Biogeographical Landscape of the Human Face Skin Microbiome Viewed in High Definition.

The bacterial community that colonizes the human face imparts physiochemical and physiological effects on the facial skin. These skin-microbe interactions impact dermatological, cosmetic and skincare applications due to the centrality of the human face in daily interactions. However, fine-scale characterization of the human face skin microbiome is lacking. Using 16S rRNA sequencing and 3D cartography, this study plotted and characterized the facial skin microbiome in high- definition, based on 1,649 samples from 12 individuals. Analysis yielded a number of novel insights, including that of the relative uniformity of skin microbiome composition within skin sites, site localization of certain microbes, and the interpersonal variability of the skin microbiome. The results show that high-resolution topographical mapping of the skin microbiome is a powerful tool for studying the human skin microbiome. Despite a decade of skin microbiome research, there is still much to be discovered.

Microbial exposure during early human development primes fetal immune cells.

The human fetal immune system begins to develop early during gestation; however, factors responsible for fetal immune-priming remain elusive. We explored potential exposure to microbial agents in utero and their contribution toward activation of memory T cells in fetal tissues. We profiled microbes across fetal organs using 16S rRNA gene sequencing and detected low but consistent microbial signal in fetal gut, skin, placenta, and lungs in the 2nd trimester of gestation. We identified several live bacterial strains including Staphylococcus and Lactobacillus in fetal tissues, which induced in vitro activation of memory T cells in fetal mesenteric lymph node, supporting the role of microbial exposure in fetal immune-priming. Finally, using SEM and RNA-ISH, we visualized discrete localization of bacteria-like structures and eubacterial-RNA within 14th weeks fetal gut lumen. These findings indicate selective presence of live microbes in fetal organs during the 2nd trimester of gestation and have broader implications toward the establishment of immune competency and priming before birth.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Transcriptional profiling reveals a subset of human breast tumors that retain wt TP53 but display mutant p53-associated features.

TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild-type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut-like features in response to growth factors or cancer-mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2- patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53-mutated tumors. Moreover, similar to TP53-mutated tumors, these 'pseudomutant' cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53-associated activities without having to accrue TP53 mutations.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.

Skin Microbiome Compositional Changes in Atopic Dermatitis Accompany Dead Sea Climatotherapy.

Dead Sea climatotherapy (DSC) is a well-established therapeutic modality for the treatment of several diseases, including atopic dermatitis. Skin microbiome studies have shown that skin microbiome diversity is anticorrelated with both atopic dermatitis severity and concurrent Staphylococcus aureus overgrowth. This study aimed to determine whether DSC induces skin microbiome changes concurrent with clinical improvements in atopic dermatitis. We sampled 35 atopic dermatitis patients and ten healthy controls on both the antecubital and popliteal fossa. High-resolution microbial community profiling was attained by sequencing multiple regions of the 16S rRNA gene. Dysbiosis was observed in both lesional and nonlesional sites, which was partially attenuated following treatment. Severe AD skin underwent the most significant community shifts, and Staphylococcus epidermidis, Streptococcus mitis and Micrococcus luteus relative abundance were significantly affected by Dead Sea climatotherapy. Our study highlights the temporal shifts of the AD skin microbiome induced by Dead Sea climatotherapy and offers potential explanations for the success of climatotherapy on a variety of skin diseases, including AD.

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling.

BACKGROUND: Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA. RESULTS: This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis. CONCLUSIONS: SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).

Temporal Stability of the Healthy Human Skin Microbiome Following Dead Sea Climatotherapy.

Dead Sea climatotherapy (DSC) is a therapeutic modality for a variety of chronic skin conditions, yet there has been scarce research on the relationship between the cutaneous microbiota and disease states in response to DSC. We characterized the skin bacterial and fungal microbiome of healthy volunteers who underwent DSC. Bacterial community diversity remained similar before and after treatment, while fungal diversity was significantly reduced as a result of the treatment. Individuals showed greater inter-individual than temporal bacterial community variance, yet the opposite was true for fungal community composition. We further identified Malassezia as the genus driving temporal mycobiome variations. The results indicate that the microbiome remains stable throughout DSC, while the mycobiome undergoes dramatic community changes. The results of this study will serve as an important baseline for future investigations of microbiome and mycobiome temporal phenomena in diseased states.

Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine.

Growing evidence suggests that microbes can influence the efficacy of cancer therapies. By studying colon cancer models, we found that bacteria can metabolize the chemotherapeutic drug gemcitabine (2',2'-difluorodeoxycytidine) into its inactive form, 2',2'-difluorodeoxyuridine. Metabolism was dependent on the expression of a long isoform of the bacterial enzyme cytidine deaminase (CDDL), seen primarily in Gammaproteobacteria. In a colon cancer mouse model, gemcitabine resistance was induced by intratumor Gammaproteobacteria, dependent on bacterial CDDL expression, and abrogated by cotreatment with the antibiotic ciprofloxacin. Gemcitabine is commonly used to treat pancreatic ductal adenocarcinoma (PDAC), and we hypothesized that intratumor bacteria might contribute to drug resistance of these tumors. Consistent with this possibility, we found that of the 113 human PDACs that were tested, 86 (76%) were positive for bacteria, mainly Gammaproteobacteria.

Coordinated Pulses of mRNA and of Protein Translation or Degradation Produce EGF-Induced Protein Bursts.

Protein responses to extracellular cues are governed by gene transcription, mRNA degradation and translation, and protein degradation. In order to understand how these time-dependent processes cooperate to generate dynamic responses, we analyzed the response of human mammary cells to the epidermal growth factor (EGF). Integrating time-dependent transcript and protein data into a mathematical model, we inferred for several proteins their pre-and post-stimulus translation and degradation coefficients and found that they exhibit complex, time-dependent variation. Specifically, we identified strategies of protein production and degradation acting in concert to generate rapid, transient protein bursts in response to EGF. Remarkably, for some proteins, for which the response necessitates rapidly decreased abundance, cells exhibit a transient increase in the corresponding degradation coefficient. Our model and analysis allow inference of the kinetics of mRNA translation and protein degradation, without perturbing cells, and open a way to understanding the fundamental processes governing time-dependent protein abundance profiles.

Pathway-based personalized analysis of breast cancer expression data.

INTRODUCTION: Most analyses of high throughput cancer data represent tumors by "atomistic" single-gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of "coarse grained" pathway-based variables. METHODS: We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Score (PDS). Individual samples are represented in terms of their PDSs calculated for several hundred pathways, and the samples of the data set are analyzed and stratified on the basis of their profiles over these "coarse grained", biologically meaningful variables. RESULTS: We identified nine tumor subtypes; a new subclass (comprising about 7% of the samples) exhibits high deregulation in 38 PKA pathways, induced by overexpression of the gene PRKACB. Another interesting finding is that basal tumors break into two subclasses, with low and high deregulation of a cluster of immune system pathways. High deregulation corresponds to higher concentrations of Tumor Infiltrating Lymphocytes, and the patients of this basal subtype have better prognosis. The analysis used 1000 "discovery set" tumors; our results were highly reproducible on 1000 independent "validation" samples. CONCLUSIONS: The coarse-grained variables that represent pathway deregulation provide a basis for relevant, novel and robust findings for breast cancer. Our analysis indicates that in breast cancer reliable prognostic signatures are most likely to be obtained by treating separately different subgroups of the patients.