Browsing by Author "Zhang, Ge"

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    De novo variants in genes regulating stress granule assembly associate with neurodevelopmental disorders
    (American Association for the Advancement of Science, 2022) Jia, Xiangbin; Zhang, Shujie; Tan, Senwei; Du, Bing; He, Mei; Qin, Haisong; Chen, Jia; Duan, Xinyu; Luo, Jingsi; Chen, Fei; Ouyang, Luping; Wang, Jian; Chen, Guodong; Yu, Bin; Zhang, Ge; Zhang, Zimin; Lyu, Yongqing; Huang, Yi; Jiao, Jian; Chen, Jin Yun (Helen); Swoboda, Kathryn J.; Agolini, Emanuele; Novelli, Antonio; Leoni, Chiara; Zampino, Giuseppe; Cappuccio, Gerarda; Brunetti-Pierri, Nicola; Gerard, Benedicte; Ginglinger, Emmanuelle; Richer, Julie; McMillan, Hugh; White-Brown, Alexandre; Hoekzema, Kendra; Bernier, Raphael A.; Kurtz-Nelson, Evangeline C.; Earl, Rachel K.; Meddens, Claartje; Alders, Marielle; Fuchs, Meredith; Caumes, Roseline; Brunelle, Perrine; Smol, Thomas; Kuehl, Ryan; Day-Salvatore, Debra-Lynn; Monaghan, Kristin G.; Morrow, Michelle M.; Eichler, Evan E.; Hu, Zhengmao; Yuan, Ling; Tan, Jieqiong; Xia, Kun; Shen, Yiping; Guo, Hui; Pediatrics, School of Medicine
    Stress granules (SGs) are cytoplasmic assemblies in response to a variety of stressors. We report a new neurodevelopmental disorder (NDD) with common features of language problems, intellectual disability, and behavioral issues caused by de novo likely gene-disruptive variants in UBAP2L, which encodes an essential regulator of SG assembly. Ubap2l haploinsufficiency in mouse led to social and cognitive impairments accompanied by disrupted neurogenesis and reduced SG formation during early brain development. On the basis of data from 40,853 individuals with NDDs, we report a nominally significant excess of de novo variants within 29 genes that are not implicated in NDDs, including 3 essential genes (G3BP1, G3BP2, and UBAP2L) in the core SG interaction network. We validated that NDD-related de novo variants in newly implicated and known NDD genes, such as CAPRIN1, disrupt the interaction of the core SG network and interfere with SG formation. Together, our findings suggest the common SG pathology in NDDs.
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    In Silico Investigation of the Clinical Translatability of Competitive Clearance Glucose-Responsive Insulins
    (American Chemical Society, 2023-09-18) Yang, Jing Fan; Yang, Sungyun; Gong, Xun; Bakh, Naveed A.; Zhang, Ge; Wang, Allison B.; Cherrington, Alan D.; Weiss, Michael A.; Strano, Michael S.; Biochemistry and Molecular Biology, School of Medicine
    The glucose-responsive insulin (GRI) MK-2640 from Merck was a pioneer in its class to enter the clinical stage, having demonstrated promising responsiveness in in vitro and preclinical studies via a novel competitive clearance mechanism (CCM). The smaller pharmacokinetic response in humans motivates the development of new predictive, computational tools that can improve the design of therapeutics such as GRIs. Herein, we develop and use a new computational model, IM3PACT, based on the intersection of human and animal model glucoregulatory systems, to investigate the clinical translatability of CCM GRIs based on existing preclinical and clinical data of MK-2640 and regular human insulin (RHI). Simulated multi-glycemic clamps not only validated the earlier hypothesis of insufficient glucose-responsive clearance capacity in humans but also uncovered an equally important mismatch between the in vivo competitiveness profile and the physiological glycemic range, which was not observed in animals. Removing the inter-species gap increases the glucose-dependent GRI clearance from 13.0% to beyond 20% for humans and up to 33.3% when both factors were corrected. The intrinsic clearance rate, potency, and distribution volume did not apparently compromise the translation. The analysis also confirms a responsive pharmacokinetics local to the liver. By scanning a large design space for CCM GRIs, we found that the mannose receptor physiology in humans remains limiting even for the most optimally designed candidate. Overall, we show that this computational approach is able to extract quantitative and mechanistic information of value from a posteriori analysis of preclinical and clinical data to assist future therapeutic discovery and development.