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Ultrasensitive sensors reveal the spatiotemporal landscape of lactate metabolism in physiology and disease.

ABSTRACT: Despite its central importance in cellular metabolism, many details remain to be determined regarding subcellular lactate metabolism and its regulation in physiology and disease, as there is sensitive spatiotemporal resolution of lactate distribution, and dynamics remains a technical challenge. Here, we develop and characterize an ultrasensitive, highly responsive, ratiometric lactate sensor, named FiLa, enabling the monitoring of subtle lactate fluctuations in living cells and animals. Utilizing FiLa, we demonstrate that lactate is highly enriched in mammalian mitochondria and compile an atlas of subcellular lactate metabolism that reveals lactate as a key hub sensing various metabolic activities. In addition, FiLa sensors also enable direct imaging of elevated lactate levels in diabetic mice and facilitate the establishment of a simple, rapid, and sensitive lactate assay for point-of-care clinical screening. Thus, FiLa sensors provide powerful, broadly applicable tools for defining the spatiotemporal landscape of lactate metabolism in health and disease.

SUBMITTER: Li X

PROVIDER: S-EPMC10560847 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Ultrasensitive sensors reveal the spatiotemporal landscape of lactate metabolism in physiology and disease.

Li Xie X Zhang Yinan Y Xu Lingyan L Wang Aoxue A Zou Yejun Y Li Ting T Huang Li L Chen Weicai W Liu Shuning S Jiang Kun K Zhang Xiuze X Wang Dongmei D Zhang Lijuan L Zhang Zhuo Z Zhang Zeyi Z Chen Xianjun X Jia Wei W Zhao Aihua A Yan Xinfeng X Zhou Haimeng H Zhu Linyong L Ma Xinran X Ju Zhenyu Z Jia Weiping W Wang Congrong C Loscalzo Joseph J Yang Yi Y Zhao Yuzheng Y

Cell metabolism 20221028 1

Despite its central importance in cellular metabolism, many details remain to be determined regarding subcellular lactate metabolism and its regulation in physiology and disease, as there is sensitive spatiotemporal resolution of lactate distribution, and dynamics remains a technical challenge. Here, we develop and characterize an ultrasensitive, highly responsive, ratiometric lactate sensor, named FiLa, enabling the monitoring of subtle lactate fluctuations in living cells and animals. Utilizin ...[more]

PMID: 36309010

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Project description:BackgroundLactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).MethodsA gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.ResultsIn total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.ConclusionThe newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients' immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

Project description:BackgroundPrevious research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown.ObjectiveIn this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time.MethodsWe conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window.ResultsWe have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92).ConclusionsOur contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

Dataset Information

Ultrasensitive sensors reveal the spatiotemporal landscape of lactate metabolism in physiology and disease.

Publications

Ultrasensitive sensors reveal the spatiotemporal landscape of lactate metabolism in physiology and disease.

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