Biology subjects

Seal, S.

Publications and source records attributed to Seal, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Hypoxia inducible factors drive miRNA-mediated downregulation of L2HGDH and HIF1A in clear cell renal cancer independent of 14q deletion

Clear cell renal cell carcinoma (ccRCC) is characterized by pseudohypoxic signaling as well as metabolic and epigenetic aberrations of increasing severity during its pathogenesis and progression. Upon identifying that the markedly lower expression of L2HGDH and HIF1A in ccRCC tumors is not explained only by 14q deletion (40% of ccRCC), we sought to elucidate the mechanisms underscoring their down-regulation independent of 14q deletion, given the marked translational and scientific importance. While lower L2HGDH was found to portend a strikingly worse prognosis in a multivariate survival analysis of ccRCC patients (n=509; TCGA) highlighting its translational importance, the lower expression of HIF1A in ccRCC despite the upregulated HIF1 protein (secondary to VHL loss) is of significant scientific interest. Using a comprehensive array of in vitro assays with genetic manipulation, clinicogenomic bioinformatic analyses of large ccRCC datasets, and the Cdh16-Cre Vhl-fl mouse model with kidney specific Vhl-loss, we demonstrate that the downregulation of both L2HGDH and HIF1A in ccRCC is driven by the hypoxia-inducible factors via miR21-3p and miR155-5p, respectively, during pathogenesis and progression. In doing so, we unveil a conserved inhibitory feedback loop for HIF1A in ccRCC (HIFs[->]miR155[->]HIF1A), providing a mechanistic explanation for HIF1A downregulation despite elevated HIF1 protein. Furthermore, and most importantly, we show that inhibition of hypoxia-inducible factors reverses the adverse loss of L2HGDH and hydroxymethylcytosine in ccRCC, implicating both HIF2 and HIF1 in these metabolic-epigenetic aberrations. Translational StatementThis study demonstrates that hypoxia-inducible factors (HIFs) drive early and sustained metabolic and epigenetic dysregulation in clear cell renal cell carcinoma (ccRCC). Notably, these aberrations begin early in carcinogenesis. We show that inhibition of HIFs can reverse these pathological changes - most significantly, restoring levels of 5-hydroxymethylcytosine, a key epigenetic marker lost in ccRCC due to metabolic inhibition of TET enzymes. Our findings support the integration of HIF2 inhibition earlier in the therapeutic approach for advanced ccRCC. Moreover, the data suggest that dual inhibition of HIF2 and HIF1 - particularly in tumors with high expression of both factors - may yield a more robust reversal of epigenetic abnormalities and merits further investigation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/625494v2_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@a75aborg.highwire.dtl.DTLVardef@b36704org.highwire.dtl.DTLVardef@3057c4org.highwire.dtl.DTLVardef@1d24074_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Microbiota drives the sexually dimorphic infection outcomes in mealworm beetles

Sexually dimorphic responses to pathogenic infections in animals may stem from sex-specific differences in their life history and immune investment. Recent evidence highlights that such sex-specific variations in immune responses can also be critically regulated by microbiota. However, direct experiments to test how microbiota jointly impacts sex-specific immunity and vulnerability to pathogens are still limited. To this end, we used Tenebrio molitor beetles to first establish that sexes appear to differ in their microbiota composition and infection responses. Females were more vulnerable to bacterial infections and carried a higher bacterial load than males. When we depleted the microbiome, only females improved their post-infection survival, leading to a loss of sex-specific infection outcomes. Males, on the other hand, remained unaffected. Microbiota reconstitution (via feeding on faecal matter) of microbiota-depleted females increased their susceptibility to infection again, restoring the sexual dimorphism. We thus found a causal association between microbiome and infection responses. We also found reduced expression of an antimicrobial peptide tenecin 1 in females, which could be associated with their higher infection susceptibility, but such immune gene-vs-phenotypic associations were not consistent across microbiota manipulations. Immune strategies that are required to mediate the causal links between microbiome and infection response might thus vary with microbiota manipulations, warranting future investigations.

evolutionary biology↗

bioRxiv2024

cytoKernel: Robust kernel embeddings for assessing differential expression of single cell data

High-throughput sequencing of single-cell data can be used to rigorously evlauate cell specification and enable intricate variations between groups or conditions. Many popular existing methods for differential expression target differences in aggregate measurements (mean, median, sum) and limit their approaches to detect only global differential changes. We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. cytoKernel is specifically designed to assess the differential expression of single cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving aggregate changes, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets. The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

bioinformatics↗

bioRxiv2024

Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery

Recent advances in machine learning methods for materials science have significantly enhanced accurate predictions of the properties of novel materials. Here, we explore whether these advances can be adapted to drug discovery by addressing the problem of prospective validation - the assessment of the performance of a method on out-of-distribution data. First, we tested whether k-fold n-step forward cross-validation could improve the accuracy of out-of-distribution small molecule bioactivity predictions. We found that it is more helpful than conventional random split cross-validation in describing the accuracy of a model in real-world drug discovery settings. We also analyzed discovery yield and novelty error, finding that these two metrics provide an understanding of the applicability domain of models and an assessment of their ability to predict molecules with desirable bioactivity compared to other small molecules. Based on these results, we recommend incorporating a k-fold n-step forward cross-validation and these metrics when building state-of-the-art models for bioactivity prediction in drug discovery.

bioinformatics↗

bioRxiv2024

Calibrated prediction of scarce adverse drug reaction labels with conditional neural processes

Adverse drug reactions (ADRs) are a major source of concern in the development of novel pharmaceuticals. ADRs may be identified in the late stages of development or even after commercialization, which may lead to failure or discontinuation after spending enormous resources on candidate molecules. Thus, predicting ADRs early in the process could help reduce costs by avoiding future failures. However, due to the low number of drugs approved, the amount of historical datapoints on ADRs is limited, which makes their prediction challenging for traditional chemoinformatics methods. Interestingly, each approved drug may have been annotated for hundreds of ADRs, which opens the door to framing ADR prediction as a multi-task or meta-learning problem. In this work, we adopt a meta-learning approach to ADR prediction by applying conditional neural processes (CNPs) to the publicly available Side Effect Resource (SIDER). Our results suggest that CNPs are competitive against single-task baselines even when trained on sparse datasets with missing labels. Furthermore, we find that their predictions are well-calibrated. Finally, we evaluate their performance on ADRs associated to different physiological systems and confirm good predictions across organ classes. Our findings suggest that meta-learning strategies may be beneficial for data-limited clinical endpoints like ADRs.

bioinformatics↗

bioRxiv2024

Genome assembly and annotation of the red flour beetle (Tribolium castaneum) from India

The largest insect order, Coleoptera, includes several economically important beetles that also serve as major model species for biological research. Perhaps foremost among these is the red flour beetle, a global pest of stored grains and flour whose genome was sequenced in 2008. However, the currently available reference genome (Tcas5.2) is incomplete, fragmented and contains many gaps, and the Y chromosome is not assembled. Here we present inTcas1, an updated genome assembly and annotation of T. castaneum collected from India, assembled using both short and long read sequencing, and annotated using two transcriptome datasets. We report that inTcas1 has fewer gaps, less fragmentation, and many new genes and new isoforms of previously annotated genes. This new resource provides a useful update, comparison, and reference for new beetle genome assemblies. The first Y chromosome assembly for this species also provides critical data to study the evolution of insect sex chromosomes and sex determination systems. SIGNIFICANCEWe present here an improved T. castaneum genome assembly (inTcas1) for beetles sampled from India, including the first Y chromosome of this important pest and laboratory model species. The new genome should facilitate more comprehensive analysis of Coleoptera genome and transcriptome datasets, especially beetle populations from Asia - the previously available genome, Tcas5.2, was assembled using the GA2 strain from USA. The updated genome should also facilitate analyses of genome evolution, including sex determination and sex chromosome dynamics; and the new gene annotations can expand the genetic toolkit for this beetle.

genomics↗

bioRxiv2024

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Paintings ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

cell biology↗

bioRxiv2024

PATHOGEN GROWTH AND VIRULENCE DYNAMICS DRIVE THE HOST EVOLUTION AGAINST COINFECTIONS

Coinfections, or the simultaneous infection of hosts by multiple pathogens, are widespread in nature with significant negative impacts on global health. Can hosts evolve against such coinfections as effectively as they would against individual pathogens? Also, what roles do individual pathogens play during such evolution? Here, we combined theoretical models and experiments with Tribolium castaneum populations evolving against two coinfecting bacterial pathogens, with contrasting growth and virulence dynamics, to reveal that fast-growing pathogens inflicting rapid mortality surges (i.e., fast-acting) restrict adaptive success against coinfections. While hosts rapidly evolved better survival against slow-growing bacteria causing long-lasting infections, evolution against coinfection was significantly delayed and resembled slow adaptation against fast-acting pathogens. Moreover, limited scopes of immunomodulation against fast-acting pathogens during coinfections can drive the observed adaptive patterns. Overall, we provide new insights into how adaptive dynamics and mechanistic bases against coinfections are critically regulated by individual pathogens growth and virulence dynamics.

evolutionary biology↗

bioRxiv2024

PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules

Drug exposure is a key contributor to the safety and efficacy of drugs. It can be defined using human pharmacokinetics (PK) parameters that affect the blood concentration profile of a drug, such as steady-state volume of distribution (VDss), total body clearance (CL), half-life (t[1/2]), fraction unbound in plasma (fu) and mean residence time (MRT). In this work, we used molecular structural fingerprints, physicochemical properties, and predicted animal PK data as features to model the human PK parameters VDss, CL, t[1/2], fu and MRT for 1,283 unique compounds. First, we predicted animal PK parameters [VDss, CL, fu] for rats, dogs, and monkeys for 372 unique compounds using molecular structural fingerprints and physicochemical properties. Second, we used Morgan fingerprints, Mordred descriptors and predicted animal PK parameters in a hyperparameter-optimised Random Forest algorithm to predict human PK parameters. When validated using repeated nested cross-validation, human VDss was best predicted with an R2 of 0.55 and a Geometric Mean Fold Error (GMFE) of 2.09; CL with accuracies of R2=0.31 and GMFE=2.43, fu with R2=0.61 and GMFE=2.81, MRT with R2=0.28 and GMFE=2.49, and t[1/2] with R2=0.31 and GMFE=2.46 for models combining Morgan fingerprints, Mordred descriptors and predicted animal PK parameters. We evaluated models with an external test set comprising 315 compounds for VDss (R2=0.33 and GMFE=2.58) and CL (R2=0.45 and GMFE=1.98). We compared our models with proprietary pharmacokinetic (PK) models from AstraZeneca and found that model predictions were similar with Pearson correlations ranging from 0.77-0.78 for human PK parameters of VDss and fu and 0.46-0.71 for animal (dog and rat) PK parameters of VDss, CL and fu. To the best of our knowledge, this is the first work that publicly releases PK models on par with industry-standard models. Early assessment and integration of predicted PK properties are crucial, such as in DMTA cycles, which is possible with models in this study based on the input of only chemical structures. We developed a webhosted application PKSmart (https://broad.io/PKSmart) which users can access using a web browser with all code also downloadable for local use. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/578658v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1a4b673org.highwire.dtl.DTLVardef@c64c5aorg.highwire.dtl.DTLVardef@bec0b3org.highwire.dtl.DTLVardef@15d5756_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure:C_FLOATNO For TOC Only C_FIG

bioinformatics↗

bioRxiv2024

Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/575128v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1b3346org.highwire.dtl.DTLVardef@1c3460dorg.highwire.dtl.DTLVardef@12c38cdorg.highwire.dtl.DTLVardef@455aee_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

PIEZO1-HaloTag hiPSCs: Bridging Molecular, Cellular and Tissue Imaging

PIEZO1 is critical to numerous physiological processes, transducing diverse mechanical stimuli into electrical and chemical signals. Recent studies underscore the importance of visualizing endogenous PIEZO1 activity and localization to understand its functional roles. To enable physiologically and clinically relevant studies on human PIEZO1, we genetically engineered human induced pluripotent stem cells (hiPSCs) to express a HaloTag fused to endogenous PIEZO1. Combined with advanced imaging, our chemogenetic platform allows precise visualization of PIEZO1 localization dynamics in various cell types. Furthermore, the PIEZO1-HaloTag hiPSC technology facilitates the non-invasive monitoring of channel activity across diverse cell types using Ca2+-sensitive HaloTag ligands, achieving temporal resolution approaching that of patch clamp electrophysiology. Finally, we used lightsheet imaging of hiPSC-derived neural organoids to achieve molecular scale imaging of PIEZO1 in three-dimensional tissue organoids. Our advances offer a novel platform for studying PIEZO1 mechanotransduction in human cells and tissues, with potential for elucidating disease mechanisms and targeted therapeutic development.

biophysics↗

bioRxiv2023

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/562398v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@755906org.highwire.dtl.DTLVardef@27c43borg.highwire.dtl.DTLVardef@c430dborg.highwire.dtl.DTLVardef@63b2ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/549031v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1a6169forg.highwire.dtl.DTLVardef@11757eforg.highwire.dtl.DTLVardef@18e3fc2org.highwire.dtl.DTLVardef@1fc0a90_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFSeal et al. used machine learning models and feature selection approaches to group cell morphological features from Cell Painting assays and to describe the shared role of these morphological features in various cell health phenotypes. The resulting BioMorph space improves the ability to understand the mechanism of action and toxicity of compounds and to generate hypotheses to guide future experiments. HIGHLIGHTSO_LICombining Cell Painting and Cell Health imaging data defines the BioMorph space. C_LIO_LIBioMorph space allows detecting less common mechanisms for bioactive compounds. C_LIO_LIBioMorph space can generate MOA hypotheses to guide experimental validation. C_LIO_LIBioMorph space is more biologically relevant and interpretable than Cell Painting features. C_LI

cell biology↗

bioRxiv2023

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA

MotivationMultiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. ResultsWe present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. AvailabilityThe associated R package can be found here, https://github.com/sealx017/SpaceANOVA. Contactsealso@musc.edu Supplementary informationThe supplementary material is attached.

bioinformatics↗

bioRxiv2023

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.

bioinformatics↗

bioRxiv2022

BindingSiteAugmentedDTA: Enabling A Next-Generation Pipeline for Interpretable Prediction Models in Drug-Repurposing

While research into Drug-Target Interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning-based framework, called BindingSite-AugmentedDTA, which improves Drug-Target Affinity (DTA) predictions by reducing the search space of potential binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of 4 widely used evaluation metrics, including Concordance Index (CI), Mean Squared Error (MSE), modified squared correlation coefficient [Formula], and the Area Under the Precision Curve (AUPC). We also contribute to the two most commonly used DTA benchmark datasets, namely Kiba and Davis, by including additional information on 3D structure of all proteins contained in these two datasets. We manually extracted this information from Protein Data Bank (PDB) files of proteins available at https://www.uniprot.org/. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. We measure the binding interaction between several drug candidate compounds for the inhibition of binding between (SARS-CoV-2 S-protein RBD) Spike and ACE-2 (host cell binding target) proteins. We then compare the computationally-predicted results against the ones experimentally-observed in the laboratory. The relatively high agreement between computationally-predicted and experimentally-observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.

bioinformatics↗

bioRxiv2022

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY O_FLOATNOFigure:C_FLOATNO For TOC Only C_FIG_DISPLAY

bioinformatics↗

bioRxiv2022

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

bioinformatics↗