Microbial Reference Frames Reveal Distinct Shifts in the Skin Microbiota after Cleansing

Skin cleansing represents a process of mechanical and chemical removal of dirt, pollutants as well as microbiota from the skin. While skin cleansing can help maintain good health, protect us from infections, illnesses and ailments, skin cleansing can also strip away lipids and moisture from the skin, leading to irritation, barrier impairment and disturbance of the delicate cutaneous microbiome. This study investigated how skin cleansing impacts skin’s microbial composition. Thirty Caucasian women were enrolled in a placebo controlled clinical study where participants applied on their volar forearms a liquid body wash twice daily for 1 week in order to mimic frequent showering. Skin microbiome samples were collected by swabbing at defined timepoints and 16S rRNA sequencing was performed. Using “reference frames”, we could identify shifts in the microbial composition and several microbiota were identified as being characteristically associated with the presence of saccharide isomerate, a well-known skin moisturizer. The microbial shift was quite immediate, and we could observe it already at 1 h post cleansing. Interestingly, the new microbial composition reached a certain dynamic equilibrium at day 1 which was then maintained until the end of the study. Paracoccus marcusii, a potentially beneficial carotenoid-producer microorganism, was enriched by the active treatment and, at the same time, the abundance of several potential pathogenic taxa, Brevibacterium casei and Rothia mucilaginosa, diminished.

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White-nose syndrome restructures bat skin microbiomes

10.1101/614842 ◽

2019 ◽

Cited By ~ 2

Author(s):

Meghan Ange-Stark ◽

Tina L. Cheng ◽

Joseph R. Hoyt ◽

Kate E. Langwig ◽

Katy L. Parise ◽

...

Keyword(s):

Fungal Species ◽

Myotis Lucifugus ◽

Skin Microbiome ◽

Pseudogymnoascus Destructans ◽

Fungal Community Composition ◽

Microbial Composition ◽

Pathogen Invasion ◽

White Nose Syndrome ◽

Bacterial Microbiome ◽

Species Specific

AbstractThe skin microbiome is an essential line of host defense against pathogens, yet our understanding of microbial communities and how they change when hosts become infected is limited. We investigated skin microbial composition in three North American bat species (Myotis lucifugus,Eptesicus fuscus, andPerimyotis subflavus) that have been impacted by the infectious disease, white-nose syndrome, caused by an invasive fungal pathogen,Pseudogymnoascus destructans. We compared bacterial and fungal composition from 154 skin swab samples and 70 environmental samples using a targeted 16S rRNA and ITS amplicon approach. We found that forM. lucifugus, a species that experiences high mortality from white-nose syndrome, bacterial microbiome diversity was dramatically lower whenP. destructansis present. Key bacterial families—including those potentially involved in pathogen defense—significantly differed in abundance in bats infected withP. destructanscompared to uninfected bats. However, skin bacterial diversity was not lower inE. fuscusorP. subflavuswhenP. destructanswas present, despite populations of the latter species declining sharply from white-nose syndrome. The fungal species present on bats substantially overlapped with the fungal taxa present in the environment at the site where the bat was sampled, but fungal community composition was unaffected by the presence ofP. destructansfor any of the three bat species. This species-specific alteration in bat skin bacterial microbiomes after pathogen invasion may suggest a mechanism for the severity of WNS inM. lucifugus, but not for other bat species impacted by white-nose syndrome.

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Insights into the desert living skin microbiome: geography, soil depth, and crust type affect biocrust microbial communities and networks in Mojave Desert, USA

10.1101/810002 ◽

2019 ◽

Cited By ~ 1

Author(s):

Nuttapon Pombubpa ◽

Nicole Pietrasiak ◽

Paul De Ley ◽

Jason E Stajich

Keyword(s):

Microbial Communities ◽

Mojave Desert ◽

Plant Pathogens ◽

Spatial Organization ◽

Soil Depth ◽

Ecosystem Functions ◽

Skin Microbiome ◽

Microbial Composition ◽

Green Algal ◽

Crust Type

AbstractBiocrusts are the living skin of drylands, comprising diverse microbial communities that are essential to desert ecosystems. Although we have extensive knowledge on biocrust ecosystem function and what drives biodiversity in lichen and moss dominated biocrusts, much less is understood about the impacts of diversity among microbial communities. Moreover, most biocrust microbial composition studies have primarily focused on bacteria. We used amplicon-based metabarcode sequencing to explore composition of both fungal and bacterial communities in biocrusts. Specifically we tested how geography, soil depth, and crust type structured biocrust microbial communities or fungal-bacterial networks. Microbial communities were surveyed from biocrust surface and subsurface soils collected from Joshua Tree National Park, Granite Mountain, Kelso Dunes, and Cima volcanic flows located within the Mojave Desert, USA. Five biocrust types were examined: Light-algal, Cyano-lichen, Green-algal lichen, Smooth moss, and Rough moss crust types. We found the primary characteristics structuring biocrust microbial diversity were 1) geography, as central and southern Mojave sites displayed different community signatures, 2) presence of plant associated fungi (plant pathogens and wood saprotrophs), indicator, and endemic species were identified at each site, 3) soil depth patterns, as surface and subsurface microbial communities were distinctly structured, and 4) the crust type, which predicted distinct microbial compositions. Network analysis showed that Cyanobacteria and Dothideomycetes (Pleosporales) were the major hubs of overall biocrust microbial community. Such hierarchical spatial organization of biocrust communities and their associated biotic networks can have pronounced impacts to ecosystem functions. Our findings provide crucial insights for dryland restoration and sustainable management.

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The skin microbiome of cow-nose rays (Rhinoptera bonasus) in an aquarium touch-tank exhibit

10.7287/peerj.preprints.2341 ◽

2016 ◽

Author(s):

Patrick J Kearns ◽

Jennifer L Bowen ◽

Michael F Tlusty

Keyword(s):

Microbial Community ◽

New England ◽

High Throughput Sequencing ◽

Ventral Surface ◽

Rrna Gene ◽

Skin Microbiome ◽

Microbial Composition ◽

Human Interactions ◽

Rhinoptera Bonasus ◽

Public Aquarium

Public aquarium exhibits offer numerous educational opportunities for visitors while touch tank exhibits offer guests the ability to directly interact with marine life. However, despite the popularity of these exhibits, the effect of human interactions on the host-associated microbiome or the habitat microbiome remains unclear. Microbial communities, both host-associated and habitat associated can have great implications for host health and habitat function. To better understand the link between human interactions and the microbiome of a touch tank we used high-throughput sequencing of the 16S rRNA gene to analyze the microbial community on the dorsal and ventral surfaces of cow-nose rays (Rhinoptera bonasus) as well as its environment in a frequently visited touch tank exhibit at the New England Aquarium. Our analyses revealed a distinct microbial community associated with the skin of the ray that had lower diversity than the surrounding habitat. The ray skin was dominated by three orders: Burkholderiales (~55%), Flavobacteriales (~19%) and Pseudomonadales (~12%), suggesting a potentially important role of these taxa in ray health. Further, there was no difference between dorsal and ventral surface of the ray in terms of microbial composition or diversity, and a very low presence of common human-associated microbial taxa (<1.5%). Our results suggest that human contact has a minimal effect on the skin and habitat microbiome of the cow-nose ray and that the ray skin harbors a distinct and lower diversity microbial community than its environment.

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Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

10.1101/2020.07.02.184713 ◽

2020 ◽

Author(s):

Anna Paola Carrieri ◽

Niina Haiminen ◽

Sean Maudsley-Barton ◽

Laura-Jayne Gardiner ◽

Barry Murphy ◽

...

Keyword(s):

Artificial Intelligence ◽

Smoking Status ◽

Menopausal Status ◽

Human Microbiome ◽

Skin Aging ◽

Skin Hydration ◽

Skin Microbiome ◽

Microbial Composition ◽

Microbiome Composition ◽

Non Invasive

AbstractAlterations in the human microbiome have been observed in a variety of conditions such has asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in our health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions.We combine the effort of collecting a corpus of leg skin microbiome samples of two healthy cohorts of women with the development of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes and explanations. The explanations are expressed in terms of variations in the abundance of key microbes that drive the predictions.We predict skin hydration, subject’s age, pre/post-menopausal status and smoking status from the leg skin microbiome. The key changes in microbial composition linked to skin hydration can accelerate the development of personalised treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring.Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any conditions from microbiome samples and has the potential to accelerate the development of microbiome-based personalised therapeutics and non-invasive diagnostics.

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Idealisation and Formulation in Structural Dynamics Using Modal Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.418-420.1022 ◽

2011 ◽

Vol 418-420 ◽

pp. 1022-1025

Author(s):

Muhammad Danish ◽

Vinay Kumar Pingali ◽

Somnath Chattopadhyaya ◽

N.K. Singh ◽

A.K. Ray

Keyword(s):

Reference Frame ◽

Structural Dynamics ◽

Reference Frames ◽

Dynamic Equilibrium ◽

Equations Of Motion ◽

Complex Structure ◽

Complicated Structure ◽

Multiple Reference Frames ◽

Lumped Masses ◽

Multiple Reference

The crux feature of this paper is the equations of motion in a structural dynamics with respect to single reference frame that can be easily derived, and the results are well defined and converged. However, problem occurs, when the analysis of any complex, complicated structure is considered and its equation of motion is extracted with respect to single reference frame. The results are indecipherable, ambiguous and less converged. Thus, for such a complex structure, the results obtain with respect to multiple reference frames. In present study, an approximated model with a set of lumped masses, properly interconnected, along with discrete spring and damper elements are in consideration for continuous vibrating system. This results in dynamic equilibrium, which in turn results in formulation and idealization. As, rightly said by scientist Steve Lacy- “To me, there is spirit in a reed. It is a living thing, a weed, really and it does not contain spirit of sort. It’s really an ancient vibration”

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On the Euler–Lagrange Equation for Planar Systems of Rigid Bodies or Lumped Masses

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4044145 ◽

2019 ◽

Vol 14 (9) ◽

Author(s):

R. Wiebe ◽

P. S. Harvey

Keyword(s):

Rigid Body ◽

Reference Frames ◽

Dynamic Equilibrium ◽

Lagrange Equation ◽

Equations Of Motion ◽

Rigid Bodies ◽

Double Pendulum ◽

Governing Equations ◽

Lumped Mass ◽

Planar Systems

The Euler–Lagrange equation is frequently used to develop the governing dynamic equilibrium expressions for rigid-body or lumped-mass systems. In many cases, however, the rectangular coordinates are constrained, necessitating either the use of Lagrange multipliers or the introduction of generalized coordinates that are consistent with the kinematic constraints. For such cases, evaluating the derivatives needed to obtain the governing equations can become a very laborious process. Motivated by several relevant problems related to rigid-body structures under seismic motions, this paper focuses on extending the elegant equations of motion developed by Greenwood in the 1970s, for the special case of planar systems of rigid bodies, to include rigid-body rotations and accelerating reference frames. The derived form of the Euler–Lagrange equation is then demonstrated with two examples: the double pendulum and a rocking object on a double rolling isolation system. The work herein uses an approach that is used by many analysts to derive governing equations for planar systems in translating reference frames (in particular, ground motions), but effectively precalculates some of the important stages of the analysis. It is hoped that beyond re-emphasizing the work by Greenwood, the specific form developed herein may help researchers save a significant amount of time, reduce the potential for errors in the formulation of the equations of motion for dynamical systems, and help introduce more researchers to the Euler–Lagrange equation.

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Composition of the North American wood frog (Rana sylvatica) skin microbiome and seasonal variation in community structure

10.1101/2020.01.28.921544 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexander J. Douglas ◽

Laura. A. Hug ◽

Barbara A. Katzenback

Keyword(s):

Seasonal Variation ◽

North American ◽

Frog Skin ◽

Rana Sylvatica ◽

Wood Frog ◽

Skin Microbiome ◽

Environment Impact ◽

Skin Microbiota ◽

Microbial Composition ◽

Α Diversity

AbstractWhile a number of amphibian microbiomes have been characterized, it is unclear how microbial communities might vary in response to seasonal changes in the environment and the behaviors which many amphibians exhibit. Given recent studies demonstrating the importance of the skin microbiome in frog innate immune defenses against pathogens, investigating how changes in the environment impact the microbial species present, and thus their potential contribution to skin host defense, will provide a better understanding of conditions that may alter host susceptibility to pathogens in their environment. We sampled the skin microbiome of North American wood frogs (Rana sylvatica) from two breeding ponds in the spring, along with the microbial community present in their vernal breeding pools, and frogs from the nearby forest floor in the summer and fall to determine whether the microbial composition differs by sex, vernal pond site, or temporally across season (spring, summer, fall). Taxon abundance data reveals a profile of bacterial phyla similar to those previously described on anuran skin, with Proteobacteria, Bacteroidetes, and Actinobacteria dominating the wood frog skin microbiome. Our results indicate that sex had no significant effect on skin microbiota diversity, however, this may be due to our limited female sample size. Vernal pool site had a small but significant effect on skin microbiota, but skin-associated communities were more similar to each other than to the communities observed in the frogs’ respective pond water. Across seasons, diversity analyses suggest there are significant differences between the skin microbiome of frogs from spring and summer/fall groups while the average α-diversity per frog remained consistent. Bacterial genera known to have antifungal properties such as Pseudomonas spp. and Rhizobium spp. were prevalent, and several were considered core microbiota during at least one season. These results illustrate seasonal variation in wood frog skin microbiome structure and highlight the importance of considering temporal trends in an amphibian microbiome, particularly for species whose life history requires recurrent shifts in habitat and behavior.

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An enhanced characterization of the human skin microbiome: a new biodiversity of microbial interactions

10.1101/2020.01.21.914820 ◽

2020 ◽

Author(s):

Akintunde Emiola ◽

Wei Zhou ◽

Julia Oh

Keyword(s):

Human Skin ◽

De Novo ◽

Microbial Interactions ◽

Skin Microbiome ◽

Metagenomic Sequencing ◽

Interspecies Interactions ◽

Microbial Composition ◽

Healthy Human ◽

Skin Site ◽

Cutibacterium Acnes

ABSTRACTThe healthy human skin microbiome is shaped by skin site physiology, individual-specific factors, and is largely stable over time despite significant environmental perturbation. Studies identifying these characteristics used shotgun metagenomic sequencing for high resolution reconstruction of the bacteria, fungi, and viruses in the community. However, these conclusions were drawn from a relatively small proportion of the total sequence reads analyzable by mapping to known reference genomes. ‘Reference-free’ approaches, based on de novo assembly of reads into genome fragments, are also limited in their ability to capture low abundance species, small genomes, and to discriminate between more similar genomes. To account for the large fraction of non-human unmapped reads on the skin—referred to as microbial ‘dark matter’—we used a hybrid de novo and reference-based approach to annotate a metagenomic dataset of 698 healthy human skin samples. This approach reduced the overall proportion of uncharacterized reads from 42% to 17%. With our refined characterization, we revisited assumptions about the skin microbiome, and demonstrated higher biodiversity and lower stability, particularly in dry and moist skin sites. To investigate hypotheses underlying stability, we examined growth dynamics and interspecies interactions in these communities. Surprisingly, even though most skin sites were relatively stable, many dominant skin microbes, including Cutibacterium acnes and staphylococci, were actively growing in the skin, with poor or no relationship between growth rate and relative abundance, suggesting that host selection or interspecies competition may be important factors maintaining community homeostasis. To investigate other mechanisms facilitating adaptation to a specific skin site, we identified Staphylococcus epidermidis genes that are likely involved in stress response and provide mechanisms essential for growth in oily sites. Finally, horizontal gene transfer—another mechanism of competition by which strains may swap antagonistic or virulent coding regions—was relatively limited in healthy skin, but suggested exchange of different metabolic and environmental tolerance pathways. Altogether, our findings underscore the value of a combined reference-based and de novo approach to provide significant new insights into microbial composition, physiology, and interspecies interactions to maintain community homeostasis in the healthy human skin microbiome.

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Establishing microbial composition measurement standards with reference frames

Nature Communications ◽

10.1038/s41467-019-10656-5 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 88

Author(s):

James T. Morton ◽

Clarisse Marotz ◽

Alex Washburne ◽

Justin Silverman ◽

Livia S. Zaramela ◽

...

Keyword(s):

Reference Frames ◽

Microbial Composition ◽

Measurement Standards ◽

Composition Measurement

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Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Scientific Reports ◽

10.1038/s41598-021-83922-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Anna Paola Carrieri ◽

Niina Haiminen ◽

Sean Maudsley-Barton ◽

Laura-Jayne Gardiner ◽

Barry Murphy ◽

...

Keyword(s):

Artificial Intelligence ◽

Human Health ◽

Smoking Status ◽

Menopausal Status ◽

Human Microbiome ◽

Skin Aging ◽

Skin Hydration ◽

Skin Microbiome ◽

Microbial Composition ◽

Non Invasive

AbstractAlterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

Download Full-text