Biology subjects

Ma, J.

Publications and source records attributed to Ma, J..

457 records · Page 26Linked to original sources

bioRxiv2017

Molecular Basis For The Specific And Multivariate Recognitions Of RNA Substrates By Human hnRNPA2/B1

Human hnRNPA2/B1 is an RNA-binding protein that plays important roles in a variety of biological processes, from mRNA maturation, trafficking and translation to regulation of gene expression mediated by long non-coding RNAs and microRNAs. hnRNPA2/B1 contains two RNA recognition motifs (RRM) that provide sequence-specific recognition of widespread RNA substrates including recently reported m6A-containing motifs. Here we determined the first crystal structures of tandem RRM domains of hnRNPA2/B1 in complex with various RNA substrates. Our structures reveal that hnRNPA2/B1 can bind two RNA elements in an antiparallel fashion with a sequence preference for AGG and UAG by RRM1 and RRM2, respectively, suggesting an RNA matchmaker mechanism during the hnRNPA2/B1 function. However, our combined studies did not observe specific binding of m6A by either the RRM domains or the full-length hnRNPA2/B1, implying that the \"reader\" function of hnRNPA2/B1 may adopt an unknown mechanism that remains to be characterized.

biochemistry↗

bioRxiv2017

The Y-Chromosome Clarifies The Evolutionary History Of Sus scrofa By Large-Scale Deep Genome Sequencing

The genetics and evolution of sex chromosomes are largely distinct from autosomes and mitochondrial DNA (mtDNA). The Y chromosome offers unique genetic perspective on male-line inheritance. Here, we uncover novel evolutionary history of Sus scrofa based on 205 high-quality genomes from worldwide-distributed different wild boars and domestic pig breeds. We find that only two haplotypes exist in the distal and proximal blocks of at least 7.7 Mb on chromosome Y in pigs across European and Asian continents. And the times of most recent common ancestors (TMRCA) within both haplotypes, approximately 0.14 and 0.10 million years, are far smaller than their divergence time of around 1.07 million years. Whats more, the relationship between Sumatran and Eurasian continent Sus scrofa is much closer than that we knew before. And surprisingly, European pigs share the same haplotype with many Chinese pigs, which is not consistent with their deep splitting status on autosome and mtDNA. Further analyses show that the haplotype in Chinese pigs was likely introduced from European wild boars via ancient gene flow before pig domestication about 24k years ago. Low mutation rates and no recombination in the distal and proximal blocks on chromosome Y help us detect this male-driven ancient gene flow. Taken together, our results update the knowledge of pig demography and evolution, and might shed insight into the genetics and evolution studies on chromosome Y in other mammals.

evolutionary biology↗

bioRxiv2017

DeepBound: Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields

MotivationReconstructing the full-length expressed transcripts (a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.\n\nResultsWe present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods.\n\nAvailabilityDeepBound is freely available at https://github.com/realbigws/DeepBound.\n\nContactmingfu.shao@cs.cmu.edu, realbigws@gmail.com

bioinformatics↗

bioRxiv2017

T-bet+ CD11c+ B Cells Are Critical For Anti-Chromatin IgG Production In The Development Of Lupus

A hallmark of systemic lupus erythematosus is high titers of circulating autoantibody. A novel CD11c+ B cell subset has been identified that is critical for the development of autoimmunity. However, the role of CD11c+ B cells in the development of lupus is unclear. Chronic graft-versus-host disease (cGVHD) is a lupus-like syndrome with great autoantibody production. In the present study we investigated the role of CD11c+ B cells in the pathogenesis of lupus in the cGVHD model. Here, we found the percentage and absolute number of CD11c+ B cells and titer of sera anti-chromatin IgG and IgG2a antibody were increased in cGVHD mice. CD11c+ plasma cells from cGVHD mice produced large amounts of anti-chromatin IgG2a upon stimulation. Depletion of CD11c+ B cells reduced anti-chromatin IgG and IgG2a production. T-bet expression was further shown to be upregulated in CD11c+ B cells. Knockout of T-bet in B cells alleviated cGVHD. The percentage of T-bet+ CD11c+ B cells was elevated in lupus patients and positively correlated with serum anti-chromatin levels. Our findings suggest T-bet+ CD11c+ B cells contribute to the pathogenesis of lupus and provides potential target for therapeutic intervention.

immunology↗

bioRxiv2017

An overexpressed Q allele leads to increased spike density and improved processing quality

Spike density and processing quality are important traits during the evolution of wheat, which is controlled by multiple gene loci. The associated gene loci have been heavily studied with slow progress. A common wheat mutant with extremely compact spikes and good processing quality was isolated. The gene (Qc1) responsible for the mutant phenotype was mapped and cloned, and the cellular mechanism for the mutant phenotype was investigated. Qc1 originated from a point mutation that interferes with the miR172-directed cleavage of the Q gene, leading to its overexpression. Qc1 reduces the longitudinal cell size of rachises, resulting in an increased spike density. Qc1 increases the number of vascular bundles, which suggests a higher efficiency in the transportation of assimilates in the spikes of the mutant than in the WT. This could account for the improved processing quality. The effects of Qc1 on spike density and wheat processing quality were confirmed by the identification of nine common wheat mutants having four different Qc alleles. These results deepen our understanding of the key role of Q gene, one of the most important domestication gene for wheat, and provide new insights for the potential application of Qc allele in wheat breeding.

genetics↗

bioRxiv2016

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

neuroscience↗

bioRxiv2016

Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks

In the human genome, distal enhancers are involved in regulating target genes through proxi-mal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named \"SPEID\") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.

bioinformatics↗