Biology subjects

Zhang, J. D.

Publications and source records attributed to Zhang, J. D..

14 recordsLinked to original sources

bioRxiv2024

CellEKT: A robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors

The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probes ALX005 and ALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/616061v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@5dd891org.highwire.dtl.DTLVardef@1353379org.highwire.dtl.DTLVardef@1c67382org.highwire.dtl.DTLVardef@1c964eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples

Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.

genomics↗

bioRxiv2024

A bottom-up approach identifies the antipsychotic and antineoplastic trifluoperazine and the ribose derivative deoxytubercidin as novel microglial phagocytosis inhibitors.

Phagocytosis is an indispensable function of microglia, the brain professional phagocytes. Microglia are particularly efficient phagocytosing cells that undergo programmed cell death (apoptosis) in physiological conditions. However, mounting evidence suggests microglial phagocytosis dysfunction in multiple brain disorders. These observations prompted us to search for phagocytosis modulators (enhancers or inhibitors) with therapeutic potential. We used a bottom-up strategy that consisted on the identification of phagocytosis modulators using phenotypic high throughput screenings (HTSs) in cell culture and validation in organotypic cultures and in vivo. We performed two complementary HTS campagnes: at Achucarro, we used primary cultures of mouse microglia and compounds of the Prestwick Chemical Library; at Roche, we used human iPSC derived macrophage-like cells and a proprietary chemo-genomic library with 2,200 compounds with known mechanism-of-action. Next, we validated the more robust compounds using hippocampal organotypic cultures and identified two hits: trifluoperazine, a dopaminergic and adrenergic antagonist used as an antipsychotic and antineoplastic; and deoxytubercidin, a ribose derivative. Finally, we tested whether these compounds were able to modulate phagocytosis of apoptotic newborn cells in the adult hippocampal neurogenic niche in vivo by administering them into the mouse hippocampus using osmotic minipumps. We confirmed that both trifluoperazine and deoxytubercidin have anti-phagocytic activity in vivo, and validated our bottom-up strategy to identify novel phagocytosis modulators. These results show that chemical libraries with anotated mechanism of action are an starting point for the pharmacological modulation of microglia in drug discovery projects aiming at the therapeutic manipulation of phagocytosis in brain diseases. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/599284v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@13ed994org.highwire.dtl.DTLVardef@13ea710org.highwire.dtl.DTLVardef@4a1c7dorg.highwire.dtl.DTLVardef@1945f00_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Kinex infers causal kinases from phosphoproteomics data

MotivationPhosphoproteomics data are essential for characterising signalling pathways, identifying drug targets, and evaluating efficacy and safety profiles of drug candidates. Emerging resources, including a substrate-specificity atlas and drug-induced phosphoproteomics profiles, may transform the inference of causal kinases. However, there is currently no open-source software that leverages insights derived from these resources. ResultsWe introduce Kinex, a workflow implemented in the same-name Python package, which infers causal serine/threonine kinases from phosphoproteomics data. Kinex allows users to score kinase-substrate interactions, perform enrichment analysis, visualise candidates of causal regulators, and query similar profiles in a database of drug-induced kinase activities. Analysing seven published studies and one newly generated dataset, we demonstrate that analysis with Kinex recovers causal effects of perturbations and reveals novel biological insights. We foresee that Kinex will become an indispensable tool for basic and translational research including drug discovery. AvailabilityKinex is released with the GNU General Public License and available at https://github.com/bedapub/kinex.

bioinformatics↗

bioRxiv2023

G-PLIP: Knowledge graph neural network for structure-free protein-ligand affinity prediction

Protein-ligand interaction (PLI) shapes efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computation to predict PLI, making us wonder whether it is possible to perform structure-free PLI prediction with low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information of protein-ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, protein-protein interaction network, and structural similarities between ligands. The novel model performs competitively with or better than structure-aware models. Our observations suggest that existing PLI-prediction methods may be further improved by using representation learning techniques that embed biological and chemical knowledge.

bioinformatics↗

bioRxiv2023

splicekit: a comprehensive toolkit for splicing analysis from short-read RNA-seq

MotivationAnalysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. ResultsWe introduce splicekit, a python package that provides a comprehensive set of tools for conducting splicing analysis. Availability and implementationhttps://github.com/bedapub/splicekit and over PyPI.

bioinformatics↗

bioRxiv2022

Gene symbol recognition with GeneOCR

MotivationFigures in biomedical publications often contain lists of gene symbols. Human intelligence and manual curation is required to recognize, extract, and validate the symbols in order to reuse them. The task is laborious, error prone, and amenable to automation. ResultsWe introduce GeneOCR, a web-based software to recognize, extract, and check gene symbols from figures. It features both an interactive user interface and programmatic access. Simulation-based benchmark reveals that GeneOCR recognizes or suggests correct gene symbols in more than 80% cases. The performance qualifies GeneOCR as an enhancement to manual curation. AvailabilityThe source code of GeneOCR is available with GNU v3 license at https://github.com/bedapub/GeneOCR-UI (frontend) and https://github.com/bedapub/GeneOCR-API (backend). A Docker/Singularity image is available via BioContainers. Contactjitao_david.zhang@roche.com

bioinformatics↗

bioRxiv2021

Optimization of the TeraTox assay for preclinical teratogenicity assessment

Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives, and more sensitive approaches of toxicity prediction are required. To address this issue, we characterized the TeraTox test, a newly developed multi-lineage differentiation assay for human teratogenicity prediction using 3D human induced pluripotent stem cells. TeraTox produces as primary output concentration-dependent data sets for each test compound on cytotoxicity and altered gene expression. These data are then fed into a prediction model based on an interpretable machine-learning approach. The final information obtained relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. This way, it was possible to relate the test predictions to known human or animal toxicity. The TeraTox had an accuracy of 69% (specificity: 53%, sensitivity: 79%). It clearly performed better than two quantitative structure-activity relationship (QSAR) models and it had a higher sensitivity than the murine embryonic stem cell test (mEST) run in the same laboratory. By combining TeraTox and mEST data, the overall prediction accuracy was further improved. The knowledge on the pattern of altered gene expression may provide additional value in grouping toxicologically similar compounds and possibly deducing common modes of action. The assay will thus be a helpful additional tool in drug discovery, and the dataset provided here will be a valuable resource for the field of stem cell-based drug profiling.

pharmacology and toxicology↗

bioRxiv2021

A novel, anatomy-similar in vitro model of 3D airway epithelial for anti-coronavirus drug discovery

SARS-CoV-2 and its induced COVID-19 remains as a global health calamity. Severe symptoms and high mortality, caused by cytokine storm and acute respiratory distress syndrome in the lower respiratory airway, are always associated with elderly individuals and those with comorbidities; whereas mild or moderate COVID-19 patients have limited upper respiratory flu-like symptoms. There is an urgent need to investigate SARS-CoV-2 and other coronaviruses replication and immune responses in human respiratory systems. The human reconstituted airway epithelial air-liquid interface (ALI) models are the most physiologically relevant model for the investigation of coronavirus infection and virus-triggered innate immune signatures. We established ALI models representing both the upper and the lower respiratory airway to characterize the coronavirus infection kinetics, tissue pathophysiology, and innate immune signatures from upper and lower respiratory tract perspective. Our data suggested these in vitro ALI models maintain high physiological relevance with human airway tissues. The coronavirus induced immune response observed in these upper and lower respiratory airway models are similar to what has been reported in COVID-19 patients. The antiviral efficacy results of a few promising anti-coronavirus drugs in these models were consistent with previous reports and could be valuable for the human dose prediction. Taken together, our study demonstrates the importance of 3D airway epithelial ALI model for the understanding of coronavirus pathogenesis and the discovery and development of anti-coronavirus drugs.

microbiology↗

bioRxiv2020

Tissue heterogeneity is prevalent in gene expression studies

BackgroundLack of reproducibility in gene expression studies has recently attracted much attention in and beyond the biomedical research community. Previous efforts have identified many underlying factors, such as batch effects and incorrect sample annotations. Recently, tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, was proposed as a source of variance that exacerbates irreproducibility and is commonly ignored. ResultsHere, we systematically analyzed 2,692 publicly available gene expression datasets including 78,332 samples for tissue heterogeneity. We found a prevalence of tissue heterogeneity in gene expression data that affects on average 5-15% of the samples, depending on the tissue type. We distinguish cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, from cases of moderate heterogeneity, which are more likely caused by tissue infiltration or sample contamination. ConclusionsTissue heterogeneity is a widespread issue in publicly available gene expression datasets and thus an important source of variance that should not be ignored. We advocate the application of quality control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.

bioinformatics↗

bioRxiv2020

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Single-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.

bioinformatics↗

bioRxiv2019

Gene-set enrichment with regularized regression

MotivationCanonical methods for gene-set enrichment analysis assume independence between gene-sets. In practice, heterogeneous gene-sets from diverse sources are frequently combined and used, resulting in gene-sets with overlapping genes. They compromise statistical modelling and complicate interpretation of results.\n\nResultsWe rephrase gene-set enrichment as a regression problem. Given some genes of interest (e.g. a list of hits from an experiment) and gene-sets (e.g. functional annotations or pathways), we aim to identify a sparse list of gene-sets for the genes of interest. In a regression framework, this amounts to identifying a minimum set of gene-sets that optimally predicts whether any gene belongs to the given genes of interest. To accommodate redundancy between gene-sets, we propose regularized regression techniques such as the elastic net. We report that regression-based results are consistent with established gene-set enrichment methods but more parsimonious and interpretable.\n\nAvailabilityWe implement the model in gerr (gene-set enrichment with regularized regression), an R package freely available at https://github.com/TaoDFang/gerr and submitted to Bioconductor. Code and data required to reproduce the results of this study are available at https://github.com/TaoDFang/GeneModuleAnnotationPaper.\n\nContactJitao David Zhang (jitao_david.zhang@roche.com), Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124, 4070 Basel, Switzerland.

bioinformatics↗

bioRxiv2018

Comprehensive evaluation of cell-type quantification methods for immuno-oncology

MotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of [~]11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and [~]1,800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AvailabilityA snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Contactg.sturm@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Cross-reactive immunity drives global oscillation and opposed alternation patterns of seasonal influenza A viruses

Several human pathogens exhibit distinct patterns of seasonality and circulate as pairs of discrete strains. For instance, the activity of the two co-circulating influenza A virus subtypes oscillates and peaks during winter seasons of the worlds temperate climate zones. These periods of increased activity are usually caused by a single dominant subtype. Alternation of dominant strains in successive influenza seasons makes epidemic forecasting a major challenge. From the start of the 2009 influenza pandemic we enrolled influenza A virus infected patients (n = 2,980) in a global prospective clinical study. Complete hemagglutinin (HA) sequences were obtained from 1,078 A/H1N1 and 1,033 A/H3N2 viruses and were linked to patient data. We then used phylodynamics to construct high resolution spatio-temporal phylogenetic HA trees and estimated global influenza A effective reproductive numbers (R) over time (2009-2013). We demonstrate that R, a parameter to define host immunity, oscillates around R = 1 with a clear opposed alternation pattern between phases of the A/H1N1 and A/H3N2 subtypes. Moreover, we find a similar alternation pattern for the number of global virus migration events between the sampled geographical locations. Both observations suggest a between-strain competition for susceptible hosts on a global level. Extrinsic factors that affect person-to-person transmission are a major driver of influenza seasonality, which forces influenza epidemics to coincide with winter seasons. The data presented here indicate that also cross-reactive host immunity is a key intrinsic driver of global influenza seasonality, which determines the outcome of competition between influenza A virus strains at the onset of each epidemic season.\n\nSignificance statementAnnual influenza epidemics coincide with winter seasons in many parts of the world. Environmental factors, such as air humidity variation or temperature change, are commonly believed to drive these seasonality patterns. Interestingly, three out of the four latest pandemics (1918, 1968 and 2009) did not spread in winter initially, but during summer. This questions to what extent other factors could also impact virus spread among humans. We demonstrate that cross-reactive host immunity is a key factor. It drives the well-known seasonal patterns of virus activity oscillation and alternation of the dominant influenza virus subtype in successive seasons. Furthermore, this factor may also explain the efficient spread of pandemic viruses during summer when cross-reactive host immunity is relatively low.

epidemiology↗