Biology subjects

Sirota, M.

Publications and source records attributed to Sirota, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Searching for influencers among placental immune cells in preeclampsia.

Cells in maternal and fetal immune systems may communicate, leading to immune tolerance during pregnancy; however, this hypothesis remains controversial. Here, we profiled single-cell transcriptional signatures in placental layers comprising the maternal-fetal interface and deep placenta, then searched for genes associated with preeclampsia. To investigate the underlying principle of the failure of immune tolerance, we started by clarifying the systemic framework, comprising models of immune interaction frequency (IIF) and specific triggers (i.e., influencers) of tolerance (IT). We generated single-cell transcriptional profiles of normal term (Norms) and preeclampsia preterm (PePT) parturitions. Fetal and maternal cells are admixed across the placenta, for both Norms and PePTs, rejecting the IIF model of immune failure during pregnancy posed by excessive interactions between fetomaternal cells. Whereas placental layers are well mixed with maternal cells, we identified a conserved gradual immune transition of fetal T-cells in both PePT and Norm, disproving the IIF model. To search for influencers of PePT in the IT model, we established and validated a classification model for PePT and Norm immune cells, including T-cells, and then prioritized major contributors to the classifier model, which are highly enriched in ligands and receptors (p = 5.98e-5). Among the prioritized ligand receptors, SPP1 and CD44 are suggested as influencers of inflammation signatures and were experimentally validated by the exclusive colocalization of SPP1- and CD44-expressing cells in the PePT placentas. Different interleukin-4 and interferon-{psi} levels in the serum and urine of PePTs further support the contribution of SPP1 to associated pathways, including allograft rejection. Our findings provide insight into the influence of specific immune interactions between cells in the human placenta and their influencer-derived impact on PePT.

bioinformatics↗

bioRxiv2024

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease

Alzheimers disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/627436v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1e67fd5org.highwire.dtl.DTLVardef@1d2b779org.highwire.dtl.DTLVardef@10b6efforg.highwire.dtl.DTLVardef@1a44394_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

molecular biology↗

bioRxiv2023

Coordinated immune dysregulation in Juvenile Dermatomyositis revealed by single-cell genomics

Juvenile Dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I interferon response and autoantibodies. Treatment options are limited due to incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of JDM patients at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment towards an immature naive state as a hallmark of JDM. Furthermore, we find that these changes in B cells are paralleled by signatures of Th2-mediated inflammation. Additionally, our work identified SIGLEC-1 expression in monocytes as a composite measure of heterogeneous type I interferon activity in disease. We applied network analysis to reveal that hyperactivation of the type I interferon response in all immune populations is coordinated with dysfunctional protein processing and regulation of cell death programming. This analysis separated the ubiquitously expressed type I interferon response into a central hub and revealed previously masked cell states. Together, these findings reveal the coordinated immune dysregulation underpinning JDM and provide novel insight into strategies for restoring balance in immune function.

immunology↗

bioRxiv2023

Gene Expression Meta-Analysis Reveals Aging and Cellular Senescence Signatures in Scleroderma-associated Interstitial Lung Disease

Aging and cellular senescence are increasingly recognized as key contributors to pulmonary fibrosis. However, our understanding in the context of scleroderma associated interstitial lung disease (SSc-ILD) is limited. To investigate, we leveraged previously established lung aging and cell-specific senescence signatures to determine their presence and potential relevance to SSc-ILD. We performed a gene expression meta-analysis of lung tissue from 38 SSc-ILD and 18 healthy controls and found markers (GDF15, COMP, CDKN2A) and pathways (p53) of senescence were significantly increased in SSc-ILD. When probing the established aging and cellular senescence signatures, we found epithelial and fibroblast senescence signatures had a 3.6-fold and 3.7-fold enrichment respectively in the lung tissue of SSc-ILD and that lung aging genes (CDKN2A, FRZB, PDE1A, NAPI12) were increased in SSc-ILD. These signatures were also enriched in SSc skin and associated with degree of skin involvement (limited vs. diffuse cutaneous). To further support these findings, we examined telomere length (TL), a surrogate for aging, in lung tissue and found independent of age, SSc-ILD had significantly shorter telomeres than controls in type II alveolar cells in the lung. TL in SSc-ILD was comparable to idiopathic pulmonary fibrosis, a disease of known aberrant aging. Taken together, this study provides novel insight into the possible mechanistic effects of accelerated aging and aberrant cellular senescence in SSc-ILD pathogenesis.

bioinformatics↗

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2023

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes

Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes. In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival. We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2-receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel-resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

cell biology↗

bioRxiv2023

Development and characterization of new patient-derived xenograft (PDX) models of osteosarcoma with distinct metastatic capacities

Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology, especially for highly aggressive cancers with a propensity for metastatic spread. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, a large panel of models is needed to fully elucidate key aspects of disease biology and to recapitulate clinically-relevant phenotypes. We describe the development and characterization of osteosarcoma patient-derived xenografts (PDXs) and a panel of PDX-derived cell lines. Matched patient samples, PDXs, and PDX-derived cell lines were comprehensively evaluated using whole genome sequencing and RNA sequencing. PDXs and PDX-derived cell lines largely maintained the expression profiles of the patient from which they were derived despite the emergence of whole-genome duplication (WGD) in a subset of cell lines. These cell line models were heterogeneous in their metastatic capacity and their tissue tropism as observed in both intravenous and orthotopic models. As proof-of-concept study, we used one of these models to test the preclinical effectiveness of a CDK inhibitor on the growth of metastatic tumors in an orthotopic amputation model. Single-agent dinaciclib was effective at dramatically reducing the metastatic burden in this model. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/524562v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@17ea2a8org.highwire.dtl.DTLVardef@e8fcc7org.highwire.dtl.DTLVardef@2b9f8corg.highwire.dtl.DTLVardef@1f7b802_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Global Endometrial DNA Multi-omics Analysis Reveals Insights into mQTL Regulation and Associated Endometriosis Disease Risk

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Identifying biologic and genetic effects on DNA methylation (DNAm) in endometrium increases understanding of mechanisms that influence gene regulation predisposing to endometriosis and offers an opportunity for novel therapeutic target discovery. Herein, we characterize variation in endometrial DNAm and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genome-wide genotype data and methylation at 759,345 DNAm sites in endometrial samples from 984 deeply-phenotyped participants. We identify significant differences in DNAm profiles between menstrual cycle phases and at four DNAm sites between stage III/IV endometriosis and controls. We estimate that 15.4% of the variation in endometriosis is captured by DNAm, and identify DNAm networks associated with endometriosis. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTL including some tissue-specific effects. We find significant differences in DNAm profiles between endometriosis sub- phenotypes and a significant association between genetic regulation of methylation in endometrium and disease risk, providing functional evidence for genomic targets contributing to endometriosis risk and pathogenesis.

systems biology↗

bioRxiv2022

Perturbations of the T-cell immune repertoire in kidney transplant rejection

In this cross-sectional and longitudinal analysis of mapping the T-cell repertoire in kidney transplant recipients, we have investigated and validated T-cell clonality, immune repertoire chronology at rejection, and contemporaneous allograft biopsy quantitative tissue injury, to better understand the pathobiology of acute T cell and antibody-mediated kidney transplant rejection. To follow the dynamic evolution of T-cell repertoire changes before and after engraftment and during biopsy-confirmed acute rejection, we sequenced 323 peripheral blood samples from 200 unique kidney transplant recipients, with (n=100) and without (n=100) biopsyconfirmed acute rejection. The results of these studies highlight, for the first time, that patients who develop acute allograft rejection, have lower (p=0.01) T cell fraction even before transplantation, followed by its rise after transplantation and at the time of acute rejection accompanied by high TCR repertoire turnover (p=0.004). Acute rejection episodes occurring after the first 6 months post-transplantation, and those with a component of antibody-mediated rejection, had the highest turnover; p=0.0016) of their TCRs. In conclusion, further prospective validation studies are needed to evaluate the clinical utility of peripheral blood TCR analysis for both pre- and post-transplant immune risk assessment and prediction of different mechanisms of graft rejection.

immunology↗

bioRxiv2022

Robust Harmonization of Microbiome Studies by Phylogenetic Scaffolding with MaLiAmPi

Microbiome science is difficult to translate back to patients due to an inability to harmonize 16S rRNA gene-based microbiome data, as differences in the technique will result in different amplicon sequence variants (ASV) from the same microbe. Here we demonstrate that placement of ASV onto a common phylogenetic tree of full-length 16S rRNA alleles can harmonize microbiome studies. Using in silico data approximating 100 healthy human stool microbiomes we demonstrated that phylogenetic placement of ASV can recapitulate the true relationships between communities as compared closed-OTU based approaches (Spearman R 0.8 vs 0.2). Using real data from thousands of human gut and vaginal microbiota, we demonstrate phylogenetic placement, but not closed OTUs, were able to group communities by origin (stool vs vaginal) without being confounded by technique and integrate new data into existing ordination/clustering models for precision medicine. This enables meta-analysis of microbiome studies and the microbiome as a biomarker.

bioinformatics↗

bioRxiv2022

Perturbation of ATP6V0A1 as a Driver and Druggable Target for Renal Proximal Tubular Cellular Injury in Nephropathic Cystinosis

Understanding the unique susceptibility of the human kidney to pH dysfunction and injury in cystinosis is paramount to developing new therapies to preserve renal function. Renal proximal tubular epithelial cells (RPTECs) and fibroblasts isolated from patients with cystinosis were transcriptionally profiled. Lysosomal fractionation, immunoblotting, confocal microscopy, intracellular pH, TEM, mitochondrial stress test, and membrane integrity assays were performed for validation. CRISPR, CTNS -/- RPTECs were generated. Alterations in cell stress, pH, autophagic turnover, and mitochondrial energetics highlighted key changes in the vacuolar (V)-ATPases in patient-derived and CTNS-/- RPTECs. ATP6V0A1 was significantly downregulated in cystinosis and highly co-regulated with loss of CTNS. Correction of ATP6V0A1 rescued cell stress and mitochondrial function. Treatment of CTNS -/- RPTECs with antioxidants astaxanthin (ATX) induced ATP6V0A1 expression and improved autophagosome turnover and mitochondrial integrity. In conclusion, our exploratory transcriptional and in vitro cellular and functional studies confirm that loss of cystinosin in RPTECs, results in a reduction in ATP6V0A1 expression, with changes in intracellular pH, mitochondrial integrity, mitochondrial function, and autophagosome-lysosome clearance. The novel findings are ATP6V0A1s role in cystinosis-associated renal pathology and among other antioxidants, ATX specifically upregulated ATP6V0A1, improved autophagosome turnover or reduced autophagy and mitochondrial integrity. This is a pilot study highlighting a novel mechanism of tubular injury in cystinosis and requires further study in animal models to clarify its utility in clinical settings.

cell biology↗

bioRxiv2021

Transcriptional fidelity enhances cancer cell line selection in pediatric cancers

Multi-omic technologies have allowed for comprehensive profiling of patient-derived tumor samples and the cell lines that are intended to model them. Yet, our understanding of how cancer cell lines reflect native pediatric cancers in the age of molecular subclassification remains unclear and represents a clinical unmet need. Here we use Treehouse public data to provide an RNA-seq driven analysis of 799 cancer cell lines, focusing on how well they correlate to 1,655 pediatric tumor samples spanning 12 tumor types. For each tumor type we present a ranked list of the most representative cell lines based on correlation of their transcriptomic profiles to those of the tumor. We found that most (8/12) tumor types best correlated to a cell line of the closest matched disease type. We furthermore showed that inferred molecular subtype differences in medulloblastoma significantly impacted correlation between medulloblastoma tumor samples and cell lines. Our results are available as an interactive web application to help researchers select cancer cell lines that more faithfully recapitulate pediatric cancer.

cancer biology↗

bioRxiv2021

Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single cell RNAseq, we comprehensively characterized immune and nonimmune cell types in endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue. Teaser SentenceCell type deconvolution and single cell RNAseq analysis identify altered endometrial cellular compositions in women with endometriosis

bioinformatics↗

bioRxiv2021

Nurturing Diversity and Inclusion in AI in Biomedicine through a Virtual Summer Program for High School Students

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to flawed systems that perpetuate gender and racial biases, and limit how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by promoting diversity and inclusion in AI. The program targets high school students from underrepresented backgrounds in AI and gives them a chance to learn about AI with a focus on biomedicine. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at our final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There was also a nominally significant increase in the students knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater inclusion in AI.

scientific communication and education↗

bioRxiv2021

Large-scale placenta DNA methylation mega-analysis reveals fetal sex-specific differentially methylated CpG sites and regions

Although male-female differences in placental structure and function have been observed, little is understood about their molecular underpinnings. Here, we present a mega-analysis of 14 publicly available placenta DNA methylation (DNAm) microarray datasets to identify individual CpGs and regions associated with fetal sex. In the discovery dataset of placentas from full term pregnancies (N = 532 samples), 5,212 CpGs met genome-wide significance (p < 1E-8) and were enriched in pathways such as keratinization (FDR p-value = 7.37E-14), chemokine activity (FDR p-value = 1.56E-2), and eosinophil migration (FDR p-value = 1.83E-2). Nine differentially methylated regions were identified (fwerArea < 0.1) including a region in the promoter of ZNF300 that showed consistent differential DNAm in samples from earlier timepoints in pregnancy and appeared to be driven predominately by effects in the trophoblast cell type. We describe the largest study of fetal sex differences in placenta DNAm performed to date, revealing genes and pathways characterizing sex-specific placenta function and health outcomes later in life.

genomics↗

bioRxiv2020

Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

bioinformatics↗

bioRxiv2020

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

bioinformatics↗