PreAnalyze #3 - Scalp Dandruff
Last updated
Last updated
Recent advancements in sequencing technology and database development have provided new insights into microbial communities. Notably, many microbial genomes have been sequenced from metagenomes without official descriptions. With up-to-date databases, especially those incorporating genomospecies, and improved software, we can revisit existing datasets to uncover previously overlooked organisms.
Our goal with these reAnalyze blog posts is to utilize EzBioCloud’s comprehensive databases and proprietary pipeline to explore public metagenomic datasets and share relevant findings.
Let's reAnalyze: Longitudinal study of the scalp microbiome suggests coconut oil to enrich healthy scalp commensals by Saxena et al., 2021. This paper explores the potential health benefits of coconut oil over 12 weeks for patients suffering from dandruff. These scalp microbiome samples are compared with healthy microbiome and dandruff microbiome samples with shampoo treatment as controls.
In , the cheek skin microbiomes of healthy young and ageing individuals was dominated by Cutibacterium acnes (Zhou et al., 2023). For case three, C. acnes is dominant again but to a much lesser extent with a greater diversity and species evenness in the scalp microbiome (Figure 1). These samples were taken prior to treatment, at time point one of three. Staphylococcus capitis (green bars) appears predominant in the dandruff scalp microbiomes. Whereas, C. acnes appears proportionally larger in the healthy profiles, which was also the case for younger skin microbiomes in .
Two familiar names from the group composition analysis are relevant in this differential abundance analysis: S. capitis and C. acnes. Although there were many candidates associated with dandruff found by ANCOMBC and LEfSe independently (Table 1a. indicated by score: 1), only one species was agreed by more than a single tool: S. capitis (score: 5). Several species are negatively associated with the dandruff scalp microbiome samples, which indicates they are relevant to healthy scalp microbiomes (Table 1b. scores < -1). The common skin commensal, C. acnes alongside Corynebacterium marquesiae, Cutibacterium modestum, Sphingobium olei, and many more appear as healthy scalp candidiate biomarkers.
The paper reported a reduction of dandruff symptoms in patients treated with coconut oil over several weeks. Would this reduction of symptoms correlate to a reduction in the candidate biomarker S. capitis? Do we see any changes in the dandruff microbiome after treatment?
Thinks are getting funky with a new top contender: Cellulosimicrobium funkei (Figure 3). This species was present ubiquitously in the pretreatment group composition (Figure 1) but, now, is more prevalent than C. acnes, the most common skin commensal, in control and treated dandruff microbiomes.
Interestingly, C. funkei and other Cellulosimicrobium spp. were only found in the metagenomic samples that used fungal-targeting DNA extraction protocol. In the paper, they removed 'contaminant' bacterial reads from these fungal DNA samples in a post-processing step. We incorporated the raw (pre-processed) bacterial and fungal samples, together, without removing respective contaminants. Perhaps the pretreatment of the bacterial DNA extraction protocol removed Cellulsimicrobium spp., post-processing removal, contamination of the fungal samples, or even the lysis of fungal cell walls may have given access to this genus.
Considering the reduction in symptoms of oil-treated scalps, guess which is the most popular biomarker candidate in this 'healthier' group?
If you guessed C. acnes… you were close. From the same genus, Cutibacterium namnetense takes the lead with C. acnes and Acinetobacter junni group also significantly enriched in oil-treated scalp microbiomes, agreed upon by five and two DAA tools, respectively (Table 2a).
I hypothesised that S. capitis might have been reduced in the oil-treated scalp microbiomes. However, it seems to still be present at similar abundances in both the control and treated dandruff groups (Table 2b). Only three taxa were associated with the control dandruff group: three Corynebacterium spp. Notably, these taxa were identified by LEfSe alone which is a generous DAA tool.
Now let’s look at the time scale of oil treatment of dandruff patients. Saxena et al., took samples at three time points for each cohort: day 0 (pre-treatment), day 84 (post-treatment), and day 112 (relapse-treatment).
As mentioned, dandruff symptoms were reduced in scalp microbiomes treated with coconut oil. Here we compare the dandruff microbiomes of oil-treated scalps, before and after treatment.
Calidethermus timidus and Brevundiomonas aurantiaca are interesting to examine - associated with pre-treatment dandruff (positively associated with untreated (no coconut oil as of yet) scalp microbiomes).
LEfSe generously giving out candidates (S. capitis), perhaps not enough to go on but wait until later!
If potentially significant species are present in dandruff microbiomes of both oil-treated and shampoo-treated scalp microbiomes such as S. capitis (observed in previous analysis and also in this analysis (e.g. S. capitis insignificantly different in dandruff microbiomes before oil treatment and after oil treatment)). This biomarker indicates differential abundance; it could still be a relevant species in that the metabolites it produces, or any sort of irritation directly or indirectly linked to this species, are potentially reduced by coconut oil treatment yet its presence remains.
In the paper, they refer to how scalp commensal Staphylococcus epidermidis is often associated with dandruff symptoms, which they found to be significantly higher in dandruff microbiomes. We observed it in the group composition but without any differentially abundant significance. Another species that seemed much more significant under the Staphylococcus genus was not mentioned in the paper and kept appearing in our analyses: S. capitis, a bacteria associated with skin disease (Tett et al., 2017). Below, you can explore our database entrances for each species and subspecies.
In the above pre-treatment datasets, some individuals are represented twice: by bacterial and fungal DNA-extraction protocols. Reducing to individuals represented only once decreases the amount of data available yet provides a more uniform look at the population samples.
Most individuals were represented twice: once by bacteria and once by fungi, but there are three extra individuals in fungal (66, 67, 69) and some only in bacteria (8, 7, 9, 1, 2, 4, 5, 11, 12, 18, and more). Their reasoning for using two methods for extracting microbial DNA from the same samples was to reduce the bias of extraction of different protocols.
To validate any potential findings we described previously, we ran a subset of data, representing individuals only once by bacterial DNA-extraction protocols (i.e. excluding fungal-targeted duplicate samples). We analysed this modified pre-treatment subset between healthy and dandruff cohorts under the same parameters. This includes Alpha: 0.05 which is strict under this reduced dataset.
S. capitis is the only consistent biomarker between the two datasets, associated with dandruff microbiomes.
This reduced dataset is missing some taxa that were found previously, yet all of these indicated here were associated with healthy scalp microbiomes. Again, Cutibacterium seems to be a significant biomarker of healthy scalp microbiomes.
Is this Cutibacterium genus just as prevalent in ‘healthy’ scalp microbiomes? Below we will explore how these microbiomes cluster around genus- and species-driven taxa without being influenced by cohort status i.e. unsupervised clustering of microbiomes represented by different taxa.
Traditionally, enterotyping was used to find gut microbiome 'types' (Type 1: high Bacteroides levels, Type 2: low Bacteroides but common Prevotella, and Type 3: high Ruminococcus levels (Arumugam et al., 2011)). However, such clustering methods can be used on microbiome data from any region to discover potential types.
PAM clustering determined two 'enterotypes' which, in this case, are clusters of genera (Figure 6a). We can see in the blue that Cutibacterium is a driving taxa of this cluster. Knowing that this genus was repeatedly found as a biomarker candidate in healthy scalp microbiomes, perhaps E1 (blue) represents the healthy samples. Let's see what proportions of dandruff and healthy scalp microbiomes belong to each 'enterotype'.
Using some Excel wizardry, we can determine the type proportions belonging to dandruff and healthy microbiomes at the genus level (Table 5a).
Dandruff
Healthy
E1
17
16
E2
11
12
E1 Percentage
60.71429
57.14286
E2 Percentage
39.28571
42.85714
Table 5a. PAM clustering of healthy and dandruff scalp microbiomes at the genus level.
Despite E1 being driven by Cutibacterium, the healthy and dandruff microbiomes are predominantly in this cluster (57% and 61%, respectively). Interestingly, E1 also contains our notorious biomarker for dandruff symptoms Staphylococcus. Driven by both the healthy and unhealthy genera biomarkers, this E1 type at the genus level may not be at a suitable taxa resolution. Let's go deeper, to species-level clustering.
PAM clustering at the species level gives us three types: E1-E3 (Figure 6b). Two of our favourite scalp species: C. acnes, a healthy biomarker, and S. capitis, a dandruff biomarker, are now separated into E1 and E2, respectively. The third type (E3) is represented by Cellulosimicrobium spp., Limosilactobacillus reuteri, and Luteimicrobium xylanilyticum. Perhaps this higher resolution clustering will better represent scalp microbiome status compared with genus-level.
Dandruff
Healthy
E1
7
14
E2
10
2
E3
11
12
E1 Percentage
25
50
E2 Percentage
35.71429
7.142857
E3 Percentage
39.28571
42.85714
Table 5b. PAM clustering of healthy and dandruff scalp microbiomes at the species level.
Healthy microbiomes are 7% in the E2 (dandruff) type and 50% in the E1 (healthy) type. Dandruff microbiomes seem more evenly distributed across the three types.
Out of curiosity, we made an index using the measure of 'total dandruff score' and arbitrarily selected parameter values: 0 = 'clear', <5 = 'present', and >5 = 'excess'. Using this metric, treatments can be ignored, focusing only on dandruff symptoms. The results aligned with the previous findings.
Some potential biomarkers associated with clear conditions, of course, including C. acnes and C. modestum being two of the most identified by DAA tools alongside two other Cutibacterium spp. A genomospecies was also identified: Brevilactibacter MSSCI00298188_s in the top five associated with clear symptoms.
Look who is back! The notorious S. capitis! The clearest biomarker against clear symptoms.
It is important to acknowledge that fungi, particularly Malassezia restricta, are often abundantly present in scalp microbiomes (Clavaud et al., 2013). However, using genomic content to compare bacterial and fungal populations can be problematic. Fungal genomes are highly complex, often exhibiting significant variation in ploidy, ranging from haploid to dikaryotic to polyploid, which makes direct comparisons challenging. Additionally, fungal genomes are generally much larger and more intricate than bacterial genomes. For instance, the haploid genome of C. acnes is approximately 2.5 million base pairs (Mbp) in size, whereas the genome of M. restricta is approximately 8.9 Mbp.
Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., ... & Bork, P. (2011). Enterotypes of the human gut microbiome. nature, 473(7346), 174-180.
Clavaud, C., Jourdain, R., Bar-Hen, A., Tichit, M., Bouchier, C., Pouradier, F., ... & Mouyna, I. (2013). Dandruff is associated with disequilibrium in the proportion of the major bacterial and fungal populations colonizing the scalp. PloS one, 8(3), e58203.
Saxena, R., Mittal, P., Clavaud, C., Dhakan, D. B., Roy, N., Breton, L., ... & Sharma, V. K. (2021). Longitudinal study of the scalp microbiome suggests coconut oil to enrich healthy scalp commensals. Scientific reports, 11(1), 1-14.
Tett, A., Pasolli, E., Farina, S., Truong, D. T., Asnicar, F., Zolfo, M., ... & Segata, N. (2017). Unexplored diversity and strain-level structure of the skin microbiome associated with psoriasis. NPJ biofilms and microbiomes, 3(1), 14.
Zhou, W., Fleming, E., Legendre, G., Roux, L., Latreille, J., Gendronneau, G., ... & Oh, J. (2023). Skin microbiome attributes associate with biophysical skin ageing. Experimental dermatology, 32(9), 1546-1556.
Staphylococcus capitis
Staphylococcus capitis subsp. Capitis (type)
Staphylococcus capitis subsp. Urealyticus
Staphylococcus epidermidis
