Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorders.

Significant trauma histories and post-traumatic stress disorder (PTSD) are common in persons with substance use disorders (SUD) and often associate with increased SUD severity and poorer response to SUD treatment. As such, this sub-population has been associated with unique risk factors and treatment needs. Understanding the distinct etiological profile of persons with co-occurring SUD and PTSD is therefore crucial for advancing our knowledge of underlying mechanisms and the development of precision treatments. To this end, we employed supervised machine learning algorithms to interrogate the responses of 160 participants with SUD on the multidimensional NIDA Phenotyping Assessment Battery. Significant PTSD symptomatology was correctly predicted in 75% of participants (sensitivity: 80%; specificity: 72.22%) using a classification-based model based on anxiety and depressive symptoms, perseverative thinking styles, and interoceptive awareness. A regression-based machine learning model also utilized similar predictors, but failed to accurately predict severity of PTSD symptoms. These data indicate that even in a population already characterized by elevated negative affect (individuals with SUD), especially severe negative affect was predictive of PTSD symptomatology. In a follow-up analysis of a subset of 102 participants who also completed neurocognitive tasks, comorbidity status was correctly predicted in 86.67% of participants (sensitivity: 91.67%; specificity: 66.67%) based on depressive symptoms and fear-related attentional bias. However, a regression-based analysis did not identify fear-related attentional bias as a splitting factor, but instead split and categorized the sample based on indices of aggression, metacognition, distress tolerance, and interoceptive awareness. These data indicate that within a population of individuals with SUD, aberrations in tolerating and regulating aversive internal experiences may also characterize those with significant trauma histories, akin to findings in persons with anxiety without SUD. The results also highlight the need for further research on PTSD-SUD comorbidity that includes additional comparison groups (i.e., persons with only PTSD), captures additional comorbid diagnoses that may influence the PTSD-SUD relationship, examines additional types of SUDs (e.g., alcohol use disorder), and differentiates between subtypes of PTSD.

Recombinant vesicular stomatitis vaccine against Nipah virus has a favorable safety profile: Model for assessment of live vaccines with neurotropic potential.

Nipah virus (NiV) disease is a bat-borne zoonosis responsible for outbreaks with high lethality and is a priority for vaccine development. With funding from the Coalition of Epidemic Preparedness Innovations (CEPI), we are developing a chimeric vaccine (PHV02) composed of recombinant vesicular stomatitis virus (VSV) expressing the envelope glycoproteins of both Ebola virus (EBOV) and NiV. The EBOV glycoprotein (GP) mediates fusion and viral entry and the NiV attachment glycoprotein (G) is a ligand for cell receptors, and stimulates neutralizing antibody, the putative mediator of protection against NiV. PHV02 is identical in construction to the registered Ebola vaccine (Ervebo) with the addition of the NiV G gene. NiV ephrin B2 and B3 receptors are expressed on neural cells and the wild-type NiV is neurotropic and causes encephalitis in affected patients. It was therefore important to assess whether the NiV G alters tropism of the rVSV vector and serves as a virulence factor. PHV02 was fully attenuated in adult hamsters inoculated by the intramuscular (IM) route, whereas parental wild-type VSV was 100% lethal. Two rodent models (mice, hamsters) were infected by the intracerebral (IC) route with graded doses of PHV02. Comparator active controls in various experiments included rVSV-EBOV (representative of Ebola vaccine) and yellow fever (YF) 17DD commercial vaccine. These studies showed PHV02 to be more neurovirulent than both rVSV-EBOV and YF 17DD in infant animals. PHV02 was lethal for adult hamsters inoculated IC but not for adult mice. In contrast YF 17DD retained virulence for adult mice inoculated IC but was not virulent for adult hamsters. Because of the inconsistency of neurovirulence patterns in the rodent models, a monkey neurovirulence test (MNVT) was performed, using YF 17DD as the active comparator because it has a well-established profile of quantifiable microscopic changes in brain centers and a known reporting rate of neurotropic adverse events in humans. In the MNVT PHV02 was significantly less neurovirulent than the YF 17DD vaccine reference control, indicating that the vaccine will have an acceptable safety profile for humans. The findings are important because they illustrate the complexities of phenotypic assessment of novel viral vectors with tissue tropisms determined by transgenic proteins, and because it is unprecedented to use a heterologous comparator virus (YF vaccine) in a regulatory-enabling study. This approach may have value in future studies of other novel viral vectors.

Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research.

Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene-behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a 'meta-PCA' on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic-behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.

A NEW approach for characterizing mouse urinary pathophysiologies.

The void spot assay (VSA) is a cost-effective method for evaluating and quantifying mouse urinary voiding phenotypes. The VSA has been used to differentiate voiding behaviors between experimental groups, but not as a diagnostic assay. To build toward this goal, we used the VSA to define voiding patterns of male mice with diabetic diuresis (BTBR.Cg-Lepob /WiscJ mice), irritative urinary dysfunction (E. coli UTI89 urinary tract infection), and obstructive urinary dysfunction (testosterone and estradiol slow-release implants) compared to their respective controls. Many studies compare individual VSA endpoints (urine spot size, quantity, or distribution) between experimental groups. Here, we consider all endpoints collectively to establish VSA phenomes of mice with three different etiologies of voiding dysfunction. We created an approach called normalized endpoint work through (NEW) to normalize VSA outputs to control mice, and then applied principal components analysis and hierarchical clustering to 12 equally weighted, normalized, scaled, and zero-centered VSA outcomes collected from each mouse (the VSA phenome). This approach accurately classifies mice based on voiding dysfunction etiology. We used principal components analysis and hierarchical clustering to show that some aged mice (>24 m old) develop an obstructive or a diabetic diuresis VSA phenotype while others develop a unique phenotype that does not cluster with that of diabetic, infected, or obstructed mice. These findings support use of the VSA to identify specific urinary phenotypes in mice and the continued use of aged mice as they develop urinary dysfunction representative of the various etiologies of LUTS in men.

Lessons and tips for designing a machine learning study using EHR data.

Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.

Statistical Approaches to Candidate Biomarker Panel Selection.

The statistical analysis of robust biomarker candidates is a complex process, and is involved in several key steps in the overall biomarker development pipeline (see Fig. 22.1, Chap. 19 ). Initially, data visualization (Sect. 22.1, below) is important to determine outliers and to get a feel for the nature of the data and whether there appear to be any differences among the groups being examined. From there, the data must be pre-processed (Sect. 22.2) so that outliers are handled, missing values are dealt with, and normality is assessed. Once the processed data has been cleaned and is ready for downstream analysis, hypothesis tests (Sect. 22.3) are performed, and proteins that are differentially expressed are identified. Since the number of differentially expressed proteins is usually larger than warrants further investigation (50+ proteins versus just a handful that will be considered for a biomarker panel), some sort of feature reduction (Sect. 22.4) should be performed to narrow the list of candidate biomarkers down to a more reasonable number. Once the list of proteins has been reduced to those that are likely most useful for downstream classification purposes, unsupervised or supervised learning is performed (Sects. 22.5 and 22.6, respectively).

Transcriptomics of Environmental Enrichment Reveals a Role for Retinoic Acid Signaling in Addiction.

There exists much variability in susceptibility/resilience to addiction in humans. The environmental enrichment paradigm is a rat model of resilience to addiction-like behavior, and understanding the molecular mechanisms underlying this protective phenotype may lead to novel targets for pharmacotherapeutics to treat cocaine addiction. We investigated the differential regulation of transcript levels using RNA sequencing of the rat nucleus accumbens after environmental enrichment/isolation and cocaine/saline self-administration. Ingenuity Pathways Analysis and Gene Set Enrichment Analysis of 14,309 transcripts demonstrated that many biofunctions and pathways were differentially regulated. New functional pathways were also identified for cocaine modulation (e.g., Rho GTPase signaling) and environmental enrichment (e.g., signaling of EIF2, mTOR, ephrin). However, one novel pathway stood out above the others, the retinoic acid (RA) signaling pathway. The RA signaling pathway was identified as one likely mediator of the protective enrichment addiction phenotype, an interesting result given that nine RA signaling-related genes are expressed selectively and at high levels in the nucleus accumbens shell (NAcSh). Subsequent knockdown of Cyp26b1 (an RA degradation enzyme) in the NAcSh of rats confirmed this role by increasing cocaine self-administration as well as cocaine seeking. These results provide a comprehensive account of enrichment effects on the transcriptome and identify RA signaling as a contributing factor for cocaine addiction.

Convergent transcriptomics and proteomics of environmental enrichment and cocaine identifies novel therapeutic strategies for addiction.

Transcriptomic and proteomic approaches have separately proven effective at identifying novel mechanisms affecting addiction-related behavior; however, it is difficult to prioritize the many promising leads from each approach. A convergent secondary analysis of proteomic and transcriptomic results can glean additional information to help prioritize promising leads. The current study is a secondary analysis of the convergence of recently published separate transcriptomic and proteomic analyses of nucleus accumbens (NAc) tissue from rats subjected to environmental enrichment vs. isolation and cocaine self-administration vs. saline. Multiple bioinformatics approaches (e.g. Gene Ontology (GO) analysis, Ingenuity Pathway Analysis (IPA), and Gene Set Enrichment Analysis (GSEA)) were used to interrogate these rich data sets. Although there was little correspondence between mRNA vs. protein at the individual target level, good correspondence was found at the level of gene/protein sets, particularly for the environmental enrichment manipulation. These data identify gene sets where there is a positive relationship between changes in mRNA and protein (e.g. glycolysis, ATP synthesis, translation elongation factor activity, etc.) and gene sets where there is an inverse relationship (e.g. ribosomes, Rho GTPase signaling, protein ubiquitination, etc.). Overall environmental enrichment produced better correspondence than cocaine self-administration. The individual targets contributing to mRNA and protein effects were largely not overlapping. As a whole, these results confirm that robust transcriptomic and proteomic data sets can provide similar results at the gene/protein set level even when there is little correspondence at the individual target level and little overlap in the targets contributing to the effects.

S-Nitrosylation Proteome Profile of Peripheral Blood Mononuclear Cells in Human Heart Failure.

Nitric oxide (NO) protects the heart against ischemic injury; however, NO- and superoxide-dependent S-nitrosylation (S-NO) of cysteines can affect function of target proteins and play a role in disease outcome. We employed 2D-GE with thiol-labeling FL-maleimide dye and MALDI-TOF MS/MS to capture the quantitative changes in abundance and S-NO proteome of HF patients (versus healthy controls, n = 30/group). We identified 93 differentially abundant (59-increased/34-decreased) and 111 S-NO-modified (63-increased/48-decreased) protein spots, respectively, in HF subjects (versus controls, fold-change | ≥1.5|, p ≤ 0.05). Ingenuity pathway analysis of proteome datasets suggested that the pathways involved in phagocytes' migration, free radical production, and cell death were activated and fatty acid metabolism was decreased in HF subjects. Multivariate adaptive regression splines modeling of datasets identified a panel of proteins that will provide >90% prediction success in classifying HF subjects. Proteomic profiling identified ATP-synthase, thrombospondin-1 (THBS1), and vinculin (VCL) as top differentially abundant and S-NO-modified proteins, and these proteins were verified by Western blotting and ELISA in different set of HF subjects. We conclude that differential abundance and S-NO modification of proteins serve as a mechanism in regulating cell viability and free radical production, and THBS1 and VCL evaluation will potentially be useful in the prediction of heart failure.

Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens.

Invasive pulmonary aspergillosis (IPA) is an opportunistic fungal infection in patients undergoing chemotherapy for hematological malignancy, hematopoietic stem cell transplant, or other forms of immunosuppression. In this group, Aspergillus infections account for the majority of deaths due to mold pathogens. Although early detection is associated with improved outcomes, current diagnostic regimens lack sensitivity and specificity. Patients undergoing chemotherapy, stem cell transplantation and lung transplantation were enrolled in a multi-site prospective observational trial. Proven and probable IPA cases and matched controls were subjected to discovery proteomics analyses using a biofluid analysis platform, fractionating plasma into reproducible protein and peptide pools. From 556 spots identified by 2D gel electrophoresis, 66 differentially expressed post-translationally modified plasma proteins were identified in the leukemic subgroup only. This protein group was rich in complement components, acute-phase reactants and coagulation factors. Low molecular weight peptides corresponding to abundant plasma proteins were identified. A candidate marker panel of host response (9 plasma proteins, 4 peptides), fungal polysaccharides (galactomannan), and cell wall components (ß-D glucan) were selected by statistical filtering for patients with leukemia as a primary underlying diagnosis. Quantitative measurements were developed to qualify the differential expression of the candidate host response proteins using selective reaction monitoring mass spectrometry assays, and then applied to a separate cohort of 57 patients with leukemia. In this verification cohort, a machine learning ensemble-based algorithm, generalized pathseeker (GPS) produced a greater case classification accuracy than galactomannan (GM) or host proteins alone. In conclusion, Integration of host response proteins with GM improves the diagnostic detection of probable IPA in patients undergoing treatment for hematologic malignancy. Upon further validation, early detection of probable IPA in leukemia treatment will provide opportunities for earlier interventions and interventional clinical trials.

Molecular classification of outcomes from dengue virus -3 infections.

OBJECTIVES: Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. STUDY DESIGN: We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. RESULTS: 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. CONCLUSIONS: Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions.

Serum-mediated activation of macrophages reflects TcVac2 vaccine efficacy against Chagas disease.

Chagas disease is endemic in Latin America and an emerging infectious disease in the United States. No effective treatments are available. The TcG1, TcG2, and TcG4 antigens are highly conserved in clinically relevant Trypanosoma cruzi isolates and are recognized by B and T cells in infected hosts. Delivery of these antigens as a DNA prime/protein boost vaccine (TcVac2) elicited lytic antibodies and type 1 CD8(+) T cells that expanded upon challenge infection and provided >90% control of parasite burden and myocarditis in chagasic mice. Here we determined if peripheral blood can be utilized to capture the TcVac2-induced protection from Chagas disease. We evaluated the serum levels of T. cruzi kinetoplast DNA (TckDNA), T. cruzi 18S ribosomal DNA (Tc18SrDNA), and murine mitochondrial DNA (mtDNA) as indicators of parasite persistence and tissue damage and monitored the effect of sera on macrophage phenotype. Circulating TckDNA/Tc18SrDNA and mtDNA were decreased by >3- to 5-fold and 2-fold, respectively, in vaccinated infected mice compared to nonvaccinated infected mice. Macrophages incubated with sera from vaccinated infected mice exhibited M2 surface markers (CD16, CD32, CD200, and CD206), moderate proliferation, a low oxidative/nitrosative burst, and a regulatory/anti-inflammatory cytokine response (interleukin-4 [IL-4] plus IL-10 > tumor necrosis factor alpha [TNF-α]). In comparison, macrophages incubated with sera from nonvaccinated infected mice exhibited M1 surface markers, vigorous proliferation, a substantial oxidative/nitrosative burst, and a proinflammatory cytokine response (TNF-α ≫ IL-4 plus IL-10). Cardiac infiltration of macrophages and TNF-α and oxidant levels were significantly reduced in TcVac2-immunized chagasic mice. We conclude that circulating TcDNA and mtDNA levels and macrophage phenotype mediated by serum constituents reflect in vivo levels of parasite persistence, tissue damage, and inflammatory/anti-inflammatory state and have potential utility in evaluating disease severity and efficacy of vaccines and drug therapies.

Discovery proteomics and nonparametric modeling pipeline in the development of a candidate biomarker panel for dengue hemorrhagic fever.

Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.

A three-component biomarker panel for prediction of dengue hemorrhagic fever.

Dengue virus infections are a major cause of morbidity in tropical countries. Early detection of dengue hemorrhagic fever (DHF) may help identify individuals that would benefit from intensive therapy. Predictive modeling was performed using 11 laboratory values of 51 individuals (38 DF and 13 DHF) obtained on initial presentation using logistic regression. We produced a robust model with an area under the curve of 0.9615 that retained IL-10 levels, platelets, and lymphocytes as the major predictive features. A classification and regression tree was developed on these features that were 86% accurate on cross-validation. The IL-10 levels and platelet counts were also identified as the most informative features associated with DHF using a Random Forest classifier. In the presence of polymerase chain reaction-proven acute dengue infections, we suggest a complete blood count and rapid measurement of IL-10 can assist in the triage of potential DHF cases for close follow-up or clinical intervention improving clinical outcome.

Aging and microRNA expression in human skeletal muscle: a microarray and bioinformatics analysis.

A common characteristic of aging is loss of skeletal muscle (sarcopenia), which can lead to falls and fractures. MicroRNAs (miRNAs) are novel posttranscriptional modulators of gene expression with potential roles as regulators of skeletal muscle mass and function. The purpose of this study was to profile miRNA expression patterns in aging human skeletal muscle with a miRNA array followed by in-depth functional and network analysis. Muscle biopsy samples from 36 men [young: 31 ± 2 (n = 19); older: 73 ± 3 (n = 17)] were 1) analyzed for expression of miRNAs with a miRNA array, 2) validated with TaqMan quantitative real-time PCR assays, and 3) identified (and later validated) for potential gene targets with the bioinformatics knowledge base software Ingenuity Pathways Analysis. Eighteen miRNAs were differentially expressed in older humans (P < 0.05 and >500 expression level). Let-7 family members Let-7b and Let-7e were significantly elevated and further validated in older subjects (P < 0.05). Functional and network analysis from Ingenuity determined that gene targets of the Let-7s were associated with molecular networks involved in cell cycle control such as cellular proliferation and differentiation. We confirmed with real-time PCR that mRNA expression of cell cycle regulators CDK6, CDC25A, and CDC34 were downregulated in older compared with young subjects (P < 0.05). In addition, PAX7 mRNA expression was lower in older subjects (P < 0.05). These data suggest that aging is characterized by a higher expression of Let-7 family members that may downregulate genes related to cellular proliferation. We propose that higher Let-7 expression may be an indicator of impaired cell cycle function possibly contributing to reduced muscle cell renewal and regeneration in older human muscle.

Analysis of the differential host cell nuclear proteome induced by attenuated and virulent hemorrhagic arenavirus infection.

Arenaviruses are important emerging pathogens and include a number of hemorrhagic fever viruses classified as NIAID category A priority pathogens and CDC potential biothreat agents. Infection of guinea pigs with the New World arenavirus Pichindé virus (PICV) has been used as a biosafety level 2 model for the Lassa virus. Despite continuing research, little is known about the molecular basis of pathogenesis, and this has hindered the design of novel antiviral therapeutics. Modulation of the host response is a potential strategy for the treatment of infectious diseases. We have previously investigated the global host response to attenuated and lethal arenavirus infections by using high-throughput immunoblotting and kinomics approaches. In this report, we describe the differential nuclear proteomes of a murine cell line induced by mock infection and infection with attenuated and lethal variants of PICV, investigated by using two-dimensional gel electrophoresis. Spot identification using tandem mass spectrometry revealed the involvement of a number of proteins that regulate inflammation via potential modulation of NF-kappaB activity and of several heterogeneous nuclear ribonuclear proteins. Pathway analysis revealed a potential role for transcription factor XBP-1, a transcription factor involved in major histocompatibility complex II (MHC-II) expression; differential DNA-binding activity was revealed by electrophoretic mobility shift assay, and differences in surface MHC-II expression were seen following PICV infection. These data are consistent with the results of several previous studies and highlight potential differences between transcriptional and translational regulation. This study provides a number of differentially expressed targets for further research and suggests that key events in pathogenesis may be established early in infection.

Identification of differentially activated cell-signaling networks associated with pichinde virus pathogenesis by using systems kinomics.

Phosphorylation plays a key role in regulating many signaling pathways. Although studies investigating the phosphorylated forms of signaling pathways are now commonplace, global analysis of protein phosphorylation and kinase activity has lagged behind genomics and proteomics. We have used a kinomics approach to study the effect of virus infection on host cell signaling in infected guinea pigs. Delineating the host responses which lead to clearance of a pathogen requires the use of a matched, comparative model system. We have used two passage variants of the arenavirus Pichinde, used as a biosafety level 2 model of Lassa fever virus as it produces similar pathologies in guinea pigs and humans, to compare the host cell responses between infections which lead to either a mild, self-limiting infection or lethal disease. Using this model, we can begin to understand the differences in signaling events which give rise to these markedly different outcomes. By contextualizing these data using pathway analysis, we have identified key differences in cellular signaling matrices. By comparing these differentially involved networks, we have identified a number of key signaling "nodes" which show differential phosphorylations between mild and lethal infections. We believe that these nodes provide potential targets for the development of antiviral therapies by acting at the level of the host response rather than by directly targeting viral proteins.