Firmicutes preponderate the communities in the biofilm samples, and Proteobacteria stand next in the population in all the treated condenser materials. At the genus level, Lactobacillaceae and Azospirillaceae dominated the biofilm community. The metagenome data suggested that the attached community is different from those biofilm samples based on the environment that influences the bacterial community. The outcome of the present study depicts that copper coating was effective against biofouling and corrosion resistance of titanium condenser material for designing long-term durability.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data.

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

Comparison of databases useful for the analysis of vaginal microbiota in Japanese women using next-generation sequencing data (QIIME 2 software).

AIMS: Approximately 10% of children are born prematurely, and bacterial vaginosis during pregnancy is associated with preterm delivery. Highly accurate species-level vaginal microflora analysis helps control bacteria-induced preterm birth. Therefore, we aimed to conduct a bioinformatic analysis of gene sequences using 16S databases and compare their efficacy in comprehensively identifying potentially pathogenic vaginal microbiota in Japanese women. METHODS AND RESULTS: The 16 s rRNA databases, Silva, Greengenes, and the basic local alignment search tool (BLAST) were compared to determine whether the classification quality could be improved using the V3-V4 region next-generation sequencing (NGS) sequences. It was found that NGS data were aligned using the BLAST database with the QIIME 2 platform, whose classification quality was higher than that of Silva, and the combined Silva and Greengenes databases based on the mutual complementarity of the two databases. CONCLUSIONS: The reference database selected during the bioinformatic processing influenced the recognized sequence percentage, taxonomic rankings, and accuracy. This study showed that the BLAST database was the best choice for NGS data analysis of Japanese women's vaginal microbiota.

Dietary Flaxseed and Flaxseed Oil Differentially Modulate Aspects of the Microbiota Gut-Brain Axis Following an Acute Lipopolysaccharide Challenge in Male C57Bl/6 Mice.

The microbiota gut-brain axis (mGBA) is an important contributor to mental health and neurological and mood disorders. Lipopolysaccharides (LPS) are endotoxins that are components of Gram-negative bacteria cell walls and have been widely shown to induce both systemic and neuro-inflammation. Flaxseed (Linum usitatissimum) is an oilseed rich in fibre, n3-poly-unsaturated fatty acid (alpha-linolenic acid (ALA)), and lignan, secoisolariciresinol diglucoside, which all can induce beneficial effects across varying aspects of the mGBA. The objective of this study was to determine the potential for dietary supplementation with flaxseed or flaxseed oil to attenuate LPS-induced inflammation through modulation of the mGBA. In this study, 72 5-week-old male C57Bl/6 mice were fed one of three isocaloric diets for 3 weeks: (1) AIN-93G basal diet (BD), (2) BD + 10% flaxseed (FS), or (3) BD + 4% FS oil (FO). Mice were then injected with LPS (1 mg/kg i.p) or saline (n = 12/group) and samples were collected 24 h post-injection. Dietary supplementation with FS, but not FO, partially attenuated LPS-induced systemic (serum TNF-α and IL-10) and neuro-inflammation (hippocampal and/or medial prefrontal cortex IL-10, TNF-α, IL-1ß mRNA expression), but had no effect on sickness and nest-building behaviours. FS-fed mice had enhanced fecal microbial diversity with increased relative abundance of beneficial microbial groups (i.e., Lachnospiraceae, Bifidobacterium, Coriobacteriaceae), reduced Akkermansia muciniphila, and increased production of short-chain fatty acids (SCFAs), which may play a role in its anti-inflammatory response. Overall, this study highlights the potential for flaxseed to attenuate LPS-induced inflammation, in part through modulation of the intestinal microbiota, an effect which may not be solely driven by its ALA-rich oil component.

Influence of Gallic Acid-Containing Mouth Spray on Dental Health and Oral Microbiota of Healthy Dogs: A Pilot Study.

The pilot study aimed to investigate the effects of GAMS on oral microbiota in healthy dog subjects. Thirty-eight dogs were recruited and randomly allocated to the placebo (n = 19) and treatment groups (n = 19). The dogs were treated with mouth spray once daily for 42 days. The changes in the gingival index (GI), plaque index (PI), and calculus index (CI) were measured at baseline (day 0) and end of the study (42nd day). The changes in the oral microbial composition of representative dogs (placebo, n = 7; and treatment, n = 7) were also evaluated at baseline and end of the study. Oral microbial composition was assessed by sequencing. The sequences were annotated using the QIIME 2.0TM. The GI, PI, and CI indexes were reduced after the GAMS usage. The abundance of the commensal bacterial phylum Actinobacteria and Chloroflexi, genera Frederiksenia, and Bergeyella was improved after six weeks of GAMS usage. GAMS reduced the pathogenic bacterial species, including Neisseria sp., Desulfobulbus sp., Capnocytophaga canis, and Corynebacterium mustelae. Moreover, some pathogenic bacterial abundances were increased at the end of the study. All the microbial variations were observed within the group. The inter-group analysis revealed that the changes were unrelated to GAMS usage. Further studies need to be carried out using more experimental subjects to confirm the effectiveness of GAMS. More metagenomic data are required to evidence the GMAS impact on the oral microbiome of healthy dogs.

Identification of Gut Microbiome and Metabolites Associated with Acute Diarrhea in Cats.

Changes in diet and environment can lead to acute diarrhea in companion animals, but the composition and interactions of the gut microbiome during acute diarrhea remain unclear. In this multicenter case-control study, we investigated the relationship between intestinal flora and acute diarrhea in two breeds of cats. Acutely diarrheic American Shorthair (MD, n = 12) and British Shorthair (BD, n = 12) and healthy American Shorthair (MH, n = 12) and British Shorthair (BH, n = 12) cats were recruited. Gut microbial 16S rRNA sequencing, metagenomic sequencing, and untargeted metabolomic analysis were performed. We observed significant differences in beta-diversity (Adonis, P < 0.05) across breeds and disease state cohorts. Profound differences in gut microbial structure and function were found between the two cat breeds. In comparison to healthy British Shorthair cats, Prevotella, Providencia, and Sutterella were enriched while Blautia, Peptoclostridium, and Tyzzerella were reduced in American Shorthair cats. In the case-control cohort, cats with acute diarrhea exhibited an increased abundance of Bacteroidota, Prevotella, and Prevotella copri and a decreased abundance of Bacilli, Erysipelotrichales, and Erysipelatoclostridiaceae (both MD and BD cats, P < 0.05). Metabolomic analysis identified significant changes in the BD intestine, affecting 45 metabolic pathways. Moreover, using a random forest classifier, we successfully predicted the occurrence of acute diarrhea with an area under the curve of 0.95. Our findings indicate a distinct gut microbiome profile that is associated with the presence of acute diarrhea in cats. However, further investigations using larger cohorts of cats with diverse conditions are required to validate and extend these findings. IMPORTANCE Acute diarrhea is common in cats, and our understanding of the gut microbiome variations across breeds and disease states remains unclear. We investigated the gut microbiome of two cat breeds (British Shorthair and American Shorthair) with acute diarrhea. Our study revealed significant effects of breeds and disease states on the structure and function of the gut microbiota in cats. These findings emphasize the need to consider breed-related factors in animal nutrition and research models. Additionally, we observed an altered gut metabolome in cats with acute diarrhea, closely linked to changes in bacterial genera. We identified a panel of microbial biomarkers with high diagnostic accuracy for feline acute diarrhea. These findings provide novel insights into the diagnosis, classification, and treatment of feline gastrointestinal diseases.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Body Metrics and the Gut Microbiome in Response to Macronutrient Limitation in the Zebrafish Danio rerio.

Background: Healthy and predictable physiologic homeostasis is paramount in animal models for biomedical research. Proper macronutrient intake is an essential and controllable environmental factor for maintaining animal health and promoting experimental reproducibility. Objective and Methods: Evaluate reductions in dietary macronutrient composition on body weight metrics, composition, and gut microbiome in Danio rerio. Methods: D. rerio were fed reference diets deficient in either protein or lipid content for 14 weeks. Results: Diets of reduced-protein or reduced-fat resulted in lower weight gain than the standard reference diet in male and female D. rerio. Females fed the reduced-protein diet had increased total body lipid, suggesting increased adiposity compared with females fed the standard reference diet. In contrast, females fed the reduced-fat diet had decreased total body lipid compared with females fed the standard reference diet. The microbial community in male and female D. rerio fed the standard reference diet displayed high abundances of Aeromonas, Rhodobacteraceae, and Vibrio. In contrast, Vibrio spp. were dominant in male and female D. rerio fed a reduced-protein diet, whereas Pseudomonas displayed heightened abundance when fed the reduced-fat diet. Predicted functional metagenomics of microbial communities (PICRUSt2) revealed a 3- to 4-fold increase in the KEGG (Kyoto Encyclopedia of Genes and Genomes) functional category of steroid hormone biosynthesis in both male and female D. rerio fed a reduced-protein diet. In contrast, an upregulation of secondary bile acid biosynthesis and synthesis and degradation of ketone bodies was concomitant with a downregulation in steroid hormone biosynthesis in females fed a reduced-fat diet. Conclusions: These study outcomes provide insight into future investigations to understand nutrient requirements to optimize growth, reproductive, and health demographics to microbial populations and metabolism in the D. rerio gut ecosystem. These evaluations are critical in understanding the maintenance of steady-state physiologic and metabolic homeostasis in D. rerio. Curr Dev Nutr 20xx;x:xx.

A Practical Guide to 16S rRNA Microbiome Analysis in Musculoskeletal Disorders.

Microbial taxonomic assignment based on 16S marker gene amplification requires multiple data transformations, often encompassing the use of a variety of computational platforms. Bioinformatics analysis may represent a bottleneck for researchers as many tools require programmatic access in order to implement the software. Here we describe a step-by-step approach for taxonomic assignment using QIIME2 and highlight the utility of graphical-based microbiome tools for further analysis and identification of biological relevant taxa with reference to an outcome of interest.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis.

BACKGROUND: The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles. RESULTS: Of the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%). CONCLUSIONS: Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

High-throughput amplicon sequencing datasets of coastal sediments from three locations of the Gulf of Mexico, USA.

We present high-throughput amplicon sequence (HTS) datasets of the purified microbial metacommunity DNA of coastal surface sediments from Portersville Bay (PVB) (n = 3), Bayou La Batre (BLB) (n = 3), and Mobile Bay (MOB) (n = 3) of the U.S. Gulf of Mexico (U.S. Gulf Coast). The PVB samples were collected from the oyster aquaculture Shellevator™ system; the BLB samples were from locations on the shoreline adjacent to wild oysters attached to rocks and likely polluted from sewage and possibly chemical contamination from boats, shipyards, and seafood processing facilities; and MOB samples were adjacent to aquaculture oysters in bottom cages. The amplicons of the V4 hypervariable segment of the 16S rRNA gene from each sample were sequenced on an Illumina MiSeq to generate these HTS datasets. The raw sequences were quality-checked, demultiplexed into FASTQ files, denoised using DADA2, and subsampled. Then, the FASTA formatted sequences were assigned the taxonomic ids to amplicon sequence variants (ASVs) against the silva-138-99-nb-classifier using the Quantitative Insights Into Microbial Ecology (QIIME2 v2022.2). The applicability of the HTS datasets was confirmed by microbial taxa analysis at the phylum level using the "qiime taxa collapse" command. All HTS datasets are available through the BioSample Submission Portal under the BioProject ID PRJNA876773 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA876773).

Variations in gut microbiome and metabolites of dogs with acute diarrhea in poodles and Labrador retrievers.

For different breeds of dogs with acute diarrhea, the gut microbiota and metabolome profiles are unclear. This prospective observational study analyzed the gut microbiomes of poodles with acute diarrhea and Labrador retrievers with acute diarrhea based on 16S amplicon sequencing, with respective healthy dogs as controls. Fecal non-target metabolomics and metagenomics were performed on poodles with acute diarrhea. This study found that the diversity and structure of the microbial community differed significantly between the two breeds in cohorts of healthy dogs. Two breeds of dogs with acute diarrhea demonstrated different changes in microbial communities and metabolic functions. The metabolism of starch and sucrose was significantly decreased in dogs with acute diarrhea, which may be attributed to the reduced activity of dextran dextrinase. Non-targeted metabolomics identified 21 abnormal metabolic pathways exhibited by dogs with acute diarrhea, including starch, amino acid, bile acid metabolism, etc., and were closely related to specific intestinal flora. This study provided new insights into breed specificity and the development of dietary treatment strategy in canine gastrointestinal disease.

Bioinformatic Approaches for Describing the Oral Microbiota.

Cultivation-independent (molecular) analysis of the oral microbiota can provide a comprehensive picture of microbial community composition, yet there is an at-times bewildering array of approaches that can be employed. This chapter introduces some of the key considerations when undertaking microbiota research and describes two alternative bioinformatic pipelines for conducting such studies. The descriptions are based on analysis of bacterial 16S ribosomal RNA gene sequences, but can be easily adapted for analysis of other microbial taxa such as fungi.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice.

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2.

Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.