Biology subjects

Tao, S.

Publications and source records attributed to Tao, S..

4 recordsLinked to original sources

bioRxiv2018

Unbiased Age-Appropriate Structural Brain Atlases for Chinese Pediatrics

In magnetic resonance imaging (MRI) studies of children brain development, structural brain atlases usually serve as important references of pediatric population in which individual images are spatially normalized into a common or standard stereotactic space. However, the existing popular children brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD atlases) are made mostly based on MR images from Western populations, and are thus insufficient to characterize the brains of Chinese children due to the neuroanatomical differences that are relevant to genetic and environmental factors. By collecting high-quality T1- and T2- weighted MR images from 328 typically developing Chinese children aged from 6 to 12 years old, we created a set of age-appropriate Chinese pediatric (CHN-PD) atlases using an unbiased template construction algorithm. The CHN-PD atlases included the head/brain templates, the symmetric brain template, the gender-specific brain templates and the corresponding tissue probability atlases. Moreover, the atlases contained multiple age-specific templates with a one-year interval. A direct comparison of the CHN-PD and the NIH-PD atlases revealed remarkable anatomical differences bilaterally in the lateral frontal and parietal regions and somatosensory cortex. While applying the CHN-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71, respectively), machine-learning regression approaches revealed higher prediction accuracy on brain ages than the usage of NIH-PD atlases. These results suggest that the CHN-PD brain atlases are necessary and important for future typical and atypical developmental studies in Chinese pediatric population. Currently, the CHN-PD atlases have been released on the NITRC website (https://www.nitrc.org/projects/chn-pd).

neuroscience↗

bioRxiv2018

Template-assisted synthesis of adenine-mutagenized cDNA by a retroelement protein complex

Diversity-generating retroelements (DGRs) create unparalleled levels of protein sequence variation through mutagenic retrohoming. Sequence information is transferred from an invariant template region (TR), through an RNA intermediate, to a protein-coding variable region. Selective infidelity at adenines during transfer is a hallmark of DGRs from disparate bacteria, archaea, and microbial viruses. We recapitulated selective infidelity in vitro for the prototypical Bordetella bacteriophage DGR. A complex of the DGR reverse transcriptase bRT and pentameric accessory variability determinant (Avd) protein along with DGR RNA were necessary and sufficient for synthesis of template-primed, covalently linked RNA-cDNA molecules, as observed in vivo. We identified RNAcDNA molecules to be branched and most plausibly linked through 2'-5' phosphodiester bonds. Adenine-mutagenesis was intrinsic to the bRT-Avd complex, which displayed unprecedented promiscuity while reverse transcribing adenines of either DGR or non-DGR RNA templates. In contrast, bRT-Avd processivity was strictly dependent on the template, occurring only for the DGR RNA. This restriction was mainly due to a noncoding segment downstream of TR, which specifically bound Avd and created a privileged site for processive polymerization. Restriction to DGR RNA may protect the host genome from damage. These results define the early steps in a novel pathway for massive sequence diversification.

biochemistry↗

bioRxiv2018

Deciphering the rules which mRNA structures differs from vivo and vitro in Saccharomyces cerevisiae by deep neural networks

The structure of mRNA in vivo is influenced by various factors involved in the translation process, resulting in significant differentiation of mRNA structure from that in vitro. Because multiple factors cause the differentiation of in vivo and in vitro mRNA structures, it was difficult to perform a more accurate analysis of mRNA structures in previous studies. In this study, we have proposed a novel application of a deep neural network (DNN) model to predict the structural stability of mRNA in vivo by fitting six quantifiable features that may affect mRNA folding: ribosome density, minimum folding free energy, GC content, mRNA abundance, ribosomal initial density and position of mRNA structure. Simulated mutations of the mRNA structure were designed and then fed into the trained DNN model to compute their structural stability. We found unique effects of these six features on mRNA structural stability in vivo. Strikingly, the ribosome density of the structural region is the most important factor affecting the structural stability of mRNA in vivo, and the strength of the mRNA structure in vitro should have a relatively small effect on its structural stability in vivo. The recruitment of DNNs provides a new paradigm to decipher the differentiation of mRNA structure in vivo and in vitro. This improved knowledge on the mechanisms of factors influencing mRNA structural stability will facilitate the design and functional analysis of mRNA structure in vivo.

bioinformatics↗

bioRxiv2018

Identification of key genes related to dexamethasone-resistance in acute lymphoblastic leukemia

Drug resistance is the main cause of poor chemotherapy response in acute leukemia. Despite the extensive use of dexamethasone(DEX) in the treatment of acute lymphoblastic leukemia for many years, the mechanisms of dexamethasone - resistance has not been fully understood. We choose GSE94302 from GEO database aiming to identify key genes that contribute to the DEX resistance in acute lymphoblastic leukemia. Differentially expressed gene(DEGs) are selected by using GEO2R tools. A total of 837 DEGs were picked out, including 472 up-regulated and 365 down-regulated DEGs. All the DEGs were underwent gene ontology(GO) analysis and Kyoto Encyclopedia of Gene and Genome(KEGG) pathway analysis. In addition, the DEGs-encoded protein-protein interaction (PPI) was screened by using Cytoscape and Search Tool for the Retrieval of Interacting Genes(STRING). Total 20 genes were found as key genes related to DEX resistance with high degree of connectivity, including CDK1, PCNA, CCNB1, MYC, KPNA2, AURKA, NDC80, HSPA4, KIF11, UBE2C, PIK3CG, CD44, CD19, STAT1, DDX41, LYN, BCR, CD48, JAK1 and ITGB1. They could be used as biomarkers to identify the DEX-resistant acute lymphoblastic leukemia.

bioinformatics↗