Biology subjects

Zhong, Q.

Publications and source records attributed to Zhong, Q..

54 records · Page 3Linked to original sources

bioRxiv2022

Transformer-based deep learning integrates multi-omic data with cancer pathways

Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet. HighlightsO_LIDeePathNet integrates biological pathways for enhanced cancer analysis. C_LIO_LIDeePathNet utilizes Transformer-based deep learning for superior accuracy. C_LIO_LIDeePathNet outperforms existing models in drug response prediction. C_LIO_LIDeePathNet enables pathway-level biomarker discovery in cancer research. C_LI

bioinformatics↗

bioRxiv2022

m6A epitranscriptomic modification regulates neural progenitor-to-glial cell transition in the retina

N6-methyladenosine (m6A) is the most prevalent mRNA internal modification and has been shown to regulate the development, physiology and pathology of various tissues. However, the functions of the m6A epitranscriptome in the visual system remain unclear. In this study, using a retina-specific conditional knockout mouse model, we show that retinas deficient in Mettl3, the core component of the m6A methyltransferase complex, exhibit structural and functional abnormalities beginning at the end of retinogenesis. Immunohistological and scRNA-seq analyses of retinogenesis processes reveal that retinal progenitor cells (RPCs) and Muller glial cells are the two cell types primarily affected by Mettl3 deficiency. Integrative analyses of scRNA-seq and MeRIP-seq data suggest that m6A fine-tunes the transcriptomic transition from RPCs to Muller cells by promoting the degradation of RPC transcripts, the disruption of which leads to abnormalities in late retinogenesis and compromises the glial functions of Muller cells. Finally, overexpression of m6A-regulated RPC-enriched transcripts in late RPCs partially recapitulates the Mettl3-deficient retinal phenotype. Collectively, our study reveals an epitranscriptomic mechanism governing progenitor-to-glial cell transition during late retinogenesis, which is essential for the homeostasis of the mature retina. The mechanism revealed in this study might also apply to other nervous systems.

developmental biology↗

bioRxiv2022

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗

bioRxiv2021

Multiscale profiling of enzyme activity in cancer

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. Methods to quantify enzyme activity, rather than just expression, are therefore critical to our ability to understand the pathological roles of enzymes in cancer and to harness this class of biomolecules as diagnostic and therapeutic targets. Here we present an integrated set of methods for measuring specific enzyme activities across the organism, tissue, and cellular levels, which we unify into a methodological hierarchy to facilitate biological discovery. We focus on proteases for method development and validate our approach through the study of tumor progression and treatment response in an autochthonous model of Alk-mutant lung cancer. To quantitatively measure activity dynamics over time, we engineered multiplexed, peptide-based nanosensors to query protease activity in vivo. Machine learning analysis of sensor measurements revealed dramatic protease dysregulation in lung cancer, including significantly enhanced proteolytic cleavage of one peptide, S1 (Padj < 0.0001), which returned to healthy levels within three days after initiation of targeted therapy. Next, to link these organism-level observations to the in situ context, we established a multiplexed assay for on-tissue localization of enzyme activity and pinpointed S1 cleavage to endothelial cells and pericytes of the tumor vasculature. Lastly, to directly link enzyme activity measurements to cellular phenotype, we designed a high-throughput method to isolate and characterize proteolytically active cells, uncovering profound upregulation of pro-angiogenic transcriptional programs in S1-positive cells. Together, these methods allowed us to discover that protease production by angiogenic vasculature responds rapidly to targeted therapy against oncogene-addicted tumor cells, identifying a highly dynamic interplay between tumor cells and their microenvironment. This work provides a generalizable framework to functionally characterize enzyme activity in cancer.

bioengineering↗

bioRxiv2021

The genome of an apodid holothuroid (Chiridota heheva) provides insights into its adaptation to deep-sea reducing environment

Cold seeps and hydrothermal vents are deep-sea reducing environments that are characterized by a lack of oxygen, photosynthesis-derived nutrients and a high concentration of reducing chemicals. Apodida is an order of deep-sea echinoderms lacking tube feet and complex respiratory trees, which are commonly found in holothurians. Chiridota heheva Pawson & Vance, 2004 (Apodida: Chiridotidae) is one of the few echinoderms that resides in deep-sea reducing environments. Unlike most cold seep and hydrothermal vent-dwelling animals, C. heheva does not survive by maintaining an epi- or endosymbiotic relationship with chemosynthetic microorganisms. The species acquires nutrients by extracting organic components from sediment detritus and suspended material. Here, we report a high-quality genome of C. heheva as a genomic reference for echinoderm adaptation to reducing environments. Chiridota heheva likely colonized its current habitats in the early Miocene. The expansion of the aerolysin-like protein family in C. heheva compared with other echinoderms might be involved in the disintegration of microbes during digestion, which in turn facilitates the species adaptation to cold seep environments. Moreover, several hypoxia-related genes were subject to positive selection in the genome of C. heheva, which contributes to their adaptation to hypoxic environments.

genomics↗

bioRxiv2021

Extensive Chromatin Structure-Function Association Revealed by Accurate Compartmentalization Characterization

Chromosome conformation capture-based experiments have shown that eukaryotic chromosomes are partitioned into A and B compartments conventionally identified by the first eigenvector (EV1) of dimension reduction methods. However, many genomic regions show marginal EV1 values, indicating the ambiguity of A/B compartment scheme on these regions. We develop MOSAIC (MOdularity and Singular vAlue decomposition-based Identification of Compartments), an accurate compartmental state detection scheme. MOSAIC reveals that those ambiguous regions segregate into two additional compartmental states, which typically correspond to small genomic regions flanked by large canonical A/B compartments with opposite activities. They are denoted as micro-compartments accordingly. In contrast to the canonical A/B compartments, micro-compartments cover ~30% of the genome and are highly dynamic between cell types. More importantly, distinguishing the micro-compartments underpins accurate characterization of chromatin structure-function relationship. By applying MOSAIC to GM12878 and K562 cells, we identify CD86, ILDR1 and GATA2 which show concordance between gene expression and compartmental states beyond the scheme of A/B compartments. Taken together, MOSAIC uncovers fine-scale and dynamic structures underlying canonical A/B compartments. Our results suggest dynamic chromatin compartmentalization is underlying transcriptional regulation and disease.

bioinformatics↗

bioRxiv2021

CoolBox: A flexible toolkit for visual analysis of genomics data

We developed CoolBox, an open source toolkit for visual analysis of genomics data, which is highly compatible with the Python ecosystem, easy to use and highly customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife, for example, to produce high-quality genome track plots or fetch common used genomic data files with a Python script or command line, interactively explore genomic data within Jupyter environment or web browser. Moreover, owing to the highly extensible API design, users can customize their own tracks without difficulty, which can greatly facilitate analytical, comparative genomic data visualization tasks.

bioinformatics↗

bioRxiv2021

Structural variant selection for high-altitude adaptation using single-molecule long-read sequencing

Structural variants (SVs) can be important drivers of human adaptation with strong effects, but previous studies have focused primarily on common variants with weak effects. Here, we used large-scale single-molecule long-read sequencing of 320 Tibetan and Han samples, to show that SVs are key drivers of selection under high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict driver enhancers, target genes, upstream regulators, and biological functions, which we validate using enhancer reporter and DNA pull-down assays. We reveal diverse Tibetan-specific SVs affecting the cis- and trans-regulatory circuitry of diverse biological functions, including hypoxia response, energy metabolism, lung function, etc. Our study greatly expands the global SV landscape, reveals the central role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles that SVs can play in human biology.

genomics↗

bioRxiv2021

Binary Interactome Models of Inner- Versus Outer-Complexome Organization

Hundreds of different protein complexes that perform important functions across all cellular processes, collectively comprising the "complexome" of an organism, have been identified1. However, less is known about the fraction of the interactome that exists outside the complexome, in the "outer-complexome". To investigate features of "inner"- versus outer-complexome organisation in yeast, we generated a high-quality atlas of binary protein-protein interactions (PPIs), combining three previous maps2-4 and a new reference all-by-all binary interactome map. A greater proportion of interactions in our map are in the outer-complexome, in comparison to those found by affinity purification followed by mass spectrometry5-7 or in literature curated datasets8-11. In addition, recent advances in deep learning predictions of PPI structures12 mirror the existing experimentally resolved structures in being largely focused on the inner complexome and missing most interactions in the outer-complexome. Our new PPI network suggests that the outer-complexome contains considerably more PPIs than the inner-complexome, and integration with functional similarity networks13-15 reveals that interactions in the inner-complexome are highly detectable and correspond to pairs of proteins with high functional similarity, while proteins connected by more transient, harder-to-detect interactions in the outer-complexome, exhibit higher functional heterogeneity.

systems biology↗

bioRxiv2021

The glycosphingolipid inhibitor eliglustat inhibits autophagy in osteoclasts to increase bone mass and reduce myeloma bone disease

Multiple myeloma (MM) is a fatal hematological malignancy, where the majority of patients are diagnosed with, or develop, destructive and debilitating osteolytic bone lesions. Current treatments for MM bone disease such as the bisphosphonate zoledronic acid can result in deleterious side effects at high doses. In this study, eliglustat, an FDA approved glycosphingolipid inhibitor, was shown to reduce MM bone disease in preclinical models of MM. Mechanistically, eliglustat alters the lipid composition and plasma membrane fluidity and acts as an autophagy flux inhibitor in bone-resorbing osteoclasts (OC). Autophagic degradation of the signaling molecule TRAF3 is key step in OC differentiation; this was prevented by eliglustat in OC precursors. In addition, eliglustat works depend on TRAF3 in vivo. Furthermore, the combination of eliglustat and zoledronic acid was found to have an additive effect to reduce MM bone disease, suggesting the potential for combination therapies that would allow for drug dose reductions. Taken together, this project identifies a novel mechanism in which glycosphingolipid inhibition reduces osteoclastogenesis via autophagy and highlights the translational potential of eliglustat for the treatment of bone loss disorders such as MM. One Sentence SummaryTranslational use of eliglustat as an autophagy inhibitor to limit bone lesions in multiple myeloma.

cell biology↗

bioRxiv2021

Antibody Humanization: Intra and Inter VH-VL Non-bond Energy Sort Best Humanized Antibody

Humanization of non-human derived antibody is necessary to reduce the immunogenicity of antibody drug. In recent years, computer aided antibody humanization became an efficient and rapid routine process. We report here a new computational humanization pipeline which includes CDR grafting onto the crystal structures of homologous antibody and CDR grafted humanized antibody structures. Then, the intra and inter VH-VL binding energies are calculated and sorted for optimized humanized antibody. The resulted humanized antibodies are ranked and compared to experimental dataset, which confirms the validity of humanized antibody design by intra and inter VH-VL binding energy.

bioinformatics↗

bioRxiv2020

Activatable zymography probes enable in situ localization of protease dysregulation in cancer

Recent years have seen the emergence of conditionally activated diagnostics and therapeutics that leverage protease-cleavable peptide linkers to enhance their specificity for cancer. However, due to a lack of methods to measure and localize protease activity directly within the tissue microenvironment, the design of protease-activated agents has been necessarily empirical, yielding suboptimal results when translated to patients. To address the need for spatially resolve d protease activity profiling in cancer, we developed a new class of in situ probes that can be applied to fresh-frozen tissue sections in a manner analogous to immunofluorescence staining. These activatable zymography probes (AZPs) detected dysregulated protease activity in human prostate cancer biopsy samples, enabling disease classification. We then leveraged AZPs within a generalizable framework to design conditional cancer diagnostics and therapeutics, and demonstrated the power of this approach in the Hi-Myc mouse model of prostate cancer, which models features of early pathogenesis. Multiplexed screening against barcoded substrates yielded a peptide, S16, that was robustly and specifically cleaved by tumor-associated metalloproteinases in the Hi-Myc model. In situ labeling with an AZP incorporating S16 revealed a potential role of metalloproteinase dysregulation in proliferative, pre-malignant Hi-Myc prostatic glands. Last, we incorporated S16 into an in vivo imaging probe that, after systemic administration, perfectly classified diseased and healthy prostates, supporting the relevance of ex vivo activity assays to in vivo translation. We envision AZPs will enable new insights into the biology of protease dysregulation in cancer and accelerate the development of conditional diagnostics and therapeutics for multiple cancer types.

bioengineering↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2019

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

biochemistry↗

bioRxiv2018

Polymeric perfluoropentane nanoemulsions are a versatile platform for ultrasonic drug uncaging

Catheter-based intra-arterial drug therapies have proven effective for a range of oncologic, neurologic, and cardiovascular applications. However, these procedures are limited by their invasiveness, as well as the relatively broad drug spatial distribution that is achievable with selective arterial catheterization. The ideal technique for local pharmacotherapy would be noninvasive and would flexibly deliver a given drug to any region of the body. Combining polymeric perfluorocarbon nanoemulsions with existent clinical focused ultrasound systems could in principle enable noninvasive targeted drug delivery, but it has not been clear whether these nanoparticles could provide the necessary drug loading, stability, and generalizability across a range of drugs to meet these needs, beyond a few niche applications. Here, we directly address all of those challenges and fully develop polymeric perfluorocarbon nanoemulsions into a generalized platform for ultrasound-targeted drug delivery with high potential for clinical translation. We demonstrate that a wide variety of drugs may be effectively uncaged with ultrasound using these nanoparticles, with drug loading increasing with hydrophobicity. We also set the stage for clinical translation by delineating production protocols that hew to clinical standards and yield stable and optimized ultrasound-activated drug-loaded nanoemulsions. Finally, as a new potential clinical application for these nanoemulsions, we exhibit their in vivo efficacy and performance for cardiovascular applications, by achieving local vasodilation in the highest flow vessel of the body, the aorta. This work establishes the power of polymeric perfluorocarbon nanoemulsions as a clinically-translatable platform for effective noninvasive ultrasonic drug uncaging for myriad targets in the brain and body.

bioengineering↗

bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗

bioRxiv2018

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Many tumors are characterized by large genomic heterogeneity and it remains unclear to what extent this impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of 30 biopsy-scale prostate tissues using pressure cycling technology and SWATH mass spectrometry. We quantified 8,248 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied significantly depending on proteins and tissue types. Benign tissues exhibited generally more complex ITH patterns than malignant tissues. Spatial variability of ten prostate biomarkers was further validated by immunohistochemistry in an independent cohort (n=83) using tissue microarrays. PSA was preferentially variable in benign prostatic hyperplasia, while GDF15 substantially varied in prostate adenocarcinomas. Further, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery by quantifying spatial proteome variation and it demonstrates the feasibility by exploiting recent technological progress.

systems biology↗

bioRxiv2017

Cyclic-di-adenosine monophosphate (c-di-AMP) is required for osmotic regulation in Staphylococcus aureus but dispensable for viability in anaerobic conditions

Cyclic di-adenosine monophosphate (c-di-AMP) is a recently discovered signaling molecule important for the survival of Firmicutes, a large bacterial group that includes notable pathogens such as Staphylococcus aureus. However, the exact role of this molecule has not been identified. dacA, the S. aureus gene encoding the diadenylate cyclase enzyme required for c-di-AMP production, cannot be deleted when bacterial cells are grown in rich medium, indicating that c-di-AMP is required for growth in this condition. Here, we report that an S. aureus dacA mutant can be generated in chemically defined medium. Consistent with previous findings, this mutant had a severe growth defect when cultured in rich medium. Using this growth defect in rich medium, we selected for suppressor strains with improved growth to identify c-di-AMP-requiring pathways. Mutations bypassing the essentiality of dacA were identified in alsT and opuD, encoding a predicted amino acid and osmolyte transporter, the latter of which we show here to be the main glycine betaine-uptake system in S. aureus. Inactivation of these transporters likely prevents the excessive osmolyte and amino acid accumulation in the cell, providing further evidence for a key role of c-di-AMP in osmotic regulation. Suppressor mutations were also obtained in hepS, hemB, ctaA and qoxB, coding for proteins required for respiration. Furthermore, we show that dacA is dispensable for growth in anaerobic conditions. Together, these finding reveal an essential role for the c-di-AMP signaling network in aerobic, but not anaerobic, respiration in S. aureus.

microbiology↗