Profiling true microbial community of newborn beef calves using low microbial biomass samples
Creators: Nakandalage Don, Ranga
Publisher: name: University of Alberta Library
Year: 2023

<p>Amplicon sequencing (16S rRNA gene sequencing) is widely used to profile host-associated microbial communities. Rapid advancements in sequencing and user-friendly bioinformatics platforms have improved the knowledge of microbial community composition. However, profiling samples containing low microbial biomass (biological samples containing limited microbial materials) using amplicon sequencing is challenging, as the presence of low levels of microbial genetic materials in samples leads to the generation of higher levels of artificial sequences during the sequencing process. The use of appropriate approaches to control contaminations during sample processing and sequencing, and the optimization of bioinformatics pipelines determine the accuracy of next-generation sequencing (NGS) based microbial profiling. QIIME2 is one of the most used bioinformatics pipelines that allows users to perform quality filtering, classification, community analysis, visualization, and statistical analysis through one open-source software package. Denoising in QIIME2 is one of the important plugins for quality filtering, which should be handled carefully to generate credible outcomes from microbial community profiling. The first study (Chapter 2) of this thesis aimed to optimize the denoising parameters to increase the accuracy of microbial community data analysis when using low-microbial biomass samples. This study used primers targeting the V1V3 region of the 16S rRNA gene to profile the fecal microbial communities of newborn beef calves sampled using swabs and data were analyzed using QIIME2 with customized quality filtering steps to remove environmental contaminations and to filter out low abundant amplicon sequencing variants (ASVs). Use of optimized (truncation: forward – 294; reverse – 241; median quality score - ≥25) denoising parameters increased the percentage of merged read (default – 1%; optimized – 45%), and the number of samples used for downstream analysis compared to default approach in QIIME 2, which is based on trimming reads based on mean quality score (truncation: forward – 281; reverse – 207; mean quality score - ≥25). Moreover, the optimization of denoising parameters increased microbial diversity and classified taxa. Our study confirmed that optimization of denoising parameters enhances the accuracy of outcomes and interpretation of host-associated microbial community compositions compared to default denoising parameters, especially when the biological sample contains low microbial biomass. Our findings revealed that the default settings of the bioinformatics tools might not be suitable for all microbial analyses. Customizing parameters in bioinformatics pipelines need to be considered to obtain credible outcomes in microbial community assessments. The second study (chapter 3) compared amplicon sequencing-based microbial profiles generated by different genetic materials (DNA vs. RNA) and hypervariable regions of the 16S rRNA gene (V1V3 vs. V3V4). Rectal and oral swabs (n=40) were collected from 20 newborn beef calves and used to extract DNA and RNA. Both DNA- and RNA-based amplicon sequencing were performed by targeting the V3V4 region of the 16S rRNA gene. In addition, only DNA-based sequencing was performed by targeting both V1V3 and V3V4 regions of the 16S rRNA gene. All sequence runs included no template controls (NTC) and positive controls (Clostridium butyricum). Data were analyzed using the QIIME2 platform as defined in chapter 2. Sequencing analysis revealed that sequences generated from NTC could be assigned to bacterial taxa irrespective of the genetic materials and target regions, suggesting that the amplicon sequencing process introduces contaminations. When comparing the impact of the target region, alpha diversity was higher (p&lt;0.05) in the fecal and oral bacterial profiles generated from the V1V3 region compared to those of the V3V4 region. Taxonomic assignment of bacterial profiles generated using two hypervariable regions revealed distinct bacterial communities. For example, Actinobacteria (fecal - 0.41±0.09%; oral - 0.51±0.10%) was abundant in bacterial profiles generated from the V1V3 region, whereas Firmicutes (fecal - 0.37±0.11%; oral - 0.34±0.10%) was abundant in those of V3V4 region when comparing D1 (prior to suckle colostrum-Day 1) samples. When comparing different genetic materials, DNA-based bacterial profiles (both oral and fecal) had a diverse microbial community compared to RNA-based profiles on D1. In contrast, the diversity of the RNA-based profiles was higher than DNA-based profiles on D2 (after suckling colostrum from cows). In conclusion, the diversity and composition of microbial communities derived from low microbial biomass samples depend on the choice of genetic materials and the hypervariable region of the 16S rRNA gene. The inclusion of appropriate controls is crucial to increase the accuracy of results, regardless of the sequencing technique.</p>