Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cell Line”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease

Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3 , the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC ) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1 . Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of HEK-293 Cell Lines Constitutively Expressing Flaviviral Antigens for Use in Diagnostics

Flaviviruses are important human pathogens worldwide. Diagnostic testing for these viruses is difficult because many of the pathogens require specialized biocontainment. To address this issue, we generated 39 virus-like particle (VLP)- and nonstructural protein 1 (NS1)-secreting stable cell lines in HEK-293 cells of 13 different flaviviruses, including dengue, yellow fever, Japanese encephalitis, West Nile, St. Louis encephalitis, Zika, Rocio, Ilheus, Usutu, and Powassan viruses. Antigen secretion was stable for at least 10 cell passages, as measured by enzyme-linked immunosorbent assays and immunofluorescence assays. Thirty-five cell lines (90%) had stable antigen expression over 10 passages, with three of these cell lines (7%) increasing in antigen expression and one cell line (3%) decreasing in antigen expression. Antigen secretion in the HEK-293 cell lines was higher than in previously developed COS-1 cell line counterparts. These antigens can replace current antigens derived from live or inactivated virus for safer use in diagnostic testing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Engineered Cell Line Imaging Assay Differentiates Pathogenic from Non-Pathogenic Bacteria

Cell culture systems have greatly expanded our understanding of how bacterial pathogens target signaling pathways to manipulate the host and cause infection. Advances in genetic engineering have allowed for the creation of fluorescent protein readouts within signaling pathways, but these techniques have been underutilized in pathogen biology. Here, we genetically engineered a lung cell line with fluorescent reporters for extracellular signal-related kinase (ERK) and the downstream transcription factor FOS-related antigen 1 (Fra1) and evaluated signaling after inoculation with pathogenic and non-pathogenic bacteria. Cells were inoculated with 100 colony-forming units of Acinetobacter baylyi, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus agalactiae, or Staphylococcus epidermidis and imaged in a multi-mode reader. The alamarBlue cell viability assay was used as a reference test and showed that pathogenic P. aeruginosa induced significant (p < 0.05) cell death after 8 h in both wild-type and engineered cell lines compared to non-pathogenic S. epidermidis. In engineered cells, we found that Fra1 signaling was disrupted in as little as 4 h after inoculation with bacterial pathogens compared to delayed disruption in signaling by non-pathogenic S. epidermidis. Overall, we demonstrate that low levels of pathogenic versus non-pathogenic bacteria can be rapidly and sensitively screened based on ERK-Fra1 signaling

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2021

Netrin-like domain of sFRP4, a Wnt antagonist inhibits stemness, metastatic and invasive properties by specifically blocking MMP-2 in cancer stem cells from human glioma cell line U87MG

Rapid proliferation, high stemness potential, high invasiveness and apoptotic evasion are the distinctive hallmarks of glioma malignancy. The dysregulation of the Wnt/β-catenin pathway is the key factor regulating glioma malignancy. Wnt antagonist, secreted frizzled-related protein 4 (sFRP4), which has a prominent pro-apoptotic role in glioma stem cells, has two functional domains, the netrin-like domain (NLD), and cysteine-rich domain (CRD) both of which contribute to apoptotic properties of the whole protein. However, there are no reports elucidating the specific effects of individual domains of sFRP4 in inhibiting the invasive properties of glioma. This study explores the efficacy of the domains of sFRP4 in inhibiting the key hallmarks of glioblastoma such as invasion, metastasis, and stemness. We overexpressed sFRP4 and its domains in the glioblastoma cell line, U87MG cells and observed that both CRD and NLD domains played prominent roles in attenuating cancer stem cell properties. Significantly, we could demonstrate for the first time that both NLD and CRD domains negatively impacted the key driver of metastasis and migration, the matrix metalloproteinase-2 (MMP-2). Mechanistically, compared to CRD, NLD domain suppressed MMP-2 mediated invasion more effectively in glioma cells as observed in matrigel invasion assay and a function-blocking antibody assay. Fluorescent matrix degradation assay further revealed that NLD reduces matrix degradation. NLD also significantly disrupted fibronectin assembly and decreased cell adhesion in another glioma cell line LN229. In conclusion, the NLD peptide of sFRP4 could be a potent short peptide therapeutic candidate for targeting MMP-2-mediated invasion in the highly malignant glioblastoma multiforme.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Chromosome compartmentalization alterations in prostate cancer cell lines model disease progression

Prostate cancer aggressiveness and metastatic potential are influenced by gene expression and genomic aberrations, features that can be influenced by the 3D structure of chromosomes inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, from normal epithelium to bone metastasis. We describe spatial compartment identity (A-open versus B-closed) changes with progression in these cell lines and their relation to gene expression changes in both cell lines and patient samples. In particular, 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches are accompanied by changes in the structure, size, and boundaries of topologically associating domains (TADs). Further, compartment changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation. These results suggest that discrete 3D genome structure changes play a deleterious role in prostate cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improved Zika virus plaque assay using Vero/TMPRSS2 cell line

Plaque assay is the gold standard for the quantification of viable cytopathic viruses like Zika virus (ZIKV). Some strains of ZIKV produce plaques that are very difficult to accurately visualize and count on the commonly used Vero cell line. From data generated in our lab, we became curious if Vero/TMPRSS2 cells may be a better alternative; therefore, we compared the plaque forming units of two strains of ZIKV on Vero/TMPRSS2 cells to those produced by Vero cells. We also compared the virus stock titer generated on Vero/TMPRSS2 cells to that generated by the Vero cell line. Although the Vero cells generated higher quantity of ZIKV stocks, the Vero/TMPRSS2 cells produced plaques with significantly improved morphology and visibility and may, therefore, be a better alternative to use for performing plaque assays for strains of ZIKV that are more difficult to titer on regular Vero cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

In-Depth Proteome Profiling of Small Extracellular Vesicles Isolated from Cancer Cell Lines and Patient Serum

Extracellular Vesicle (EV) secretion has been observed from most types of normal and tumor cells. These EVs contain a variety of distinctive cargo where tumor-derived serum proteins in EVs secreted into the bloodstream can be used to provide a minimally invasive method for clinical monitoring. We have therefore undertaken a comprehensive study of the protein content of the EVs from several cancer cell lines using direct Data Independent Analysis (DIA). Further, several thousand proteins were detected including many of the classic EV markers such as CD9, CD81, CD63, TSG101 and Syndecan-1 among others. Many distinctive cancer related proteins were detected and some of them are known to be potential markers of cancer detection and monitoring of cancer progression. We have further studied the protein content of EVs from patient serum for both normal controls and pancreatic (PC) and Hepatocellular Carcinoma (HCC) cancers. The EVs for these studies have been isolated by various methods for comparison including ultracentrifugation (UC) and CD9 immunoaffinity column (CD9-IC). Typically, 500-1,000 proteins were identified where most of them overlapped with the EV proteins identified from the cell lines studied. Many of the EV protein markers were identified in these serum EVs and large numbers of proteins related to pancreatic and HCC cancers were identified.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Human RNome Project draft human RNome sequence of GM12878, B-cell line, obtained by mass-spectrometry sequencing, long-read sequencing and short-read sequencing.

Here we report the first draft of the human RNome sequence, a reference map of RNA chemical modifications in a human B-cell line. RNA carries a diverse repertoire of chemical modifications that regulate gene expression, cellular function, and responses to physiological and pathological cues. Yet, unlike the genome, no reference map of RNA modifications is available for any human cell. To generate this resource, the Human RNome Project Consortium analyzed a shared RNA preparation from the well-characterized GM12878 B-cell line using short-read sequencing, long-read direct RNA sequencing, and mass spectrometry, generating more than 7.1 billion sequencing reads spanning approximately 1.2 trillion nucleotides. The resulting maps of the human RNome reveal that RNA modifications are organized according to function, transcript architecture, and cellular identity. Modifications concentrate at functional centers of ribosomal and transfer RNAs, follow the canonical topology of N6-methyladenosine in coding transcripts, and form coordinated hotspots in immune regulatory genes. This first reference human RNome provides a foundation for understanding how RNA chemistry shapes cellular identity, human disease, and the development of RNA-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs but attempts to combine different algorithms and optimize them on large datasets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in identification of 540 SAAVs in the mass spectrometry data, the most comprehensive coverage yet reported. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53 and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines

Autophagy drives drug resistance and drug-induced cancer cell cytotoxicity. Targeting the autophagy process could greatly improve chemotherapy outcomes. The discovery of specific inhibitors or activators has been hindered by challenges with reliably measuring autophagy levels in a clinical setting. We investigated drug-induced autophagy in breast cancer cell lines with differing ER/PR/Her2 receptor status by exposing them to known but divergent autophagy inducers each with a unique molecular target, tamoxifen, trastuzumab, bortezomib or rapamycin. Differential gene expression analysis from total RNA extracted during the earliest sign of autophagy flux showed both cell- and drug-specific changes. We analyzed the list of differentially expressed genes to find a common, cell- and drug-agnostic autophagy signature. Twelve mRNAs were significantly modulated by all the drugs and 11 were orthogonally verified with Q-RT-PCR (Klhl24, Hbp1, Crebrf, Ypel2, Fbxo32, Gdf15, Cdc25a, Ddit4, Psat1, Cd22, Ypel3). The drug agnostic mRNA signature was similarly induced by a mitochondrially targeted agent, MitoQ. In-silico analysis on the KM-plotter cancer database showed that the levels of these mRNAs are detectable in human samples and associated with breast cancer prognosis outcomes of Relapse-Free Survival in all patients (RSF), Overall Survival in all patients (OS), and Relapse-Free Survival in ER + Patients (RSF ER + ). High levels of Klhl24, Hbp1, Crebrf, Ypel2, CD22 and Ypel3 were correlated with better outcomes, whereas lower levels of Gdf15, Cdc25a, Ddit4 and Psat1 were associated with better prognosis in breast cancer patients. This gene signature uncovers candidate autophagy biomarkers that could be tested during preclinical and clinical studies to monitor the autophagy process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A cross-study analysis of drug response prediction in cancer cell lines

Abstract To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

African Swine Fever Vaccine Candidate ASFV-G-ΔI177L Produced in the Swine Macrophage-Derived Cell Line IPKM Remains Genetically Stable and Protective against Homologous Virulent Challenge

ASFV vaccine candidate ASFV-G-ΔI177L has been shown to be highly efficacious in inducing protection against challenges with the parental virus, the Georgia 2010 isolate, as well as against field strains isolated from Vietnam. ASFV-G-ΔI177L has been shown to produce protection even when used at low doses (10 2 HAD 50 ) and shows no residual virulence even when administered at high doses (10 6 HAD 50 ) or evaluated for a relatively long period of time (6 months). ASFV-G-ΔI177L stocks can only be massively produced in primary cell macrophages. Alternatively, its modified version (ASFV-G-ΔI177L/ΔLVR) grows in a swine-derived cell line (PIPEC), acquiring significant genomic modifications. We present here the development of ASFV-G-ΔI177L stocks in a swine macrophage cell line, IPKM, and its protective efficacy when evaluated in domestic pigs. Successive passing of ASFV-G-ΔI177L in IPKM cells produces minimal genomic changes. Interestingly, a stock of ASFV-G-ΔI177L obtained after 10 passages (ASFV-G-ΔI177Lp10) in IPKM cells showed very small genomic changes when compared with the original virus stock. ASFV-G-ΔI177Lp10 conserves similar growth kinetics in primary swine macrophage cultures than the original parental virus ASFV-G-ΔI177L. Pigs infected with 10 3 HAD 50 of ASFV-G-ΔI177Lp10 developed a strong virus-specific antibody response and were completely protected against the challenge with the parental virulent field isolate Georgia 2010. Therefore, IPKM cells could be an effective alternative for the production of ASFV vaccine stocks for those vaccine candidates exclusively growing in swine macrophages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Selective CK1α degraders exert antiproliferative activity against a broad range of human cancer cell lines

Molecular-glue degraders are small molecules that induce a specific interaction between an E3 ligase and a target protein, resulting in the target proteolysis. The discovery of molecular glue degraders currently relies mostly on screening approaches. Here, we describe screening of a library of cereblon (CRBN) ligands against a panel of patient-derived cancer cell lines, leading to the discovery of SJ7095, a potent degrader of CK1α, IKZF1 and IKZF3 proteins. Through a structure-informed exploration of structure activity relationship (SAR) around this small molecule we develop SJ3149, a selective and potent degrader of CK1α protein in vitro and in vivo. The structure of SJ3149 co-crystalized in complex with CK1α + CRBN + DDB1 provides a rationale for the improved degradation properties of this compound. In a panel of 115 cancer cell lines SJ3149 displays a broad antiproliferative activity profile, which shows statistically significant correlation with MDM2 inhibitor Nutlin-3a. These findings suggest potential utility of selective CK1α degraders for treatment of hematological cancers and solid tumors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Experimental Senecavirus A Infection of Bovine Cell Lines and Colostrum-Deprived Calves

Senecavirus A (SVA) is a causative agent for vesicular disease in swine, which is clinically indistinguishable from other vesicular diseases of swine including foot-and-mouth disease (FMD). Recently, it was reported that buffalo in Guangdong, China were experiencing clinical symptoms similar to FMD including mouth ulcers and lameness tested positive for SVA. The objective of this study was to determine the susceptibility of cattle (Bos taurus) to SVA infection. Initial in vitro work using the PrimeFlow assay demonstrated that bovine cell lines and peripheral blood mononuclear cells from cattle were susceptible to SVA infection. Subsequently, six colostrum-deprived Holstein calves were challenged with SVA intranasally. No vesicular lesions were observed after challenge. Serum, oral, nasal, and rectal swabs tested for SVA nucleic acid did not support significant viral replication and there was no evidence of seroconversion. Therefore, demonstrating cattle from this study were not susceptible to experimental SVA infection.

60 APPLIED LIFE SCIENCES↗