Showing papers by "Vladimir Jojic published in 2019"
TL;DR: The subterranean-dwelling naked mole-rat (NM-R) exhibits prolonged life span relative to its body size, is unusually cancer resistant, and manifests few physiological or molecular changes with advancing age, challenging current understanding of mammalian immunity.
Abstract: The immune system comprises a complex network of specialized cells that protects against infection, eliminates cancerous cells, and regulates tissue repair, thus serving a critical role in homeostasis, health span, and life span. The subterranean-dwelling naked mole-rat (NM-R; Heterocephalus glaber) exhibits prolonged life span relative to its body size, is unusually cancer resistant, and manifests few physiological or molecular changes with advancing age. We therefore hypothesized that the immune system of NM-Rs evolved unique features that confer enhanced cancer immunosurveillance and prevent the age-associated decline in homeostasis. Using single-cell RNA-sequencing (scRNA-seq) we mapped the immune system of the NM-R and compared it to that of the short-lived, cancer-prone mouse. In contrast to the mouse, we find that the NM-R immune system is characterized by a high myeloid-to-lymphoid cell ratio that includes a novel, lipopolysaccharide (LPS)-responsive, granulocyte cell subset. Surprisingly, we also find that NM-Rs lack canonical natural killer (NK) cells. Our comparative genomics analyses support this finding, showing that the NM-R genome lacks an expanded gene family that controls NK cell function in several other species. Furthermore, we reconstructed the evolutionary history that likely led to this genomic state. The NM-R thus challenges our current understanding of mammalian immunity, favoring an atypical, myeloid-biased mode of innate immunosurveillance, which may contribute to its remarkable health span.
66 citations
TL;DR: The Naked mole-rat immune system is characterized by a high myeloid to lymphoid cell ratio that includes a novel, lipopolysaccharide responsive, granulocyte cell subset not found in the mouse, and it is found that naked mole-rats do not have a cell subset that corresponds to natural killer cells as defined in other well-characterized mammalian species.
Abstract: Using single-cell transcriptional profiling we mapped the immune system of the naked mole-rat (Heterocephalus glaber), a small but long-lived and cancer-resistant subterranean rodent. Both splenic and circulating immune cells were examined in healthy young animals and following an infection-mimicking lipopolysaccharide challenge. Our study revealed that the naked mole-rat immune system is characterized by a high myeloid to lymphoid cell ratio that includes a novel, lipopolysaccharide responsive, granulocyte cell subset not found in the mouse. Conversely, we find that naked mole-rats do not have a cell subset that corresponds to natural killer cells as defined in other well-characterized mammalian species. Supporting this finding, we show that the naked mole-rat genome has not expanded any of the gene families encoding diverse natural killer cell receptors, which are the genomic hallmarks of species in which natural killer cells have been described. These unusual features suggest an atypical mode of immunosurveillance and a greater reliance on myeloid-biased innate immunity.
9 citations
28 Jan 2019
TL;DR: A novel method that uses geometric and physical constraints to deduce the relative tissue elasticity parameters and demonstrates the feasibility of a statistically based classifier that automatically provides a clinical T-stage and Gleason score based on the elasticity values reconstructed from computed tomography images.
Abstract: Purpose: In this paper, we describe a method for recovering the tissue properties directly from medical images and study the correlation of tissue (i.e., prostate) elasticity with the aggressiveness of prostate cancer using medical image analysis. Methods: We present a novel method that uses geometric and physical constraints to deduce the relative tissue elasticity parameters. Although elasticity reconstruction, or elastograph, can be used to estimate tissue elasticity, it is less suited for in-vivo measurements or deeply seated organs like prostate. We develop a method to estimate tissue elasticity values based on pairs of images, using a finite-element-based biomechanical model derived from an initial set of images, local displacements, and an optimization-based framework. Results: We demonstrate the feasibility of a statistically based classifier that automatically provides a clinical T-stage and Gleason score based on the elasticity values reconstructed from computed tomography images. Conclusion: We study the relative elasticity parameters by performing cancer grading/staging prediction and achieve up to 85% accuracy for cancer staging prediction and up to 77% accuracy for cancer grading prediction using a feature set, which includes recovered relative elasticity parameters and patient age information.