Biology subjects

Tham, E.

Publications and source records attributed to Tham, E..

3 recordsLinked to original sources

bioRxiv2024

Life stage impact on the human skin ecosystem: lipids and the microbial community

While research into gut-microbe interactions is common and advanced, with multiple defined impacts on human health, studies exploring the significance of skin-microbe interactions remain underrepresented. Skin is the largest human organ, has a vast surface area, and is inhabited by a plethora of microorganisms which metabolise sebaceous lipids. Sebaceous free fatty acids are metabolized into bioactive lipid mediators with immune-modulatory properties by skin-resident microbes, including Malassezia. Intriguingly, many of the same lipid mediators are also found on human skin, implying these compounds may have microbial or mixed microbial/human origin. To support this hypothesis, we isolated lipids and microbial DNA from the skin of prepubescent, adult, pre- and post-menopausal volunteers and performed correlational analyses using skin lipidomics and metagenomics to compare lipid mediator profiles and microbiome compositions on skin with either low or high sebaceous gland activity. We found that specific microbial taxonomies were positively and negatively correlated with skin lipid mediator species with high statistical significance. 2D in vitro co-cultures with Malassezia and keratinocytes also directly linked the production of specific lipid mediators, detected on healthy human skin, to secretion of immuno-stimulatory cytokines. Together, these findings further support the hypothesis that microbial-derived skin lipid mediators influence healthy skin homeostasis and skin disease development and progression, thereby spotlighting the relevance of the skin microbiomes footprint on human health.

microbiology↗

bioRxiv2020

Tough Hydrogel-Based Biocontainment of Engineered Organisms for Continuous, Self-Powered Sensing and Computation

Genetically modified microorganisms (GMMs) can enable a wide range of important applications, including environmental sensing, precision therapeutics, and responsive materials. However, containment of GMMs to prevent environmental escape and satisfy regulatory requirements is a bottleneck for real-world use1-7. While biochemical strategies have been developed to restrict unwanted growth and replication of GMMs in the environment8-12, there is a need for deployable physical containment technologies to achieve redundant, multi-layered, and robust containment2. In addition, form factors that enable easy retrieval would be useful for environmental sensing. To address this challenge, we developed a hydrogel-based encapsulation system for GMMs that incorporates a biocompatible multilayer tough shell and an alginate-based core. This DEployable Physical COntainment Strategy (DEPCOS) allows no detectable GMM escape, bacteria to be protected against environmental insults including antibiotics and low pH, controllable lifespan, and easy retrieval of genetically recoded bacteria. To highlight the versatility of a DEPCOS, we demonstrate that robustly encapsulated cells can execute useful functions, including performing cell-cell communication with other encapsulated bacteria and sensing heavy metals in water samples from the Charles River. We envision that our multilayered physical and chemical containment strategy will facilitate the realization of a wide range of real-world applications for living biosensors.

synthetic biology↗

bioRxiv2019

pyCancerSig: subclassifying human cancer with comprehensive single nucleotide, structural and microsatellite mutational signature deconstruction from whole genome sequencing

BackgroundDNA damage accumulates over the course of cancer development. The often-substantial amount of somatic mutations in cancer poses a challenge to traditional methods to characterize tumors based on driver mutations. However, advances in machine learning technology can take advantage of this substantial amount of data.\n\nResultsWe developed a command line interface python package, pyCancerSig, to perform sample profiling by integrating single nucleotide variation (SNV), structural variation (SV) and microsatellite instability (MSI) profiles into a unified profile. It also provides a command to decipher underlying cancer processes, employing an unsupervised learning technique, Non-negative Matrix Factorization, and a command to visualize the results. The package accepts common standard file formats (vcf, bam). The program was evaluated using a cohort of breast- and colorectal cancer from The Cancer Genome Atlas project (TCGA). The result showed that by integrating multiple mutations modes, the tool can correctly identify cases with known clear mutational signatures and can strengthen signatures in cases with unclear signal from an SNV-only profile.\n\nConclusionspyCancerSig has demonstrated its capability in identifying known and unknown cancer processes, and at the same time, illuminates the association within and between the mutation modes.

bioinformatics↗