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.

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.

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.

Influence of Agaricus bisporus establishment and fungicidal treatments on casing soil metataxonomy during mushroom cultivation.

The cultivation of edible mushroom is an emerging sector with a potential yet to be discovered. Unlike plants, it is a less developed agriculture where many studies are lacking to optimize the cultivation. In this work we have employed high-throughput techniques by next generation sequencing to screen the microbial structure of casing soil employed in mushroom cultivation (Agaricus bisporus) while sequencing V3-V4 of the 16S rRNA gene for bacteria and the ITS2 region of rRNA for. In addition, the microbiota dynamics and evolution (bacterial and fungal communities) in peat-based casing along the process of incubation of A. bisporus have been studied, while comparing the effect of fungicide treatment (chlorothalonil and metrafenone). Statistically significant changes in populations of bacteria and fungi were observed. Microbial composition differed significantly based on incubation day, changing radically from the original communities in the raw material to a specific microbial composition driven by the A. bisporus mycelium growth. Chlorothalonil treatment seems to delay casing colonization by A. bisporus. Proteobacteria and Bacteroidota appeared as the most dominant bacterial phyla. We observed a great change in the structure of the bacteria populations between day 0 and the following days. Fungi populations changed more gradually, with A. bisporus displacing the rest of the species as the cultivation cycle progresses. A better understanding of the microbial communities in the casing will hopefully allow us to increase the biological efficiency of the crop.

EzMAP: Easy Microbiome Analysis Platform.

BACKGROUND: The rapid advances in next-generation sequencing technologies have revolutionized the microbiome research by greatly increasing our ability to understand diversity of microbes in a given sample. Over the past decade, several computational pipelines have been developed to efficiently process and annotate these microbiome data. However, most of these pipelines require an implementation of additional tools for downstream analyses as well as advanced programming skills. RESULTS: Here we introduce a user-friendly microbiome analysis platform, EzMAP (Easy Microbiome Analysis Platform), which was developed using Java Swings, Java Script and R programming language. EzMAP is a standalone package providing graphical user interface, enabling easy access to all the functionalities of QIIME2 (Quantitative Insights Into Microbial Ecology) as well as streamlined downstream analyses using QIIME2 output as input. This platform is designed to give users the detailed reports and the intermediate output files that are generated progressively. The users are allowed to download the features/OTU table (.biom;.tsv;.xls), representative sequences (.fasta) and phylogenetic tree (.nwk), taxonomy assignment file (optional). For downstream analyses, users are allowed to perform relative abundances (at all taxonomical levels), community comparison (alpha and beta diversity, core microbiome), differential abundances (DESeq2 and linear discriminant analysis) and functional prediction (PICRust, Tax4Fun and FunGuilds). Our case study using a published rice microbiome dataset demonstrates intuitive user interface and great accessibility of the EzMAP. CONCLUSIONS: This EzMAP allows users to consolidate the microbiome analysis processes from raw sequence processing to downstream analyses specific for individual projects. We believe that this will be an invaluable tool for the beginners in their microbiome data analysis. This platform is freely available at https://github.com/gnanibioinfo/EzMAP and will be continually updated for adoption of changes in methods and approaches.

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities.

BACKGROUND: Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases. RESULTS: The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes. CONCLUSIONS: Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

Potential microbial bioindicators of phosphorus mining in a temperate deciduous forest.

AIMS: The soil microbial community plays a critical role in increasing phosphorus (P) availability in low-P, weathered soils by "mining" recalcitrant organic P through the production of phosphatase enzymes. However, there is a lack of data on the fungal and bacterial taxa which are directly involved in P mining, which could also serve as potential microbial bioindicators of low P availability. METHODS AND RESULTS: Leveraging a 5-year P enrichment experiment on low-P forest soils, high-throughput sequencing was used to profile the microbial community to determine which taxa associate closely with P availability. We hypothesized that there would be a specialized group of soil micro-organisms that could access recalcitrant P and whose presence could serve as a bioindicator of P mining. Community profiling revealed several candidate bioindicators of P mining (Russulales, Acidobacteria Subgroup 2, Acidobacteriales, Obscuribacterales and Solibacterales), whose relative abundance declined with elevated P and had a significant, positive association with phosphatase production. In addition, we identified candidate bioindicators of high P availability (Mytilinidales, Sebacinales, Chitinophagales, Cytophagales, Saccharimonadales, Opitulales and Gemmatales). CONCLUSIONS: This research provides evidence that mitigating P limitation in this ecosystem may be a specialized trait and is mediated by a few microbial taxa. SIGNIFICANCE AND IMPACT OF THE STUDY: Here, we characterize Orders of soil microbes associated with manipulated phosphorus availability in forest soils to determine bioindicator candidates for phosphorus. Likewise, we provide evidence that the microbial trait to utilize recalcitrant organic forms of P (e.g. P mining) is likely a specialized trait and not common to all members of the soil microbial community. This work further elucidates the role that a complex microbial community plays in the cycling of P in low-P soils, and provides evidence for future studies on microbial linkages to human-induced ecosystem changes.

Fish gill microbiome from India's largest Brahmaputra River-a trans-border biodiversity hotspot region.

In this study, we sequenced the V3-V4 region of 16S rRNA gene amplicon using paired-end Illumina HiSeq to study the bacterial community in the gills of fish from the bank of the trans-border river of Brahmaputra, Northeast India. Metagenome data consisted of 278,784 reads, 248-bp length, and 56.48% GC content with 85% sequence having a Phred score Q = 30. Community metagenomics revealed a total of 631 genera belonging to 22 different phyla, dominated by Proteobacteria (118,222 features), Firmicutes (101,043 features), Actinobacteria (34,189 features), Bacteroidetes (17,977 features), and Cyanobacteria (2730 features). The bacterial community identified was composed of both pathogenic zoonotic and non-harmful groups. The pathway or functional analysis of the fish gill microbiome exhibited 21 different pathways which also included the pathogenic-related functions. Our data detected a wide group of bacterial communities that will be useful in further isolating and characterizing the pathogenic bacteria from the fish and also to understand the bacterial association in highly consumed fish.

Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota.

BACKGROUND: In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear. RESULTS: Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition. CONCLUSION: Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.

Exploring the eukaryotic diversity in rumen of Indian camel (Camelus dromedarius) using 18S rRNA amplicon sequencing.

In addition to a wide variety of anaerobic and facultative anaerobic bacteria, camel rumen also harbors a diverse of eukaryotic organisms. In the present study, the eukaryotic communities of camel rumen were characterized using 18S rRNA amplicon sequencing. Metagenomic DNA was isolated from rumen samples of fourteen adult Bikaneri and Kachchhi breeds of camel fed different diets containing Jowar, Bajra, Maize, and Guar. Illumina sequencing generated 27,161,904 number of reads corresponding to 1543 total operational taxonomic units (OTUs). Taxonomic classification of community metagenome sequences from all the samples revealed the presence of 92 genera belonging to 16 different divisions, out of which Ciliophora (73%), Fungi (13%) and Streptophyta (9%) were found to be the most dominant. Notably, the abundance of Ciliophora was significantly higher in the case of Guar feed, while Fungi was significantly higher in the case of Maize feed, indicating the influence of cellulose and hemicellulose content of feedstuff on the composition of eukaryotes. The results suggest that the camel rumen eukaryotes are highly dynamic and depend on the type of diet given to the animal. Pearson's correlation analysis suggested the ciliate protozoa and fungi were negatively correlated with each other. To the best of our knowledge, this is first systematic study to characterize camel rumen eukaryotes, which has provided newer information regarding eukaryotic diversity patterns amongst camel fed on different diets.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.