Biology subjects

Deng, S.

Publications and source records attributed to Deng, S..

4 recordsLinked to original sources

bioRxiv2018

Distinct CED-10/Rac1 Domains Confer Context-Specific Functions in Neuronal Development

Rac GTPases act as master switches to coordinate multiple interweaved signaling pathways. A major function for Rac GTPases is to control neurite development by influencing downstream effector molecules and pathways. In Caenorhabditis elegans, the Rac proteins CED-10, RAC-2 and MIG-2 act in parallel to control axon outgrowth and guidance. Here, we have identified a single glycine residue in the CED-10/Rac1 Switch 1 region that confers a non-redundant function in axon outgrowth but not guidance. Mutation of this glycine to glutamic acid (G30E) reduces GTP binding and inhibits axon outgrowth but does not affect other canonical CED-10 functions. This demonstrates previously unappreciated domain-specific functions within the CED-10 protein. Further, we reveal that when CED-10 function is diminished, the adaptor protein NAB-1 (Neurabin) and its interacting partner SYD-1 (Rho-GAP-like protein) can act as inhibitors of axon outgrowth. Together, we reveal that specific domains and residues within Rac GTPases can confer context-dependent functions during animal development.

neuroscience↗

bioRxiv2018

Dynamics of the sex ratio in Tetrahymena thermophila

Sex is often hailed as one of the major successes in evolution, and in sexual organisms the maintenance of proper sex ratio is crucial. As a large unicellular eukaryotic lineage, ciliates exhibit tremendous variation in mating systems, especially the number of sexes and the mechanism of sex determination (SD), and yet how the populations maintain proper sex ratio is poorly understood. Here Tetrahymena thermophila, a ciliate with seven mating types (sexes) and probabilistic SD mechanism, is analyzed from the standpoint of population genetics. It is found based on a newly developed population genetics model that there are plenty of opportunities for both the co-existence of all seven sexes and the fixation of a single sex, pending on several factors, including the strength of natural selection. To test the validity of predictions, five experimental populations of T. thermophila were maintained in the laboratory so that the factors that can influence the dynamics of sex ratio could be controlled and measured. Furthermore, whole-genome sequencing was employed to examine the impact of newly arisen mutations. Overall, it is found that the experimental observations highly support theoretical predictions. It is expected that the newly established theoretical framework is applicable in principle to other multi-sex organisms to bring more insight into the understanding of the maintenance of multiple sexes in a natural population.

evolutionary biology↗

bioRxiv2017

TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

The use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. It significantly enhances the specificity for downstream ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects data demonstrate that the proposed algorithm consistently outperforms a current, state of the art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. TNER is publicly available at https://github.com/ctDNA/TNER.

bioinformatics↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗