Biology subjects

Cai, Z.

Publications and source records attributed to Cai, Z..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Endothelial cell-derived lactate triggers mesenchymal stem cell histone lactylation to attenuate osteoporosis

Blood vessels play a role in osteogenesis and osteoporosis; however, the role of vascular metabolism is unclear. The present study found that ovariectomized mice exhibit reductions in bone blood vessel density and expression of endothelial glycolytic regulator pyruvate kinase M2 (PKM2). Additional data showed that endothelial cell (EC)-specific deletion of Pkm2 impair osteogenesis and worsen osteoporosis in mice. This was attributed to the impaired differentiation ability toward osteoblast of bone mesenchymal stem cells (BMSCs). Mechanistically, EC-specific deletion of Pkm2 reduce serum lactate levels secreted by ECs, which affect histone lactylation of BMSCs. We identified collagen type I alpha 2 chain, cartilage oligomeric matrix protein, ectonucleotide pyrophosphatase/phosphodiesterase 1, and transcription factor 7 like 2 as histone H3K18 lactylation-regulated osteogenic genes using joint CUT&Tag and RNA-sequencing analyses. The overexpression of PKM2 in ECs, addition of lactate, and exercise were observed to restore the phenotype of endothelial Pkm2-deficient mice. Furthermore, metabolomics of the serum indicated that osteoporosis patients showed a relatively low lactate level. The histone lactylation and related osteogenic genes of BMSCs in osteoporosis patients also decreased. In conclusion, the glycolysis of ECs fuels the differentiation of BMSCs into osteoblasts through histone lactylation, and exercise partially ameliorates osteoporosis through increased serum lactate.

cell biology↗

bioRxiv2023

Neuronal Transcriptome Disruption, Tau Accumulation and Synapse Loss in Alzheimer's Knock-in Mice Require Cellular Prion Protein

BackgroundCellular prion protein (PrPC) is a high-affinity cell-surface receptor for Amyloid-{beta} oligomers (A{beta}o). In certain overexpression models of Alzheimers Disease (AD), pharmacology and genetics demonstrate its essential role for synaptic plasticity impairment, memory deficits and synapse loss. However, PrPCs role in AD-related phenotypes with endogenous expression levels, its role in tau accumulation and its effect on imaging biomarkers are unknown. The necessity of PrPC for transcriptomic alterations driven by A{beta} across cell types is unexplored. MethodsThe role of PrPC was examined as a function of age in homozygous AppNL-G-F/hMapt double knock-in mice (DKI). Phenotypes of AppNL-G-F/hMapt mice with a deletion of Prnp expression (DKI; Prnp-/-) were compared with DKI mice with intact Prnp, mice with a targeted deletion of Prnp (Prnp-/-), and mice with intact Prnp (WT). Phenotypes examined included behavioral deficits, synapse loss by PET imaging, synapse loss by immunohistology, tau pathology, gliosis, inflammatory markers, and snRNA-seq transcriptomic profiling. ResultsBy 9 months age, DKI mice showed learning and memory impairment, but DKI; Prnp-/- and Prnp-/- groups were indistinguishable from WT. Synapse loss in DKI brain, measured by [18F]SynVesT-1 SV2A PET or anti-SV2A immunohistology, was prevented by Prnp deletion. Accumulation of Tau phosphorylated at aa 217 and 202/205, C1q tagging of synapses, and dystrophic neurites were all increased in DKI mice but each decreased to WT levels with Prnp deletion. In contrast, astrogliosis, microgliosis and A{beta} levels were unchanged between DKI and DKI; Prnp-/- groups. Single-nuclei transcriptomics revealed differential expression in neurons and glia of DKI mice relative to WT. For DKI; Prnp-/- mice, the majority of neuronal genes differentially expressed in DKI mice were no longer significantly altered relative to WT, but most glial DKI-dependent gene expression changes persisted. The DKI-dependent neuronal genes corrected by Prnp deletion associated bioinformatically with synaptic function. Additional genes were uniquely altered only in the Prnp-/-or the DKI; Prnp-/- groups. ConclusionsA functional Prnp gene is required in AppNL-G-F/hMapt double knock-in mice for synapse loss, phospho-tau accumulation and neuronal gene expression. These data support the efficacy of targeting the A{beta}o-PrPC interaction to prevent A{beta}o-neurotoxicity and pathologic tau accumulation in AD.

neuroscience↗

bioRxiv2022

Mechanism of pH sensing in the human voltage-gated proton channel hHv1

The molecular mechanisms governing the human voltage-gated proton channel hHv1 remain elusive. Here we used membrane-enabled hybrid-solvent continuous constant pH molecular dynamics (CpHMD) simulations with pH replica exchange to further evaluate the recently obtained structural models of hHv1 in hyperpolarized (closed channel) and depolarized (open channel) states (Geragotelis, Tobias et al., Proc. Natl. Acad. Sci. USA 2020) and explore potential pH-sensing residues. The CpHMD titration at a set of symmetric pH conditions revealed three residues that can gain or lose protons upon channel depolarization. Among them residue H168 at the intracellular end of the S3 helix switches from the deprotonated to the protonated state and its protonation is correlated with the increased tilting of the S3 helix during the transition from the hyper-to the depolarized state. Thus, the simulation data suggest H168 as an interior pH sensor, in support of a recent finding based on electrophysiological experiments of Hv1 mutants (Cherny, DeCoursey et al., J. Gen. Physiol. 2018). Our work represents an important step towards deciphering the pH-dependent gating mechanism of hHv1. SIGNIFICANCEThe human voltage-gated proton channel hHv1 is comprised of a proton-selective voltage sensing domain and responsible for cellular pH homeostasis. Despite intense experimental and theoretical investigations, its pH-dependent gating mechanism is not understood. Our simulation data offer strong evidence supporting the role of H168 as a pHi sensor (Cherny, DeCoursey et al., J. Gen. Physiol. 2018). Deciphering the interior pH sensor moves us a step closer to elucidating the structure-function relationship of hHv1.

biophysics↗

bioRxiv2022

Rapid generation of CD19 CAR-T Cells by minicircle DNA enables anti-tumor activity and prevents fatal CAR-B leukemia

Manufacturing CAR (chimeric antigen receptor)-T cell with viral vector is expensive and time-consuming. Besides, during viral transduction, the genes encoding CARs are randomly integrated into the genome, which could cause oncogenesis or produce devastating CAR-tumor cells. Here, using virus-free and non-transgenic minicircle DNA (mcDNA) vector, we enable rapid generation of CD19 CAR-T cells within two days. Further, we demonstrate in vitro and in xenograft models that the anti-tumor effects of CD19 CAR-T cells produced by mcDNA are as effective as those produced by viral vectors. Finally, we show that our manufacturing process can avoid the production of fatal CAR-tumor cells. Taken together, we provide a fast, effective, and therapeutically safe method to generate CD19 CAR-T cells for treating leukemia.

cancer biology↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

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

First identification and investigation of piRNAs in the larval guts of Asian honey bee, Apis cerana

Piwi-interacting RNAs (piRNAs), a kind of small non-coding RNAs (ncRNAs), play pivotal parts in maintaining the genomic stability and modulating biological processes such as growth and development via regulation of gene expression. However, piRNAs in Asian honey bee (Apis cerana) is still largely unknown at present. In this current work, on basis of previously gained high-quality small RNA-seq datasets, piRNAs in the larval guts of Apis cerana cerana, the nominate species of A. cerana, was for the first time identified, followed by in-depth investigation of the regulatory roles of differentially expressed piRNAs (DEpiRNAs) in the developmental process of the A. c. cerana. Here, a total of 621 piRNAs were identified in the A. c. cerana larval guts, among which 499 piRNAs were shared by 4- (Ac4 group), 5- (Ac5 group), and 6-day-old (Ac6 group) larval guts, while the numbers of unique ones were 79, 37, and 11, respectively. piRNAs each group were ranged from 24 nt to 33 nt in length, and the first base of piRNAs had a cytosine (C) bias. Additionally, five up-regulated and five down-regulated piRNAs were identified in the Ac4 vs. Ac5 comparison group, 9 of which could target 9, 011 mRNAs; these targets were involved in 41 GO terms and 137 pathways. Comparatively, 22 up-regulated piRNAs were detected in the Ac5 vs. Ac6 comparison group, 21 of which could target 28, 969 mRNAs; these targets were engaged in 46 functional terms and 164 pathways. The results suggested the overall alteration of expression pattern of piRNAs during the developmental process of A. c. cerana larvae. Regulatory network analysis showed that piR-bmo-748815 and piR-bmo-512574 in the Ac4 vs. Ac5 comparison group as well as piR-bmo-716466 and piR-bmo-828146 in the Ac5 vs. Ac6 comparison group linked to the highest number of targets. Further investigation indicated that targets of DEpiRNAs in the above-mentioned two comparison groups could be annotated to several growth and development-associated pathways, such as Jak/STAT, TGF-{beta}, and Wnt signaling pathways, indicating the involvement of DEpiRNAs in modulating larval gut development via these crucial pathways. Moreover, the expression trends of six randomly selected DEpiRNAs were verified using a combination of stem-loop RT-PCR and RT-qPCR. These results not only provide a novel insight into the development of the A. c. cerana larval guts, but also lay a foundation for uncovering the epigenetic mechanism underlying the larval gut development.

molecular biology↗

bioRxiv2022

Reconstruct a eukaryotic chromosome arm by de novo design and synthesis

The genome of an organism is inherited from its ancestor and keeps evolving over time, however, how much the current version could be altered remains unknown. Here, we use the left arm of chromosome XII (chrXIIL) as an example to probe the genome plasticity in Saccharomyces cerevisiae. A neochromosome was designed to harbor originally dispersed genes. The essentiality of sequences in chrXIIL was dissected by targeted DNA removal, chromosome truncation and random deletion. Notably, 12 genes were sufficient for survival, while 25 genes are required to retain robust fitness. Next, we demonstrated these genes could be reconstructed using synthetic regulatory sequences and recoded open-reading frames with "one-amino-acid-one-codon" strategy. Finally, we built a neochromsome, which could substitute for chrXIIL for cell viability, with these reconstructed genes. Our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional chromosomes with completely artificial sequences. HIGHLIGHTSO_LIA neochromosome was designed to facilitate the assembly of exogenous DNA for stable expression in yeast C_LIO_LIThe left arm of chrXII could be minimized to just 12 genes to maintain viability, but additional genes were required to retain robust fitness C_LIO_LIComprehensive recoding and transcriptional refactoring using artificial regulatory sequences produced a functional chromosome arm C_LIO_LIA completely reconstructed neochromosome could replace the chrXIIL to maintain comparable fitness C_LI

synthetic biology↗

bioRxiv2022

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

BackgroundMagnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. MethodWe validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. ResultsThe MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. ConclusionThis study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. HighlightsO_LIValidation of MEG source imaging with intracranial EEE atlas C_LIO_LIAssessment of resting state human brain oscillations from healthy brain C_LIO_LIAdapted source imaging method, wMEM, to localize resting state oscillations C_LIO_LIIdentified brain regions with oscillations accurately estimated by MEG C_LIO_LIMEG estimated spectra dominated by oscillations in the alpha band C_LI

neuroscience↗

bioRxiv2022

Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation

Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and impute multimodal datasets with mosaic measurements. A key advance is the use of a missing mask for learning the conditional distribution of unobserved modalities and features, which makes scVAEIT flexible to combine different panels of measurements from multimodal datasets accurately and in an end-to-end manner. Imputing the masked features serves as a supervised learning procedure while preventing overfitting by regularization. Focusing on gene expression, protein abundance, and chromatin accessibility, we validate that scVAEIT robustly imputes the missing modalities and features of cells biologically different from the training data. scVAEIT also adjusts for batch effects while maintaining the biological variation, which provides better latent representations for the integrated datasets. We demonstrate that scVAEIT significantly improves integration and imputation across unseen cell types, different technologies, and different tissues. Significance StatementSingle-cell multimodal assays provide an unprecedented opportunity for investigating heterogeneity of cell types and novel associations with disease and development. Although analyses of such multimodal datasets have the potential to provide new insights that cannot be inferred with a single modality, access typically requires the integration of multiple data sources. We propose a probabilistic variational autoencoder model for mosaic integration, which involves merging data sources that include features shared across datasets and features exclusive to a single data source. Our model is designed to provide a lower dimensional representation of the cells for visualization, clustering, and other downstream tasks; accurate imputation of missing features and observations; and transfer learning for robustly imputing new datasets when only partial measurements are available.

genetics↗

bioRxiv2022

Chiral Derivatization-enabled Discrimination and Visualization of Proteinogenic Amino Acids by Ion Mobility Mass Spectrometry

The importance of chiral amino acids (AAs) in living organisms has been widely recognized since the discovery of endogenous D-AAs as potential biomarkers in several metabolic disorders. Chiral analysis by ion mobility spectrometry-mass spectrometry (IMS-MS) has the advantages of high speed and sensitivity but is still in its infancy. Here, a N-(2,4-dinitro-5-fluorophenyl)-L-alaninamide (FDAA) derivatization is combined with trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) for chiral AA analysis. For the first time, we demonstrate the simultaneous separation of 19 pairs of chiral proteinogenic AAs in a single fixed condition TIMS-MS run. The utility of this approach presents for mouse brain extracts by direct-infusion TIMS-MS. The robust separation ability in complex biological sample was proven in MALDI TIMS mass spectrometry imaging (MSI) as well by directly depositing 19 pairs of AAs on a tissue slide following on-tissue derivatization. In addition, endogenous chiral amino acids were also detected and distinguished. The developed methods show compelling application prospects in biomarker discovery and biological research. Entry for the Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/498692v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@19af718org.highwire.dtl.DTLVardef@d457d7org.highwire.dtl.DTLVardef@d2f932org.highwire.dtl.DTLVardef@e64831_HPS_FORMAT_FIGEXP M_FIG C_FIG The combination of chiral derivatization and trapped ion mobility-mass spectrometry provides the first insights into the separation of 19 pairs of chiral proteinogenic D/L-amino acids in a single run and further visualization of chiral amino acids under complex biological matrix.

biochemistry↗

bioRxiv2022

The application of mixed linear models for the estimation of functional effects on bovine stature based on SNP summary statistics from a whole-genome association study

Genome-Wide Association Studies (GWAS) help identify polymorphic sites or genes linked to phenotypic variance, but a few identified genes / Single Nucleotide Polymorphisms are unlikely to explain a large part of the phenotypic variability of complex traits. In this study, the focus was moved from single loci to functional units, expressed by the metabolic pathways: Kyoto Encyclopaedia of Genes and Genomes (KEGG). Consequently, this study aimed to estimate KEGG effects on stature in three Nordic dairy cattle breeds using SNPs effects from GWAS as the dependent variable. The SNPs were annotated to genes, then the genes to KEGG pathways. The effects of KEGG were estimated separately for each breed using a mixed linear model incorporating the similarity between pathways expressed by common genes. The KEGG pathway D-amino acid metabolism (map00473) was estimated as significant on stature in two of the analysed breeds and revealed a borderline significance in the third breed. Interestingly, biological evidence exists that described the importance of D-amino acids for growth in experimental organisms as well as in cattle.

bioinformatics↗

bioRxiv2022

SARS-CoV-2 S protein antagonizes type I interferon downstream signal pathway through interacting and attenuating phosphorylation of STAT1/STAT2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may keep patients in a clinically asymptomatic state by blocking cellular innate antiviral immunity, but the molecular mechanism remains unclear. Here, we screened the viral proteins of SARS-CoV-2 and found that the spike (S) protein inhibits the activation of interferon-stimulated genes (ISGs) and even reduces the expression of these genes to below background values. Mechanistically, the S protein interacted with STAT1, STAT2, and IRF9 and impedes the phosphorylation of STAT1/STAT2, thus preventing the formation of the interferon-stimulating gene factor 3 (ISGF3) complex and inhibiting the downstream production of Interferon-stimulated genes (ISGs). Remarkably, we also have found that the inhibitory mechanism of the S protein was conservative among SARS-CoV-2 variants and other human coronaviruses, including SARS-CoV, MERS-CoV, HCoV-229E, HCoV-NL63, and HCoV-HKU1. Truncation studies indicated that the most conserved S2 domain played a major inhibitory role. Altogether, our findings unveil a new mechanism by which SARS-CoV-2 S protein attenuated the hosts antiviral immune response and provide new insights into the pathogenic mechanism of coronavirus.

microbiology↗

bioRxiv2022

vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is public available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely-used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. The study would greatly facilitate effective identification of vsRNAs.

bioinformatics↗

bioRxiv2022

Model-free Prediction Test with Application to Genomics Data

Testing the significance of prediction in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that, given confounding variables Z, X does not significantly contribute to the prediction of Y under the model-free setting, where X and Z are possibly high dimensional. We propose a general framework that first fits nonparametric regression models on the Y|X and Y|(X, Z), then compares the prediction power of the two models. The proposed method allows us to leverage the strength of the most powerful regression algorithms developed from the modern machine learning community. The p-value for the test can be easily obtained by permutation. In simulations, we find that the proposed method is more powerful compared to existing methods. The proposed method allows us to draw biologically meaningful conclusions from two gene expression data analyses without strong distributional assumptions: (a) testing prediction power of sequencing RNA for the proteins in CITE-seq data, and (b) identification of spatially variable genes in spatially resolved transcriptomics data.

genomics↗

bioRxiv2022

Microglial Piezo1 senses Aβ fibrils stiffness to restrict Alzheimer's disease

The pathology of Alzheimers disease (AD) is associated with the extracellular amyloid-{beta} (A{beta}) plaques that perturb the mechanical properties of brain tissue. Microglia sense and integrate biochemical cues in their local microenvironment, intimate linking with AD progress. However, neither the microglial mechanosensing pathway nor its impact on AD pathogenesis is well studied. Here, we showed that the mechanosensitive ion channel Piezo1 is increased in microglia upon stiffness stimuli of A{beta} fibrils. The upregulation of Piezo1 in A{beta} plaque-associated microglia was observed in AD mouse models and human patients. Microglia lacking Piezo1 disturbed microglial clustering, phagocytosis, and compaction of A{beta} plaques, resulting in the exacerbation of A{beta} and neurodegenerative pathologies in AD. Conversely, pharmacological activation of Piezo1 ameliorated brain A{beta} burden and cognitive impairment in the 5xFAD mouse. Together, our results reveal Piezo1, as a mechanosensor of A{beta} fibrils stiffness in microglia, could represent a promising therapeutic target for AD.

neuroscience↗

bioRxiv2022

R-BIND 2.0: An Updated Database of Bioactive RNA-Targeting Small Molecules and Associated RNA Secondary Structures

Discoveries of RNA roles in cellular physiology and pathology are raising the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an "RNA-privileged" chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA Structure Search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different to that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.

biochemistry↗

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↗