Biology subjects

Zhang, Q.

Publications and source records attributed to Zhang, Q..

797 records · Page 45Linked to original sources

bioRxiv2017

Molecular Correlate Of Mouse Executive Function. Top-Down And Bottom-Up Information Flows Complementation By Ntng Gene Paralogs

Executive function (EF) is a regulatory construct of learning and general cognitive abilities. Genetic variations underlying the architecture of cognitive phenotypes are likely to affect EF and associated behaviors. Mice lacking one of Ntng gene paralogs, encoding the vertebrate brain-specific presynaptic Netrin-G proteins, exhibit prominent deficits in the EF control. Brain areas responsible for gating the bottom-up and top-down information flows differentially express Ntng1 and Ntng2, distinguishing neuronal circuits involved in perception and cognition. As a result, high and low cognitive demand tasks (HCD and LCD, respectively) modulate Ntng1 and Ntng2 associations either with attention and impulsivity (AI) or working memory (WM), in a complementary manner. During the LCD Ntng2supported neuronal gating of AI and WM dominates over the Ntng1-associated circuits. This is reversed during the HCD, when the EF requires a larger contribution of cognitive control, supported by Ntng1, over the Ntng2 pathways. Since human NTNG orthologs have been reported to affect human IQ (1), and an array of neurological disorders (2), we believe that mouse Ntng gene paralogs serve an analogous role but influencing brain executive functioning.

animal behavior and cognition↗

bioRxiv2017

The microprotein Minion controls cell fusion and muscle formation

Although recent evidence has pointed to the existence of small open reading frame (smORF)-encoded microproteins in mammals, the functional repertoire of this microproteome remains to be determined1. In skeletal muscle, proper development requires fusion of mononuclear progenitors to form multinucleated myotubes, a critical but poorly understood process2,3. Here we report the identification of a small ORF encoding an essential skeletal muscle specific microprotein we term Minion (microprotein inducer of fusion). Myogenic progenitors lacking Minion differentiate normally but fail to form syncytial myotubes, and Minion-deficient mice die perinatally with marked reduction in fused muscle fibers. This fusogenic activity is conserved to the human Minion ortholog, previously annotated as a long noncoding RNA. Loss-of-function studies demonstrate that Minion is the factor providing muscle specific fusogenic function for the transmembrane protein Myomaker4. Remarkably, we demonstrate that co-expression of Minion and Myomaker is sufficient to induce rapid cytoskeletal rearrangement and homogeneous cellular fusion, even in non-muscle cells. These findings establish Minion as a novel microprotein required for muscle development, and define a two-component program for the induction of mammalian cell fusion, enabling both research and translational applications. Importantly, these data also significantly expand the known functions of smORF-encoded microproteins, an under-explored source of proteomic diversity.

cell biology↗

bioRxiv2017

Classification of RNA-Seq Data via Gaussian Copulas

RNA-sequencing (RNA-Seq) has become a preferred option to quantify gene expression, because it is more accurate and reliable than microarrays. In RNA-Seq experiments, the expression level of a gene is measured by the count of short reads that are mapped to the gene region. Although some normal-based statistical methods may also be applied to log-transformed read counts, they are not ideal for directly modeling RNA-Seq data. Two discrete distributions, Poisson distribution and negative binomial distribution, have been commonly used in the literature to model RNA-Seq data, where the latter is a natural extension of the former with allowance of overdispersion. Due to the technical difficulty in modeling correlated counts, most existing classifiers based on discrete distributions assume that genes are independent of each other. However, as we show in this paper, the independence assumption may cause non-ignorable bias in estimating the discriminant score, making the classification inaccurate. To this end, we drop the independence assumption and explicitly model the dependence between genes using Gaussian copula. We apply a Bayesian approach to estimate covariance matrix and the overdispersion parameter in negative binomial distribution. Both synthetic data and real data are used to demonstrate the advantages of our model.

bioinformatics↗

bioRxiv2017

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately or severely violated, making it unsuitable to deal with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine Expectation-Maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling dataset. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

bioinformatics↗

bioRxiv2016

Inter-Subject Alignment of MEG Datasets at the Neural Representational Space

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the underdetermined inverse problem given the high dimensional source space. In this paper, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multi-set canonical correlation analysis (M-CCA), to transform individual subject data to a low dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On a MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Lastly, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information.

neuroscience↗