5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Exploration and comparison of bacterial communities present in bovine faeces, milk and blood using 16S rRNA metagenomic sequencing

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Cattle by-products like faeces, milk and blood have many uses among rural communities; aiding to facilitate everyday household activities and occasional rituals. Ecologically, the body sites from which they are derived consist of distinct microbial communities forming a complex ecosystem of niches. We aimed to explore and compare the faecal, milk and blood microbiota of cows through 16S rRNA sequencing. All downstream analyses were performed using applications in R Studio (v3.6.1). Alpha-diversity metrics showed significant differences between faeces and blood; faeces and milk; but non-significant between blood and milk using Kruskal-Wallis test, P < 0,05. The beta-diversity metrics on Principal Coordinate Analysis and Non-Metric Dimensional Scaling significantly clustered samples by type (PERMANOVA test, P < 0,05). The overall analysis revealed a total of 30 phyla, 74 classes, 156 orders, 243 families and 408 genera. Firmicutes, Bacteroidota and Proteobacteria were the most abundant phyla overall. A total of 58 genus-level taxa occurred concurrently between the body sites. The important taxa could be categorized into four potentially pathogenic clusters i. e. arthropod-borne; food-borne and zoonotic; mastitogenic; and metritic and abortigenic. A number of taxa were significantly differentially abundant (DA) between sites based on the Wald test implemented in DESeq2 package. Majority of the DA taxa ( i. e. Romboutsia, Paeniclostridium, Monoglobus, Akkermansia, Turicibacter, Bacteroides, Candidatus_Saccharimonas, UCG-005 and Prevotellaceae_UCG-004) were significantly enriched in faeces in comparison to milk and blood, except for Anaplasma which was greatly enriched in blood and was in turn the largest microbial genus in the entire analysis. This study provides insights into the microbial community composition of the sampled body sites and its extent of overlapping. It further highlights the potential risk of disease occurrence and transmission between the animals and the community of Waaihoek in KwaZulu-Natal, Republic of South Africa pertaining to their unsanitary practices associated with the use of cattle by-products.

          Related collections

          Most cited references80

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: Data curation
                Role: ConceptualizationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 August 2022
                2022
                : 17
                : 8
                : e0273799
                Affiliations
                [1 ] Biomedical Sciences Department, Tshwane University of Technology, Pretoria, South Africa
                [2 ] Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa
                [3 ] Faculty of Veterinary Science, Department of Veterinary Tropical Diseases, University of Pretoria, Onderstepoort, South Africa
                [4 ] Study Management, ClinVet International, Bainsvlei, Bloemfontein, South Africa
                [5 ] Sequencing Core Facility, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa
                [6 ] Faculty of Science, Department of Biochemistry and Microbiology, Engineering and Agriculture, University of Venda, Thohoyandou, South Africa
                University of Illinois College of Medicine, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-3493-4066
                https://orcid.org/0000-0002-7923-5077
                https://orcid.org/0000-0003-4672-5915
                Article
                PONE-D-21-40353
                10.1371/journal.pone.0273799
                9432762
                36044481
                fb01e508-1a18-4709-8dce-2d358010484b
                © 2022 Mtshali et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 December 2021
                : 15 August 2022
                Page count
                Figures: 8, Tables: 1, Pages: 29
                Funding
                Funded by: National Research Foundation (RSA)
                Award ID: 107416
                Award Recipient :
                K received funding from the National Research Foundation – Thuthuka PhD Track Funding Instrument in collaboration with the Tshwane University of Technology, unique grant No. 107416. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Nutrition
                Diet
                Beverages
                Milk
                Medicine and Health Sciences
                Nutrition
                Diet
                Beverages
                Milk
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Milk
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Milk
                Biology and Life Sciences
                Physiology
                Body Fluids
                Milk
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Blood
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Blood
                Biology and Life Sciences
                Physiology
                Body Fluids
                Blood
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Anaplasma
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Anaplasma
                Biology and Life Sciences
                Organisms
                Bacteria
                Rickettsiales
                Anaplasma
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Ribosomal RNA
                Biology and life sciences
                Biochemistry
                Ribosomes
                Ribosomal RNA
                Biology and life sciences
                Cell biology
                Cellular structures and organelles
                Ribosomes
                Ribosomal RNA
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Bacteroides
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Custom metadata
                All FastQ sequence files generated from this work are available from the National Center for Biotechnology Information's Short Reads Archive, under BioProject number PRJNA777568, Accession numbers SRI168760 -SRI168784. All relevant data are within the manuscript and its Supporting Information files.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article