Biology subjects

Li, H. Y.

Publications and source records attributed to Li, H. Y..

8 recordsLinked to original sources

bioRxiv2025

A First-in-Class Therapy Targeting Transcription-Replication Conflicts Drives Synthetic Lethality in Homologous Recombination Deficient Cancers

Hidden beneath the layer of tumor malignancy lies unchecked proliferation driven by hyperactive transcription, which fosters the formation of RNA:DNA hybrids. These structural intermediates precipitate collisions between the transcription and replication machineries. Central to mitigating this genomic threat is interleukin enhancer-binding factor 2 (ILF2), which orchestrates the recruitment of RNA:DNA helicases to resolve R-loops and, in doing so, reveals cancer cells dependency on stress-mitigating mechanisms to avert genome catastrophe. Our discovery of Molephantin and its lead derivative, NYH0002 is the outcome of delineating ILF2s function. NYH002 exerts direct binding to the ILF2 complex and disrupts RNA:DNA helicase activity to elicit genome-wide DNA breakages. Tumors with elevated cyclin E and E2F1 expression are sensitive to NYH002, while those deficient in homologous recombination repair exhibit lethality. Collectively, these findings position ILF2 as a key regulator of R-loop homeostasis and reveal a therapeutic vulnerability that NYH002 exploits to suppress tumor malignancy.

cancer biology↗

bioRxiv2024

Establishing the foundations for a data-centric AI approach for virtual drug screening through a systematic assessment of the properties of chemical data

Researchers have adopted model-centric artificial intelligence (AI) approaches in cheminformatics by using newer, more sophisticated AI methods to take advantage of growing chemical libraries. It has been shown that complex deep learning methods outperform conventional machine learning (ML) methods in QSAR and ligand-based virtual screening1-3 but such approaches generally lack explanability. Hence, instead of developing more sophisticated AI methods (i.e., pursuing a model-centric approach), we wanted to explore the potential of a data-centric AI paradigm for virtual screening. A data-centric AI is an intelligent system that would automatically identify the right type of data to collect, clean and curate for later use by a predictive AI and this is required given the large volumes of chemical data that exist in chemical databases - PubChem alone has over 100 million unique compounds. However, a systematic assessment of the attributes and properties of suitable data is needed. We show here that it is not the result of deficiencies in current AI algorithms but rather, poor understanding and erroneous use of chemical data that ultimately leads to poor predictive performance. Using a new benchmark dataset of BRAF ligands that we developed, we show that our best performing predictive model can achieve an unprecedented accuracy of 99% with a conventional ML algorithm (SVM) using a merged molecular representation (Extended + ECFP6 fingerprints), far surpassing past performances of virtual screening platforms using sophisticated deep learning methods. Thus, we demonstrate that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening because conventional ML can perform exceptionally well if given the right data and representation. We also show that the common use of decoys for training leads to high false positive rates and its use for testing will result in an over-optimistic estimation of a models predictive performance. Another common practice in virtual screening is defining compounds that are above a certain pharmacological threshold as inactives. Here, we show that the use of these so-called inactive compounds lowers a models sensitivity/recall. Considering that some target proteins have a limited number of known ligands, we wanted to also observe how the size and composition of the training data impact predictive performance. We found that an imbalance training dataset where inactives outnumber actives led to a decrease in recall but an increase in precision, regardless of the model or molecular representation used; and overall, we observed a decrease in the models accuracy. We highlight in this study some of the considerations that one needs to take into account in future development of data-centric AI for CADD.

cancer biology↗

bioRxiv2023

Benchmarking Reverse Docking through AlphaFold2 Human Proteome

Predicting binding of a small molecule to the human proteome by reverse docking methods, we can predict the target interactions of drug compounds in the human body, as well as further evaluate their potential off-target effects or toxic side effects. In this study, we constructed 11 pipelines to evaluate and benchmark thoroughly the predictive capabilities of these reverse docking pipelines. The pipelines were built using site prediction tools (PointSite and SiteMap) based on the AF2 human proteome, docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, OnionNet-SFCT). The results show that pipeline glide_sfct (PS) exhibited the best target prediction ability and successfully predicted the similar proteins of native targets. This finding provides important clues for understanding the promiscuity between the drug ligand and the whole human proteome. In general, our study has the potential to increase the success rate and reduce the development timeline of drug discovery, thereby saving costs.

bioinformatics↗

bioRxiv2022

Erythrocyte signalling is critical for Plasmodium falciparum invasion

Successful Plasmodium falciparum merozoite invasion requires the activation of red blood cell (RBC) signalling pathways. The binding of parasite ligand reticulocyte binding protein homologue 5 (RH5) to its host receptor Basigin is essential for merozoite invasion and triggers a Ca2+ influx in RBCs. Here we observed that RH5-bound RBCs form a multimeric protein complex containing Basigin, CD44 and {beta}2-adrenergic receptor ({beta}2AR), suggesting that RH5-Basigin interaction is functionally associated with the host cAMP signalling pathway. Interestingly, we detected a characteristic rise in cAMP levels in the RBC upon RH5-Basigin interaction, which can be blocked by G protein and cAMP-synthesising adenylyl cyclase (AC) inhibitors. Furthermore, we demonstrated that RBC L-type Ca2+ channel inhibitor and cAMP signalling inhibitors are able to block merozoite invasion. Checkerboard invasion inhibition assay containing different combinations of signalling inhibitors also exhibited a drastic amplification of inhibition levels, indicating that these signalling proteins are functioning in a common signalling cascade to activate the L-type Ca2+ channels. Taken together, this study provides new insights into the role of a host cAMP-Ca2+ signalling pathway during merozoite invasion and sheds new light on antimalarial therapeutic strategies to tackle the high infection rate and growing threat of drug resistant parasites. Key PointsO_LIA pre-existing Basigin-associated membrane protein complex undergoes increased protein assembly upon RH5 binding on the RBC surface. C_LIO_LIPlasmodium falciparum merozoite exploits host cAMP signalling to initiate Ca2+ influx in the RBC. C_LI

microbiology↗

bioRxiv2022

Genomic, transcriptomic, and metabolomic analysis of Traditional Chinese Medicine plant Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites

Natural products from traditional medicinal plants are valuable candidates for clinical cancer therapy. Plants from the Oldenlandia-Hedyotis complex are popular ingredients of Traditional Chinese Medicine (TCM), however a major hurdle in the plant bioprospecting process of TCM plants is that the active metabolites, their biosynthetic pathways, and mode of action are often unknown. We show that Oldenlandia corymbosa extracts are active against breast cancer cell lines. To study the genes involved in the biosynthesis of active compounds in this medicinal plant, we assembled a high-quality genome. We show that the main active compound is ursolic acid and that abiotic stresses cause changes in anti-cancer activity, metabolite composition, and gene expression of plants. To reveal the mode of action of ursolic acid, we show that cancer cells undergo mitotic catastrophe, and we identify three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking.

plant biology↗

bioRxiv2020

Cancer Cell Intrinsic Expression of MHCII Regulates the Immune Microenvironment and Response to Anti-PD-1 Therapy in Lung Adenocarcinoma

MHC class II (MHCII) expression is usually restricted to antigen presenting cells, but can be expressed by cancer cells. We examined the effect of cancer cell-intrinsic MHC class II (csMHCII) expression in lung adenocarcinoma on T cell recruitment to tumors and response to anti-PD-1 therapy. The functional significance of altering csMHCII expression was explored using two orthotopic immunocompetent murine models of non-small cell lung cancer: CMT167 (CMT) and Lewis Lung Carcinoma (LLC). We previously showed that CMT167 tumors are eradicated by anti-PD1 therapy, while LLC tumors are resistant. RNA-seq analysis of cancer cells recovered from tumors revealed that csMHCII correlated with response to anti-PD1 therapy, with immunotherapy-sensitive CMT167 cells being csMHCII positive, while resistant LLC cells were csMHCII negative. To test the functional effects of csMHCII, MHCII expression was altered on the cancer cells through loss- and gain-of-function of CIITA, a master regulator of the MHCII pathway. Loss of CIITA in CMT167 decreased csMHCII, and converted tumors from anti-PD-1-sensitive to anti-PD-1-resistant. This was associated with decreased T cell infiltration, lower levels of Th1 cytokines, increased B cell number and decreased macrophage recruitment. Conversely, overexpression of CIITA in LLC cells resulted in csMHCII in vitro and in vivo. Enforced expression of CIITA increased T cell infiltration and sensitized tumors to anti-PD-1 therapy. csMHCII expression was also examined in a subset of surgically resected human lung adenocarcinomas by multispectral imaging, provided a survival benefit and positively correlated with T cell infiltration. These studies demonstrate a functional role for csMHCII in regulating T cell infiltration and sensitivity to anti-PD-1.

cancer biology↗

bioRxiv2020

High-throughput 3D tracking reveals the importance of near wall swimming and initial attachment behaviors of P. aeruginosa for biofilm formation on a vertical wall

Studying the swimming behaviour of bacteria in 3 dimensions (3D) allows us to understand critical biological processes, such as biofilm formation. It is still unclear how near wall swimming behaviour may regulate the initial attachment and biofilm formation. It is challenging to address this as visualizing the movement of bacteria with reasonable spatial and temporal resolution in a high-throughput manner is technically difficult. Here, we compared the near wall (vertical) swimming behaviour of P. aeruginosa (PAO1) and its mutants {Delta}dipA (reduced in swarming motility and increased in biofilm formation) and {Delta}fimX (deficient in twitching motility and reduced in biofilm formation) using our new imaging technique based on light sheet microscopy. We found that P. aeruginosa (PAO1) increases its speed and changes its swimming angle drastically when it gets closer to a wall. In contrast, {Delta}dipA mutant moves toward the wall with steady speed without changing of swimming angle. The near wall behavior of {Delta}dipA allows it to be more effective to interact with the wall or wall-attached cells, thus leading to more capture events and a larger biofilm volume during initial attachment when compared with PAO1. Furthermore, we found that {Delta}fimX has a similar near wall swimming behavior as PAO1, however, it has a higher dispersal frequency and smaller biofilm formation when compared with PAO1 which can be explained by its poor twitching motility. Together, we propose that near wall swimming behavior of P. aeruginosa plays an important role in the regulation of initial attachment and biofilm formation. ImportanceBacterial biofilm is a community of bacteria on surfaces which leads to serious problems in medical devices, food industry, and aquaculture. The initial attachment and subsequent microcolony formation play critical roles in bacterial biofilm formation. However, it is unclear how the initial attachment is regulated, in particular, on a vertical surface. To study this, we have developed a novel imaging technique based on light sheet microscopy, which overcame the limitations of other imaging techniques, to understand how 3D bacterial motility near a wall may regulate initial attachment during biofilm formation. Using our technique, we discovered that near wall swimming behavior of the bacteria, P. aeruginosa, plays an important role in the regulation of biofilm formation during initial attachment.

microbiology↗

bioRxiv2019

Tumor-Intrinsic Response to IFNgamma Shapes the Tumor Microenvironment and Anti-PD-1 Response in NSCLC

Targeting PD-1/ PD-L1 is only effective in ~20% of lung cancer patients, but determinants of this response are poorly defined. We previously observed differential responses of two murine K-Ras lung cancer cell lines to anti-PD-1 therapy: CMT167 tumors were eliminated while LLC tumors were resistant. The goal of this study was to define mechanism(s) mediating this difference. RNA-Seq analysis of cancer cells recovered from lung tumors revealed that CMT167 cells induced an IFN{gamma} signature that was absent in LLC cells. Silencing Ifngr1 in CMT167 resulted in tumors resistant to IFN{gamma} and anti-PD-1 therapy. Conversely, LLC cells had high basal expression of Socs1, an inhibitor of IFN{gamma}. Silencing Socs1 increased response to IFN{gamma} in vitro and sensitized tumors to anti-PD-1. This was associated with a reshaped TME, characterized by enhanced T cell infiltration and enrichment of PD-L1 high myeloid cells. These studies demonstrate that targeted enhancement of tumor-intrinsic IFN{gamma} signaling can induce of cascade of changes associated with increased therapeutic vulnerability. SummaryMechanisms regulating response to anti-PD-1 therapy in lung cancer are not well defined. This study, using orthotopic immunocompetent mouse models of lung cancer, demonstrates that intrinsic sensitivity of cancer cells to IFN{gamma} determines anti-PD-1 responsiveness through alterations in the tumor microenvironment.

cancer biology↗