Probable Donor-Derived Human Adenovirus Type 34 Infection in 2 Kidney Transplant Recipients From the Same Donor.

Human adenovirus type 34 (HAdV-34) infection is a recognized cause of transplant-associated hemorrhagic cystitis and, in rare cases, tubulointerstitial nephritis. The source of such infections is often difficult to assess, that is, whether acquired as a primary infection, exposure to a pathogen in the transplanted organ, or reactivation of an endogenous latent infection. We present here 2 cases of likely transplant-acquired HAdV-34 infection from the same organ donor, manifesting as tubulointerstitial nephritis in 1

High-resolution label-free imaging of tissue morphology with confocal phase microscopy

Label-free imaging approaches seek to simplify and augment histopathologic assessment by replacing the current practice of staining by dyes to visualize tissue morphology with quantitative optical measurements. Quantitative phase imaging (QPI) operates with visible/UV light and thus provides a resolution matched to current practice. Here we introduce and demonstrate confocal QPI for label-free imaging of tissue sections and assess its utility for manual histopathologic inspection. Imaging cancerous and normal adjacent human breast and prostate, we show that tissue structural organization can be resolved with high spatial detail comparable to conventional hematoxylin and eosin (H&E) stains. Our confocal QPI images are found to be free of halo, solving this common problem in QPI. We further describe a virtual imaging system based on finite-difference time-domain (FDTD) calculations and combine it with numerical tissue phantoms to quantitatively show the absence of halo and the improved clarity in resolving subcellular features with confocal QPI compared to wide-field QPI. Confocal QPI bears the potential to become a common tool for label-free disease diagnosis, while the presented FDTD method provides a flexible platform to evaluate the diagnostic potential of QPI methods

TLR2 and its co-receptors determine responses of macrophages and dendritic cells to lipoproteins of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) signals through Toll-like receptor 2 (TLR2) to regulate antigen presenting cells (APCs). Mtb lipoproteins, including LpqH, LprA, LprG and PhoS1, are TLR2 agonists, but their co-receptor requirements are unknown. We studied Mtb lipoprotein-induced responses in TLR2(-/-), TLR1(-/-), TLR6(-/-), CD14(-/-) and CD36(-/-) macrophages. Responses to LprA, LprG, LpqH and PhoS1 were completely dependent on TLR2. LprG, LpqH, and PhoS1 were dependent on TLR1, but LprA did not require TLR1. None of the lipoproteins required TLR6, although a redundant contribution by TLR6 cannot be excluded. CD14 contributed to detection of LprA, LprG and LpqH, whereas CD36 contributed only to detection of LprA. Studies of lung APC subsets revealed lower TLR2 expression by CD11b(high)/CD11c(low) lung macrophages than CD11b(low)/CD11c(high) alveolar macrophages, which correlated with hyporesponsiveness of lung macrophages to LpqH. Thus, lung APC subsets differ in TLR expression, which may determine differences in responses to Mtb

Mycobacterium tuberculosis Lipoproteins Directly Regulate Human Memory CD4+ T Cell Activation via Toll-Like Receptors 1 and 2▿

The success of Mycobacterium tuberculosis as a pathogen relies on its ability to regulate the host immune response. M. tuberculosis can manipulate adaptive T cell responses indirectly by modulating antigen-presenting cell (APC) function or by directly interacting with T cells. Little is known about the role of M. tuberculosis molecules in direct regulation of T cell function. Using a biochemical approach, we identified lipoproteins LprG and LpqH as major molecules in M. tuberculosis lysate responsible for costimulation of primary human CD4+ T cells. In the absence of APCs, activation of memory CD4+ T cells with LprG or LpqH in combination with anti-CD3 antibody induces Th1 cytokine secretion and cellular proliferation. Lipoprotein-induced T cell costimulation was inhibited by blocking antibodies to Toll-like receptor 2 (TLR2) and TLR1, indicating that human CD4+ T cells can use TLR2/TLR1 heterodimers to directly respond to M. tuberculosis products. M. tuberculosis lipoproteins induced NF-κB activation in CD4+ T cells in the absence of TCR co-engagement. Thus, TLR2/TLR1 engagement alone by M. tuberculosis lipoprotein triggered intracellular signaling, but upregulation of cytokine production and proliferation required co-engagement of the TCR. In conclusion, our results demonstrate that M. tuberculosis lipoproteins LprG and LpqH participate in the regulation of adaptive immunity not only by inducing cytokine secretion and costimulatory molecules in innate immune cells but also through directly regulating the activation of memory T lymphocytes

Mesenchymal gene program-expressing ovarian cancer spheroids exhibit enhanced mesothelial clearance.

Metastatic dissemination of ovarian tumors involves the invasion of tumor cell clusters into the mesothelial cell lining of peritoneal cavity organs; however, the tumor-specific factors that allow ovarian cancer cells to spread are unclear. We used an in vitro assay that models the initial step of ovarian cancer metastasis, clearance of the mesothelial cell layer, to examine the clearance ability of a large panel of both established and primary ovarian tumor cells. Comparison of the gene and protein expression profiles of clearance-competent and clearance-incompetent cells revealed that mesenchymal genes are enriched in tumor populations that display strong clearance activity, while epithelial genes are enriched in those with weak or undetectable activity. Overexpression of transcription factors SNAI1, TWIST1, and ZEB1, which regulate the epithelial-to-mesenchymal transition (EMT), promoted mesothelial clearance in cell lines with weak activity, while knockdown of the EMT-regulatory transcription factors TWIST1 and ZEB1 attenuated mesothelial clearance in ovarian cancer cell lines with strong activity. These findings provide important insights into the mechanisms associated with metastatic progression of ovarian cancer and suggest that inhibiting pathways that drive mesenchymal programs may suppress tumor cell invasion of peritoneal tissues

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

Abstract While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types