Biology subjects

Ye, E.

Publications and source records attributed to Ye, E..

3 recordsLinked to original sources

bioRxiv2024

Genome-wide CRISPR screen identifies AC9 as a key regulator of ER calcium homeostasis involved in neuronal differentiation.

Endoplasmic reticulum (ER) calcium (Ca2+) homeostasis is essential for maintaining normal cellular physiological functions. Its disturbance is strongly linked to the onset and progression of human diseases, including cancer, developmental defects, and neurodegenerative disorders. The lack of sensitive ratiometric ER Ca2+ indicators, nevertheless, hinders systematic investigation of ER Ca2+ modulators and the underlying mechanisms. Capitalizing on two ultra-sensitive ER Ca2+ indicators and CRISPR-based genome-wide screening, we identified a set of proteins capable of reducing the ER Ca2+ content. Further comparative analysis and qPCR validation pinpointed adenylate cyclase 9 (AC9), which is upregulated during neuronal differentiation, as a key ER-Ca2+-reducing regulator. Mechanistically, AC9-mediated production of cAMP is not essential for its ability to reduce ER Ca2+ content. Instead, AC9 inhibits store operated calcium entry (SOCE) by acting on Orai1, ultimately causing attenuation of ER Ca2+ level. More physiologically relevant, upregulation of AC9 in neurons is essential for reducing ER Ca2+ levels during Drosophila brain development. Collectively, this study lays a solid groundwork for further in-depth exploration of the regulatory mechanisms dictating ER Ca2+ homeostasis during neuronal differentiation and brain development.

cell biology↗

bioRxiv2023

Molecular understanding of anthocyanin biosynthesis activated by PAP1 in engineered red Artemisia annua cells and regulation of 2, 4-dichlorophenoxyacetic acid

Artemisia annua is an effective antimalarial medicinal crop. We have established anthocyanin-producing red cell cultures from this plant with the overexpression of Production of Anthocyanin Pigment 1 (PAP1) encoding a R2R3MYB transcription factor. To understand the molecular mechanism by which PAP1 activated the entire anthocyanin pathway, we mined the genomic sequences of A. annua and obtained eight promoters of the anthocyanin pathway genes. Sequence analysis identified four types of AC cis-elements from six promoters, the MYB response elements (MRE) bound by PAP1. In addition, six promoters were determined to have at least one G-Box cis-element. Eight promoters were cloned for activity analysis. Duel luciferase assays showed that PAP1 significantly enhanced the promoting activity of seven promoters, indicating that PAP1 turned on the biosynthesis of anthocyanins via the activation of these pathway gene expression. To understand how 2,4-dichlorophenoxyacetic acid (2,4-D), an auxin, regulates the PAP1-activated anthocyanin biosynthesis, five different concentrations (0, 0.05, 0.5, 2.5, and 5 M) were tested to characterize anthocyanin production and profiles. The resulting data showed that the concentrations tested decreased the fresh weight of callus growth, anthocyanin levels, and the production of anthocyanins per petri dish. HPLC-qTOF-MS/MS based profiling showed that these concentrations did not alter anthocyanin profiles. Real time RT-PCR was completed to characterize the expression PAP1 and four representative pathway genes. The results showed that the five concentrations reduced the expression levels of the constitutive PAP1 transgene and three pathway genes significantly and eliminated chalcone synthase gene in expression either significantly or slightly. These data indicate that the constitutive PAP1 expression depends on gradients added in the medium. Based on these findings, the regulation of 2,4-D is discussed for anthocyanin engineering in red cells of A. annua. ConclusionPromoters of eight anthocyanin pathway genes were cloned. Four types of AC cis-elements were identified from six promoters and G-Box elements were also determined from six promoters. PAP1 enhanced the activity of eight promoters. 2,4-D downregulated the expression of the constitutive PAP1 transgene leading to the decrease the biosynthesis of anthocyanins.

plant biology↗

bioRxiv2023

Measuring chondrocyte viability of articular cartilage based on label-free two-photon microscopy and deep learning image analysis

ObjectiveChondrocyte viability (CV) is an important indicator of articular cartilage health. Two-photon excitation autofluorescence (TPAF) and second harmonic generation (SHG) microscopy provide a label-free method for imaging chondrocytes. In this study, we propose an automated assessment of CV using deep learning cell segmentation and counting based on acquired TPAF/SHG images. DesignLabel-free TPAF/SHG images of cartilage samples from rats and porcine were acquired using both commercial and home-built two-photon microscopes, respectively. TPAF/SHG images were merged to form RGB color images with red, green, and blue channels assigned to TPAF (two channels) and SHG signals, respectively. To make the training datasets for the deep learning networks, individual chondrocyte areas on the RGB color images were manually circled and live or dead chondrocytes were validated by using Calcein-AM and Ethidium homodimer-1 dye labeling. We first built a chondrocyte viability network (MCV-Net) using the Mask R-CNN architecture, which could provide individual segmented cellular areas with live or dead status. Wiener deconvolution preprocessing was added before the input of MCV-Net to improve the accuracy of the CV analysis, forming the Wiener deconvolution CV network (wMCV-Net). ResultsTraining (300 images) and test (120 images) datasets were built for rats and porcine cartilage respectively. Wiener deconvolution could improve the Peak Signal-to-Noise Ratio (PSNR) for 30-40%. We demonstrated that both MCV-Net and wMCV-Net significantly improved the accuracy of the CV measurement. ConclusionA custom desktop TPAF/SHG microscope was used in collaboration with deep learning algorithm wMCV-Net based label-free method to assess the CV and get 95% accuracy with both rats and porcine samples.

bioengineering↗