Background The human skin microbiota is considered to be essential for skin homeostasis and barrier function. Comprehensive analyses of its function would substantially benefit from a catalog of reference genes derived from metagenomic sequencing. The existing catalog for the human skin microbiome is based on samples from limited individuals from a single cohort on reference genomes, which limits the coverage of global skin microbiome diversity. Results In the present study, we have used shotgun metagenomics to newly sequence 822 skin samples from Han Chinese, which were subsequently combined with 538 previously sequenced North American samples to construct an integrated Human Skin Microbial Gene Catalog (iHSMGC). The iHSMGC comprised 10,930,638 genes with the detection of 4,879,024 new genes. Characterization of the human skin resistome based on iHSMGC confirmed that skin commensals, such as Staphylococcus spp, are an important reservoir of antibiotic resistance genes (ARGs). Further analyses of skin microbial ARGs detected microbe-specific and skin site-specific ARG signatures. Of note, the abundance of ARGs was significantly higher in Chinese than Americans, while multidrug-resistant bacteria (“superbugs”) existed on the skin of both Americans and Chinese. A detailed analysis of microbial signatures identified Moraxella osloensis as a species specific for Chinese skin. Importantly, Moraxella osloensis proved to be a signature species for one of two robust patterns of microbial networks present on Chinese skin, with Cutibacterium acnes indicating the second one. Each of such “cutotypes” was associated with distinct patterns of data-driven marker genes, functional modules, and host skin properties. The two cutotypes markedly differed in functional modules related to their metabolic characteristics, indicating that host-dependent trophic chains might underlie their development. Conclusions The development of the iHSMGC will facilitate further studies on the human skin microbiome. In the present study, it was used to further characterize the human skin resistome. It also allowed to discover the existence of two cutotypes on the human skin. The latter finding will contribute to a better understanding of the interpersonal complexity of the skin microbiome.

Macrophages, an important class of innate immune cells that maintain body homeostasis and ward off foreign pathogens, exhibit a high degree of plasticity and play a supportive role in different tissues and organs. Thus, dysfunction of macrophages may contribute to advancement of several diseases, including cancer. Macrophages within the tumor microenvironment are known as tumor-associated macrophages (TAMs), which typically promote cancer cell initiation and proliferation, accelerate angiogenesis, and tame anti-tumor immunity to promote tumor progression and metastasis. Massive infiltration of TAMs or enrichment of TAM-related markers usually indicates cancer progression and a poor prognosis, and consequently tumor immunotherapies targeting TAMs have gained significant attention. Here, we review the interaction between TAMs and cancer cells, discuss the origin, differentiation and phenotype of TAMs, and highlight the role of TAMs in pro-cancer functions such as tumor initiation and development, invasive metastasis, and immunosuppression. Finally, we review therapies targeting TAMs, which are very promising therapeutic strategies for malignant tumors.

Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%-9.3%; (2) As the observed values increased, the LSR and kNN