New publication from Mohn Lab shows diversity and significance of steroid-degrading bacteria is largely underestimated

Steroids in the environment accumulate from both natural and anthropogenic sources. Cholesterol, for example, is an essential part of cellular membranes and a natural source of steroids in the environment. Anthropogenic sources include steroid hormones associated with birth control pills. Regardless of where they originate, however, steroids have been found to accumulate in soil, wastewater treatment plants, and aquatic environments, where even at low concentrations they have negative impacts on animals—including humans. So far, only a few types of bacteria are known to degrade steroids in the environment and these species will play a big role in regulating steroidal pollution and its impacts.

To better understand the distribution and ecological significance of these steroid degraders, researchers from the Canadian universities of British Columbia and Waterloo, along with collaborators from Georgetown University in Washington, set out to apply a metagenomics approach to studying these bacteria. This approach uses DNA sequencing to find genes from the 9, 10-seco pathway responsible for steroid degradation in environmental samples and not the bacteria itself. The team then builds phylogenies to find the bacteria according to the phyla in which these genes occur.

The results of this paper supported earlier work showing that bacteria using the 9, 10-seco pathway belong to the Actinobacteria and Proteobacteria phyla. Members of both phyla coexist in wastewater, while species of Actinobacteria alone are found in soil and rhizospheres. While the complete set of genes used in this pathway were not assigned to any other phylum, evidence for steroid degradation ability was found for the first time in the alphaproteobacterial lineages Hyphomonadaceae, Rhizobiales, and Rhodobacteraceae, as well as the gammaproteobacterial lineages Spongiibacteraceae and Halieaceae. Actniobacterial degraders were found in the deep ocean samples while alpha- and gammaproteobacterial degraders were found in other marine samples, including sponges. Furthermore, the authors confirmed that the steroid-degrading bacteria from sponges, Spongiibacteraceae and Halieaceae, catabolize steroids.

The metagenomics approach is a useful one because many bacterial species cannot be cultured and identified directly. However, the techniques involved in DNA extraction and sequencing have inherent biases that cannot be avoided. It is therefore important to note that the absence of steroid degradation proteins from a sample does not definitely mean that the bacteria are not present. Despite this potential underestimation, this study is, according to researchers, “the first analysis of aerobic steroid degradation in diverse natural, engineered, and host-associated environments via bioinformatic analysis of an extensive metagenome data set.” Not only does this confirm the usefulness of the technique; it also demonstrates that the ecological significance and taxonomic and biochemical diversity of these bacteria have been largely underestimated.

Holert J, Cardenas E, Bergstrand LH, et al. Metagenomes reveal global distribution of bacterial steroid catabolism in natural, engineered, and host environments. MBio. 2018; 9: e02345-17.

New paper from Microbiome Insights co-founder on critical window for the gut microbiome in infants and the later occurrence of asthma

Among serious and chronic childhood diseases, asthma is the most prevalent. Currently there exists no cure for asthma—only treatments designed to help manage symptoms. Recently, a body of research attempting to unravel how this condition develops  in young children has emerged, so that prevention may one day eliminate or reduce the burden of this chronic condition.

Recent work identified the existence of a critical window during the early lives of both mice and children, during which gut microbial changes are associated with the development of asthma. This provided an avenue to explore the role of the gut microbiome during early childhood development and the onset of chronic diseases like asthma. Importantly though, we know the gut microbiome varies greatly among those raised in different geographic regions. Therefore, understanding how changes in gut microbiota related to asthma development differ globally may provide valuable insights into the mechanism of asthma development.

A new paper, led by Microbiome Insights co-founder Brett Finlay and published in The Journal of Allergy and Clinical Immunology, evaluated the associations of fungal and bacterial changes (dysbiosis) in infants raised in the non-industrialized setting of rural Ecuador. The research was conducted as a collaboration between members of the Universities of British Colombia and Calgary, the BC Children’s Hospital, and Universidad Internacional del Ecuador. Children with atopic wheeze (27 in total) along with 70 healthy controls were identified and their bacterial and eukaryotic gut microbiota analysed at age 3 months. Stool samples were collected and sequencing of the 16S and 18S regions predicted bacterial metagenomes while fecal short chain fatty acids were determined via gas chromatography.

Results indicated that, similar to the previous findings in Canadian children, microbial dysbiosis in Ecuadorian infants at 3 months was associated with the subsequent development of atopic wheeze. Surprisingly though, the dysbiosis observed in Ecuador involved different bacteria taxa as well as some fungal species, and this was more pronounced than in Canada. Some predictions based on the metagenome analysis also emphasized significant dysbiosis-associated differences in genes involved in carbohydrate and taurine metabolism. The fecal short-chain fatty acid acetate was reduced while caproate was increased in children at 3 months who later developed atopic wheeze.

This work continues to provide evidence that there is a critical window during the first 100 days of life during which microbial dysbiosis is strongly associated with development of atopic wheeze. The study also yielded several valuable pieces of information. Despite the involvement of different bacteria taxa, both the Canadian and Ecuadorian populations had decreased fecal acetate, suggesting alterations to fermentation patterns may be a common factor associated with atopic wheeze. Furthermore, the pronounced role of fungal dysbiosis in this study led researchers to recommend that “the role of P. kudriavzevii and other yeasts should be explored in mechanistic studies using animal models.”

Along with more studies characterizing the early microbiome in more communities around the world, optimized biomarker studies of microbial taxa and metabolites could lead to better predictions of risk and therapeutic strategies to restore gut microbial health as a prevention method.


How to Design a Skin Microbiome Study, Part II: Amplicon Sequencing

In this post, the second of a 2-part series on skin microbiome research, we will discuss technical issues surrounding sequencing of human skin microbes. Read the first blog post here.

At this point, the microbial ecologist conducting a skin microbiome study has now collected all the skin samples she needs, and the DNA has been extracted. We turn to the question of how to decide on the sequencing strategy. Metagenome shotgun sequencing, in which the entire community of microbes is sequenced in an untargeted manner, can provide invaluable information about the functional potential of the microbiome, but – despite continually dropping sequencing costs – it is still expensive. The researcher in this case settles for 16S marker gene sequencing, which targets a specific region of the gene. Now, which primer pair should she choose?

The current dogma in the field is that primers targeting regions V1-V3 are better at describing skin bacterial communities than the V4 region primer pair. (The V4 region is commonly used for studying gut communities and other environments.) This is because V1-V3-sequenced communities better recapitulate the taxonomic composition and relative abundance of “mock community” controls (Meisel et al., 2016). And V4 primers poorly amplify typical skin microbes, notably Propionibacterium and some Staphylococcus species (Meisel et al., 2016). But should V4 be discarded in favor of V1-V3?

The reason behind the V4 region’s underestimation of Propionibacterium is a single mismatch at the end of the primer that prevents efficient binding to a specific group of bacteria. To evaluate if V4 region may be a suitable target for characterizing skin bacteria, our team re-designed the V4 primer pair and tested in silico its ability to improve the coverage of underrepresented propionibacteria. With these new candidate primers, we are able (theoretically) to increase the coverage of Propionibacterium to over 67%–from less than 3%–without losing coverage of the other bacterial groups. Our next step is to evaluate the accuracy of this approach using a mock community as the standard.

There are advantages to using existing V4 primers. They can detect the genera Finegoldia and Peptoniphilus, which are increased in persons with primary immunodeficiencies (Oh et al ., 2013). Zeeuwen et al., citing previous work, also pointed out that the 27F primer used for the V1-V3 region inefficiently amplifies Gardnerella and Lactobacillus, which have been found to be associated with females (Zeeuwen et al., 2012). In general, V1-V3 classifies fewer populations down to the genus level (Meisel et al., 2016). Because the V1-V3 region is longer than the V4 region, paired-end reads generated with the Illumina MiSeq will not fully overlap. And without full overlap, denoising of reads is not as effective. Using the V3 chemistry (a 600-cycle kit, longer than the 500-cycle kit of the V2 version) will not solve the problem and may even make it worse, because the sequence quality drops after 500 cycles.

In this two-part blog series, we have discussed how to collect enough microbial biomass to run a skin microbiome study, and how to deal with environmental contamination. We have seen that even relatively minor changes in primer sequences may improve the detection of bacteria relevant to skin microbiomes. Feel free to reach out to our team for more information on designing your own skin microbiome study!

How to Design a Skin Microbiome Study, Part I: Sampling

In this post, the first of a 2-part series on skin microbiome research, we will discuss technical issues surrounding sampling of human skin microbes.

Let’s say a researcher sets out to study bacteria on the human skin—the body’s largest organ, which is teeming with microbes from each domain of life, and viruses. The scientific question has been identified and the funding to conduct a pilot study has been secured. Perhaps her group has some experience studying the gut microbiome. For the most part, she has discovered, getting bacteria out of stool is not too difficult; a small amount of material contains enough microbial DNA to sequence the most prominent members of the microbiome. But unlike the intestine, the skin does not support a high-biomass microbiome. If microbes on the skin are present in low abundance, how does our researcher decide on reasonable sampling and sequencing strategies that together capture a representative picture of bacterial diversity?

Before collecting samples, the researcher must consider key advantages and limitations of available sampling protocols. Commonly used methods involve variations of swabbing – the repeated rubbing of a defined area of the skin with a sterile, pre-moistened swab. When the objective is to obtain sufficient microbial DNA from skin sites with variable and/or low microbial biomass, swabbing can be complemented with scalpel scrapping (Oh et al., 2014). If access to deeper layers, including the dermis, is required, punch biopsies are a viable alternative, but require specialized expertise and are more invasive, reducing the number of sites that can be sampled from the same subject.

Because each method samples a slightly different environment, we would expect different microbial profiles to arise from variations in sampling method. This prediction has not been thoroughly tested (but see Chng et al., 2016). As part of ongoing efforts to improve sampling methods, the Microbiome Insights team is currently evaluating whether D-Squame and Sebutape tape stripping, used for peeling off epidermal layers and sebum, respectively, provide a reliable means of sampling skin microbes. Compared with swabbing, tape stripping recovers ~2- to 3-fold less bacterial DNA. We have yet to evaluate, via amplicon sequencing, whether lower bacterial yield results in different microbial profiles. We are also exploring if coating pre-wetted swabs with aluminum oxide particles maximizes bacterial DNA recovery. The results of this experiment will be made available in a forthcoming technical note.

Handling samples with low microbial biomass is challenging. Even if the sampling method affects how much microbial biomass is collected, the amount of DNA recovered from skin is always low. (Of course, the DNA extraction method affects DNA yield and microbial composition from study to study. But because most studies are comparative in nature, methodological consistency is vitally important.) As most of the DNA is human, obtaining enough genetic material for microbial profiling can be difficult. Microbial load can be increased by instructing participants not to wash with soap or bathe at least 24 hrs prior to sampling, although the effect of cleansing is probably minor compared with the combined influence of sampling and extraction.

Another challenge of low microbial biomass samples is dealing with environmental contamination. Contamination can be introduced during sample collection, DNA extraction, and sequencing library preparation. For instance, bacterial DNA is often found in DNA extraction kits and in other reagents used for preparing samples. And while it is tempting to create lists of “usual suspect” contaminants, this may be futile when studying skin microbes or other human-associated bacteria because, for example, Staphylococcus, a common skin inhabitant, and Escherichia have been identified as potential kit reagent contaminants (Salter et al., 2014).

Processing negative controls alongside low-biomass specimens is critical, because the proportion of microbial DNA attributable to contamination is higher in low-biomass samples compared with high-biomass samples. Usually, we include at least four replicates for each of two types of negative controls on each 16S sequencing run: (1) DNA extraction controls, to assess if kit reagents carry a detectable signal, and (2) template-free PCR blanks, to pinpoint contamination that may arise during downstream processing. For skin microbiome analysis, sterile swabs opened at the site of sample collection are co-processed with the swabbed samples. In general, the number of sequencing reads in our negative controls is about 3- to 4-fold lower than the average in samples derived from skin sites. This is what we would expect for samples containing little to no DNA.

Stay tuned for the second post in this series: amplicon sequencing in skin microbiome studies.