Researcher’s Profile

Chaolin Zhang, PhD

Body: 

The Chaolin Zhang lab works at the interface of Systems Biology, RNA biology and Molecular Neuroscience to understand the organizational principles and functional impact of RNA regulatory networks in the nervous system. Regulation of RNA processing is dictated by interaction of at least several hundred RNA-binding proteins (RBPs) with their target transcripts. These interactions have a profound impact on the output of the transcriptome, specifically for the development and function of the nervous system.

To study RNA regulatory networks in motor neurons, we utilize CRISPR genome engineering technology in in vivo and in vitro model systems combined with high-throughput biochemical and molecular biology assays that profile transcriptomes and protein-RNA interactomes. We apply these assays to specific neuronal RBPs and intersect them with statistical and machine learning approaches to identify exons under cell type-specific regulation and predict specific protein-RNA interactions at single nucleotide resolution. We have developed an integrative modeling approach to combine multiple modalities of data to infer direct and functional targets of specific RBPs with high accuracy and sensitivity.

Our current studies focus on investigating systematically how multiple RBPs work together to achieve dynamic regulation during neurodevelopment (e.g., differentiation of spinal motor neurons from embryonic stem cells). We are also interested in mutations that disrupt RNA regulation observed in several neuronal diseases. Our work relies heavily on high-throughput technologies which produce an enormous amount of data and on algorithms, developed in the Zhang lab, to transform the acquired data into useful information. Ultimately, the goal is to better understand how the interplay between RNA regulation and RBPs may play a role in the development of various neuronal diseases.

Research Statement: 

The Chaolin Zhang lab works at the interface of Systems Biology, RNA biology and Molecular Neuroscience to understand the organizational principles and functional impact of RNA regulatory networks in the nervous system. Regulation of RNA processing is dictated by interaction of at least several hundred RNA-binding proteins (RBPs) with their target transcripts. These interactions have a profound impact on the output of the transcriptome, specifically for the development and function of the nervous system.

To study RNA regulatory networks in motor neurons, we utilize CRISPR genome engineering technology in in vivo and in vitro model systems combined with high-throughput biochemical and molecular biology assays that profile transcriptomes and protein-RNA interactomes. We apply these assays to specific neuronal RBPs and intersect them with statistical and machine learning approaches to identify exons under cell type-specific regulation and predict specific protein-RNA interactions at single nucleotide resolution. We have developed an integrative modeling approach to combine multiple modalities of data to infer direct and functional targets of specific RBPs with high accuracy and sensitivity.

Our current studies focus on investigating systematically how multiple RBPs work together to achieve dynamic regulation during neurodevelopment (e.g., differentiation of spinal motor neurons from embryonic stem cells). We are also interested in mutations that disrupt RNA regulation observed in several neuronal diseases. Our work relies heavily on high-throughput technologies which produce an enormous amount of data and on algorithms, developed in the Zhang lab, to transform the acquired data into useful information. Ultimately, the goal is to better understand how the interplay between RNA regulation and RBPs may play a role in the development of various neuronal diseases.

Publications: 

Feng, H., Zhang, X., Zhang, C., 2015. mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA sequencing data. Nat Comm. 6:7816. doi: 10.1038/ncomms8816.

Yan, Q., Weyn-Vanhentenryck, S.M.,Wu, J., Sloan, S.A., Zhang, Y., Chen, K., Wu, J.-Q., Barres, B.A., Zhang, C., 2015. Systematic discovery of regulated and conserved alternative exons in the mammalian brain reveals NMD modulating chromatin regulators. Proc. Nat. Acad. Sci. USA. Mar 3. pii: 201502849.

Weyn-Vanhentenryck, S.M., Mele,A., Yan,Q., Sun,S., Farny,N., Zhang,Z., Xue,C., Herre,M., Silver,P.A., Zhang, M.Q., Krainer,A.R., Darnell,R.B. , Zhang,C.  2014. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 10.1016/j.celrep.2014.02.005.

Moore, M., Zhang, C., Gantman, E.C., Mele, A., Darnell, J.C., Darnell, R.B. 2014. Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat Protocols, 9:263-293.

Zhang, C., Lee, K.-Y., Swanson, M.S., Darnell, R.B. 2013. Prediction of clustered RNA-binding protein motif sites in the mammalian genome. Nucleic Acids Res. 41:6793-6807.

Wu,J., Anczukow, O., Krainer, A.R., Zhang,M.Q. , Zhang,C. , 2013. OLego: Fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res. 41:5149-5163.

Darnell, J.C., Van Driesche, S.J., Zhang,C., Hung, K.Y.S., Mele, A., Fraser, C.E., Stone, E.F., Chen, C., Fak, J.J., Chi, S.W., Licatalosi, D.D., Richter, J.D., Darnell, R.B., 2011. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell, 146:247-261.

Zhang,C., Darnell, R.B. 2011. Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat. Biotech. 29:607-614.

Zhang,C., Frias, M.A., Mele, A., Ruggiu, M., Eom, T., Marney, C.B., Wang, H., Licatalosi, D.D., Fak, J.J., Darnell, R.B. 2010. Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science. 329: 439-443.

Zhang,C., Zhang, Z., Castle, J., Sun, S., Johnson, J., Krainer, A.R. and Zhang, M.Q. 2008. Defining the regulatory network of the tissue-specific splicing factors Fox-1 and Fox-2. Genes Dev. 22:2550-2563.