Biology subjects

Qi, R.

Publications and source records attributed to Qi, R..

25 records · Page 2Linked to original sources

bioRxiv2020

Construction of a liquid-liquid phase separation system from the gel-sol transition of elongated protein microgels in a crowding agent

Liquid proteinaceous materials have been frequently found in cells or tissues and are crucial for various biological processes. Unlike their solid-state counterparts, liquid-state protein compartments are challenging to engineer and control at the microscale. Conventionally, gelation (sol-gel transition) of biological molecules has been thought to be the intermediate step between liquid-liquid phase separation (LLPS) states and insoluble aggregates that are related to protein functions, malfunctions and even diseases. However, the opposite process, i.e., the gel-sol transition of materials, has not been broadly explored. Here we describe a thermoresponsive gel-sol transition of a protein in a crowded environment that results in a demixed LLPS state, contradicting the common consequence of a one-phase protein solution by the end of such transition at elevated temperature without crowding agents. We also demonstrate a simple method to monitor the gel-sol transition by showing that elongated gelatin microgels can evolve towards a spherical morphology in the crowding agents because of interfacial tension. The LLPS system was explored for the diffusion of small particles for drug-release application scenarios. Our results demonstrate a route for the rapid construction of LLPS models, where the gel-sol transition of the protein-rich phase is monitorable. The models are featured with tunable size and dimensional monodispersity of dispersed condensates. The present study can be employed in biophysics and bioengineering with practices such as 3D printing and temperature sensing.

biophysics↗

bioRxiv2020

Machine learning models for predicting protein condensate formation from sequence determinants and embeddings

Intracellular phase separation of proteins into biomolecular condensates is increasingly recognised as an important phenomenon for cellular compartmentalisation and regulation of biological function. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, here, we established an in silico strategy for understanding on a global level the associations between protein sequence and condensate formation, and used this information to construct machine learning classifiers for predicting liquid-liquid phase separation (LLPS) from protein sequence. Our analysis highlighted that LLPS-prone sequences are more disordered, hydrophobic and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database, and have their disordered regions enriched in polar, aromatic and charged residues. Using these determining features together with neural network based word2vec sequence embeddings, we developed machine learning classifiers for predicting protein condensate formation. Our model, trained to distinguish LLPS-prone sequences from structured proteins, achieved high accuracy (93%; 25-fold cross-validation) and identified condensate forming sequences from external independent test data at 97% sensitivity. Moreover, in combination with a classifier that had developed a nuanced insight into the features governing protein phase behaviour by learning to distinguish between sequences of varying LLPS propensity, the sensitivity was supplemented with high specificity (approximated ROC-AUC of 0.85). These results provide a platform rooted in molecular principles for understanding protein phase behaviour. The predictor is accessible from https://deephase.ch.cam.ac.uk/. Significance StatementThe tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behaviour have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behaviour, here, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase separating proteins. Moreover, by combining this insight with neural network based sequence embeddings, we trained machine learning classifiers that identified phase separating sequences with high accuracy, including from independent external test data. The predictor is available from https://deephase.ch.cam.ac.uk/.

biophysics↗

bioRxiv2020

scREAD: A single-cell RNA-Seq database for Alzheimer's Disease

SummaryAlzheimers disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimers Disease), which is the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq datasets from human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 55 datasets from eight brain regions, including control atlas construction, cell type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons. Availability and ImplementationscREAD is a one-stop and user-friendly interface and freely available at https://bmbls.bmi.osumc.edu/scread/. The backend workflow can be downloaded from https://github.com/OSU-BMBL/scread/tree/master/script, to enable more discovery-driven analyses. Contactqin.ma@osumc.edu or hongjun.fu@osumc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimers disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

bioinformatics↗

bioRxiv2020

Rapid characterisation of protein phase behaviour using droplet microfluidics

The assembly of intracellular proteins into biomolecular condensates is a fundamental process underlying the organisation of intracellular space and the regulation of many cellular processes. Mapping and characterising phase behaviour of biomolecules is essential to understand the mechanisms of condensate assembly, and to develop therapeutic strategies targeting biomolecular condensate systems. A central concept for characterising phase-separating systems is the phase diagram. Phase diagrams are typically built from numerous individual measurements sampling different parts of the parameter space. However, even when performed in microwell plate format, this process is slow, low throughput and requires significant sample consumption. To address this challenge, we present here a combinatorial droplet microfluidic platform, termed PhaseScan, for rapid and high-resolution acquisition of multidimensional biomolecular phase diagrams. Using this platform, we characterise the phase behaviour of a wide range of systems under a variety of conditions and demonstrate that this approach allows the quantitative characterisation of the effect of small molecules on biomolecular phase transitions.

biophysics↗

bioRxiv2020

Nucleolin is essential for rabbit hemorrhagic disease virus replication by providing a physical link in replication complex formation

Rabbit hemorrhagic disease virus (RHDV) is an important member of the Caliciviridae family and cannot be propagated in vitro, which has greatly impeded progress of investigating its replication mechanism. Construction of an RHDV replicon system has recently provided a platform for exploring RHDV replication in host cells. Here, aided by this replicon system and using two-step affinity purification, we purified the RHDV replicase and identified its associated host factors. We identified rabbit nucleolin (NCL) as a physical link required for the formation of RHDV replication complexes (RCs), by mediating the interaction between other host proteins and the viral RNA replicase, RNA-dependent RNA polymerase (RdRp). We found that RHDV RdRp uses an amino acid (aa) region spanning residues 448-478 to directly interact with NCLs RNA-recognition motif 2. We also found that the viral p16 protein uses a highly conserved region (35Cys-Ile-Arg-Ala38 or CIRA motif) to specifically bind the N-terminal region of NCL (aa 1-110) and that RHDV p23 uses a specific domain (aa 90-145) to bind NCLs RNA-recognition motif 1. Disrupting these protein-protein interactions severely weakened viral replication. Furthermore, NCL overexpression or knockdown significantly increased or severely impaired, respectively, RHDV replication. Collectively, these results indicate that the host protein NCL is essential for RHDV replication and plays a key role in the formation of RHDV RCs. The mechanisms by which NCL promotes viral replicase assembly reported here shed light on viral RC biogenesis and may inform antiviral therapies. Author summaryRabbit hemorrhagic disease virus (RHDV) is the causative agent of highly contagious and lethal hemorrhagic disease in the European rabbit, but the host factors involved in RHDV replication remain poorly understood. In the present study, we isolated RHDV replication complex (RC) for the first time and identified its main components. We found that nucleolin (NCL) plays a key role in the formation of the RHDV RC. NCL not only interacts with viral replicase (RdRp), it also specifically binds to other important host factors. In addition, we proved that NCL is necessary for RHDV replication because the level of RHDV replication is significantly affected by knocking down the NCL gene in cells. Together, our data suggest that RHDV completes its replication by hijacking NCL to recruit other viral proteins and host factors, thereby assembling the RC of RHDV.

microbiology↗

bioRxiv2017

Epigenetic Reprogramming of Tissue-Specific Transcription Promotes Metastasis

Tumor metastasis is the cause of death for 90% of cancer patients, and no currently-available therapies target this multi-step process in which cancer cells spread from the local tissue of a primary tumor to distant organs where they establish secondary tumors1. Although epithelial-to-mesenchymal transition2, tumor-secreted exosomes3, epigenetic regulators as well as other genes4-8 have been implicated in metastasis, little is known about how cells adapt to and colonize new tissue environments. Here, we show that the epigenetics-mediated reprogramming of tissue-specific gene transcription in cancer cells promotes metastasis. Using colorectal cancer (CRC) as a model, we found in both clinical and cell line studies that metastatic CRC cells lose their colon-specific gene transcription program and gain a liver-specific gene transcription program as they metastasize in the liver. Further, we found this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical and super-enhancers. Chemical inhibition of enhancer activity disrupts the ability of cells to execute altered transcription programs and consequently inhibits metastasis. Binding motif analysis of the enhancers in liver metastatic CRC cells identified the liver-specific transcription factor FOXA2 as a key regulator, and knocking down of FOXA2 expression prevents the colonization of metastatic CRC cells in the liver of a mice xenograft model. These results, together with additional observations of similar reprogramming in several cohorts of clinical CRC tumor samples and in multiple other forms of metastatic cancers, indicate that this reprogramming may be a common feature of metastasis in multiple cancers and suggest the targeted disruption of this epigenetic reprogramming as a strategy for the development of therapies to treat metastasis, the leading cause of cancer-related mortality.

cancer biology↗