Ptpn2 and KLRG1 regulate the generation and function of tissue-resident memory CD8 + T cells in skin

Tissue-resident memory T cells (T cells) are key elements of tissue immunity. Here, we investigated the role of the regulator of T cell receptor and cytokine signaling, Ptpn2, in the formation and function of T cells in skin. Ptpn2-deficient CD8 T cells displayed a marked defect in generating CD69 CD103 T cells in response to herpes simplex virus type 1 (HSV-1) skin infection. This was accompanied by a reduction in the proportion of KLRG1 memory precursor cells and a transcriptional bias toward terminal differentiation. Of note, forced expression of KLRG1 was sufficient to impede T cell formation. Normalizing memory precursor frequencies by transferring equal numbers of KLRG1− cells restored T generation, demonstrating that Ptpn2 impacted skin seeding with precursors rather than downstream T cell differentiation. Importantly, Ptpn2-deficient T cells augmented skin autoimmunity but also afforded superior protection from HSV-1 infection. Our results emphasize that KLRG1 repression is required for optimal T cell formation in skin and reveal an important role of Ptpn2 in regulating TRM cell functionality.K. Hochheiser was supported by the German Research Council (grant HO 5417/1-1) and is a Rhian and Paul Brazis Fellow in Translational Melanoma Immunology administered by the Peter MacCallum Cancer Foundation. T. Gebhardt is a Senior Biomedical Research Fellow supported by the Sylvia and Charles Viertel Charitable Foundatio

Systematic identification of abundant A-to-I editing sites in the human transcriptome

RNA editing by members of the double-stranded RNA-specific ADAR family leads to site-specific conversion of adenosine to inosine (A-to-I) in precursor messenger RNAs. Editing by ADARs is believed to occur in all metazoa, and is essential for mammalian development. Currently, only a limited number of human ADAR substrates are known, while indirect evidence suggests a substantial fraction of all pre-mRNAs being affected. Here we describe a computational search for ADAR editing sites in the human transcriptome, using millions of available expressed sequences. 12,723 A-to-I editing sites were mapped in 1,637 different genes, with an estimated accuracy of 95%, raising the number of known editing sites by two orders of magnitude. We experimentally validated our method by verifying the occurrence of editing in 26 novel substrates. A-to-I editing in humans primarily occurs in non-coding regions of the RNA, typically in Alu repeats. Analysis of the large set of editing sites indicates the role of editing in controlling dsRNA stability.Comment: Pre-print version. See http://dx.doi.org/10.1038/nbt996 for a reprin

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP

Estrogenic Plant Extracts Reverse Weight Gain and Fat Accumulation without Causing Mammary Gland or Uterine Proliferation

Long-term estrogen deficiency increases the risk of obesity, diabetes and metabolic syndrome in postmenopausal women. Menopausal hormone therapy containing estrogens might prevent these conditions, but its prolonged use increases the risk of breast cancer, as wells as endometrial cancer if used without progestins. Animal studies indicate that beneficial effects of estrogens in adipose tissue and adverse effects on mammary gland and uterus are mediated by estrogen receptor alpha (ERα). One strategy to improve the safety of estrogens to prevent/treat obesity, diabetes and metabolic syndrome is to develop estrogens that act as agonists in adipose tissue, but not in mammary gland and uterus. We considered plant extracts, which have been the source of many pharmaceuticals, as a source of tissue selective estrogens. Extracts from two plants, Glycyrrhiza uralensis (RG) and Pueraria montana var. lobata (RP) bound to ERα, activated ERα responsive reporters, and reversed weight gain and fat accumulation comparable to estradiol in ovariectomized obese mice maintained on a high fat diet. Unlike estradiol, RG and RP did not induce proliferative effects on mammary gland and uterus. Gene expression profiling demonstrated that RG and RP induced estradiol-like regulation of genes in abdominal fat, but not in mammary gland and uterus. The compounds in extracts from RG and RP might constitute a new class of tissue selective estrogens to reverse weight gain, fat accumulation and metabolic syndrome in postmenopausal women

Transcriptional Analysis of T Cells Resident in Human Skin.

Human skin contains various populations of memory T cells in permanent residence and in transit. Arguably, the best characterized of the skin subsets are the CD8(+) permanently resident memory T cells (TRM) expressing the integrin subunit, CD103. In order to investigate the remaining skin T cells, we isolated skin-tropic (CLA(+)) helper T cells, regulatory T cells, and CD8(+) CD103(-) T cells from skin and blood for RNA microarray analysis to compare the transcriptional profiles of these groups. We found that despite their common tropism, the T cells isolated from skin were transcriptionally distinct from blood-derived CLA(+) T cells. A shared pool of genes contributed to the skin/blood discrepancy, with substantial overlap in differentially expressed genes between each T cell subset. Gene set enrichment analysis further showed that the differential gene profiles of each human skin T cell subset were significantly enriched for previously identified TRM core signature genes. Our results support the hypothesis that human skin may contain additional TRM or TRM-like populations

Middle-Tier Extensible Data Management

this paper, we discuss how extensible, middle-tier data management can address the twin challenges of flexibility and efficiency for today's e-commerce applications. Specifically, we make several contributions: . We present an architecture for deploying eXtensible Data Management in the middle tier of an ecommerce applicatio

Extensible Data Management in the Middle-Tier

Current data management solutions are largely optimized for intra-enterprise, client-server applications. They depend on predictability, predefined structure, and universal administrative control, and cannot easily cope with change and lack of structure. However, modern ecommerce applications are dynamic, unpredictable, organic, and decentralized, and require adaptability. eXtensible Data Management (XDM) is a new approach that enables rapid development and deployment of networked, data-intensive services by providing semantically-rich, high-performance middle-tier data management, and allows heterogeneous data from different sources to be accessed in a uniform manner. Here, we discuss how middle tier extensible data management can benefit an enterprise, and present technical details and examples from the Index Fabric, an XDM engine we have implemented. 1