Biology subjects

Wen, C.

Publications and source records attributed to Wen, C..

3 recordsLinked to original sources

bioRxiv2018

The Population Genetic Variation Analysis of Bitter Gourd Wilt Caused by Fusarium oxysporum f. sp. momordicae in China by Inter Simple Sequence Repeats (ISSR) Molecular Marker

The bitter gourd fusarium wilt caused by Fusarium oxysporum f.sp. momordicae (FOM) was a devastating disease in China and leading to great economic losses every year. A total of 152 isolates, which have the typical Fusarium oxysporum characteristics with abundant microconidia and macroconidia on the white or ruby colonies, were obtained from diseased plant tissues with typical fusarium wilt symptoms. The BLASTn analysis of rDNA-ITS showed 99% identity with F.oxysporum species. Among the tested isolates, three isolates infected tower gourd, and five isolates were pathogenic to bottle gourd. However, they were all pathogenic to bitter gourd. Based on the molecular and morphologic results, the isolates were identified as FOM. For genetic variation analysis, forty ISSR primers were screened and eleven primers were used in PCR amplification. Totally, 121 loci were detected, of which 52 loci were polymorphic at rate of 42.98%. The POPGENE analysis showed that Neis gene diversity index (H) and Shannons information index (I) were 0.0902 and 0.1478, respectively, which indicated that the genetic diversity for the tested 152 isolates was relatively low. It also means that each geographical population was a relatively independent unit. While the value of coefficient of gene differentiation (Gst=0.4929 > 0.15) pointed to the genetic differentiation was mainly among populations. The strength of gene flow (Nm=0.5143<1.0) was weaker, indicating that gene exchanges were blocked to some degree. The dendrogram based on ISSR markers showed that the eight geographical populations were clustered into four groups at the threshold of genetic similar coefficient 0.96. Fujian, Jiangxi and Guangdong populations were clustered into Group I. Group II contained Hunan and Guangxi populations. Group III only had Hainan population. Group IV consisted Shandong and Henan populations. The geographical populations closer to each other grouped together, suggesting a correlationship between geographical origin and genetic differentiation. Two hybridization events were observed between Hainan and Hunan populations and between Guangdong and Guangxi by Structure analysis. Our findings enrich the knowledge on genetic variation characteristics of the FOM populations with helpful of development of effective disease management programs and disease resistance breeding.

microbiology↗

bioRxiv2018

COMPASS Family Histone Methyltransferase ASH2L Mediates Corticogenesis via Transcriptional Regulation of Wnt Signalling

Cell fate specification in neural progenitor cells (NPCs) is orchestrated via extrinsic and intrinsic molecular programs, and histone methylation in these decisions has been ascribed to a crucial function regulating gene expression. Here, we show that the COMPASS family histone methyltransferase co-factor ASH2L is required in NPCs proliferation and upper layer cortical projection neurons production and position. Deletion of Ash2l impairs trimethylation of H3K4 and transcriptional machinery specifically for subsets of Wnt-{beta}-catenin signalling, disrupting their transcription and consequently inhibiting the proliferation ability of NPCs in late stages of neurogenesis. Consistently, Ash2l conditional mutants exhibit thinning neocortex with reduced upper layer neurons and altered neuronal position. Moreover, overexpressing {beta}-catenin after Ash2l elimination or knockdown can rescue the proliferation deficiency of NPCs both in vivo and in vitro. These results demonstrate an essential and highly specific role for Ash2l in controlling NPCs proliferation and late-born neurons lamination in corticogenesis via transcriptionally regulating Wnt-{beta}-catenin signalling, and provide clues to how the COMPASS family epigenetic factors coordinate cell fate determination during cortex development.

neuroscience↗

bioRxiv2018

Deep-learning-based flexible pipeline for segmenting and tracking cells in 3D image time series for whole brain imaging

The brain is a complex system that operates based on coordinated neuronal activities. Brain-wide cellular calcium imaging techniques have quickly advanced in recent years and become powerful tools for understanding the neuronal activities of small animal models. The whole brain imaging generally requires to extract the neuronal activities from three-dimensional (3D) image series. Unfortunately, the 3D image series are obtained under imaging conditions different among laboratories and extracting neuronal activities from the data requires multiple processes. Therefore researchers need to develop their own software, which has prevented the application of whole-brain imaging experiments in more laboratories. Here, we combined traditional image processing techniques with the powerful deep-learning method which can be flexibly modified to fit 3D image data in the nematode Caenorhabditis elegans obtained under different conditions. We first trained the 3D U-net deep network to classify each pixel into cell and non-cell categories. Cells merged as a whole region were further separated into individual cells by watershed segmentation. The cells were then tracked in 3D space over time with the combination of a feedforward network and a point set registration method to use local and global relative positions of the cells, respectively. Remarkably, one manually annotated 3D image combined with data augmentation was sufficient for training the deep networks to obtain satisfactory tracking results. Our method correctly tracked more than 98% of neurons in three different image datasets and successfully extracted brain-wide neuronal activities. Our method worked well even when the sampling rate was reduced: 86% correct in case 4/5 frames were removed, and when artificial noise was added into the raw images: 91% correct in case 35 times of background-level noise was added. Our results proved that deep learning is widely applicable to different datasets and can help us in establishing a flexible pipeline for extracting whole brain activities.

bioinformatics↗