Host Gene Expression Patterns Research Articles

Microbiota and mucosal gene expression of fecal microbiota transplantation or placebo treated patients with chronic pouchitis

ABSTRACT Altered microbiota and impaired host immune function have been linked to the pathogenesis of pouchitis. We used 16S rRNA gene sequencing and RNA sequencing data from a previous randomized clinical trial (RCT) on fecal microbiota transplantation (FMT) therapy in 26 chronic pouchitis patients with one-year follow-up. We analyzed changes in both luminal and mucosal microbiota composition, as well as in host mucosal gene expression to gain insights into the host–microbiota interactions possibly underlying clinical outcomes of the patients. Antibiotic type and pattern of use were significant drivers of the luminal microbiota at baseline. Differential gene expression analysis indicated transition from ileal to colonic gene expression in the pouch, and upregulation in inflammation- and immune system-related pathways in the pouch. At 4 weeks, the non-relapsed FMT patients had a lower microbiota dissimilarity to the donor than the non-relapsed placebo patients (p = .02). While two FMT-treated patients showed a shift toward the donor’s microbiota during the one-year follow-up, the overall FMT microbiota modulation effect was low. Patient’s luminal and mucosal microbiota profiles were unstable in both FMT and placebo groups. Expression of the chemokine receptor CXCR4 was downregulated at 52 weeks compared to the baseline in the non-relapsed patients in both FMT and placebo groups. Microbiota modulation by FMT seems to be low in this patient group. The microbiota composition or alterations did not explain the relapse status of the patients. Some evidence for remission-related host gene expression pattern was found; specifically, CXCR4 expression may have a role in sustained remission.

305. PBMC-Derived Transcriptomic Signatures Accurately Discriminate Between Viral, Bacterial, and Fungal Infections and can be Translated to Real-World Human Infections

Abstract Background Analysis of host gene expression patterns (‘signatures’) can provide diagnostic information to determine the etiology of acute infection. In vitro experiments can be used to supplement our knowledge about these signatures. Methods We performed in vitro human peripheral blood mononuclear cell (PBMC) challenges with influenza virus, gram-negative (Escherichia coli) and gram-positive (Streptococcus pneumoniae) bacteria, and fungal pathogens (Candida albicans, Cryptococcus neoformans and gattii). Exposed human cells were analyzed for differential gene expression utilizing Affymetrix microarrays. Logistic regression models were applied to the gene expression data to identify signatures capable of identifying each infection etiology. PBMC-derived signatures for each pathogen class were then applied to a microarray dataset of peripheral blood samples from human patients with candidemia. Results A 41-gene multinomial classifier was developed which correctly classified viral, bacterial, or fungal infection with 94-98% accuracy. Gene expression patterns in PBMC challenges show substantial overlap with gene expression changes in natural infection. For fungal infection, 31 of the top 50 differentially expressed genes (DEGs) from the PBMC challenge overlapped with responses seen in patients with candidemia. Twenty of these genes were upregulated, including those involved in IgG binding (FCGR1A and FCGR1B). A subset of these genes were able to accurately identify human cases of candidemia in an independent dataset with an area under the receiver operating characteristic curve (auROC) of 0.94. Pathway analysis of differentially expressed genes revealed pathogen class-specific immune responses. For fungal infections in particular, a marked upregulation of inflammatory responses was seen including cellular responses to stress, eosinophil chemotaxis (CCL24, SOCS1/2) and T cell proliferation and migration (IL2, CXCL9). Conclusion In vitro PBMC challenges can support ability to generate host transcriptomic signatures able to discriminate between bacterial, viral, and fungal infection, and their performance can be translated to real-world human infections. Disclosures Julie M. Steinbrink, MD, MHS, Duke University: Methods to Detect and Treat a Fungal Infection Ricardo Henao, PhD, Duke University: Methods to Diagnose and Treat Acute Respiratory Infections Ephraim L. Tsalik, MD, PhD, Danaher Diagnostics: VP and Chief Scientific Officer for Infectious Disease|Duke University: Methods to Diagnose and Treat Acute Respiratory Infections; Methods to Detect and Treat a Fungal Infection Geoffrey S. Ginsburg, MD, PhD, Duke University: Methods to Diagnose and Treat Acute Respiratory Infections Elizabeth Ramsburg, PhD, Spark Therapeutics: Head Of Central Nervous System & Research Christopher W. Woods, MD, MPH, Biomeme Inc: Methods to diagnose and treat acute respiratory infections|Biomeme Inc: Chief Medical Consultant|Biomeme Inc: Ownership Interest Micah T. McClain, MD, PhD, Biomeme Inc: Methods to diagnose and treat acute respiratory infections

A multicenter prospective study of comprehensive metagenomic and transcriptomic signatures for predicting outcomes of patients with severe community-acquired pneumonia

Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients. In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study. We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98). In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP. National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).

Regulation of host gene expression by gastrointestinal tract microbiota in Chinook Salmon (Oncorhynchus tshawytscha).

Differences in gut microbiome composition are linked with health, disease and ultimately host fitness; however, the molecular mechanisms underlying that relationship are not well characterized. Here, we modified the fish gut microbiota using antibiotic and probiotic feed treatments to address the effect of host microbiome on gene expression patterns. Chinook salmon (Oncorhynchus tshawytscha) gut gene expression was evaluated using whole transcriptome sequencing (RNA-Seq) on hindgut mucosa samples from individuals treated with antibiotic, probiotic and control diets to determine differentially expressed (DE) host genes. Fifty DE host genes were selected for further characterization using nanofluidic qPCR chips. We used 16S rRNA gene metabarcoding to characterize the rearing water and host gut microbiome (bacterial) communities. Daily administration of antibiotics and probiotics resulted in significant changes in fish gut and aquatic microbiota as well as more than 100 DE genes in the antibiotic and probiotic treatment fish, relative to healthy controls. Normal microbiota depletion by antibiotics mostly led to downregulation of different aspects of immunity and upregulation of apoptotic process. In the probiotic treatment, genes related to post-translation modification and inflammatory responses were up-regulated relative to controls. Our qPCR results revealed significant effects of treatment (antibiotic and probiotic) on rabep2, aifm3, manf, prmt3 gene transcription. Moreover, we found significant associations between members of Lactobacillaceae and Bifidobacteriaceae with host gene expression patterns. Overall, our analysis showed that the microbiota had significant impacts on many host signalling pathways, specifically targeting immune, developmental and metabolic processes. Our characterization of some of the molecular mechanisms involved in microbiome-host interactions will help develop new strategies for preventing/ treating microbiome disruption-related diseases.

Begomovirus: Exploiting the Host Machinery for Their Survival

Begomoviruses are the most prevalent plant-infecting virus genus, identified by a circular single-stranded DNA genome. The proteins encoded by these viruses are multifunctional and effectively modulate the host's cellular response during infection. Begomovirus-host interactions alter host gene expression patterns, redirect signaling pathways, disturb protein degradation pathways, and reprogram cellular metabolism to establish a successful infection. This review summarises known studies about the genome organisation of begomoviruses and altered cellular processes during infection at a molecular level. In addition, we emphasise proteins involved in intercellular trafficking of the viral genome for systemic infection and counteracting host small interfering RNA silencing and discuss different approaches for virus management based on the virus’s offensive and the host’s defensive strategies.

HIV silencing and cell survival signatures in infected T cell reservoirs

Rare CD4 T cells that contain HIV under antiretroviral therapy represent an important barrier to HIV cure1–3, but the infeasibility of isolating and characterizing these cells in their natural state has led to uncertainty about whether they possess distinctive attributes that HIV cure-directed therapies might exploit. Here we address this challenge using a microfluidic technology that isolates the transcriptomes of HIV-infected cells based solely on the detection of HIV DNA. HIV-DNA+ memory CD4 T cells in the blood from people receiving antiretroviral therapy showed inhibition of six transcriptomic pathways, including death receptor signalling, necroptosis signalling and antiproliferative Gα12/13 signalling. Moreover, two groups of genes identified by network co-expression analysis were significantly associated with HIV-DNA+ cells. These genes (n = 145) accounted for just 0.81% of the measured transcriptome and included negative regulators of HIV transcription that were higher in HIV-DNA+ cells, positive regulators of HIV transcription that were lower in HIV-DNA+ cells, and other genes involved in RNA processing, negative regulation of mRNA translation, and regulation of cell state and fate. These findings reveal that HIV-infected memory CD4 T cells under antiretroviral therapy are a distinctive population with host gene expression patterns that favour HIV silencing, cell survival and cell proliferation, with important implications for the development of HIV cure strategies.

An automated DNA computing platform for rapid etiological diagnostics.

Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

Host gene expression is associated with viral shedding magnitude in blue-winged teals (Spatula discors) infected with low-path avian influenza virus

Intraspecific variation in host infectiousness affects disease transmission dynamics in human, domestic animal, and many wildlife host-pathogen systems including avian influenza virus (AIV); therefore, identifying host factors related to host infectiousness is important for understanding, controlling, and preventing future outbreaks. Toward this goal, we used RNA-seq data collected from low pathogenicity avian influenza virus (LPAIV)-infected blue-winged teal (Spatula discors) to determine the association between host gene expression and intraspecific variation in cloacal viral shedding magnitude, the transmissible fraction of virus. We found that host genes were differentially expressed between LPAIV-infected and uninfected birds early in the infection, host genes were differentially expressed between shed level groups at one-, three-, and five-days post-infection, host gene expression was associated with LPAIV infection patterns over time, and genes of the innate immune system had a positive linear relationship with cloacal viral shedding. This study provides important insights into host gene expression patterns associated with intraspecific LPAIV shedding variation and can serve as a foundation for future studies focused on the identification of host factors that drive or permit the emergence of high viral shedding individuals.

Influence of African Swine Fever Virus on Host Gene Transcription within Peripheral Blood Mononuclear Cells from Infected Pigs.

African swine fever virus (ASFV) has become a global threat to the pig production industry and has caused enormous economic losses in many countries in recent years. Peripheral blood mononuclear cells (PBMCs) from pigs infected with ASFV not only express ASFV genes (almost 200 in number) but have altered patterns of host gene expression as well. Both up- and down-regulation of host cell gene expression can be followed using RNAseq on poly(A)+ mRNAs harvested from the PBMCs of pigs collected at different times post-infection. Consistent with the time course of changes in viral gene expression, only few and limited changes in host gene expression were detected at 3 days post-infection (dpi), but by 6 dpi, marked changes in the expression of over 1300 host genes were apparent. This was co-incident with the major increase in viral gene expression. The majority of the changes in host gene expression were up-regulation, but many down-regulated genes were also identified. The patterns of changes in gene expression within the PBMCs detected by RNAseq were similar in each of the four infected pigs. Furthermore, changes in the expression of about twenty selected host genes, known to be important in host defence and inflammatory responses, were confirmed using high-throughput microfluidic qPCR assays.

Toxoplasma infection in male mice alters dopamine-sensitive behaviors and host gene expression patterns associated with neuropsychiatric disease.

During chronic infection, the single celled parasite, Toxoplasma gondii, can migrate to the brain where it has been associated with altered dopamine function and the capacity to modulate host behavior, increasing risk of neurocognitive disorders. Here we explore alterations in dopamine-related behavior in a new mouse model based on stimulant (cocaine)-induced hyperactivity. In combination with cocaine, infection resulted in heightened sensorimotor deficits and impairment in prepulse inhibition response, which are commonly disrupted in neuropsychiatric conditions. To identify molecular pathways in the brain affected by chronic T. gondii infection, we investigated patterns of gene expression. As expected, infection was associated with an enrichment of genes associated with general immune response pathways, that otherwise limits statistical power to identify more informative pathways. To overcome this limitation and focus on pathways of neurological relevance, we developed a novel context enrichment approach that relies on a customized ontology. Applying this approach, we identified genes that exhibited unexpected patterns of expression arising from the combination of cocaine exposure and infection. These include sets of genes which exhibited dampened response to cocaine in infected mice, suggesting a possible mechanism for some observed behaviors and a neuroprotective effect that may be advantageous to parasite persistence. This model offers a powerful new approach to dissect the molecular pathways by which T. gondii infection contributes to neurocognitive disorders.

Testing Two Somatic Cell Count Cutoff Values for Bovine Subclinical Mastitis Detection Based on Milk Microbiota and Peripheral Blood Leukocyte Transcriptome Profile.

Simple SummarySetting a proper SCC cut-off value for determining the intramammary infection status of each individual cow is essential to early mastitis detection and prevention. In this study, we compared the effect of two commonly used SCC thresholds on distinguishing milk microbiota and host gene expression patterns, and demonstrated that the microbial composition and peripheral blood leukocyte transcriptome profiles did have conspicuous differences between cows with SCC above and below 100,000 cells/mL, respectively, which may help establish why 100,000 cells/mL is a more suitable cow-level cut-off value of subclinical mastitis diagnosis than 200,000 cells/mL from the perspective of microbiota and transcriptomic responses. Somatic cell count (SCC) is an important indicator of the health state of bovine udders. However, the exact cut-off value used for differentiating the cows with healthy quarters from the cows with subclinical mastitis remains controversial. Here, we collected composite milk (milk from four udder quarters) and peripheral blood samples from individual cows in two different dairy farms and used 16S rRNA gene sequencing combined with RNA-seq to explore the differences in the milk microbial composition and transcriptome of cows with three different SCC levels (LSCC: <100,000 cells/mL, MSCC: 100,000–200,000 cells/mL, HSCC: >200,000 cells/mL). Results showed that the milk microbial profiles and gene expression profiles of samples derived from cows in the MSCC group were indeed relatively easily discriminated from those from cows in the LSCC group. Discriminative analysis also uncovered some differentially abundant microbiota at the genus level, such as Bifidobacterium and Lachnospiraceae_AC2044_group, which were more abundant in milk samples from cows with SCC below 100,000 cells/mL. As for the transcriptome profiling, 79 differentially expressed genes (DEGs) were found to have the same direction of regulation in two sites, and functional analyses also showed that biological processes involved in inflammatory responses were more active in MSCC and HSCC cows. Overall, these results showed a similarity between the milk microbiota and gene expression profiles of MSCC and HSCC cows, which presented further evidence that 100,000 cells/ml is a more optimal cut-off value than 200,000 cells/mL for intramammary infection detection at the cow level.

A Multi-Omics Study of Familial Lung Cancer: Microbiome and Host Gene Expression Patterns.

BackgroundInherited susceptibility and environmental carcinogens are crucial players in lung cancer etiology. The lung microbiome is getting rising attention in carcinogenesis. The present work sought to investigate the microbiome in lung cancer patients affected by familial lung cancer (FLC) and indoor air pollution (IAP); and further, to compare host gene expression patterns with their microbiome for potential links.MethodsTissue sample pairs (cancer and adjacent nonmalignant tissue) were used for 16S rRNA (microbiome) and RNA-seq (host gene expression). Subgroup microbiome diversities and their matched gene expression patterns were analyzed. Significantly enriched taxa were screened out, based on different clinicopathologic characteristics.ResultsOur FLC microbiome seemed to be smaller, low-diversity, and inactive to change; we noted microbiome differences in gender, age, blood type, anatomy site, histology type, TNM stage as well as IAP and smoking conditions. We also found smoking and IAP dramatically decreased specific-OTU biodiversity, especially in normal lung tissue. Intriguingly, enriched microbes were in three categories: opportunistic pathogens, probiotics, and pollutant-detoxication microbes; this third category involved Sphingomonas, Sphingopyxis, etc. which help degrade pollutants, but may also cause epithelial damage and chronic inflammation. RNA-seq highlighted IL17, Ras, MAPK, and Notch pathways, which are associated with carcinogenesis and compromised immune system.ConclusionsThe lung microbiome can play vital roles in carcinogenesis. FLC and IAP subjects were affected by fragile lung epithelium, vulnerable host-microbes equilibrium, and dysregulated immune surveillance and response. Our findings provided useful information to study the triple interplay among environmental carcinogens, population genetic background, and diversified lung microbiome.

1023. Host Gene Expression Biomarkers to Distinguish Between Causes of Acute Respiratory Symptoms in Lung Transplant Recipients

BackgroundLong-term immunosuppression after lung transplantation increases susceptibility to a variety of respiratory infections that are often difficult to diagnose. Host gene expression patterns in circulating leukocytes may provide additional diagnostic information in these settings.Methods107 lung transplant recipients (79% with cystic fibrosis) were enrolled at Duke University Medical Center over a 2-year period – 59% with acute respiratory symptoms, the remainder as healthy controls. Whole blood was collected by PAXGene for RNA sequencing. Prior to undergoing biomarker analysis, each case was adjudicated to the appropriate clinical phenotype: bacterial infection, viral infection, allograft rejection, and healthy. Logistic regression models were applied to gene expression data to identify classifiers capable of identifying each etiology.ResultsIn lung transplant recipients, 117 genes were upregulated at least 2-fold in the presence of viral infection compared to healthy transplant controls. These genes clustered into expected antiviral pathways, including type I interferon signaling, interferon gamma mediated signaling, and defense response to virus, although the magnitude of gene expression was significantly less than that seen in non-transplant cohorts.Similar results were seen during bacterial infection (defense response to bacterium, antibacterial humoral response) and rejection (upregulation in defensins DEFA3 and DEFA4). Interestingly, despite the presence of immunosuppression, a previously published gene expression signature of respiratory infection (derived from non-immunosuppressed subjects) was able to differentiate between bacterial and viral infection with 100% accuracy.ConclusionEven in the presence of systemic immunosuppression and regardless of presence/absence of cystic fibrosis, core canonical components of the host response to infection and rejection are seen. Gene expression signatures based on these conserved components offer the potential for diagnostic capability in the setting of nonspecific respiratory illness in these vulnerable hosts. Disclosures Julie M. Steinbrink, MD, CareDx (Research Grant or Support) Alice Gray, MD, CareDx (Advisor or Review Panel member, Research Grant or Support, Speaker’s Bureau)Polarean (Advisor or Review Panel member)

The Impact of Microbiome and Microbiota-Derived Sodium Butyrate on Drosophila Transcriptome and Metabolome Revealed by Multi-Omics Analysis.

The host microbiome plays an important role in regulating physiology through microbiota-derived metabolites during host-microbiome interactions. However, molecular mechanism underly host-microbiome interactions remains to be explored. In this study, we used Drosophila as the model to investigate the influence of microbiome and microbiota-derived metabolite sodium butyrate on host transcriptome and metabolome. We established both a sterile Drosophila model and a conventional Drosophila model to demonstrate the role of sodium butyrate. Using multi-omics analysis, we found that microbiome and sodium butyrate could impact host gene expression patterns in both the sterile Drosophila model and the conventional Drosophila model. The analysis of gut microbial using 16S rRNA sequencing showed sodium butyrate treatment also influenced Drosophila bacterial structures. In addition, Drosophila metabolites identified by ultra-high performance liquid chromatography-MS/MS were shown to be affected by sodium butyrate treatment with lipids as the dominant changed components. Our integrative analysis of the transcriptome, the microbiome, and the metabolome data identified candidate transcripts that are coregulated by sodium butyrate. Taken together, our results reveal the impact of the microbiome and microbiota-derived sodium butyrate on host transcriptome and metabolome, and our work provides a better understanding of host-microbiome interactions at the molecular level with multi-omics data.

Epigenetic Lens to Visualize the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Infection in COVID-19 Pandemic.

In <20 years, we have witnessed three different epidemics with coronaviruses, SARS-CoV, MERS-CoV, and SARS-CoV-2 in human populations, causing widespread mortality. SARS-CoV-2, through its rapid global spread, has led to the pandemic that we call COVID-19. As of February 1, 2021, the global infections linked to SARS-CoV-2 stand at 103,503,340, with 2,236,960 deaths, and 75,108,099 recoveries. This review attempts to highlight host-pathogen interaction with particular emphasis on the role of epigenetic machinery in regulating the disease. Although researchers, since the start of the pandemic, have been intensely engaged in diverse areas to understand the mechanisms involved in SARS-CoV-2 infection to find answers that can bring about innovative ways to swiftly treat and prevent disease progression, this review provides an overview on how the host epigenetics is modulated and subverted by SARS-CoV-2 to enter the host cells and drive immunopathogenesis. Epigenetics is the study that combines genetic and non-genetic factors controlling phenotypic variation, which are primarily a consequence of external and environmental stimuli. These stimuli alter the activity of a gene without impinging on the DNA code. In viral-host interactions, DNA/RNA methylation, non-coding RNAs, chromatin remodeling, and histone modifications are known to regulate and modulate host gene expression patterns. Viruses such as Coronaviruses (an RNA virus) show intrinsic association with these processes. They have evolved the ability to tamper with host epigenetic machinery to interfere with immune sensing pathways to evade host immune response, thereby enhancing its replication and pathogenesis post-entry. These epigenetic alterations allow the virus to weaken the host's immune response to successfully spread infection. How this occurs, and what epigenetic mechanisms are altered is poorly understood both for coronaviruses and other respiratory RNA viruses. The review highlights several cutting-edge aspects of epigenetic work primarily pertinent to SARS-CoV-2, which has been published between 2019 and 2020 to showcase the current knowledge both in terms of success and failures and take lessons that will assist us in understanding the disease to develop better treatments suited to kill SARS-CoV-2.

Elucidating micro RNAs role in different plant-pathogen interactions.

Bacteria, fungi, virus and nematode constitute the primary class of pathogens causing plant diseases. Plant-pathogen interactions are crucial for the identification of the host and pathogen and further establishments of a network of interaction that can cross regulate the gene expressions in both sides. After infection, the correct identification of pathogen through various molecular interactions elicit a defense response against the pathogen by alteration of gene expression by the host. Co-evolution of pathogen gives them the ability to counter the virulence response of the host and pathogen can also modulate the host gene expression pattern to make it more susceptible to the infection. Small non-coding RNA molecules (siRNAs and miRNAs) efficiently modulate gene expression at the transcriptional and post-transcriptional level and play a vital role in host defense. The pathogen can also use this double-sided sward in their defense by deregulating the plant immunity via transcriptional control of plant genes utilizing RNA interference or suppressing the host RNA interference response with the help of various RNA silencing suppressor proteins. This mini-review focused on the miRNAs involvement in host defense and how different families of these non-coding regulatory RNAs regulate the defense response against the pathogen.

Immunosuppressive FK506 treatment leads to more frequent EBV-associated lymphoproliferative disease in humanized mice.

Post-transplant lymphoproliferative disorder (PTLD) is a potentially fatal complication after organ transplantation frequently associated with the Epstein-Barr virus (EBV). Immunosuppressive treatment is thought to allow the expansion of EBV-infected B cells, which often express all eight oncogenic EBV latent proteins. Here, we assessed whether HLA-A2 transgenic humanized NSG mice treated with the immunosuppressant FK506 could be used to model EBV-PTLD. We found that FK506 treatment of EBV-infected mice led to an elevated viral burden, more frequent tumor formation and diminished EBV-induced T cell responses, indicative of reduced EBV-specific immune control. EBV latency III and lymphoproliferation-associated cellular transcripts were up-regulated in B cells from immunosuppressed animals, akin to the viral and host gene expression pattern found in EBV-PTLD. Utilizing an unbiased gene expression profiling approach, we identified genes differentially expressed in B cells of EBV-infected animals with and without FK506 treatment. Upon investigating the most promising candidates, we validated sCD30 as a marker of uncontrolled EBV proliferation in both humanized mice and in pediatric patients with EBV-PTLD. High levels of sCD30 have been previously associated with EBV-PTLD in patients. As such, we believe that humanized mice can indeed model aspects of EBV-PTLD development and may prove useful for the safety assessment of immunomodulatory therapies.

Molecular analysis of lymphoid tissue from rhesus macaque rhadinovirus-infected monkeys identifies alterations in host genes associated with oncogenesis.

Rhesus macaque (RM) rhadinovirus (RRV) is a simian gamma-2 herpesvirus closely related to human Kaposi’s sarcoma-associated herpesvirus (KSHV). RRV is associated with the development of diseases in simian immunodeficiency virus (SIV) co-infected RM that resemble KSHV-associated pathologies observed in HIV-infected humans, including B cell lymphoproliferative disorders (LPD) and lymphoma. Importantly, how de novo KSHV infection affects the expression of host genes in humans, and how these alterations in gene expression affect viral replication, latency, and disease is unknown. The utility of the RRV/RM infection model provides a novel approach to address these questions in vivo, and utilizing the RRV bacterial artificial chromosome (BAC) system, the effects of specific viral genes on host gene expression patterns can also be explored. To gain insight into the effects of RRV infection on global host gene expression patterns in vivo, and to simultaneously assess the contributions of the immune inhibitory viral CD200 (vCD200) molecule to host gene regulation, RNA-seq was performed on pre- and post-infection lymph node (LN) biopsy samples from RM infected with either BAC-derived WT (n = 4) or vCD200 mutant RRV (n = 4). A variety of genes were identified as being altered in LN tissue samples due to RRV infection, including cancer-associated genes activation-induced cytidine deaminase (AICDA), glypican-1 (GPC1), CX3C chemokine receptor 1 (CX3CR1), and Ras dexamethasone-induced 1 (RasD1). Further analyses also indicate that GPC1 may be associated with lymphomagenesis. Finally, comparison of infection groups identified the differential expression of host gene thioredoxin interacting protein (TXNIP), suggesting a possible mechanism by which vCD200 negatively affects RRV viral loads in vivo.

2885. A Host Transcriptional Signature for Accurate Diagnosis of Candidemia in the Hospital Setting

BackgroundCandidemia is one of the most common nosocomial bloodstream infections in the United States and causes significant morbidity and mortality in hospitalized patients. Improved rapid diagnostics capable of differentiating candidemia from other causes of febrile illness in the hospitalized patient are of paramount importance. Pathogen class-specific biomarker-based diagnostics such as those focusing on host gene expression patterns in circulating leukocytes may offer a promising alternative.MethodsRNA sequencing was performed on peripheral blood samples from 27 hospitalized patients with blood culture positive invasive candidiasis. Samples from healthy controls as well as at-risk subjects with acute febrile illness and similar clinical backgrounds but other infectious or noninfectious etiologies were used as comparator phenotypes (35 subjects with culture-proven bacterial infection, 49 with confirmed viral infection, and 17 with acute noninfectious illness). Bayesian techniques were utilized to develop infection-specific classifiers and leave one out cross-validation was used to estimate the predictive probability of each pathogen class.ResultsCandidemia triggers a unique, robust and conserved transcriptomic response in human hosts with 1,170 genes differentially upregulated compared with healthy controls. Based on this strength of signal, we developed a transcriptomic classifier that was capable of identifying candidemia, viral, or bacterial infection with a high degree of accuracy (auROC for Candida 0.93, Bacterial 0.98, Viral 0.99). The Candida component of this classifier (29-genes) was able to diagnose candidemia with a sensitivity of 88% and specificity of 100%.ConclusionThe host transcriptomic response during candidemia in hospitalized adults is highly conserved and unique from the genomic responses to acute viral and bacterial infection. This approach shows promise for the development of host response-based classifiers capable of differentiating multiple types of clinical illnesses at once in at-risk febrile patients.DisclosuresEphraim L. Tsalik, MD MHS PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.

Validation of a host response test to distinguish bacterial and viral respiratory infection.

BackgroundDistinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases.MethodsSubjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin.Findings151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85–0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02).InterpretationThe RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.