Do we need a standardized 16S rRNA gene amplicon sequencing analysis protocol for poultry microbiota research?

Abstract

Bacteria are the major component of poultry gastrointestinal tract (GIT) microbiota and play an important role in host health, nutrition, physiology regulation, intestinal development, and growth. Bacterial community profiling based on the 16S ribosomal RNA (rRNA) gene amplicon sequencing approach has become the most popular method to determine the taxonomic composition and diversity of the poultry microbiota. The 16S rRNA gene profiling involves numerous steps, including sample collection and storage, DNA isolation, 16S rRNA gene primer selection, Polymerase Chain Reaction (PCR), library preparation, sequencing, raw sequencing reads processing, taxonomic classification, α- and β-diversity calculations, and statistical analysis. However, there is currently no standardized protocol for 16S rRNA gene analysis profiling and data deposition for poultry microbiota studies. Variations in DNA storage and isolation, primer design, and library preparation are known to introduce biases, affecting community structure and microbial population analysis leading to over- or under-representation of individual bacteria within communities. Additionally, different sequencing platforms, bioinformatics pipeline, and taxonomic database selection can affect classification and determination of the microbial taxa. Moreover, detailed experimental design and DNA processing and sequencing methods are often inadequately reported in poultry 16S rRNA gene sequencing studies. Consequently, poultry microbiota results are often difficult to reproduce and compare across studies. This manuscript reviews current practices in profiling poultry microbiota using 16S rRNA gene amplicon sequencing and proposes the development of guidelines for protocol for 16S rRNA gene sequencing that spans from sample collection through data deposition to achieve more reliable data comparisons across studies and allow for comparisons and/or interpretations of poultry studies conducted worldwide.

Keywords: 16S rRNA gene sequencing; Microbiota; Next-generation sequencing; Poultry; Standardized protocol.

Fig. 1

Number of publications related to microbiome studies using 16S rRNA amplicon sequencing (16S), metagenomics, metatranscriptomics, metaproteomics, and metabolomics approaches from 2011 to 2014. Source: PubMed.

Fig. 2

Overview of the 16S rRNA amplicon sequencing protocol. Created with BioRender.com.

Fig. 3

Anatomical and physiological factors affecting sample collection for microbiome analysis in poultry. The chicken GIT contains several anatomical and physiological aspects that inform sample collection in the experimental design of a poultry microbiome study. (A) Bacterial microbial density increases longitudinally along the GIT from oral cavity through cloaca. (B) Each GIT region can be grossly divided into proximal, middle, and distal sections that provide sub-section physiological differences and harbor different density of microbial communities. (C) Bacterial microbial density changes along the transverse aspect of the gut, with lowest microbial population within the mucosa to most dense in the luminal content. (D) Regions with gut associated lymphoid tissues, such as the cecal tonsils or Peyer’s patches may harbor unique microbial populations that are shaped by the close proximity to immune cells. Created with BioRender.com.

Fig. 4

Motility patterns of chicken GIT tract can influence sample collection for poultry microbiota research. (A) Physiological peristaltic movements propel the intestinal content from the oral (crop) to anal side (cloaca) of the GIT tract. Luminal content varies, depending on region, viscosity and consistency, and microbial density. (B) Reverse peristalsis can move luminal contents backwards from cloaca to crop, potentially redistributing microbial populations in the chicken GIT tract. Created with BioRender.com.

Fig. 5

Sample storage techniques of chicken GIT tract samples for downstream microbiome analyses. The ‘gold standard’ for sample (luminal content, mucosal scraping or cloacal swabs) storage is snap freezing (in liquid nitrogen or dry ice) followed by storage at -80°C. When snap freezing is not available immediately at the farm, samples for microbiome analysis can be placed in a preservative solution and kept at room temperature and then stored at -80°C when the farm work is completed, and the researcher is back at the laboratory. A sample that is stored in the freezer must be thawed before DNA extraction and sequencing. The appropriate procedure to thaw the samples’ microbiome analysis is a consideration that requires best practice testing. Created with BioRender.com.

Fig. 6

Positive and negative controls during the experimental workflow of microbiome analysis in poultry. The experimental workflow of microbiome analysis in chicken intestinal samples can be summarized in five steps and each step requires positive and negative controls. 1) Sample collection. During sample collection at the farm, scientists can include or “spike-in” a mock microbial community as positive control into the collected primary sample. An empty collection vessel (e.g. cotton swabs or tubes) should be opened in the same environment in which the primary samples are collected and represent the negative control of this step. Dust in the farm, litter, feathers and insect can contaminate the primary sample as they might contain the same bacterial taxa found in the chicken ileal or cecal lumen. 2) DNA extraction. DNA extraction must be performed on a mock microbial community as a positive control and on molecular grade water as negative control. 3) Library preparation and 4) DNA sequencing. The library preparation and sequencing must be performed on a mock DNA standard as a positive control for determining the presence of any bias from a specific protocol step (i.e., amplification). Molecular grade water can be included as negative control or “library blank” to assess if reagents used in library preparation are a source of contamination. 5) Bioinformatics. The correctness of bioinformatics interpretation of sequence reads can be assessed by comparing the taxonomic composition of the mock microbial community obtained from the data analysis with that reported by the manufacturer (positive control). Unknown reads identified in negative control samples (molecular grade water) can be subtracted if they also found in the actual sample. Created with BioRender.com.

Fig. 7

Data integration methods. Microbiota sequencing data can be integrated with host data including genomics, transcriptomics, proteomics, metabolomics, feed composition, immune responses, organ function and environmental conditions, providing a “big picture” for host-microbial interactions that underlie physiological and pathological conditions in poultry. Created with BioRender.com.