Microorganisms can be found across all environments on Earth and are critical to Earth’s systems and cycles. Microbiome research encompasses many areas including: air, soil and aquatic microbial ecology, bioremediation, microbiome of the built environment, geomicrobiology, agriculture, and the human microbiome.

The human microbiome is all of of microbes including bacteria, fungi, protozoa and viruses that live on and inside the human body.  The microbiome contains more genes than the human genome. The gut microbiome is an important component of human health, aiding digestion and regulating the immune system.

The environmental microbiome has a significant impact on human health. The microbial composition of the built environment has been linked to the development of disease. Understanding how factors like humidity, pH, chemical exposures, temperature, filtration, surface materials, and air flow can impact microbes and microbial communities are critical to engineering an environment optimal for human health [1].

The human microbiome is essential for maintaining health. Getting a better understanding of the microbial makeup of a healthy person is as important as identifying microbiome phenomenon that enable disease. There are diverse, complex communities of microbes in our bodies inhabiting many organs ranging from the gastrointestinal tract and the mouth to our skin and reproductive organs  that are interacting with cells in the body helping them to absorb nutrients and modulating various immunological functions. Studying the microbiome can help us find new ways to keep the microbiome healthy, repair it and leverage it to prevent and treat disease.

• Cross-sectional study: cross sectional studies are used to find differences in microbiome composition between different populations, such as healthy individuals and those with a disease, or comparing people from different geographic regions.cross sectional studies are useful for finding differences in microbial communities between different human populations, such as healthy individuals and those with diseases, or individuals living in different geographic regions.  

• Longitudinal study: Longitudinal studies take measurements from the same individuals over time. Longitudinal studies that collect baseline samples before disease onset, can help establish causality. It is important to collect data on all possible confounding variables to control for their effects. Otherwise, if not controlled for, confounding variables can obscure patterns in microbiome data. For examples studies that reported changes in the microbiome of people with diabetes were confounded by the effects of the drug metformin [2].

• Interventional study: Interventional studies involve measuring the effect of a treatment or other intervention on the microbiomes of the subjects., Common interventions include the use of drugs or a probiotic. A double-blind randomized control study is an example of an interventional study that allows the identification of how a treatment effects the microbiome and disease state.   

• Environmental study: Environmental studies are concerned with the analysis of environmental microbes that are native to various environments. Those environments can range from lakes and permafrost to deserts and other extreme sites such as sulfur springs or fire gas craters. An environmental study of a particular group of microbes that thrive in the environment can give insights into their interaction with both the biotic and abiotic components of their environment. 

There are currently two methods to perform power calculations for 16S marker sequencing studies:

● PERMANOVA:[1]  A permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances [3].

○ Advantages:

● Powerful in hypothesis testing

● Accurate in quantifying effect size

● Multiple factors can be addressed at once

● Also ideal for investigating interaction terms

○   Disadvantages:

● Requires precise sampling design with multiple replications

● Relies on assumptions of normality and common variances (in that distribution of means across samples is normal)

● Dirichlet multinomial: Parametric model based on Dirichlet multinomial distribution [4].

○ Advantages:

● Ideal when communities are diverse with different sizes and the matrix is sparse

● Identifies envirotypes or enterotypes, groups of communities with a similar composition

○ Disadvantages:

● All variables must share a common variance parameter

● All variables are mutually independent, up to the constraint that they must sum up to one

● Does not take into account that microbial taxa are related evolutionarily [5, 6]

• Cage effects: The microbiomes of co-housed animal models tend to become more similar. In studies using rodents and zebrafish the strongest association is often which animals are co-housed together [8, 9]. Due to this experiments must be replicated across cages.  
• Parental effects: Early life exposure greatly influences the development of an individual's microbiome and immune system [10]. Due to this littermates should be randomized between cages and experimental conditions.  
• Environmental factors: Diet, litter, vendor, shipment facility of origin and early life exposures have all been shown to influence the microbiome and should be considered when planning an experiment [11, 12].

Although it is difficult to account for all bias in your microbiome experiment adhering to the following recommendations can help you mitigate technical variation and subsequently increase your chances of generating robust results:  

• Use the same reagents during sample preparation (molecular biology grade water,  PCR reagents). This minimizes the confounding effects of introducing foreign microbial DNA
• Sequencing controls can help identifying contaminants
• The wider laboratory environment can also introduce contaminations [13]     
• Try to \maximize the starting sample biomass
• Take steps to minimize the risk of contamination during sample collection     
• Consider treating PCR and extraction kit reagents to reduce contaminant DNA
• Technical controls should be processed concurrently with your environmental samples
• Randomization of your samples during preparation  can avoid creating false patterns
• Using different kits/reagents for replicates helps identify contaminations  
• The use of mock communities (reference samples with a known composition) can be useful for standardizing analyses and identification of contaminants [14, 15]
• Negative controls should be used to monitor contamination as it arises.
• Longitudinal studies should include multiple baseline samples to assess intrinsic variability  
• Use blanks during sampling, DNA extraction, PCR, and sequencing to detect contamination  
• Metadata, appropriate controls, including extraction and reagent blanks should be carefully curated  
• Apply a robust study design that isolates and interrogates variables of interest
• For marker gene sequencing, in order to minimize Chimera formation it  has been proposed to minimize the amount of template DNA in the PCR, minimize the number of rounds of PCR, minimize the amount of shearing in the template DNA and to use DNA polymerases that have proofreading ability

For more further recommendations on technical variations please review:

1. Salter, Cox et al. 2014
2. Knight, R., Vrbanac, A., Taylor, B.C. et al. Best practices for analysing microbiomes. Nat Rev Microbiol 16, 410–422 (2018). https://doi.org/10.1038/s41579-018-0029-9
3. The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data Marc A. Sze, Patrick D. Schloss mSphere May 2019, 4 (3) e00163-19; DOI: 10.1128/mSphere.00163-19  

High throughput microbiome approaches

Marker gene analysis: Microbial phylogenies of a sample are determined by using primers that target specific regions of a gene of interest. This method provides only indirect evidence of the genetic content of the samples microbiome. Marker gene sequencing methods are susceptible to PCR biases such as limited primer coverage, which can result in failure to amplify some taxa or differential amplification of templates, altering the relative abundance of species distorting results.  

A cost­ effective method for obtaining a low­ resolution view of a microbial community.  

More comprehensive review under: The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data Marc A. Sze, Patrick D. Schloss mSphere May 2019, 4 (3) e00163-19; DOI: 10.1128/mSphere.00163-19

Metagenomics: Comprehensively samples all genes in all organisms present in a given complex sample. All DNA is captured including viral and eukaryotic DNA. Library construction, assembly and reference databases for annotation can all be potential sources of bias. Relatively expensive preparation, sequencing and analysis of the samples but yields detailed genomic information at high taxonomic resolution.    

More comprehensive review under: Quince, C., Walker, A. W. & Simpson, J. T. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).

Metatranscriptomics: Large-scale metatranscriptomics approaches measure the active transcription of the microbiome. RNA-Sequencing is used to profile transcription giving insights into gene expression and the functional domains of the microbiome. This method is biased towards detecting transcripts from organisms with higher transcription rates.

Method for sequencing microbial RNA to provide better insights into the functional activity of a microbial community.  

More comprehensive review under:  Bashiardes, S., Zilberman- Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights. 10, 19–25 (2016).

Metaproteomics: Metaproteomics is a method for the identification and quantification of proteins from microbial communities. This allows more direct measurement of microbiome function.  

More comprehensive review under: Kleiner, M.

Metaproteomics: Much More than Measuring Gene Expression in Microbial Communities.mSystems. 4 (3) (2019).   Metabolomics: This method is the systematic study of metabolites within cells, biofluids, tissues or organisms. Small-molecule metabolite profiles can give insights into cellular processes.  

More comprehensive review under: Riekeberg, E., & Powers, R. (2017). New frontiers in metabolomics: from measurement to insight. F1000Research, 6, 1148. doi:10.12688/f1000research.11495.1

Metabolomics: This method is the systematic study of metabolites within cells, biofluids, tissues or organisms. Small-molecule metabolite profiles can give insights into cellular processes. 

More comprehensive review under: Riekeberg, E., & Powers, R. (2017). New frontiers in metabolomics: from measurement to insight. F1000Research, 6, 1148. doi: 10.12688/f1000research.11495.1

The Earth Microbiome Project (EMP) is a massive collaborative effort to characterize the microbial life of Earth. DNA sequencing and mass spectrometry technologies are applied to understand patterns in microbial ecology across the biomes and habitats of Earth. The EMP currently has the following protocols and guidelines:

Minimum Information about any (x) Sequence (MIxS)

Specifies what information needs to be collected for genomes, metagenomes and marker genes sequences. Provides templates for how to store this metadata.

EMP Ontology (EMPO)

The EMP Ontology (EMPO) uses existing ontologies to assign samples to habitats in hierarchical framework that captures the major axes of microbial community diversity. 

DNA extraction protocol

Currently protocol: This protocol is used for proper DNA extraction for streamlined metagenomics of diverse environmental samples

Low-biomass protocol: Protocol designed for low-biomass samples. It incorporates positive and negative controls as well as bioinformatic methods to identify differences in microbial communities with as few as 50 to 500 cells

Primer ordering and resuspension

Here you can find information on how to order primers for your polymerase chain reaction as well as additional information on best practice in handling and aliquoting your primers.

16S Illumina amplicon protocol

This protocol is designed for amplification of prokaryotes (bacteria and archaea) using paired-end 16S community sequencing on the Illumina platform. Primers 515F-806R target the V4 region of the 16S SSU rRNA

18S Illumina amplicon protocol

This protocol is designed to amplify eukaryotes broadly with a focus on microbial eukaryotic lineages. It is similar to the 16S protocol but uses different primers, PCR conditions and different sequencing primers. 

ITS Illumina Amplicon protocol

This protocol is designed to amplify fungal microbial eukaryotic lineages. Paired-end community sequencing is used on the Illumina platform with primers ITS1f-ITS2.

Shipping protocol

This protocol is for sending DNA, amplicons, and primers. Proper shipping is important to avoid damaging the samples. 

The current costs for sequencing services can be found at the USF Genomics website.

Justin Gibbons has a PhD from the University of South Florida Morsani College of Medicine. His dissertation research focused on using genomics approaches to understand drug resistance in the malaria parasite Plasmodium falciparum. Justin currently works as a postdoctoral scholar for the USF Genomics Program as a computational biology consultant in the Omics Hub. He is currently working on several projects ranging from drug resistance in malaria to how the microbiome effects the health of preterm infants.

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