Nature utilizes the burnt bridges ratchet (BBR) to generate active motion in a variety of biological contexts. Here, the influence of substrate stiffness on spherical BBR dynamics is investigated.
Folates mediate one-carbon (C1) transfers, which are essential for cellular homeostasis and survival. C1 metabolism encompasses distinct cytosol (Cyto) and mitochondria (Mito) pathways connected by an interchange between serine, glycine and formate. Mito C1 metabolism provides cellular glycine and C1 units (as formate) for de novo synthesis of thymidylate and purine nucleotides in the Cyto. Polyglutamyl folates are the predominant folate forms in cells and are generally preferred for C1 transfers. Thus, folate polyglutamylation is essential for cellular homeostasis. Folate polyglutamylation is catalyzed by folypolyglutamate synthetase (FPGS), including Cyto (cFPGS) and Mito (mFPGS) isoforms. C1 metabolism is critical for tumor growth and thus offers a plethora of therapeutic targets for cancer. Of particular interest are Mito C1 inhibitors including “non-classical” pyrazolopyran compounds SHIN1 and SHIN2, and “classical” pyrrolo[3,2-d]pyrimidine antifolate compounds typified by AGF347, all targeting serine hydroxymethyltransferase 2 (SHMT2). Both SHIN1 and AGF347 effected in vitro anti-tumor efficacy, and SHIN2 and AGF347 showed in vivo efficacy toward tumor xenografts. We systematically explored the roles of cFPGS and mFPGS levels as critical determinants of C1 inhibitor target engagement and net Cyto versus Mito C1 flux, resulting in anti-tumor efficacy. We found that FPGS transcript levels significantly correlated with the effects of AGF347 toward a panel of human pancreatic cancer cell lines including MIA PaCa-2. We engineered MIA PaCa-2 cells with FPGS gene knockout to stably express inducible mFPGS or cFPGS form. FPGS in mFPGS-transfected cells (mFPGS#32) was expressed mainly in Mito over Cyto, whereas FPGS in cFPGS-transfected cells (cFPGS#3) was expressed exclusively in Cyto. Accumulation of radiolabeled folate and AGF347 increased with increasing FPGS in both Cyto and Mito for mFPGS#32, but only in Cyto for cFPGS#3. Metabolomics with [2,3,3-2H]serine in cFPGS#3 and mFPGS#32 established FPGS levels in Mito versus Cyto as important determinants of C1 fluxes. Increased FPGS levels in mFPGS#32 enhanced C1 flux in Mito far greater than in Cyto, and greater than for the increased FPGS in cFPGS#3. As a result, Cyto-targeted antifolates pemetrexed and AGF94 were only modestly impacted by increasing FPGS levels in both mFPGS#32 and cFPGS#3 (~3x). In contrast, increasing FPGS levels dramatically enhanced the inhibitory effects of Mito-targeted antifolates such as AGF347 in mFPGS#32 (~8-25x), and to a greater extent than in cFPGS#3 (~6-8x). Conversely, increasing FPGS levels substantially attenuated the cytotoxic effect of SHIN1 in mFPGS#32 (~19x). In summary, FPGS levels are an important determinant of C1 fluxes, particularly in Mito where they contribute to cytotoxic potencies of Mito-targeted C1 inhibitors at SHMT2. Citation Format: Carrie O’Connor, Jade Katinas, Mathew Schneider, Md. Junayed Nayeen, Xun Bao, Jing Li, Charles Dann, Aleem Gangjee, Larry H. Matherly, Zhanjun Hou. Mitochondrial and cytosolic folylpolyglutamate synthetase in one-carbon metabolism and anti-tumor efficacy of mitochondrial-targeted antifolates. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4902.
Single-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed “AutoSmarTrace”, the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy (AFM). We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with the ground truth. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. While trained on AFM-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary shadowing electron microscopy and fluorescence imaging.Statement of SignificanceMachine learning presents powerful capabilities to the analysis of large data sets. Here, we apply this approach to the determination of bending flexibility – described through persistence length – from single-molecule images of biological filaments. We present AutoSmarTrace, a tool for automated tracing and analysis of chain flexibility. Built on a neural network trained via machine learning, we show that AutoSmarTrace can determine persistence lengths from AFM images of a variety of biological macromolecules including collagen and DNA. While trained on AFM-like images, the algorithm works well to identify filaments in other types of images. This technique can free researchers from tedious tracing of chains in images, removing user bias and standardizing determination of chain mechanical parameters from single-molecule conformational images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.