Biology subjects

Gao, T.

Publications and source records attributed to Gao, T..

6 recordsLinked to original sources

bioRxiv2026

Structural mechanism governing radiationless energy transfer in Renilla bioluminescence

The nonradiative transport of electronic excitation from one chromophore to another, known as resonance energy transfer, lies at the root of photochemical processes in biology. Unlike photosynthesis, bioluminescence converts chemical energy into light through an enzymatic oxygenation of an energy-rich luciferin. In glowing cnidarians, the energy is relocated from an excited oxyluciferin to a fluorescent protein, shifting the colour and enhancing the quantum yield of a photogenic reaction. How protein-chromophore complexes assemble during this interplay in real space, and what this association entails for function, are unknown. Here, we report co-crystal structures of a 120-kilodalton energy-transfer complex from the luminescent soft coral Renilla reniformis. We find a heterotetrameric 2:2 assembly composed of two coelenteramide-loaded luciferases (RrLuc) docked at opposite sides of a head-to-tail dimer of green fluorescent protein (RrGFP). The edge-to-edge distance between donor and acceptor chromophores is below 3 nm, favouring the Forster-type radiationless energy transfer. Furthermore, RrGFP serves not only as a colour-switchable antenna and luminescence amplifier but also tunes the efficiency of luciferase catalysis by controlling its inherent dynamics. Our results provide detailed spatial information about intermolecular dipole-dipole coupling in Renilla bioluminescence, including the arrangement of donor-acceptor pairs that secure excited-state energy transfer with exquisite precision.

biochemistry↗

bioRxiv2018

Cytologic, Genetic, and Proteomic Analysis of a Yellow Leaf Mutant of Sesame (Sesamum indicum L.), Siyl-1

Leaf color mutation in sesame always affects the growth and development of plantlets, and their yield. To clarify the mechanisms underlying leaf color regulation in sesame, we analyzed a yellow-green leaf mutant. Genetic analysis of the mutant selfing revealed 3 phenotypes--YY, light-yellow (lethal); Yy, yellow-green; and yy, normal green--controlled by an incompletely dominant nuclear gene, Siyl-1. In YY and Yy, the number and morphological structure of the chloroplast changed evidently, with disordered inner matter, and significantly decreased chlorophyll content. To explore the regulation mechanism of leaf color mutation, the proteins expressed among YY, Yy, and yy were analyzed. All 98 differentially expressed proteins (DEPs) were classified into 5 functional groups, in which photosynthesis and energy metabolism (82.7%) occupied a dominant position. Our findings provide the basis for further molecular mechanism and biochemical effect analysis of yellow leaf mutants in plants.

genetics↗

bioRxiv2018

Gallic Acid Disrupts Aβ1-42 Aggregation and Rescues Cognitive Decline of APP/PS1 Transgenic Mouse

Alzheimers disease (AD) treatment represents one of the largest unmet medical needs. Developing drugs capable of preventing A{beta} aggregation is an excellent approach to prevent and treat AD. Here, we show that gallic acid (GA), a naturally occurring polyphenolic small molecule rich in grape seeds and fruits, has the capacity to alleviate cognitive decline of APP/PS1 transgenic mouse through reduction of A{beta}1-42 aggregation and neurotoxicity. Oral administration of GA not only improved the spatial reference memory and spatial working memory of early stage AD mice (4-month-old), but also significantly reduced the more severe deficits in spatial learning, reference memory, short-term recognition and spatial working memory of the late stage AD mice (9-month-old). The hippocampal long-term-potentiation (LTP) was also significantly elevated in the GA-treated late stage APP/PS1 AD mice. Atomic force microscopy (AFM), dynamic light scattering (DLS) and thioflavin T (ThT) fluorescence densitometry analyses showed that GA can reduce A{beta}1-42 aggregation from forming toxic oligomers and fibrils. Indeed, pre-incubating GA with oligomeric A{beta}1-42 reduced A{beta} 1-42-mediated intracellular calcium influx and neurotoxicity. Molecular docking studies identified that the 3,4,5-hydroxyle groups of GA were essential in noncovalently stabilizing GA binding to the Lys28-Ala42 salt bridge and the -COOH group is critical for disrupting the salt bridge of A{beta}1-42. The predicated covalent interaction through Schiff-base formation between the carbonyl group of the oxidized product and {varepsilon}-amino group of Lys16 is also critical for the disruption of A{beta}1-42 S-shaped triple-{beta}-motif and toxicity. Together, these studies demonstrated that GA can prevent and protect the AD brain through disrupting A{beta}1-42 aggregation.

neuroscience↗

bioRxiv2017

Efficient online-group-screening designs for agent identification

Identifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as high-throughput screening (HTS), the ratio of causal to non-causal agents is usually very low.\n\nIn this paper, we introduce a group-screening strategy to efficiently screen causal agents by grouping them into treatments. Our analysis shows that when a large number of candidates factors are screened and true agent percentage is very low (less than 1%), even in the worst case we could save up to 80% of the experiment runs. In the case where experiments span many rounds, we provide an online version of the group-screening that can determine the best strategy automatically based on the existing results. We applied this method to a real HTS experiment with 50,000 candidates that would require 9 rounds to finish in an exhaustive case. Our analysis showed that by applying the online-group-screening method, in the worst case, we can use 3 rounds and 19.7% (9828/50000) total tests to identify all the agents.\n\nFinally, we show that with minor modifications, this framework extends to more complex agent discovery problems.

bioinformatics↗

bioRxiv2017

Assessment of the World Largest Afforestation Program: Success, Failure, and Future Directions

The Three-North Afforestation Program (TNAP), initiated in 1978 and scheduled to be completed in 2050, is the worlds largest afforestation project and covers 4.07 x 106 km2 (42.4%) of China. We systematically assessed goals and outcomes of the first 30 years of the TNAP using high-resolution remote sensing and ground survey data. With almost 23 billion dollars invested between 1978 and 2008, the forested area within the TNAP region increased by 1.20 x 107 ha, but the proportion of high quality forests declined by 15.8%. The establishment of shelterbelts improved crop yield by 1.7%, much lower than the 5.9% expected once all crop fields are fully protected by shelterbelts. The area subjected to soil erosion by water decreased by 36.0% from 6.72 x 107 to 4.27 x 107 ha; the largest reductions occurred in areas where soil erosion had been most severe and forests contributed more than half of this improvement. Desertification area increased from 1978 to 2000 but decreased from 2000 to 2008; the 30-year net reduction was 13.0% (4.05x106 ha), with 8.0% being accounted for by afforestation in areas with only slight, prior desertification. In addition to its direct impacts, the TNAP has enhanced peoples awareness of environmental protection and attracted consistent attention and long-term commitment from the Chinese government to the restoration and protection of fragile ecosystems in the vast Three-North region. The significant decline in forest quality, limited success in reducing desertification, and low coverage of shelterbelts are aspects of the TNAP in need of re-assessment, and additional ca. 34 billion dollars will be needed to ensure the completion of the TNAP.

ecology↗

bioRxiv2016

Development and assessment of fully automated and globally transitive geometric morphometric methods, with application to a biological comparative dataset with high interspecific variation

Automated geometric morphometric methods are promising tools for shape analysis in comparative biology: they improve researchers abilities to quantify biological variation extensively (by permitting more specimens to be analyzed) and intensively (by characterizing shapes with greater fidelity). Although use of these methods has increased, automated methods have some notable limitations: pairwise correspondences are frequently inaccurate or lack transitivity (i.e., they are not defined with reference to the full sample). In this study, we reassess the accuracy of two previously published automated methods, cPDist [1] and auto3Dgm [2], and evaluate several modifications to these methods. We show that a substantial fraction of alignments and pairwise maps between specimens of highly dissimilar geometries were inaccurate in the study of Boyer et al. [1], despite a taxonomically sensitive variance structure of continuous Procrustes distances. We also show these inaccuracies can be remedied by utilizing a globally informed methodology within a collection of shapes, instead of only comparing shapes in a pairwise manner (c.f. [2]). Unfortunately, while global information generally enhances maps between dissimilar objects, it can degrade the quality of correspondences between similar objects due to the accumulation of numerical error. We explore a number of approaches to mitigate this degradation, quantify the performance of these approaches, and compare the generated pairwise maps (as well as the shape space characterized by these maps) to a \"ground truth\" obtained from landmarks manually collected by geometric morphometricians. Novel methods both improve the quality of the pairwise correspondences relative to cPDist, and achieve a taxonomic distinctiveness comparable to auto3Dgm.

bioinformatics↗