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We are working on RNA at the interface of Systems Biology, Computer Science and Molecular Neuroscience, currently sponsored by NIH, Simons Foundation, and Columbia University Medical Center.

Organizational principles and functional impact of neuronal RNA-regulatory networks

The importance of post-transcriptional regulation at the RNA level for orchestrated gene expression in mammalian systems is increasingly recognized. Such regulation is dictated by interaction of at least several hundred RNA-binding proteins (RBPs) with their target transcripts, or RNA-regulatory networks, and has profound impact on the output of the transcriptome, especial for the development and function of the nervous system.

To understand the function of RNA regulatory network, a first step is to infer the structure of such networks, which is challenging due in part to the fact that most RBPs recognize very short and generate sequence motifs with limited information content. In the past few years, we took advantage of advances in high-throughput biochemical and molecular biology assays (most of which are related to next-generation sequencing), such as HITS-CLIP (or CLIP-Seq) and RNA-Seq that profile transcriptomes and protein-RNA interactomes. We applied these assays to specific neuronal RBPs,mostly using mouse brain as a model system, and intersect them with statistical and machine learning approaches to identify exons under tissue-specific regulation and predict specific protein-RNA interactions at single nucleotide resolution. Importantly, we also 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. Investigation of the resulting networks allow us to make unexpected findings such as combinatorial regulation by multiple RBPs.

For our current work, one focus is to investigate systematically how multiple RBPs work together to derive dynamic regulation during neurodevelopment. We aim to find predictive rules of combinatorial regulation, and how groups of RBPs define subsets of target transcripts that are functional distinct.

• 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.

RNA-regulatory networks in evo-devo processes and in neuronal disorders

One of the ultimate goals to infer RNA-regulatory networks is to understand their forms and functions in normal physiological and pathological contexts. In real systems, the networks are almost always under combinatorial and dynamic regulation, which tremendously increase the complexity. We are working toward a better understanding of how such dynamic regulation drives the developing mammalian brain, differentiation of neurons from stem cells and (at a larger scale) mammalian evolution. We are also interested in understanding how such networks are perturbed in neurodegenerative diseases (such as ALS and SMA) and neurodevelopment disorders (such as autism).

• 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 In press.

High-throughput transcriptomic data analysis

Our work heavily relies on high-throughput technologies which produce enormous amount of data, and algorithms to transform these data into useful information. We are interested in developing better algorithms to process transcroptomic data, such as mapping RNA-Seq reads, discovering and quantifying RNA processing in specific conditions, and analyzing CLIP data to map protein-RNA interactions at a single nucleotide resolution.

• 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. (Software)

• 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. , In press. (Software)

• 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.