Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without Affecting Cancer Treatment through Inter Variability Cross-Correlation Analysis (IVCCA).

The successful treatment of side effects of chemotherapy faces two major limitations: the need to avoid interfering with pathways essential for the cancer-destroying effects of the chemotherapy drug, and the need to avoid helping tumor progression through cancer promoting cellular pathways. To address these questions and identify new pathways and targets that satisfy these limitations, we have developed the bioinformatics tool Inter Variability Cross-Correlation Analysis (IVCCA). This tool calculates the cross-correlation of differentially expressed genes, analyzes their clusters, and compares them across a vast number of known pathways to identify the most relevant target(s). To demonstrate the utility of IVCCA, we applied this platform to RNA-seq data obtained from the hearts of the animal models with oxaliplatin-induced CTX. RNA-seq of the heart tissue from oxaliplatin treated mice identified 1744 differentially expressed genes with False Discovery Rate (FDR) less than 0.05 and fold change above 1.5 across nine samples. We compared the results against traditional gene enrichment analysis methods, revealing that IVCCA identified additional pathways potentially involved in CTX beyond those detected by conventional approaches. The newly identified pathways such as energy metabolism and several others represent promising target for therapeutic intervention against CTX, while preserving the efficacy of the chemotherapy treatment and avoiding tumor proliferation. Targeting these pathways is expected to mitigate the damaging effects of chemotherapy on cardiac tissues and improve patient outcomes by reducing the incidence of heart failure and other cardiovascular complications, ultimately enabling patients to complete their full course of chemotherapy with improved quality of life and survival rates.

The chromatin landscape of healthy and injured cell types in the human kidney.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Oxaliplatin-induced cardiotoxicity in mice is connected to the changes in energy metabolism in the heart tissue.

Oxaliplatin is a platinum-based alkylating chemotherapeutic agent used for cancer treatment. At high cumulative dosage, the negative effect of oxaliplatin on the heart becomes evident and is linked to a growing number of clinical reports. The aim of this study was to determine how chronic oxaliplatin treatment causes the changes in energy-related metabolic activity in the heart that leads to cardiotoxicity and heart damage in mice. C57BL/6 male mice were treated with a human equivalent dosage of intraperitoneal oxaliplatin (0 and 10 mg/kg) once a week for eight weeks. During the treatment, mice were followed for physiological parameters, ECG, histology and RNA sequencing of the heart. We identified that oxaliplatin induces strong changes in the heart and affects the heart's energy-related metabolic profile. Histological post-mortem evaluation identified focal myocardial necrosis infiltrated with a small number of associated neutrophils. Accumulated doses of oxaliplatin led to significant changes in gene expression related to energy related metabolic pathways including fatty acid (FA) oxidation, amino acid metabolism, glycolysis, electron transport chain, and NAD synthesis pathway. At high accumulative doses of oxaliplatin, the heart shifts its metabolism from FAs to glycolysis and increases lactate production. It also leads to strong overexpression of genes in NAD synthesis pathways such as Nmrk2. Changes in gene expression associated with energy metabolic pathways can be used to develop diagnostic methods to detect oxaliplatin-induced cardiotoxicity early on as well as therapy to compensate for the energy deficit in the heart to prevent heart damage.

The chromatin landscape of healthy and injured cell types in the human kidney.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3 , KLF6 , and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1 . Further, combined perturbation of ELF3 , KLF6 , and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Heparanase Blockade as a Novel Dual-Targeting Therapy for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused over 5 million deaths worldwide. Pneumonia and systemic inflammation contribute to its high mortality. Many viruses use heparan sulfate proteoglycans as coreceptors for viral entry, and heparanase (HPSE) is a known regulator of both viral entry and inflammatory cytokines. We evaluated the heparanase inhibitor Roneparstat, a modified heparin with minimum anticoagulant activity, in pathophysiology and therapy for COVID-19. We found that Roneparstat significantly decreased the infectivity of SARS-CoV-2, SARS-CoV-1, and retroviruses (human T-lymphotropic virus 1 [HTLV-1] and HIV-1) in vitro. Single-cell RNA sequencing (scRNA-seq) analysis of cells from the bronchoalveolar lavage fluid of COVID-19 patients revealed a marked increase in HPSE gene expression in CD68+ macrophages compared to healthy controls. Elevated levels of HPSE expression in macrophages correlated with the severity of COVID-19 and the expression of inflammatory cytokine genes, including IL6, TNF, IL1B, and CCL2. In line with this finding, we found a marked induction of HPSE and numerous inflammatory cytokines in human macrophages challenged with SARS-CoV-2 S1 protein. Treatment with Roneparstat significantly attenuated SARS-CoV-2 S1 protein-mediated inflammatory cytokine release from human macrophages, through disruption of NF-κB signaling. HPSE knockdown in a macrophage cell line also showed diminished inflammatory cytokine production during S1 protein challenge. Taken together, this study provides a proof of concept that heparanase is a target for SARS-CoV-2-mediated pathogenesis and that Roneparstat may serve as a dual-targeted therapy to reduce viral infection and inflammation in COVID-19. IMPORTANCE The complex pathogenesis of COVID-19 consists of two major pathological phases: an initial infection phase elicited by SARS-CoV-2 entry and replication and an inflammation phase that could lead to tissue damage, which can evolve into acute respiratory failure or even death. While the development and deployment of vaccines are ongoing, effective therapy for COVID-19 is still urgently needed. In this study, we explored HPSE blockade with Roneparstat, a phase I clinically tested HPSE inhibitor, in the context of COVID-19 pathogenesis. Treatment with Roneparstat showed wide-spectrum anti-infection activities against SARS-CoV-2, HTLV-1, and HIV-1 in vitro. In addition, HPSE blockade with Roneparstat significantly attenuated SARS-CoV-2 S1 protein-induced inflammatory cytokine release from human macrophages through disruption of NF-κB signaling. Together, this study provides a proof of principle for the use of Roneparstat as a dual-targeting therapy for COVID-19 to decrease viral infection and dampen the proinflammatory immune response mediated by macrophages.

Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors.

Mobilized peripheral blood has become the primary source of hematopoietic stem cells for both autologous and allogeneic stem cell transplantation. Granulocyte colony-stimulating factor (G-CSF) is currently the standard agent used in the allogeneic setting. Despite the high mobilization efficacy in most donors, G-CSF requires 4-5 days of daily administration, and a small percentage of the donors fail to mobilize an optimal number of stem cells necessary for a safe allogeneic stem cell transplant. In this study, we retrospectively reviewed 1361 related allogeneic donors who underwent stem cell mobilization at Washington University. We compared the standard mobilization agent G-CSF with five alternative mobilization regimens, including GM-CSF, G-CSF+GM-CSF, GM-CSF + Plerixafor, Plerixafor and BL-8040. Cytokine-based mobilization strategies (G-CSF or in combination with GM-CSF) induce higher CD34 cell yield after 4-5 consecutive days of treatment, while CXCR4 antagonists (plerixafor and BL-8040) induce significantly less but rapid mobilization on the same day. Next, using a large dataset containing the demographic and baseline laboratory data from G-CSF-mobilized donors, we established machine learning (ML)-based scoring models that can be used to predict patients who may have less than optimal stem cell yields after a single leukapheresis session. To our knowledge, this is the first prediction model at the early donor screening stage, which may help identify allogeneic stem cell donors who may benefit from alternative approaches to enhance stem cell yields, thus ensuring safe and effective stem cell transplantation.

Increasing metadata coverage of SRA BioSample entries using deep learning-based named entity recognition.

High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE.

Histone Signatures Predict Therapeutic Efficacy in Breast Cancer.

Objective: Regulatory abnormalities caused by chromatin modifications are being increasingly recognized as contributors to cancer. While many molecularly targeted drugs have the potential to revert these modifications, their precise mechanism of action in cellular reprogramming is not known. Methods: To address this, we introduce an integrated phosphoprotein-histone-drug network (iPhDNet) approach to generate "global chromatin fingerprints of histone signatures." The method integrates proteomic/phosphoproteomic, transcriptomic and regulatory genomic data to provide a causal mechanistic network and histone signatures of drug response. Results: We demonstrate the utility of iPhDNet in identifying H3K27me3K36me3 histone mark as a key fingerprint of response, mediated by chromatin remodelers BRD4, NSD3, EZH2, and a proto-oncogene MYC when treated with CDK inhibitors. Conclusions: We construct a regulatory network of breast cancer response to treatment and show that histone H3K27me3K36me3 status changes, driven by the BRD4/MYC pathway, upon treatment with drugs are hallmarks of response to treatment.

Classical Flt3L-dependent dendritic cells control immunity to protein vaccine.

DCs are critical for initiating immunity. The current paradigm in vaccine biology is that DCs migrating from peripheral tissue and classical lymphoid-resident DCs (cDCs) cooperate in the draining LNs to initiate priming and proliferation of T cells. Here, we observe subcutaneous immunity is Fms-like tyrosine kinase 3 ligand (Flt3L) dependent. Flt3L is rapidly secreted after immunization; Flt3 deletion reduces T cell responses by 50%. Flt3L enhances global T cell and humoral immunity as well as both the numbers and antigen capture capacity of migratory DCs (migDCs) and LN-resident cDCs. Surprisingly, however, we find immunity is controlled by cDCs and actively tempered in vivo by migDCs. Deletion of Langerin(+) DC or blockade of DC migration improves immunity. Consistent with an immune-regulatory role, transcriptomic analyses reveals different skin migDC subsets in both mouse and human cluster together, and share immune-suppressing gene expression and regulatory pathways. These data reveal that protective immunity to protein vaccines is controlled by Flt3L-dependent, LN-resident cDCs.

Flt3L dependence helps define an uncharacterized subset of murine cutaneous dendritic cells.

Skin-derived dendritic cells (DCs) are potent antigen-presenting cells with critical roles in both adaptive immunity and tolerance to self. Skin DCs carry antigens and constitutively migrate to the skin-draining lymph nodes (LNs). In mice, Langerin-CD11b- dermal DCs are a low-frequency, heterogeneous, migratory DC subset that traffics to LNs (Langerin-CD11b- migDCs). Here, we build on the observation that Langerin-CD11b- migDCs are Fms-like tyrosine kinase 3 ligand (Flt3L) dependent and strongly Flt3L responsive, which may relate them to classical DCs. Examination of DC capture of FITC from painted skin, DC isolation from skin explant culture, and from the skin of CCR7 knockout mice, which accumulate migDCs, demonstrate these cells are cutaneous residents. Langerin-CD11b- Flt3L-responsive DCs are largely CD24(+) and CX3CR1(low) and can be depleted from Zbtb46-DTR mice, suggesting classical DC lineage. Langerin-CD11b- migDCs present antigen with equal efficiency to other DC subsets ex vivo, including classical CD8α cDCs and Langerin+CD103+ dermal DCs. Finally, transcriptome analysis suggests a close relationship with other skin DCs, and a lineage relationship with other classical DCs. This work demonstrates that Langerin- CD11b- dermal DCs, a previously overlooked cell subset, may be an important contributor to the cutaneous immune environment.

Diagnostic Prediction of Von Willebrand Disease using Multiple Bleeding Phenomics Datasets.

The rigorous assessment of bleeding symptoms is a key component in establishing a diagnosis in patients suspected of having von Willebrand disease (VWD) and other inherited bleeding disorders. Multiple bleeding questionnaires have been developed and validated to capture bleeding history phenotypes for assessing patients with bleeding disorders. In this study we developed a prediction model based on Naïve Bayes decision tree classifier by analyzing various phenotypic attributes derived from multiple bleeding questionnaires. We evaluated the classification effectiveness derived from the top 25 attributes with highest discriminations on prediction of VWD. We used data from 952 patients and the classifier achieved a precision of 95%, recall of 94%, and a Receiving Operating Curve (ROC) area under the curve of 0.97.

Ontology-based federated data access to human studies information.

Human studies are one of the most valuable sources of knowledge in biomedical research, but data about their design and results are currently widely dispersed in siloed systems. Federation of these data is needed to facilitate large-scale data analysis to realize the goals of evidence-based medicine. The Human Studies Database project has developed an informatics infrastructure for federated query of human studies databases, using a generalizable approach to ontology-based data access. Our approach has three main components. First, the Ontology of Clinical Research (OCRe) provides the reference semantics. Second, a data model, automatically derived from OCRe into XSD, maintains semantic synchrony of the underlying representations while facilitating data acquisition using common XML technologies. Finally, the Query Integrator issues queries distributed over the data, OCRe, and other ontologies such as SNOMED in BioPortal. We report on a demonstration of this infrastructure on data acquired from institutional systems and from ClinicalTrials.gov.

The human studies database project: federating human studies design data using the ontology of clinical research.

Human studies, encompassing interventional and observational studies, are the most important source of evidence for advancing our understanding of health, disease, and treatment options. To promote discovery, the design and results of these studies should be made machine-readable for large-scale data mining, synthesis, and re-analysis. The Human Studies Database Project aims to define and implement an informatics infrastructure for institutions to share the design of their human studies. We have developed the Ontology of Clinical Research (OCRe) to model study features such as design type, interventions, and outcomes to support scientific query and analysis. We are using OCRe as the reference semantics for federated data sharing of human studies over caGrid, and are piloting this implementation with several Clinical and Translational Science Award (CTSA) institutions.

Development and evaluation of a study design typology for human research.

A systematic classification of study designs would be useful for researchers, systematic reviewers, readers, and research administrators, among others. As part of the Human Studies Database Project, we developed the Study Design Typology to standardize the classification of study designs in human research. We then performed a multiple observer masked evaluation of active research protocols in four institutions according to a standardized protocol. Thirty-five protocols were classified by three reviewers each into one of nine high-level study designs for interventional and observational research (e.g., N-of-1, Parallel Group, Case Crossover). Rater classification agreement was moderately high for the 35 protocols (Fleiss' kappa = 0.442) and higher still for the 23 quantitative studies (Fleiss' kappa = 0.463). We conclude that our typology shows initial promise for reliably distinguishing study design types for quantitative human research.

Creating an ontology-based human phenotyping system: The Rockefeller University bleeding history experience.

The lack of standardized methods for human phenotyping is a major obstacle in translational science. We have developed a bleeding history phenotyping system comprising an ontology, a questionnaire, a Web-based phenotype recording instrument (PRI), and a database. The ontology facilitates transparency, collaboration, aggregation of data, and data analysis. The integrated system allows investigators worldwide to use the PRI, add their de-identified data to the database, and query the aggregated data. Thus, this system can increase the power to detect genotype-phenotype-environment relationships and help new investigators begin their studies. We anticipate that this approach may be applicable to other disorders.

Semi automatic indexing of PostScript files using Medical Text Indexer in medical education.

At Albert Einstein College of Medicine a large part of online lecture materials contain PostScript files. As the collection grows it becomes essential to create a digital library to have easy access to relevant sections of the lecture material that is full-text indexed; to create this index it is necessary to extract all the text from the document files that constitute the originals of the lectures. In this study we present a semi automatic indexing method using robust technique for extracting text from PostScript files and National Library of Medicine's Medical Text Indexer (MTI) program for indexing the text. This model can be applied to other medical schools for indexing purposes.

Automatic learning of the morphology of medical language using information compression.

Conversion of free-text strings in a natural language to a standard representation (codes) is an important reoccurring problem in biomedical informatics. Determining the content of a string involves identifying its meaningful constituents (morphemes). One current method of identifying these constituents is to look them up in a preexisting table (lexicon). Manual construction of lexicons and grammars in complex domains such as biomedicine is extremely laborious. As an alternative to the lexico-grammatical approach, we introduce a segmentation algorithm that automatically learns lexical and structural preferences from corpora via information compression. The method is based on the Minimum Description Length (MDL) principle from classic information theory.