Biology subjects

Wang, S.-L.

Publications and source records attributed to Wang, S.-L..

4 recordsLinked to original sources

bioRxiv2022

FAM237A, rather than peptide PEN and proCCK56-63, is a ligand of the orphan receptor GPR83

G protein-coupled receptor 83 (GPR83) is primarily expressed in the brain and is implicated in the regulation of energy metabolism and some behaviors. Recently, the PCSK1N/proSAAS-derived peptide PEN, the procholecystokinin-derived peptide proCCK56-63, and family with sequence similarity 237 member A (FAM237A) were all reported as agonists of GPR83. However, these results have not yet been reproduced by other laboratories and thus GPR83 is still officially an orphan receptor. The PEN and proCCK56-63 share sequence similarity; however, they are completely different from FAM237A, raising doubts that all of them are ligands of GPR83. To identify its actual ligand(s), in the present study we developed a NanoLuc Binary Technology (NanoBiT)-based ligand-binding assay, fluorescent ligand-based visualization, and a NanoBiT-based {beta}-arrestin recruitment assay for human GPR83. Using these assays, we demonstrated that mature human FAM237A could bind to GPR83 with nanomolar range affinity, which activated this receptor and induced its internalization in transfected human embryonic kidney 293T cells. However, we did not detect any interaction of PEN and proCCK56-63 with GPR83 using these assays. Thus, the results confirmed that FAM237A is an agonist of GPR83, but did not support PEN and proCCK56-63 as ligands of this receptor. Clarification of its actual endogenous agonist will pave the way for further functional studies of this brain-specific receptor. The present study also provided an efficient approach for the preparation of mature FAM237A, which would facilitate further functional studies of this difficult-to-make peptide in the future.

biochemistry↗

bioRxiv2019

Whole genome sequencing of 358 brown planthoppers uncovers the landscape of their migration and dispersal worldwide

The brown planthopper (BPH), Nilaparvata lugens, is a serious migratory rice pest, which is distributed in the broad area of the tropical and temperate Asian-Pacific region. However, we know little about key aspects regarding its evolution such as how they diverged and dispersed worldwide. By resequencing and analyzing 358 BPH genomes from 92 populations across the world, we uncover the genetic relationships among their worldwide populations and the history of their global dispersal. We recovered five genetic groups representing the major population structures. Of these, Australian BPHs were shown large genetic divergence with Asian BPHs; two distinct groups have formed in South and Southeast/East Asia that show strong genetic admixture in the southwest border regions of China and west Thailand with Myanmar; two local populations in Bangladesh and Fujian province of China, respectively, unexpectedly separated with surrounding populations. We also find the genetic similarity and closely phylogenetic relationships between majority of East Asian BPHs and Indo-china peninsula BPHs, indicating that Southeast Asia mainland is the major insect sources and overwintering sites for East Asia. Our study provides important molecular evidence to address BPH evolution and other key aspects of its biology such as insecticides resistance and rice varieties virulence.

evolutionary biology↗

bioRxiv2018

Artificial neural network filters for enhancing 3D optical microscopy images of neurites

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and memory formation. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (NN) architectures and conventional image enhancement filters with the aim of solving both problems. To evaluate the effects of filtering, we examine the following four image quality metrics: normalized intensity in the cross-over regions between neurites, radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that NN filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing radius of neurites by 23% to nearly 1 voxel, decreasing intensity in the cross-over regions between neurites by 22%, and reducing variations in intensity along neurites by 26%. These results suggest that including a NN filtering step, which does not require much extra time or computing power, can be beneficial for neuron tracing projects.

neuroscience↗

bioRxiv2018

Accurate registration of 3D time-lapse microscopy images

In vivo imaging experiments often require automated detection and tracking of changes in the specimen. This problem, however, can be hindered by variations in the position and orientation of the specimen relative to the microscope, as well as by linear and nonlinear deformations. Here, we present a feature-based registration method, coupled with translation, rigid, affine, and B-spline transformations, designed to address these issues in 3D time-lapse microscopy images. In this method, features are detected as local intensity maxima in the source and target image stacks, and their similarity matrix is used as an input to the Hungarian algorithm to establish initial correspondences. Random Sampling Consensus algorithm is then employed to eliminate outliers. The resulting set of corresponding features is used to determine the optimal transformations. Accuracy of the proposed algorithm was tested on fluorescently labeled axons imaged over a 48-day period with a two-photon laser scanning microscope. For this, multiple axons in individual stacks of images were traced semi-manually in 3D, and the distances between the corresponding traces were measured before and after the registration. The results show that there is a progressive improvement in the registration accuracy with increasing complexity of the transformations. In particular, an accuracy of less than 1 voxel (0.26 m) was achieved with a regularized B-spline transformation. To illustrate the utility of the proposed method, registered images were used to automatically track synaptic boutons on axons over the entire duration of the experiment, yielding 99% precision and recall.

neuroscience↗