Browsing by Author "Li, Hao"

Now showing 1 - 4 of 4
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    Discovery of first-in-class inhibitors of ASH1L histone methyltransferase with anti-leukemic activity
    (Springer Nature, 2021-05-14) Rogawski, David S.; Deng, Jing; Li, Hao; Miao, Hongzhi; Borkin, Dmitry; Purohit, Trupta; Song, Jiho; Chase, Jennifer; Li, Shuangjiang; Ndoj, Juliano; Klossowski, Szymon; Kim, EunGi; Mao, Fengbiao; Zhou, Bo; Ropa, James; Krotoska, Marta Z.; Jin, Zhuang; Ernst, Patricia; Feng, Xiaomin; Huang, Gang; Nishioka, Kenichi; Kelly, Samantha; He, Miao; Wen, Bo; Sun, Duxin; Muntean, Andrew; Dou, Yali; Maillard, Ivan; Cierpicki, Tomasz; Grembecka, Jolanta; Microbiology and Immunology, School of Medicine
    ASH1L histone methyltransferase plays a crucial role in the pathogenesis of different diseases, including acute leukemia. While ASH1L represents an attractive drug target, developing ASH1L inhibitors is challenging, as the catalytic SET domain adapts an inactive conformation with autoinhibitory loop blocking the access to the active site. Here, by applying fragment-based screening followed by medicinal chemistry and a structure-based design, we developed first-in-class small molecule inhibitors of the ASH1L SET domain. The crystal structures of ASH1L-inhibitor complexes reveal compound binding to the autoinhibitory loop region in the SET domain. When tested in MLL leukemia models, our lead compound, AS-99, blocks cell proliferation, induces apoptosis and differentiation, downregulates MLL fusion target genes, and reduces the leukemia burden in vivo. This work validates the ASH1L SET domain as a druggable target and provides a chemical probe to further study the biological functions of ASH1L as well as to develop therapeutic agents.
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    Emerging tick-borne infections in mainland China: an increasing public health threat
    (Elsevier, 2015-12) Fang, Li-Qun; Liu, Kun; Li, Xin-Lou; Liang, Song; Yang, Yang; Yao, Hong-Wu; Sun, Ruo-Xi; Sun, Ye; Chen, Wan-Jun; Zuo, Shu-Qing; Ma, Mai-Juan; Li, Hao; Jiang, Jia-Fu; Liu, Wei; Yang, X. Frank; Gray, Gregory C.; Krause, Peter J.; Cao, Wu-Chun; Department of Microbiology & Immunology, IU School of Medicine
    Since the beginning of the 1980s, 33 emerging tick-borne agents have been identified in mainland China, including eight species of spotted fever group rickettsiae, seven species in the family Anaplasmataceae, six genospecies in the complex Borrelia burgdorferi sensu lato, 11 species of Babesia, and the virus causing severe fever with thrombocytopenia syndrome. In this Review we have mapped the geographical distributions of human cases of infection. 15 of the 33 emerging tick-borne agents have been reported to cause human disease, and their clinical characteristics have been described. The non-specific clinical manifestations caused by tick-borne pathogens present a major diagnostic challenge and most physicians are unfamiliar with the many tick-borne diseases that present with non-specific symptoms in the early stages of the illness. Advances in and application of modern molecular techniques should help with identification of emerging tick-borne pathogens and improve laboratory diagnosis of human infections. We expect that more novel tick-borne infections in ticks and animals will be identified and additional emerging tick-borne diseases in human beings will be discovered.
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    Intensified Structural Overshoot Aggravates Drought Impacts on Dryland Ecosystems
    (AGU, 2024-01) Zhang, Yixuan; Liu, Liu; Cheng, Yongming; Kang, Shaozhong; Li, Hao; Wang, Lixin; Shi, Yu; Liu, Xingcai; Cheng, Lei; Earth and Environmental Sciences, School of Science
    A favorable environment can induce vegetation overgrowth to exceed the ecosystem carrying capacity, exacerbating water resource depletion and increasing the risk of lagged effects on vegetation degradation. This phenomenon is defined as structural overshoot, which can lead to large-scale forest mortality and grassland deterioration. However, the current understanding of structural overshoot remains incomplete due to the complex time-varying interactions between vegetation and climate. Here, we used a dynamic learning algorithm to decompose the contributions of vegetation and climate to drought occurrence, trace the connection between antecedent and concurrent vegetation dynamics, thus effectively capturing structural overshoot. This study focused on the climate-sensitive hotspot in Northwest China drylands, where significant vegetation greening induced by a warming and wetting climate was detected during 1982–2015, leading to soil moisture deficit and aggravating vegetation degradation risks during droughts. We found that during this period, structural overshoot induced approximately 34.6% of the drought events, and lagged effects accounted for 16.7% of the vegetation degradation for these overshoot drought events. The occurrence of overshoot droughts exhibited an increasing trend over time, which was primarily driven by vegetation overgrowth followed by precipitation variation. Although the severity of overshoot and non-overshoot droughts were generally comparable in spatial distribution, the impact of overshoot droughts is still becoming increasingly obvious. Our results indicate that the expected intensified overshoot droughts cannot be ignored and emphasize the necessity of sustainable agroecosystem management strategies.
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    SOS-EW: System for Overdose Spike Early Warning Using Drug Mover’s Distance-Based Hawkes Processes
    (Springer, 2020) Chiang, Wen-Hao; Yuan, Baichuan; Li, Hao; Wang, Bao; Bertozzi, Andrea; Carter, Jeremy; Ray, Brad; Mohler, George; Computer and Information Science, School of Science
    Opioid addictions and overdoses have increased across the U.S. and internationally over the past decade. In urban environments, overdoses cluster in space and time, with 50% of overdoses occurring in less than 5% of the city and dozens of calls for emergency medical services being made within a 48-hour period. In this work, we introduce a system for early detection of opioid overdose clusters based upon the toxicology report of an initial event. We first use drug SMILES, one hot encoded molecular substructures, to generate a bag of drug vectors corresponding to each overdose (overdoses are often characterized by multiple drugs taken at the same time). We then use spectral clustering to generate overdose categories and estimate multivariate Hawkes processes for the space-time intensity of overdoses following an initial event. As the productivity parameter of the process depends on the overdose category, this allows us to estimate the magnitude of an overdose spike based on the substances present (e.g. fentanyl leads to more subsequent overdoses compared to Oxycontin). We validate the model using opioid overdose deaths in Indianapolis and show that the model outperforms several recently introduced Hawkes-Topic models based on Dirichlet processes. Our system could be used in combination with drug test strips to alert drug using populations of risky batches on the market or to more efficiently allocate naloxone to users and health/social workers.