Biology subjects

Qian, D.

Publications and source records attributed to Qian, D..

31 records · Page 2Linked to original sources

bioRxiv2023

Modulating Nucleic Acid Phase Transitions as a Mechanism of Action for Cell-Penetrating Antimicrobial Peptides

Antimicrobial peptides (AMPs) are a vital part of the immune system that helps combat bacterial infections by disrupting the bacterial cell membrane or interacting with intracellular targets. However, the mechanisms by which AMPs act on intracellular targets are not well understood. Using machine learning-based sequence analysis, we have discovered that a significant number of AMPs have a strong tendency to form liquid-like condensates in the presence of nucleic acids, a process known as phase separation. We have demonstrated that this phase separation propensity is linked to the effectiveness of the AMPs in inhibiting transcription and translation in vitro, as well as their ability to compact nucleic acids and form clusters with bacterial nucleic acids in bacterial cells. We propose that the AMP-driven compaction of nucleic acids and modulation of their phase transitions constitute a previously unrecognised mechanism by which AMPs exert their antibacterial effects. These findings open up exciting possibilities for the development of antimicrobials that target nucleic acid phase transitions.

microbiology↗

bioRxiv2023

Adsorption of RNA to interfaces of biomolecular condensates enables wetting transitions

Biomolecular condensates form via spontaneous and driven phase transitions of multivalent proteins and nucleic acids. These macromolecules can be organized in spatially inhomogeneous ways that lead to multiple coexisting dense phases with distinct macromolecular interfaces. While considerable attention has focused on the physical driving forces that give rise to phase separation from bulk solutions, the interactions that underlie adsorption driven wetting transitions remain unclear. Here, we report that pyrimidine-rich RNAs function as adsorbents that enable cascades of wetting transitions that include partial and complete wetting of condensates formed by purine-rich RNAs. Computations show that macromolecules that are scaffolds of condensates are oriented perpendicular to condensate interfaces whereas adsorbents are oriented parallel to interfaces. Our results yield heuristics for the design of synthetic materials that can be based on RNA-rich condensates featuring bespoke interfaces and distinct local microenvironments created by the interplay between scaffolds versus adsorbents.

biophysics↗

bioRxiv2022

Multi-dimensional protein solubility optimization with an ultra-high-throughput microfluidic platform

Protein-based biologics are highly suitable for drug development, as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to very high concentrations, making insufficient solubility a critical bottleneck in drug development pipelines. Here, we report an ultra-high-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 micrograms of protein (> 10-fold improvement) and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation additives, such as salt, polysorbate, histidine, arginine and sucrose. Additionally, we can measure how solubility is affected by different concentrations of multiple additives, find a suitable pH for the formulation, and measure the impact of single mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multi-dimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

biophysics↗

bioRxiv2022

Self-consistent solution to the Flory-Huggins model and applications in protein liquid-liquid phase separation

A self-consistent analytical solution for binodal concentrations of the two-component Flory-Huggins model is derived. We show that this form extends the validity of the Ginzburg-Landau expansion away from the critical point to cover the whole phase space. Furthermore, this analytical solution reveals an exponential scaling law of the dilute phase binodal concentration as a function of interaction strength and chain length. We demonstrate explicitly the power of this approach by fitting experimental protein liquid-liquid phase separation boundaries to determine the effective chain length and the solute-solvent interaction energy. Moreover, we demonstrate that this strategy allows us to resolve the differences in the interaction energy contributions of individual amino acids. This analytical framework can serve as a new way to decode the protein sequence grammar for liquid-liquid phase separation.

biophysics↗

bioRxiv2022

Tie-lines reveal interactions driving heteromolecular condensate formation

Phase separation of biomolecules give rise to membraneless organelles that contribute to the spatiotemporal organisation of the cell. In most cases, such biomolecular condensates contain multiple components, but the manner in which interactions between components control the stability of condensates remained challenging to elucidate. Here, we develop an approach to determine tie-line gradients in ternary liquid-liquid phase separation (LLPS) systems, based on measurements of the dilute phase concentration of only one component. We show that the sign of the tie-line gradient is related to the cross-interaction energy between the polymers in the system and discriminates between competitive and cooperative phase separation. Using this approach, we studied the interaction between protein Fused in Sarcoma (FUS) and polyethylene glycol (PEG) polymer chains, and measured positive tie-line gradients. Our results show that PEG drives LLPS through an associative interaction with FUS and is not an inert crowder. We further studied the interaction between PolyA RNA (3.0{+/-}0.5kDa) and the protein G3BP1, and using the tie-line gradient as a reporter for the stoichiometry of polymers in the condensate we determined a G3BP1-to-PolyA RNA molar ratio of 1:4 in the dense phase. Our framework for measuring tie-line gradients opens up a route for the characterisation of interaction types and compositions in ternary LLPS systems.

biophysics↗

bioRxiv2022

Multiphase condensates from a kinetically arrested phase transition

The formation of biomolecular condensates through liquid-liquid phase separation from proteins and nucleic acids is emerging as a spatial organisational principle used by living cells. Many such biomolecular condensates are not, however, homogeneous fluids, but contain an internal structure consisting of distinct sub-compartments with different compositions. In many instances, such compartments inside the condensate are depleted in the biopolymers that make up the condensate. Here, we describe that this multiphase structure arises from a kinetically arrested phase transition. The combination of a change in composition coupled with a slow response to this change can lead to the spontaneous formation of multiple emulsions consisting of several inner cores within a polymer-rich middle phase. In the case of liquid-like biomolecular condensates, the slow diffusion of biopolymers causes nucleation of biopolymer-poor liquid inside of the condensate to achieve the new equilibrium composition. This framework shows that multiphase condensates can be a result of kinetic trapping, rather than thermodynamic stability, and provides and avenue to understand and control the internal structure of condensates in vitro and in vivo.

biophysics↗

bioRxiv2021

X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction

MotivationIdentifying the drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic datasets with hidden bias, which will limit the accuracy of virtual screening methods. Meanwhile, most DPIs prediction methods pay more attention to molecular representation but lack effective research on protein representation and high-level associations between different instances. To this end, we presented here a novel structure-aware multi-modal DPIs prediction model, X-DPI, performing on a curated industry-scale benchmark dataset. ResultsWe built a high-quality benchmark dataset named GalaxyDB for DPIs prediction. This industry-scale dataset along with an unbiased training procedure resulted in a more robust benchmark study. For informative protein representation, we constructed a structure-aware graph neural network method from the protein sequence by combining predicted contact maps and graph neural networks. Through further integration of structure-based representation and high-level pre-trained embeddings for molecules and proteins, our model captured more effectively the feature representation of the interactions between them. As a result, X-DPI outperformed state-of-the-art DPIs prediction methods and obtained 5.30% Mean Square Error (MSE) improved in the DAVIS dataset and 8.89% area under the curve (AUC) improved in GalaxyDB dataset. Moreover, our model is an interpretable model with the transformer-based interaction mechanism, which can accurately reveal the binding sites between molecule and protein.

bioinformatics↗

bioRxiv2021

LMO2 is critical for early metastatic events in breast cancer

Metastasis is responsible for the majority of breast cancer-related deaths, however identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage- traced cells have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNF and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis. One sentence summaryLMO2 modulates STAT3 signaling in breast cancer metastasis.

cancer biology↗

bioRxiv2021

Selection and structural characterisation of anti-TREM2 scFvs that reduce levels of shed ectodomain

Single point mutations in TREM2, a receptor expressed by microglia in the brain, are associated with an increased risk of neurodegeneration including Alzheimers disease. Numerous studies support a role for TREM2 in sensing damaging stimuli and triggering signalling cascades necessary for neuroprotection. Despite its significant role, ligands and regulators of TREM2 activation, and the mechanisms governing TREM2-dependent responses and its cleavage from the membrane, remain poorly characterised. Here, we present phage display generated scFv antibody binders to human TREM2 ectodomain. Cocrystal structures revealed the binding of two scFvs to an epitope on the globular TREM2 domain distal to the putative ligand-binding site. Enhanced functional activity was observed for oligomeric scFv species which inhibited the production of soluble TREM2 in a HEK293 cell model. We hope that detailed characterisation of their epitopes and properties will facilitate the use of these renewable binders as structural and functional biology tools for TREM2 research.

molecular biology↗

bioRxiv2020

Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer's model

Alzheimers disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD and human fetal cells harboring mutant amyloid precursor protein, we show cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it the earliest defect in the evolution of disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. Reduction of USP16 led to decreased expression of senescence gene Cdkn2a and mitigated aberrant regulation of the BMP pathway, a previously unknown function of USP16. Thus, we reveal USP16 as a novel target in an AD model that can both ameliorate the NPC defect and rescue memory and learning through its regulation of both Cdkn2a and BMP signaling. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY Schematic summarizing therapeutic approaches to mitigate the effects of mutant APP through targeting of Cdkn2a, BMI1, USP16 and BMP.

molecular biology↗

bioRxiv2020

Supraphysiological testosterone induces ferroptosis and activates NF-kappaB mediated immune pathways in prostate cancer through nucleophagy

The discovery that androgens play an important role in the progression of prostate cancer (PCa) has led to the development of androgen deprivation therapy as a first line of treatment against PCa. However, paradoxical growth inhibition has been observed, both experimentally and clinically, in a subset of PCa upon administration of supraphysiological levels of testosterone (SupraT). Here we report that SupraT activates cytoplasmic nucleic acid sensors and induces growth inhibition of SupraT-sensitive PCa cells. This is initiated by induction of two parallel autophagy-mediated processes, namely, ferritinophagy and nucleophagy. Consequently, autophagosomal DNA activates nucleic acid sensors that converge on NF-kappaB to drive immune signaling pathways. Chemokines and cytokines secreted by the tumor cells in response to SupraT results in increased migration of cytotoxic immune cells to tumor beds of animal xenografts and patient tumors. Collectively, our findings indicate that SupraT may inhibit a subset of PCa by activating nucleic acid sensors and downstream immune signaling.

cancer biology↗

bioRxiv2019

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA-sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation without prior knowledge has remained challenging. Here we describe a simple yet robust determinant of developmental potential--the number of detectably expressed genes per cell-- and leverage this measure of transcriptional diversity to develop a new framework for predicting ordered differentiation states from scRNA-seq data. When evaluated on ~150,000 single-cell transcriptomes spanning 53 lineages and five species, our approach, called CytoTRACE, outperformed previous methods and ~19,000 molecular signatures for resolving experimentally-confirmed developmental trajectories. In addition, it enabled unbiased identification of tissue-resident stem cells, including cells with long-term regenerative potential. When used to analyze human breast tumors, we discovered candidate genes associated with less-differentiated luminal progenitor cells and validated GULP1 as a novel gene involved in tumorigenesis. Our study establishes a key RNA-based correlate of developmental potential and provides a new platform for robust delineation of cellular hierarchies (https://cytotrace.stanford.edu).

genomics↗

bioRxiv2018

CDK19 is a Regulator of Triple-Negative Breast Cancer Growth

Triple-negative breast cancer (TNBC) is a poor prognosis disease with no clinically approved targeted therapies. Here, using in vitro and in vivo RNA interference (RNAi) screens in TNBC patient-derived xenografts (PDX), we identify cyclin dependent kinase 19 (CDK19) as a potential therapeutic target. Using in vitro and in vivo TNBC PDX models, we validated the inhibitory effect of CDK19 knockdown on tumor initiation, proliferation and metastases. Despite this, CDK19 knockdown did not affect the growth of non-transformed mammary epithelial cells. Using CD10 and EpCAM as novel tumor initiating cell (TIC) markers, we found the EpCAMmed/high/CD10-/low TIC sub-population to be enriched in CDK19 and a putative cellular target of CDK19 inhibition. Comparative gene expression analysis of CDK19 and CDK8 knockdowns revealed that CDK19 regulates a number of cancer-relevant pathways, uniquely through its own action and others in common with CDK8. Furthermore, although it is known that CDK19 can act at enhancers, our CHIP-Seq studies showed that CDK19 can also epigenetically modulate specific H3K27Ac enhancer signals which correlate with gene expression changes. Finally, to assess the potential therapeutic utility of CDK19, we showed that both CDK19 knockdown and chemical inhibition of CDK19 kinase activity impaired the growth of pre-established PDX tumors in vivo. Current strategies inhibiting transcriptional co-factors and targeting TICs have been limited by toxicity to normal cells. Because of CDK19s limited tissue distribution and the viability of CDK19 knockout mice, CDK19 represents a promising therapeutic target for TNBC.

cancer biology↗