Biology subjects

Smith, H. R.

Publications and source records attributed to Smith, H. R..

3 recordsLinked to original sources

bioRxiv2023

Acidification-dependent suppression of C. difficile by enterococci in vitro

Clostridioides difficile and Vancomycin-resistant Enterococcus faecium (VRE) are commonly co-isolated from hospitalized patients. We sought to develop a co-culture biofilm model to characterize interactions between these two opportunistic pathogens. Upon growth in biofilm-promoting media containing added glucose, fructose or trehalose, VRE produces sufficient acid to lower the pH and inhibit growth of C. difficile. We found this effect depended on the carbon source, and that acidification by VRE was necessary and sufficient to suppress C. difficile growth in liquid medium and in cecal content extracts from germ free mice. VRE frequently dominates the intestine of patients administered antibiotics which can predispose to the development of C. difficile infection. We reasoned that it may be possible to suppress C. difficile growth during co-infection with VRE by supplementing the mouse diet with a fermentable sugar. A VRE-dominated gut microbiota may convert the sugar to acid, lower the pH and reestablish colonization resistance to C. difficile. Supplementation of the diet of VRE colonized mice with high levels of fructose neither resulted in a lower pH, nor did it prevent colonization by C. difficile. Taken together, these data suggest that VRE can suppress growth of C. difficile by organic acid production in a carbon source-dependent manner in vitro, however, the mammalian intestine may require sophisticated approaches to lower pH therapeutically.

microbiology↗

bioRxiv2022

High-resolution glucose fate-mapping reveals LDHB-dependent lactate production by human pancreatic β cells

Using 13C6 glucose labeling coupled to GC-MS and 2D 1H-13C HSQC NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning {beta} cell function. In both mouse and human islets, the contribution of glucose to the TCA cycle is similar. Pyruvate-fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is six-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and {beta} cell-specific LDHB expression. LDHB inhibition increases glucose-dependent lactate generation in mouse and human {beta} cells, and decreases Ca2+-spiking frequency without affecting ATP/ADP levels. Thus, we show that LDHB limits glucose-stimulated lactate generation in {beta} cells. Further studies are warranted to understand how lactate impacts {beta} cell metabolism and/or function. HIGHLIGHTSO_LIHuman and rodent islets generate lactate following glucose stimulation. C_LIO_LI{beta} cells specifically express LDHB, which acts to limit lactate generation. C_LIO_LILDHB inhibition influences Ca2+ spiking frequency without affecting ATP/ADP ratio. C_LI eTOCCuozzo et al show that glucose-stimulated rodent and human islets generate lactate. Transcriptomic and imaging analyses reveal that LDHB is specifically expressed in {beta} cells and unexpectedly restrains lactate production. LDHB expression and thus regulated lactate generation might reflect a key mechanism underlying {beta} cell metabolism, function and survival.

physiology↗

bioRxiv2021

Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning

A deeper understanding of the cellular and subcellular organization of tumor cells and their interactions with the tumor microenvironment will shed light on how cancer evolves and guide effective therapy choices. Electron microscopy (EM) images can provide detailed view of the cellular ultrastructure and are being generated at an ever-increasing rate. However, the bottleneck in their analysis is the delineation of the cellular structures to enable interpretable rendering. We have mitigated this limitation by using deep learning, specifically, the ResUNet architecture, to segment cells and subcellular ultrastructure. Our initial prototype focuses on segmenting nuclei and nucleoli in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. Trained with sparse manual labels, our method results in accurate segmentation of nuclei and nucleoli with best Dice score of 0.99 and 0.98 respectively. This method can be extended to other cellular structures, enabling deeper analysis of inter- and intracellular state and interactions.

cancer biology↗