Biology subjects

Xiao, D.

Publications and source records attributed to Xiao, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Large-FOV 3D localization microscopy by spatially variant point spread function generation

Accurate characterization of the microscopic point spread function (PSF) is crucial for achieving high-performance localization microscopy (LM). Traditionally, LM assumes a spatially-invariant PSF to simplify the modeling of the imaging system. However, for large fields of view (FOV) imaging, it becomes important to account for the spatially variant nature of the PSF. In this work, we propose an accurate and fast principal component analysis (PCA)-based field-dependent 3D PSF generator (PPG3D) and localizer for LM. Through simulations and experimental 3D single molecule localization microscopy (SMLM), we demonstrate the effectiveness of PPG3D, enabling super-resolution imaging of mitochondria and microtubules with high fidelity over a large FOV. A comparison of PPG3D with three other shift-invariant and shift-variant PSF generators for 3D LM reveals a three-fold improvement in accuracy and an operation speed approximately one hundred times faster. Given its user-friendliness and conciseness, we believe that PPG3D holds great potential for widespread application in SMLM and other imaging modalities.

bioengineering↗

bioRxiv2023

A cortical information bottleneck during decision-making

Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas. SignificanceThe brain uses multiple areas for cognition, decision-making, and action, but it is unclear why cortical activity differs by brain area. Machine learning and information theory suggests that one benefit of multiple areas is that it provides an "information bottleneck" that compresses inputs into an optimal representation that is minimal and sufficient to solve the task. Combining experimental recordings from behaving animals and computational simulations, we show that later brain areas have a tendency to form such minimal sufficient representations of task inputs through preferential propagation of task-relevant information present in earlier areas. Our results thus provide insight into one possible reason why the brain uses multiple brain areas for supporting decision-making and action.

neuroscience↗

bioRxiv2023

CCR9 overexpression promotes T-ALL progression by enhancing cholesterol biosynthesis

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of the lymphoid progenitor cells contributing to [~] 20% of the total ALL cases with higher prevalence in adults than the children. Despite the important role of human T-ALL cell lines in understanding the biology and treatment options, a detailed comparison of the tumorigenic potential of two commonly used T-ALL cell lines, MOLT4 and JURKAT cells is still lacking. In the present study, we compared the leukemogenic potentials of the two T-ALL cell lines (MOLT4 and JURKAT) in NOD-PrkdcscidIL2rgdull (NTG) mice and found that MOLT4 cells possessed a relatively higher aggressive phenotype characterized by their enhanced tissue infiltration as compared to the JURKAT cells. Gene expression profiling of the two cell lines revealed numerous differentially expressed genes (DEGs) including CC chemokine receptor 9 (CCR9), which augmented the invasion and metastasis of MOLT4 and JURKAT cells in vitro. The upregulation of CCR9 also promoted the tissue infiltration of JURKAT cells in the NTG mice. CCR9 overexpression increased cholesterol production by upregulating the expression of the core regulatory genes of the cholesterol biosynthesis pathway including MSMO1, MVD, and HMGCS1. Moreover, the upregulated expression of EGR1 was also found with CCR9 overexpression that modulated the expression of MSMO1, MVD, and HMGCS1. Notably, the treatment of the cells with simvastatin and siRNA-EGR1 decreased the aggressiveness of the CCR9 overexpressing JURKAT cells in vitro, suggesting the CCR9-EGR1 axis in T-ALL progression. This study highlights the distinct tumorigenic potentials of two T-ALL cell lines and reveals CCR9-regulated enhanced cholesterol biosynthesis in T-ALL. Simple summaryT-ALL is an aggressive cancer of the blood and bone marrow. In order to understand the biological mechanism of T-ALL, in vitro T-ALL cell lines are commonly employed. However, a comprehensive comparison of two common T-ALL cell lines, MOLT4 and JURKAT cells for T-ALL development is not yet available. We compared MOLT4 and JURKAT cells for T-ALL inducing potential and found that MOLT4 cells exhibited a relatively increased aggressiveness in mice as compared to JURKAT cells. We examined the molecular characteristics of two cell lines that could lead to differences in cancer development. Transcriptional profiling of MOLT4 and JURKAT cells revealed significant changes in the expression of several genes including CCR9. This aberrant expression of CCR9 impacted the migration and invasion of the T-ALL cell lines in vitro. In addition, higher expression levels of CCR9 also promoted T-ALL progression in vivo. Transcriptome analysis and gene ontology analysis of the DEGs of CCR9 over-expressing JURKAT cells indicated the enrichment of the genes in the cholesterol biosynthesis pathway, suggesting this metabolic rewiring mechanism as a strategy to cope with the increased energy demand of the malignant T-ALL cells.

cancer biology↗

bioRxiv2023

Deep Learning Enhanced Fast Fluorescence Lifetime Imaging with A Few Photons

We present a deep learning (DL) framework, which we term FPFLI (Few-Photon Fluorescence Lifetime Imaging), for fast analyzing fluorescence lifetime imaging (FLIM) data under highly low-light conditions with only a few photon-per-pixels (PPPs). FPFLI breaks the conventional pixel-wise lifetime analysis paradigm and fully exploits the spatial correlation and intensity information of fluorescence lifetime images to estimate lifetime images, pushing the photon budget to an unprecedented low level. The DL framework can be trained by synthetic FLIM data and easily adapted to various FLIM systems. FPFLI can effectively and robustly estimate FLIM images within seconds using synthetic and experimental data. The fast analysis of low-light FLIM images made possible by FPFLI will promise a broad range of potential applications.

bioengineering↗

bioRxiv2023

Mesotrode: chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice.

Brain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.

animal behavior and cognition↗

bioRxiv2022

Longitudinal assessment of water-reaching reveals altered cortical activity and fine motor coordination defects in a Huntington Disease model

Huntington Disease (HD), caused by dominantly inherited expansions of a CAG repeat results in characteristic motor dysfunction. Although gross motor and balance defects have been extensively characterized in multiple HD mouse models using tasks such as rotarod, beam walking and gait analysis, little is known about forelimb deficits. Here we use a high-throughput alternating reward/non-reward water-reaching task conducted daily over [~]2 months to simultaneously monitor forelimb impairment and mesoscale cortical changes in GCaMP activity, comparing female zQ175 (HD) and wildtype (WT) littermate mice, starting at [~]5.5 months of age. Behavioral analysis of the water-reaching task reveals that HD mice, despite learning the water-reaching task as proficiently as WT mice, take longer to learn the alternating event sequence. Although WT mice displayed no significant changes in cortical activity and reaching trajectory throughout the testing period, HD mice exhibited an increase in cortical activity - especially in the secondary motor and retrosplenial cortices - over time, as well as longer and more variable reaching trajectories by [~]7 months of age. HD mice also experienced a progressive reduction in successful performance rates. Tapered beam and rotarod tests before and/or after water-reaching assessment confirmed these early and manifest stages of HD characterized by the absence and presence of failed water-reaching trials, respectively. Reduced DARPP-32 (marker for striatal medium spiny neurons) expression in HD mice further confirmed disease pathology. The water-reaching task can be used to inform HD and potentially other movement disorder onset, therapeutic intervention windows and test drug efficacy. Significance statementThe movement disorder, Huntington Disease (HD), has been extensively studied in preclinical settings using mouse models of disease examining gross motor and balance defects. Little however, is known regarding forelimb deficits and underlying cortical circuit changes. Using a high-throughput alternating reward/non-reward water-reaching task, we characterized early event sequence learning defects in HD mice aged [~]5.5 months. Progressive forelimb movement defects first become apparent at [~]6.5 months of age with corresponding increases in cortical activity associated with reaching observed over time. These forelimb defects revealed in the water-reaching task are coincident with gross motor defects characterized using the tapered beam and rotarod tasks, demonstrating the suitability of the water-reaching task in phenotyping HD motor deficits.

neuroscience↗

bioRxiv2022

MicroRNA-155 regulates osteogenesis and bone mass phenotype via targeting S1PR1 gene

MicroRNA-155 (miR155) is overexpressed in various inflammatory diseases and cancer, in which bone resorption and osteolysis are frequently observed. However, the role of miR155 on osteogenesis and bone mass phenotype is still unknown. Here, we report a low bone mass phenotype in the long bone of miR155-Tg mice compared with control mice. In contrast, miR155-KO mice showed a high bone mass phenotype. miR155-KO mice showed robust bone regeneration in the ectopic and orthotopic model, but miR155-Tg mice showed compromised bone regeneration compared with the control mice. Similarly, the osteogenic differentiation potential of bone marrow stromal stem cells (BMSCs) from miR155-KO mice was robust and miR155-Tg was compromised compared with that of control mice. Moreover, miR155 knockdown in BMSCs from control mice showed higher osteogenic differentiation potential, supporting the results from miR155-KO mice. TargetScan analysis predicted S1PR1 as a target gene of miR155, which was further confirmed by luciferase assay and miR155 knockdown. S1PR1 overexpression in BMSCs robustly promoted osteogenic differentiation without affecting cell viability and proliferation. Thus, miR155 showed a catabolic effect on osteogenesis and bone mass phenotype via interaction with the S1PR1 gene, suggesting inhibition of miR155 as a potential strategy for bone regeneration and bone defect healing.

cell biology↗

bioRxiv2021

SMC1A facilitates gastric cancer cells proliferation, migration and invasion via promoting SNAI2 activated EMT

Backgroundstructural maintenance of chromosomes protein 1A (SMC1A) is a crucial subunit of the cohesion protein complex and plays a vital role in cell cycle regulation, genomic stability maintenance, chromosome dynamics. Recent studies demonstrated that SMC1A participate in tumorigenesis. This reseach aims to explore the role and the underlying mechanisms of SMC1A in gastric cancer (GC). Materials and methodsRT-qPCR and western blot were used to examine the expression levels of SMC1A in GC tissues and cell lines. The role of SMC1A on GC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT)were analyzed. Furthermore,the mechanism of SMC1A action was investigated. ResultsSMC1A was highly expressed in GC tissues and cell lines. The high expression of SMC1A indicated the poor overall survival of GC patients from Kaplan-Meier Plotter. Enhancing the expression of SMC1A in AGS remarkably promoted cell proliferation, migration and invasion. While knockdown of SMC1A in HCG27 inhibited cell proliferation, migration and invasion of HGC27 cells. Moreover, its observed that SMC1A promoted EMT and malignant cell behaviors via regulating SNAI2 Conclusionour study revealed SMC1A facilitates gastric cancer cell proliferation, migration and invasion via promoting SNAI2 activated EMT, which indicated SMC1A may be a potential target for gastric cancer therapy.

cancer biology↗

bioRxiv2021

Altered cortical processing of sensory input in Huntington disease mouse models

Huntington disease (HD), a hereditary neurodegenerative disorder, manifests as progressively impaired movement and cognition. Although early abnormalities of neuronal activity in striatum are well established in HD models, there are fewer in vivo studies of the cortex. Here, we record local field potentials (LFPs) in YAC128 HD model mice versus wild-type mice. In multiple cortical areas, limb sensory stimulation evokes a greater change in LFP power in YAC128 mice. Mesoscopic imaging using voltage-sensitive dyes reveal more extensive spread of evoked sensory signals across the cortical surface in YAC128 mice. YAC128 layer 2/3 sensory cortical neurons ex vivo show increased excitatory events, which could contribute to enhanced sensory responses in vivo. Cortical LFP responses to limb stimulation, visual and auditory input are also significantly increased in zQ175 HD mice. Results presented here extend knowledge of HD beyond ex vivo studies of individual neurons to the intact cortical network.

neuroscience↗

bioRxiv2021

Differentiation of cortical brain organoids and optic nerve-like structures from retinal confluent cultures of pluripotent stem cells

Advances in the study of neurological conditions have been possible due to induced pluripotent stem cell technologies and the generation of neural cell types and organoids. Numerous studies have described the generation of neural ectoderm-derived retinal and brain structures from pluripotent stem cells. However, the field is still troubled by technical challenges, including high culture costs and organoid-to-organoid variability. Here, we describe a simple and economical protocol that reproducibly gives rise to the neural retina and cortical brain regions from confluent cultures of stem cells. The spontaneously generated cortical organoids were isolated and cultured in suspension conditions for maturation and are transcriptionally comparable to organoids generated by other methods and to human foetal cortex. Furthermore, these organoids show spontaneous functional network activity with proteomic analysis and electron microscopy demonstrating the presence of synaptic components and maturity. The generation of retinal and brain organoids in close proximity also enabled their mutual isolation. Further culture of this complex organoid system demonstrated the formation of optic nerve-like structures connecting retinal and brain organoids, which might facilitate the investigation of the mechanisms of neurological diseases of the eye and brain.

neuroscience↗

bioRxiv2021

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a pseudo-positive learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data re-sampling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ( SnapKin) incorporating the above two learning strategies into a snapshot ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

bioinformatics↗

bioRxiv2021

A D-2-hydroxyglutarate biosensor based on specific transcriptional regulator DhdR

D-2-Hydroxyglutarate (D-2-HG) is a metabolite in many physiological metabolic processes. When D-2-HG is aberrantly accumulated due to mutations in isocitrate dehydrogenases or D-2-HG dehydrogenase, it functions in a pro-oncogenic manner and is thus considered a therapeutic target and biomarker in many cancers. In this study, DhdR from Achromobacter denitrificans NBRC 15125 was identified as an allosteric transcription factor that negatively regulates D-2-HG dehydrogenase expression and responds to presence of D-2-HG. It is the first known transcription regulator specifically responding to D-2-HG across all domains of life. Based on the allosteric effect of DhdR, a D-2-HG biosensor was developed by combining DhdR with amplified luminescent proximity homogeneous assay technology. The biosensor was able to detect D-2-HG in serum, urine, and cell culture with high specificity and sensitivity. Additionally, this biosensor was also successfully used to identify the role of D-2-HG metabolism in lipopolysaccharide biosynthesis of Pseudomonas aeruginosa, demonstrating its broad usages.

microbiology↗

bioRxiv2021

Chromosome-scale assembly of the Sparassis latifolia genome obtained using long-read and Hi-C sequencing

Sparassis latifolia is a valuable edible mushroom cultivated in China. In 2018, our research group reported an incomplete and low quality genome of S. latifolia was obtained by Illumina HiSeq 2500 sequencing. These limitations in the available genome have constrained genetic and genomic studies in this mushroom resource. Herein, an updated draft genome sequence of S. latifolia was generated by Oxford Nanopore sequencing and the Hi-C technique. A total of 8.24 Gb of Oxford Nanopore long reads representing ~198.08X coverage of the S. latifolia genome were generated. Subsequently, a high-quality genome of 41.41 Mb, with scaffold and contig N50 sizes of 3.31 Mb and 1.51 Mb, respectively, was assembled. Hi-C scaffolding of the genome resulted in 12 pseudochromosomes containing 93.56% of the bases in the assembled genome. Genome annotation further revealed that 17.47% of the genome was composed of repetitive sequences. In addition, 13,103 protein-coding genes were predicted, among which 98.72% were functionally annotated. BUSCO assay results further revealed that there were 92.07% complete BUSCOs. The improved chromosome-scale assembly and genome features described here will aid further molecular elucidation of various traits, breeding of S. latifolia, and evolutionary studies with related taxa. ImportanceSparassis latifolia Y.C. Dai et Z. Wang (Sparassidaceae, Polyporales, Agaricomycetes), collections from Asian Sparassis (Dai et al., 2006) exhibits diverse biological and pharmacologic activities. To date, total fresh fruit production in Chinese factories is over 20 tons/d. Despite the significant economic and medical value of S. latifolia, the assembly level of genome was under chromosome-scale. This study assembles a high-quality chromosome-scale reference genome of S. latifolia using Oxford Nanopore sequencing combined with Hi-C (High-through chromosome conformation capture) scaffolding. The improved reference genome will facilitate molecular breeding of S. latifolia and advance our understanding of its genetics and evolution.

genomics↗

bioRxiv2020

X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis

In silico modelling and analysis of small molecules substantially accelerates the process of drug development. Representing and understanding molecules is the fundamental step for various in silico molecular analysis tasks. Traditionally, these molecular analysis tasks have been investigated individually and separately. In this study, we presented X-MOL, which applies large-scale pre-training technology on 1.1 billion molecules for molecular understanding and representation, and then, carefully designed fine-tuning was performed to accommodate diverse downstream molecular analysis tasks, including molecular property prediction, chemical reaction analysis, drug-drug interaction prediction, de novo generation of molecules and molecule optimization. As a result, X-MOL was proven to achieve state-of-the-art results on all these molecular analysis tasks with good model interpretation ability. Collectively, taking advantage of super large-scale pre-training data and super-computing power, our study practically demonstrated the utility of the idea of "mass makes miracles" in molecular representation learning and downstream in silico molecular analysis, indicating the great potential of using large-scale unlabelled data with carefully designed pre-training and fine-tuning strategies to unify existing molecular analysis tasks and substantially enhance the performance of each task.

bioinformatics↗

bioRxiv2020

First demonstration of the FLASH effect with ultrahigh dose-rate high energy X-rays

Ultrahigh dose-rate (FLASH) radiotherapy has attracted immense attention because of its tumor control efficiency and healthy tissue protection during preclinical experiments with electrons, kilo-voltage X-rays, and protons. Using high-energy X-rays (HEXs) in FLASH is advantageous owing to its deep penetration, small divergence, and cost-effectiveness. This is the first report on the implementation of HEXs with FLASH (HEX-FLASH) and its corresponding application in vivo. With a high-current and high-energy superconducting linear accelerator, FLASH with a good dose rate and high penetration was achieved. Breast cancers artificially induced in BAL b/c mice were efficiently controlled, and normal tissues surrounding the thorax/abdomen in C57BL/6 mice were protected from radiation with HEX-FLASH. Theoretical analyses of cellular responses following HEX-FLASH irradiation were performed to interpret the experimental results and design further experiments. Thus, this study highlights the generation of HEX-FLASH for the first time and its potential in future clinical applications.

cancer biology↗

bioRxiv2020

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of stably phosphorylated sites, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a signalome construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.

systems biology↗

bioRxiv2020

Tumor-stromal IL1β/ NFκB / ESE3 Signal Axis Drives Pancreatic Cancer Fibrosis, Chemoresistance, Prognosis

Pancreatic stellate cells (PSCs) play a pivotal role in pancreatic fibrosis and pancreatic ductal adenocarcinoma (PDAC) progression. The mechanisms controlling PSC activation is not completely understood. Here we investigated the role of ESE3 (Epithelium-Specific ETS factor 3) in PSC activation. We discovered that in PDAC patients ESE3 expression was increased in PSC while decreased in tumor cells. ESE3 overexpression in PSC promoted PSC activation. Condition medium from ESE3-overexprssing PSC promotes PDAC cell migration, chemoresistance, tumor growth and fibrosis. ESE3 directly induced the transcription of -SMA, Collagen 1 and IL-1{beta} by binding to ESE3 binding sites on their promoters to activate PSC. On the other hand, IL-1{beta} upregulates ESE3 in PSC through NF{kappa}B activation and ESE3 is required for PSC activation by tumor cell derived IL-1{beta}. Clinical data showed ESE3 upregulation in PSC was positively correlated with tumor size, pTNM stage, CA19-9, CEA and CA242 level in serum. ESE3 overexpression in PSC was an independent negative prognostic factor for disease-free survival and overall survival among PDAC patients. Inhibition of the IL-1{beta}/ ESE3 (PSC)/ IL-1{beta} positive feedback loop represents a promising therapeutic strategy to reduce tumor fibrosis and increase chemotherapeutic efficacy in PDAC.

cancer biology↗

bioRxiv2020

A fluorescent L-2-hydroxyglutarate biosensor

L-2-Hydroxyglutarate (L-2-HG) plays important roles in diverse physiological processes, such as carbon starvation response, tumorigenesis, and hypoxic adaptation. Despite its importance and intensively studied metabolism, regulation of L-2-HG metabolism remains poorly understood and a regulator specifically responded to L-2-HG has never been identified. Based on the genomic neighborhood analysis of the gene encoding L-2-HG oxidase (LhgO), LhgR, which represses the transcription of lhgO, was identified in Pseudomonas putida W619 in this study. LhgR was demonstrated to recognize L-2-HG as its specific effector molecule, and this allosteric transcription factor was then used as a biorecognition element for construction of L-2-HG-sensing FRET sensor. The newly developed L-2-HG sensor can conveniently monitor the concentrations of L-2-HG in various biological samples. In addition to bacterial L-2-HG generation during carbon starvation, biological functions of the L-2-HG dehydrogenase and hypoxia induced L-2-HG accumulation were also revealed by using the L-2-HG sensor in human cells.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗