The microbiome of the human facial skin is unique compared to that of other hominids

The microbiome of the human facial skin is unique compared to that of other hominids | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The microbiome of the human facial skin is unique compared to that of other hominids Samuel Degregori , Melissa B. Manus , Evan B. Qu , Calen P. Mendall , Jacob S. Baker , Lydia M. Hopper , Katherine R. Amato , View ORCID Profile Tami D. Lieberman doi: https://doi.org/10.1101/2025.01.22.634396 Samuel Degregori 1 Northwestern University, Department of Anthropology , 1810 Hinman Avenue, Evanston, IL, 60201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa B. Manus 1 Northwestern University, Department of Anthropology , 1810 Hinman Avenue, Evanston, IL, 60201 2 Department of Anthropology, University of Texas at San Antonio , 1 UTSA Circle, San Antonio, TX 78249 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Evan B. Qu 3 Institute for Medical Engineering and Science; Department of Civil and Environmental Engineering; Massachusetts Institute of Technology, Institute for Medical Engineering and Science , 77 Massachusetts Avenue, Cambridge, MA 02139 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Calen P. Mendall 3 Institute for Medical Engineering and Science; Department of Civil and Environmental Engineering; Massachusetts Institute of Technology, Institute for Medical Engineering and Science , 77 Massachusetts Avenue, Cambridge, MA 02139 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacob S. Baker 3 Institute for Medical Engineering and Science; Department of Civil and Environmental Engineering; Massachusetts Institute of Technology, Institute for Medical Engineering and Science , 77 Massachusetts Avenue, Cambridge, MA 02139 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lydia M. Hopper 4 Johns Hopkins University School of Medicine, Department of Molecular and Comparative Pathobiology , 720 Rutland Ave, Baltimore, MD 21205 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine R. Amato 1 Northwestern University, Department of Anthropology , 1810 Hinman Avenue, Evanston, IL, 60201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: tami{at}mit.edu katherine.amato{at}northwestern.edu Tami D. Lieberman 3 Institute for Medical Engineering and Science; Department of Civil and Environmental Engineering; Massachusetts Institute of Technology, Institute for Medical Engineering and Science , 77 Massachusetts Avenue, Cambridge, MA 02139 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tami D. Lieberman For correspondence: tami{at}mit.edu katherine.amato{at}northwestern.edu Abstract Full Text Info/History Metrics Preview PDF Abstract The human facial skin microbiome is remarkably similar across all people sampled to date, dominated by facultative anaerobe Cutibacterium . The origin of this genus is unknown, with no close relatives currently described from samples of primate skin. This apparent human-specific bacterial taxon could reflect the unique nature of human skin, which is significantly more oily than that of our closest primate relatives. However, previous studies have not sampled the facial skin microbiome of our closest primates. Here, we profiled the skin microbiome of zoo-housed chimpanzees ( Pan troglodytes ), and gorillas ( Gorilla gorilla gorilla ), alongside their human care staff, using both 16S and shotgun sequencing. We showed that facial skin microbiomes differ significantly across host species, with humans having the lowest diversity and most unique community among the three species. We were unable to find a close relative of Cutibacterium on either chimpanzee or gorilla facial skin, consistent with human-specificity. Hominid skin microbiome functional profiles were more functionally similar compared to their taxonomic profiles. However, we still found notable functional differences including lower proportions of fatty acid biosynthesis on humans, consistent with microbes’ reliance on host-derived lipids. Our study highlights the uniqueness of the human facial skin microbiome and supports a horizontal acquisition of its dominant resident from a yet unknown source. Importance Understanding how and why human skin bacteria differ from our closest animal relatives provides crucial insights into human evolution and health. While we have known that human facial skin hosts distinct bacteria—particularly Cutibacterium acnes —we did not know if these bacteria and their associated genes were also present on the faces of our closest relatives, chimpanzees and gorillas. Our study shows that human facial skin hosts markedly different bacteria than other primates, with C. acnes being uniquely abundant on human faces. This finding suggests that this key bacterial species may have adapted specifically to human skin, which produces more oils than other primates. MAIN TEXT Among human skin sites, human facial skin harbors the highest density of bacteria, with comparable colony-forming-units found only in the groin and axilla ( Noble, 1981 ). The composition of this community is remarkably similar across all healthy adults sampled to date (all in industrialized contexts), dominated by the species Cutibacterium acnes and other members of the Cutibacterium genus (Baker 2024, Oh 2014, Grice 2009). These facultative anaerobes live at high density in sebaceous follicles (hair follicles with large glands that produce lipid-rich sebum ( Leeming et al., 1984 ), and constitute ∼70% of the relative abundance of facial skin microbes of adults ( Baker et al., 2024 ; Grice & Segre, 2011 ; Oh et al., 2016 ). To date, no close relative of Cutibacterium has been reported on other animals, making the origin of this species mysterious; the closest relatives described have been isolated from the rumen of cows and Swiss cheese ( Scholz & Kilian, 2016 ). While the skin microbiomes of diverse mammals have been profiled ( Council et al., 2016 ; Ross et al., 2018 ), studies of facial and other sebaceous skin sites are lacking. As C. acnes has many genes for metabolizing sebaceous lipids ( Brüggemann et al., 2004 ) and humans produce considerable amounts of sebum on their face and throughout their skin, with a substantially unique lipid profile ( Nicolaides et al., 1968 ; Picardo et al., 2009 ), it is possible that C. acnes may be specific to the human skin environment. However, non-human hominid species also have largely hairless faces with high densities of sebaceous glands relative to the rest of their bodies ( Burns et al., 2008 ). Thus, C. acnes may have co-diversified with primates beyond just humans and may inhabit the facial skin of our closest relatives. Alternatively, C. acnes may have been acquired independently during human evolution and be absent from the facial skin microbiome of other host relatives. To better understand how the human facial skin microbiome differs from other hominids, we profiled the facial skin microbiome (FSM) of 4 chimpanzees and 4 gorillas at Lincoln Park Zoo in Chicago, IL, as well as 4 humans who were the apes’ primary care staff, using both 16S rRNA and metagenomics. We used V1-V3 16S primers for 16S analysis, as Cutibacterium amplifies poorly using traditional V4 primers ( Meisel et al., 2016 ). At the level of taxonomic family (sample compositions displayed in Fig. 1A ), human FSMs were significantly less diverse than gorilla and chimpanzee FSMs (p adj <0.05 Krusk-Wallis; Fig. 1B ), echoing results from other body sites ( Council et al., 2016 ). As expected, this decrease in diversity was associated with a dominance of Propionibacteriacae (of which C. acnes is a member) and Staphylococcacae in human FSMs (averaging 85% of these communities; Fig. 1B ). Download figure Open in new tab Figure 1. The facial skin microbiome (FSM) of humans has decreased diversity and is dominated by organisms with no close relatives on other hominids. A) Relative abundance taxonomic bar plot across all 16S samples, collapsed at the family level. Grey bars at the top denote binned taxa that are less than 1% in relative abundance. B) At the species level, human FSMs are significantly less diverse than chimpanzee and gorilla FSMs (Shannon’s Index). Black lines indicate means. Significant comparisons denoted with Bonferroni-corrected p values based on a Krusk-Wallis test. C) Phylogenetic analysis indicates that there is no close relative of C. acnes on chimpanzee or gorilla FSMs. In contrast, closely related Staphylococci are found on all three primates. Heatmap of species abundances from Propionibacteriacae (family level; left) species and Staphylococcus (genus level; right) species. Samples were merged by individual host (by summing all sample reads together per individual), rarefied to counts of 10k to normalize sample sequencing depth, and filtered to only include microbes that appear in at least two samples in order to focus on prominent microbes. Scale bars indicate the approximate age of branches on the phylogenies, highlighting the larger depth of the pictured Propionibacteriacae phylogeny. Read abundances are log-transformed to allow for a smoother gradient between low and high read counts. D) Phylogenomic analysis of the trace amount of C. acnes sequences found in NHP metagenomes suggests C. acnes on NHPs in this dataset has a direct human origin. We searched for evidence that C. acnes on NHPS was significantly diverged from any known C. acnes genomes. Divergence was quantified by first defining major intraspecies clades within the C. acnes phylogeny, then looking for instances where a NHP metagenome contained some, but not all, of the mutations specific to a known clade, which might suggest a deep branching event ( Fig. S1 ; Methods). C. acnes found on NHPs was not more substantially diverged from publicly available genomes than that found on human handlers (P-value is derived from a two-sided Wilcoxon rank-sum test). While all three hominid species had substantial proportions of Staphylococcacae and Mycobacteriacae (a family that includes Corynebacteria ), they each had distinct FSMs— including significantly distinct communities on gorillas and chimpanzees (P PERMANOVA =0.001, Table S1 ). Among many differences, chimpanzee FSMs had a significantly higher relative abundance of Mycobacteriacae , while gorilla FSMs had significantly high relative abundance of Neisseriacae and Lactobacillacae ( Table S3 , p adj 0.001% relative abundance, respectively) aligning with a model in which Cutibacterium has recently adapted to thrive specifically in the oily skin of humans. To investigate if the low abundance of Propionibacteriacae on chimpanzees and gorillas might contain a close relative C. acnes – therefore supporting vertical diversification and bacterial evolution on hominids ( Groussin et al., 2020 ) – we performed several analyses. First, a phylogenetic analysis of prominent amplicon sequences assigned to Propionibacteriacae revealed the absence of any non-human primate (NHP)-specific amplicon sequence variants (ASVs) that could serve as a relative of the apparently human-specific Cutibacterium ( Fig. 1C , left). In contrast, a wide range of closely related Staphylococci were found across human and NHPs at a high relative abundance ( Fig. 1C , right), with several species specific to given hosts, implying a possible diversification from a recent common ancestor. A low percentage of 16S and metagenomic reads from the non-human primates were classified as C. acnes . This could reflect transmission of C. acnes from the humans to NHPs in the zoo or contamination of NHP samples in the lab—or it might reflect a close relative of C. acnes indistinguishable using 16S. To distinguish between these possibilities, we compared metagenomic reads to a phylogeny-directed reference database containing publicly available C. acnes genomes, all collected from humans. Our analysis concluded that the trace amounts of C. acnes on NHPs were not more significantly diverged than those on the 4 people sampled, and overall neither group had strains that were highly diverged from known C. acnes genomes ( Fig. 1D ). These results suggest that the trace amounts of C. acnes in NHP samples have a direct human origin. Lastly, direct assembly from metagenomes did not return any contigs suggestive of a C. acnes relative. Thus, C. acnes likely colonized an already diverged human lineage rather than being conserved across primates through a recent common ancestor. In terms of the facial skin microbiome as a whole, gorilla and chimpanzee FSMs were significantly more similar to one another than either were to human FSMs (P<0.001; Fig. 2A ; also see Fig. 2S ). Thus, despite all hominids sharing similar facial features, and the fact that the people worked closely with the apes (in protected contact), the human faces had significantly less diverse and more compositionally distinct microbiota. Download figure Open in new tab Figure 2. The human facial skin microbiome is distinct and encodes for less lipid biosynthesis, consistent with increased usage of host-derived lipids. A) A pairwise comparison of Bray-Curtis distances between humans and NHPs and between gorillas and chimpanzees. P values denote significant differences between comparisons. B) DESeq2 results for the top differentially abundant microbial pathways (MetaCyc) across humans and NHPs. Data are represented as log2fold changes between the two host groups. C) The relative abundance of the three most abundant fatty-acid pathways across humans, gorillas, and chimpanzees. Palmitate is an abundant lipid on human skin. A Krusk-Wallis test was used to identify significant differentially abundant fatty-acid pathways across hosts and reported P values are Bonferroni-corrected. Metagenomic samples were merged by individual and rarefied to 20,000 reads for A) and C). At the level of function as determined by metagenomics, human and NHP skin microbiomes had much closer pairwise distances ( Fig. 2A ; Fig S3 ) compared to 16S samples, suggestive of functional redundancy in FSMs across host species. However, we found human FSMs had a lower relative abundance of fatty-acid biosynthesis pathways compared to NHPs (DESeq2, padj<0.001, Fig. 2B , Table S4 ). This decrease in bacterial biosynthesis of lipids is consistent with an increased reliance on host-produced sebaceous lipids, which are produced in exceptionally high amounts on human skin relative to other animals ( Nicolaides et al., 1968 ). C. acnes has many genes for hydrolyzing triglycerides ( Brüggemann et al., 2004 ), which are then liberated extracellularly for utilization by other species ( Flowers & Grice, 2020 ). Consistent with a model of increased reliance on host lipid usage in humans, depleted biosynthesis pathways included those for triglycerides found in high abundance on human skin, including palmitic acid ( Fig. 2C , Table S5 ). Skin microbes provide colonization resistance and interact with the immune system in inflammatory skin processes, including wound healing, acne, and psoriasis ( Harris-Tryon & Grice, 2022 ). The uniqueness of the human FSM revealed here – in terms of diversity, composition, and metabolic pathways – may pose a challenge for animal models which seek to replicate these complex interactions. Future studies should conduct more comprehensive samplings of hominid facial skin microbiomes from wild non-human primates and humans from more diverse geographies and lifestyles—ranging from nomadic, rural, and urban—to fully understand the effects of environment and evolution on our skin microbiome composition and function. If Cutibacterium is truly specific and universal across humans, a deeper understanding of its biology and evolution may illuminate historical events in human evolution and diversification. METHODS Sample collection We sampled the facial skin microbiomes of 4 chimpanzees, 4 gorillas, and 4 humans at Lincoln Park Zoo in Chicago, IL, USA (IRB protocol: STU00206091), in January 2020. Zoo staff acclimatized the chimpanzees and gorillas to voluntary, awake sample collection procedures prior to data collection. All facial swabs were collected from the apes in protected contact (i.e. through the animals’ enclosure mesh) by using a 9-inch plastic swab holder that extended the distance between the human care staff and the non-human primates. We sampled each individual three times: targeting different regions of the face (cheek, chin, and forehead), generating a total of 22 samples across all individuals (humans and non-human primates). Because face site has been studied in humans and found to be homogenous ( Baker et al., 2024 ; Wei et al., 2022 ), we took single swabs of the human subjects’ faces (n=4) generating a total of 28 samples. For each sample, a swab (Puritan, REF#25-1506 1PF 100) was first dipped into sterile PBS with .05% w/v Tween80, and then applied with light pressure with upward and downward strokes while moving swabs laterally and rotating the swab. The tip of each swab was immediately broken off into 1.5mL cryotubes containing 1000µL of storage solution (20:80 glycerol:PBS with 0.05% w/v Cysteine (as a reducing agent) which had been vacuum filtered at 0.22um and reduced in an anaerobic chamber). Upon completion of a sampling kit, all samples were vortexed for 60s at maximum speed and placed on dry ice. Samples were transferred to long-term storage in a -80°C freezer the same day they were collected. 16S Processing DNA was extracted from 100µl of sample using the PURElink Genomic DNA purification kit (Invitrogen, K182002) following the protocol for Gram Positive bacterial cells. The following adjustments were made to the protocol to improve the lysis of difficult to lyse organisms including C. acnes : the lysozyme treatment was increased to overnight incubation (16h) at 37°C and the incubation with proteinase K was increased to 3h at 55°C. We also extracted 4 negative controls and processed them through sequencing to assess for contamination. The V1-V3 region of the bacterial 16S ribosomal RNA gene was amplified using Kapa HIFI HotStart Readymix (Roche, 07958935001) and 27F-plex and 534R-plex primers ( Table S7 ). PCR of samples was performed as follows: 3min of 95°C; 36 cycles of 98°C for 30s, 54.5°C for 30s, and 72°C for 30s; and 72°C for 5min. Samples were cleaned following the protocol outlined in the Illumina 16S Metagenomic Sequencing Library Preparation Guide (link). Amplicon product was validated by Tapestation (Aligent) and quantified by SYBR safe (Thermo, #S33102). Samples were indexed with standard Illumina primers and cleaned again following the Illumina guide. Indexing PCR was performed as follows: 3min of 95°C; 8 cycles of 95°C for 30s, 55°C for 30s, and 72°C for 30s; and 72°C for 5min. Samples were pooled in equimolar ratios after validation of amplicon size by Tapestation (Aligent) and quantification by SYBR safe (Thermo, #S33102). Pooled samples were sequenced with the Illumina MiSeq platform using the v2 chemistry for 250bp paired end reads at the MIT BioMicroCenter. Metagenomic processing Genomic libraries for metagenomic samples were processed as previously described ( Baker et al. 2024 .) Lysates were produced with an overnight lysozyme treatment at 37°C followed by a 3h proteinase K digestion at 55°C and purified with Purelink gDNA extraction kits (Thermo, #K182104A). Libraries were prepared using a miniaturized protocol of Hackflex (Gaio et al. 2022) where sample input was reduced to 10 µl at 1ng/µl at the tagmentation step and the tagmentation stop step was omitted. Standard Illumina primers and KAPA HiFi master mix (Roche, 07958935001) were used to index samples with the following PCR protocol:72°C for 3min; 98°C for 5min; 19 cycles of 98°C for 10s, 62°C for 30s, 72°C for 30s; 72°C for 5min. Samples were cleaned, pooled, and sequenced using 150bp paired-end reads on the Illumina Nextseq500. Data processing We processed the 16S sequences using QIIME2 (v. 2023.7) using the microbiome data science platform ( Bolyen et al., 2019 , p. 2) for quality control, amplicon sequence variant (ASV) taxonomy assignment, and community diversity analyses. We demultiplexed and denoised the sequencing data using DADA2 ( Callahan et al., 2016 ) and merged the resulting output into a feature table for subsequent analysis. We assigned taxonomy to ASVs, using a naïve Bayes taxonomy classifier trained on the greengenes2 database ( McDonald et al., 2023 ), conducting reference sequence clustering at 99% similarity. To avoid unwanted reads, we removed singletons, and for analyses that involved relative abundance we removed reads that only showed up in one sample. To ensure that microbiomes only included microbial sequences, we removed any ASVs assigned to eukaryotes or chloroplasts. To trim adapters and low quality bases from the metagenomic sequences and remove host contamination we used the kneaddata pipeline (v0.11) which includes trimmomatic ( Bolger et al., 2014 ) and bowtie2 ( Langmead & Salzberg, 2012 ). To add taxonomy we mapped reads to the Web of Life database ( Zhu et al., 2019 ) and for functional annotations we used Metacyc and the KEGG database. Because we saw a stronger bias towards human-associated pathways with the KEGG database, we used the Metacyc database for a majority of differential analyses between human and NHP skin microbiomes. To bin genes into functional categories we used Woltka’s collapse function to generate functional types. In total, 16S sequencing of 26 skin samples (two samples were removed due to low sequencing depth) generated 1,409,637 reads after denoising, belonging to 19,737 amplicon sequence variants (ASVs). Samples had a median of 48,138 reads and a minimum of 19,368 reads. Shotgun sequencing generated 3,053,249 reads after trimming and host-filtering. Samples had a median of 81,638 reads with a minimum of 6,502. Using the Metacyc database, 4,296 pathways were identified while the KEGG database matched to 6,002 pathways. Statistical analysis To conduct beta-diversity analyses we utilized QIIME2’s beta diversity functions on both our taxonomic OTU table of reads and a metagenomic gene table to analyze diversity and function in parallel. We rarefied reads to 10,000 in order to retain 95% of the samples before conducting beta-diversity analyses for 16S and 20,000 reads to retain 100% of the metagenomic samples. We based the PCOA plots and PERMANOVA analyses on the taxonomic OTU table and a Bray-Curtis distance matrix. For the metagenomic table we also relied on Bray-Curtis distance matrix. We chose the Bray-Curtis metric as we could standardize this across both sequencing types (in lieu of UniFrac, for example, which is specific to 16S data), while also taking into account relative abundance for each dataset. To compare alpha diversities, we opted for Shannon’s Index since this index was comparable across both 16S and metagenomic datasets. We also supplemented this with observed read counts to ensure our results were robust across both diversity metrics. Because we found no differences across facial sites in NHP FSMs, we merged samples per individual for a majority of our analyses. We did this to better equalize sample sizes across hosts and we accounted for sequencing depth by rarefying reads to 10,000 for 16S samples and 20,000 for metagenomic samples. To analyze differentially abundant reads across host taxonomy, we employed DeSEQ2 ( Love et al., 2014 ) to quantify differentially abundant microbes and pathways. We also performed DeSEQ2 on the genes of specific of bacterial taxa by utilizing a gene count table stratified with taxonomy—made with woltka’s stratifying function—to investigate certain bacteria of interest and whether their gene counts differed across hosts. We used raw, minimally filtered (i.e. filtered for Eukaryota, Chloroplast, or unassigned reads), sequence tables for all differential analyses, following author recommendations ( Love et al., 2014 ). For visualizing differential abundance, we filtered the results by p-value (which directly correlates with and showed the top 14 hits with the smallest p-values. If less than 14 taxa or pathways were significant (padj>0.05), we showed all significant hits. To visualize Cutibacterium and Staphylococcus diversity across hosts we constructed a heatmap of the most abundant taxa within each genus. To focus on both the most abundant and prevalent taxa, we rarefied the 16S table to 10,000 reads per sample and excluded microbes that only appeared in one subject. We also constructed a similar heatmap of Propionibacteriaceae spp . (family level) to visualize the abundance of Cutibacterium relatives across hosts. We used qiime2’s heatmap plugin that uses the EMPress package ( Cantrell et al., 2021 ) that allows for phylogenetically-aware visualizations of microbiome data. To visualize the relationships between samples and microbes, we employed a cluster analysis based on Euclidian distances to annotate samples and microbes with dendrogram connections. To visualize all phylogenetic relationships, we also constructed a phylogeny of all identified microbes in the data utilizing our taxonomy table and a rooted tree made with MAFFT (v7) and divergence times between microbes were calculated with TimeTree 5 ( timetree.org ). For our analyses based on taxonomic identification rather than function, we opted to use the 16S data over the metagenomic data to minimize false-positive discovery associated with metagenomic profiling ( Meyer et al., 2022 ; Ye et al., 2019 ). We also verified our 16S taxonomic identifications with BLAST and were able to confirm the identities of taxa shown in Fig. 1C . When there was a discrepancy of species identification we opted for the BLAST result over the Greengenes result. To determine whether the C. acnes found in NHPs were native to NHP skin or had a human origin, we conducted a phylogenomic analysis of the C. acnes metagenomic sequences found on NHP samples ( Fig. S4 ). We reasoned that C. acnes that is truly native to NHPs should be diverged to some extent from C. acnes found on humans, and therefore used an early version of PHLAME, a new reference-based metagenomic strain profiler that can identify the presence of novel strains ( https://github.com/quevan/phlame ). First, we generated maximum likelihood core-genome phylogeny of C. acnes using 358 public reference genomes isolated from humans and identified major intraspecies clades. We searched for the presence of each major clade in metagenomes by identifying clade-specific SNPs, which are uniquely and unanimously shared by all members of the clade. We then looked for cases where a subset of SNPs specific to a clade were systematically missing, which is evidence of a novel strain that diverged substantially before the common ancestor of that clade. We quantified this divergence by estimating number of SNPs that were systematically missing over the total number of SNPs specific to that clade (bounded between 0 and 1). We then employed a two-sided Wilcoxon rank-sum test comparing C. acnes divergence between NHP and handler skin microbiome samples across clades as well as with the clades combined. To look for evidence of C. acnes relatives in metagenome-assembled genomes, we assembled contigs directly from NHP metagenomes using metaSPAdes (v3.13). To identify contigs that could reasonably belong to C. acnes or a close relative, we used BLAST (v2.7.1) to query all contigs against a database consisting of 24 representative C. acnes and C. granulosum genomes. For any contig that returned a hit, we used BLAST again against the NCBI nucleotide database and selected only contigs with a top hit belonging to C. acnes . We recovered only 5 contigs after this second round of filtering, which all had either alignment lengths or percent IDs that were too low to reasonably originate from C. acnes . Our STORMS checklist alonog with our metadata and 16S and metagenome sequence files can be accessed here: https://figshare.com/articles/dataset/Sequences_MG_and_16S/28135763 . DATA AVAILABILITY All source code and data used for analysis is available at https://github.com/samd1993/SkinProject . The source code for our metagenomic strain profiler, PHLAME, is available at https://github.com/quevan/phlame . Sequences for both 16S and shotgun sequencing, along with associated metadata files and a STORMS checklist, are available at https://figshare.com/articles/dataset/Sequences_MG_and_16S/28135763 . Sequences are also deposited in the SRA database under BioProject accession PRJNA1209946. Supplementary Section Download figure Open in new tab Figure S1. Facial skin microbiome alpha diversity comparisons across hosts. Both Shannon’s Index and observed ASV/pathway counts are reported. Data was rarefied prior to analyzing. Bonferroni-corrected P-values denote significant comparisons based on Krusk-Wallis tests. Sequence data from 16S and shotgun sequencing are reported. A filtering was performed for abundance; therefore count estimates are likely overestimations. Only significant P-values after Bonferroni correction are reported (see Table S2 for further details). Download figure Open in new tab Figure S2. Non-metric multidimensional scaling (NMDS) plots of hominid skin microbiomes (16S) indicates that gorilla and chimpanzee FSMs are more similar to one another than either is to human FSMs. Distances between samples are based on Bray-Curtis distance matrices for both data types. Colors denote host species. Samples are unmerged, thus chimpanzee and gorilla samples consist of cheek, chin, and forehead samples per individual. Download figure Open in new tab Figure S3. Relative abundance stacked barplot showing pathway composition across hosts. Pathways are collapsed at the type level and pathways under 0.01 relative abundance are binned in the grey bars. Y axis denotes abundance proportions out of 1. NHP samples are unmerged, with forehead, chin, and cheek samples all separated. Download figure Open in new tab Figure S4. Phylogenomic analysis suggests that C. acnes reads from non-human primates have a human origin. A) Maximum likelihood core-genome phylogeny of C. acnes was made using 358 public reference genomes isolated from humans. Major phylogenetic clades are labeled as letters according to a conventional typing scheme (Scholz & Kilian et al. 2016) B) Divergence between primate samples and handler samples across clades of C. acnes were estimated using PHLAME ( https://github.com/quevan/phlame ). Only clades with at least one detection are shown. A two-sided Wilcoxon rank-sum test comparing C. acnes divergence between NHP and human skin microbiome samples. View this table: View inline View popup Download powerpoint Table S1. Effect of host species on skin microbiome composition and function (PERMANOVA). View this table: View inline View popup Download powerpoint Table S2. Alpha Diversity Results for 16S and metagenomic (MG) data across host species. NHP samples for each face site are merged per individual to allow more direct comparisons between NHP and human samples. View this table: View inline View popup Table S3. Differential abundance across humans and NHP skin microbiomes (DESeq2 - 16S). View this table: View inline View popup Table S4. Differential abundance across human and NHP skin metagenomes (DESeq2 - MG) View this table: View inline View popup Download powerpoint Table S5. Krusk-Wallis analysis on fatty-acid biosynthesis pathway relative abundance across humans and NHP skin metagenomes View this table: View inline View popup Download powerpoint Table S6. Wilcoxon test results comparing pairwise distance values across comparisons of chimpanzee, gorilla, and human skin microbiomes. View this table: View inline View popup Download powerpoint Table S7. Sequences of the 16S rRNA gene primers used. Standard primers were used for both the V1-V3 and the V3-V4 regions of the 16S rRNA gene. View this table: View inline View popup Download powerpoint Table S8: BLAST results of the top hit for metagenome-assembled contigs that had a best hit to a Cutibacterium spp. ACKNOWLEDGEMENTS We would like to thank Lincoln Park Zoo for providing permissions for this study, the BioMicroCenter at MIT for assistance in DNA sequencing, and especially to Stephen Ross and Jill Moyse for their logistical support for all data collection. This work was supported in part by NIH grant (DP2OD028909) to T.D.L. REFERENCES ↵ Baker , J. S. , Qu , E. , Mancuso , C. P. , Tripp , A. D. , Conwill , A. , & Lieberman , T. D. ( 2024 ). Highly-resolved within-species dynamics in the human facial skin microbiome (p. 2024.01.10.575018). bioRxiv . doi: 10.1101/2024.01.10.575018 OpenUrl Abstract / FREE Full Text ↵ Bolger , A. M. , Lohse , M. , & Usadel , B. ( 2014 ). Trimmomatic: A flexible trimmer for Illumina sequence data . 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Share The microbiome of the human facial skin is unique compared to that of other hominids Samuel Degregori , Melissa B. Manus , Evan B. Qu , Calen P. Mendall , Jacob S. Baker , Lydia M. Hopper , Katherine R. Amato , Tami D. Lieberman bioRxiv 2025.01.22.634396; doi: https://doi.org/10.1101/2025.01.22.634396 Share This Article: Copy Citation Tools The microbiome of the human facial skin is unique compared to that of other hominids Samuel Degregori , Melissa B. Manus , Evan B. Qu , Calen P. Mendall , Jacob S. Baker , Lydia M. Hopper , Katherine R. Amato , Tami D. 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