Patient Perceptions of Rheumatoid Arthritis Blood Work: A Cross-Sectional Survey in the ArthritisPower Registry.

OBJECTIVE: To examine how patients with rheumatoid arthritis (RA) perceive RA-related laboratory testing and the potential utility of a blood test to predict treatment response to a new RA medication. METHODS: ArthritisPower members with RA were invited to participate in a cross-sectional survey on reasons for laboratory testing plus a choice-based conjoint analysis exercise to determine how patients value different attributes of a biomarker-based test to predict treatment response. RESULTS: Most patients perceived that their doctors ordered laboratory tests to check for active inflammation (85.9%) or assess medication side effects (81.2%). The most commonly ordered blood tests used to monitor RA were complete blood counts, liver function tests, and those measuring C-reactive protein (CRP) and erythrocyte sedimentation rate. Patients felt CRP was most helpful in understanding their disease activity. Most worried their current RA medication would eventually stop working (91.4%) and they would waste time trying a new RA medication that may not work for them (81.7%). For patients who would require a future change in RA treatment, a majority (89.2%) reported that they would be very/extremely interested in a blood test that could help predict whether such new medication would be effective. Highly accurate test results (improving the chance RA medication will work from 50% to 85-95%) were more important to patients than low out-of-pocket cost (<$20) or minimal wait time (<7 days). CONCLUSIONS: Patients consider RA-related blood work important for monitoring of inflammation and medication side effects. They worry about treatment effectiveness and would undergo testing to accurately predict treatment response.

Commentary on Cohen et al.: Role of Clinical Factors in Precision Medicine Test to Predict Nonresponse to TNFi Therapies in Rheumatoid Arthritis.

A 2021 study described the development and validation of a blood-based precision medicine test called the molecular signature response classifier (MSRC) that uses 23 features to identify rheumatoid arthritis (RA) patients who are likely nonresponders to tumor necrosis factor-α inhibitor (TNFi) therapy. Both the gene expression features and clinical components (sex, body mass index, patient global assessment, and anti-cyclic citrullinated protein) included in the MSRC were statistically significant contributors to MSRC results. In response to continued inquiries on this topic, we write this letter to provide additional insights into the contribution of clinical components to the MSRC on the Network-004 validation cohort.

The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis.

Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.

Patient outcomes improve when a molecular signature test guides treatment decision-making in rheumatoid arthritis.

BACKGROUND: The molecular signature response classifier (MSRC) predicts tumor necrosis factor-ɑ inhibitor (TNFi) non-response in rheumatoid arthritis. This study evaluates decision-making, validity, and utility of MSRC testing. METHODS: This comparative cohort study compared an MSRC-tested arm (N = 627) from the Study to Accelerate Information of Molecular Signatures (AIMS) with an external control arm (N = 2721) from US electronic health records. Propensity score matching was applied to balance baseline characteristics. Patients initiated a biologic/targeted synthetic disease-modifying antirheumatic drug, or continued TNFi therapy. Odds ratios (ORs) for six-month response were calculated based on clinical disease activity index (CDAI) scores for low disease activity/remission (CDAI-LDA/REM), remission (CDAI-REM), and minimally important differences (CDAI-MID) . RESULTS: In MSRC-tested patients, 59% had a non-response signature and 70% received MSRC-aligned therapy . In TNFi-treated patients, the MSRC had an 88% PPV and 54% sensitivity. MSRC-guided patients were significantly (p < 0.0001) more likely to respond to b/tsDMARDs than those treated according to standard care (CDAI-LDA/REM: 36.0% vs 21.9%, OR 2.01[1.55-2.60]; CDAI-REM: 10.4% vs 3.6%, OR 3.14 [1.94-5.08]; CDAI-MID: 49.5% vs 32.8%, OR 2.01[1.58-2.55]). CONCLUSION: MSRC clinical validity supports high clinical utility: guided treatment selection resulted in significantly superior outcomes relative to standard care; nearly three times more patients reached CDAI remission.

Clinicians can offer rheumatoid arthritis patients many types of therapies but the response rate for each of these drugs is low. For example, within the first year of treatment, just about one-half of patients respond to the first-line drug, csDMARD. Only one-third of methotrexate-unresponsive patients will respond to the most common second-line agent, a tumor necrosis factor-α inhibitor. These low response rates present a critical challenge to treating patients. Clinicians try different cs- and b/tsDMARD and fail to quickly identify the most effective options. Then, disease will progress, irreversibly destroying patient joints, diminishing patient health-related quality of life, and increasing risks of cardiovascular disease, cancer, and death. To help clinicians quickly identify the best drugs for patients in a treat-to-target approach, a precision-medicine test was developed to identify patients unlikely to respond to tumor necrosis factor-α inhibitors. This molecular signature response classifier considers both molecular features (patient RNA-expression levels) and clinical features (e.g. body mass index, sex) to predict patient response. To evaluate the effectiveness of this test, the outcomes of patients treated with classifier-selected drugs (in a large, tested cohort) were compared with outcomes of patients treated with conventionally selected therapies (in an external cohort of electronic-health-record data). Patients treated with classifier-selected therapies were approximately three times as likely to achieve remission than were patients treated with conventionally selected drugs. These results suggest that this molecular signature response classifier is a valuable tool for more quickly identifying optimal therapies to treat rheumatoid arthritis.

Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-ɑ inhibitor molecular signature response classifier: analysis from the study to accelerate information of molecular signatures in rheumatoid arthritis.

BACKGROUND: A blood-based molecular signature response classifier (MSRC) predicts non-response to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA). RESEARCH DESIGN AND METHODS: This is an interim analysis of data collected in the Study to Accelerate Information of Molecular Signatures (AIMS) in RA from patients who received the MSRC test between September 2020 and November 2021. Absolute changes in clinical disease activity index (CDAI) scores from baseline were evaluated at 12 weeks (n = 470) and 24 weeks (n = 274). RESULTS: Predicted TNFi non-responders who received a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) with an alternative mechanism of action (altMOA) experienced up to 1.8-fold greater improvements in CDAI scores than those treated with a TNFi (12 weeks: 12.2 vs 8.0; p-value = 0.083; 24 weeks: 14.2 vs 7.8 p-value = 0.009). In patients with a molecular signature of non-response to TNFi in high disease activity at baseline, this corresponded to 43.2% relative improvement in achieving a lower CDAI disease activity level when likely TNFi non-responders were treated with a non-TNFi therapy (38.9% vs 55.7%). Commensurate improvements in efficiency of spend are expected when TNFi are avoided in favor of altMOA. CONCLUSIONS: RA treatment selection informed by MSRC test results improves clinical outcomes in real-world care and offers improvements in efficiency of healthcare spending.

Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis.

This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients, and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N = 24 and validation N = 22). Response was defined as endoscopic and histologic healing at 4-6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.83 and inadequate response was predicted with a 100% positive predictive value and 64% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients' baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.

RETRACTED: Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors after consulting with the Editors. During a follow-up study, the authors regretfully discovered that the microarray probe-to-gene mapping was incorrect. Although the methodology and primary findings remain the same, the identity of the biomarker genes are incorrect as a result of this honest mistake. The extent of the changes to correct this information necessitated the publication of a corrected version of this article: https://doi.org/10.1016/j.trsl.2022.03.006.

Clinical utility of therapy selection informed by predicted nonresponse to tumor necrosis factor-ɑ inhibitors: an analysis from the Study to Accelerate Information of Molecular Signatures (AIMS) in rheumatoid arthritis.

BACKGROUND: The molecular signature response classifier (MSRC) is a blood-based precision medicine test that predicts nonresponders to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA) so that patients with a molecular signature of non-response to TNFi can be directed to a treatment with an alternative mechanism of action. RESEARCH DESIGN AND METHODS: This study evaluated decision choice and treatment outcomes resulting from MSRC-informed treatment selection within a real-world cohort. RESULTS: Therapy selection by providers was informed by MSRC results for 73.5% (277/377) of patients. When MSRC results were not incorporated into decision-making, 62.0% (62/100) of providers reported deviating from test recommendations due to insurance-related restrictions. The 24-week ACR50 responses in patients prescribed a therapy in alignment with MSRC results were 39.6%. Patients with a molecular signature of non-response had significantly improved responses to non-TNFi therapies compared with TNFi therapies (ACR50 34.8% vs 10.3%, p-value = 0.05). This indicates that predicted non-responders to TNFi therapies are not nonresponders to other classes of RA targeted therapy. Significant changes were also observed for CDAI, ACR20, ACR70, and for responses at 12 weeks. CONCLUSIONS: Adoption of the MSRC into patient care could fundamentally shift treatment paradigms in RA, resulting in substantial improvements in real-world treatment outcomes.

Analytical and clinical validation of an RNA sequencing-based assay for quantitative, accurate evaluation of a molecular signature response classifier in rheumatoid arthritis.

OBJECTIVES: This study reports analytical and clinical validation of a molecular signature response classifier (MSRC) that identifies rheumatoid arthritis (RA) patients who are non-responders to tumor necrosis factor-ɑ inhibitors (TNFi). METHODS: The MSRC integrates patient-specific data from 19 gene expression features, anti-cyclic citrullinated protein serostatus, sex, body mass index, and patient global assessment into a single score. RESULTS: The MSRC results stratified samples (N = 174) according to non-response prediction with a positive predictive value of 87.7% (95% CI: 78-94%), sensitivity of 60.2% (95% CI: 50-69%), and specificity of 77.3% (95% CI: 65-87%). The 25-point scale was subdivided into three thresholds: signal not detected (<10.6), high (≥10.6), and very high (≥18.5). The MSRC relies on sequencing of RNA extracted from blood; this assay displays high gene expression concordance between inter- and intra-assay sample (R2 > 0.977) and minimal variation in cumulative gene assignment diversity, read mapping location, or gene-body coverage. The MSRC accuracy was 95.8% (46/48) for threshold concordance (no signal, high, very high). Intra- and inter-assay precision studies demonstrated high repeatability (92.6%, 25/27) and reproducibility (100%, 35/35). CONCLUSION: The MSRC is a robust assay that accurately and reproducibly detects an RA patient's molecular signature of non-response to TNFi therapies.

Guided therapy selection in rheumatoid arthritis using a molecular signature response classifier: an assessment of budget impact and clinical utility.

BACKGROUND: Patients with moderate to severe rheumatoid arthritis (RA) can be treated with a range of targeted therapies following inadequate response to conventional synthetic disease-modifying antirheumatic drugs such as methotrexate. Whereas clinical practice guidelines provide no formal recommendations for initial targeted therapies, the tumor necrosis factor alpha inhibitor (TNFi) class is the prevalent first-line selection based on clinician experience, its safety profile, and/or formulary requirements, while also being the costliest. Most patients do not achieve adequate clinical response with a first-line TNFi, however. A molecular signature response classifier (MSRC) test that assesses RA-related biomarkers can identify patients who are unlikely to achieve adequate response to TNFi-class therapies. OBJECTIVE: To model cost-effectiveness of MSRC-guided, first-line targeted therapy selection compared with current standard care. METHODS: This budget impact analysis used data sourced from August to September 2020. The prevalence of each first-line targeted therapy was obtained using market intelligence from Datamonitor/Informa PLC Rheumatology Dashboard Forecast 2020, and the average first-year cost of treatment for each class was calculated using wholesale acquisition costs from IBM Micromedex RED BOOK Online. Average effectiveness for each class was based on manufacturer-reported ACR50 response rates (American College of Rheumatology adequate response criteria of 50% improvement at 6 months after therapy initiation). The impact of MSRC testing on first therapy selection was predicted based on a third party-generated decision-impact study that analyzed potential alterations in rheumatologist prescribing patterns after receiving MSRC test reports. Sensitivity analysis evaluated potential impacts of variation in first-year medication cost, adherence to MSRC report, and test price on the first-year cost of treatment. Cost for response (first-year therapy cost therapy divided by probability of achieving ACR50) was compared between standard care and MSRC-guided care. RESULTS: The estimated cost for first-year, standard-care treatment was $65,117, with 80% of patients initiating treatment with a TNFi. Cost for achieving ACR50 response was $177,046. After applying MSRC-guided patient stratification and therapy selection, the first-year cost was $56,543, net of test price, with 49.0% of patients initiating with a TNFi. First-year MSRC-guided care cost, including test price, was estimated at $117,103, a 33.9% improvement over standard care. Sensitivity analysis showed a net cost improvement for guided care vs standard care across all scenarios. Patients predicted to be inadequate TNFi responders, when modeled with lower-priced alternatives, were predicted to show increased ACR50 response rates. Those with MSRC test results indicating a first-line TNFi were predicted to show an ACR50 response rate superior to that for any other class. In this model, if implemented clinically, MSRC-guided care might save the US health care system more than $850 million annually and improve ACR50 by up to 31.3%. CONCLUSIONS: Precision medicine using MSRC-guided patient stratification and therapy selection may both decrease cost and improve efficacy of targeted RA therapies. DISCLOSURES: This work was funded in full by Scipher Medicine Corporation, which participated in data analysis and interpretation and drafting, reviewing, and approving the publication. All authors contributed to data analysis and interpretation and publication preparation, maintaining control over the final content. Arnell, Withers, and Connolly-Strong are employees of and have stock ownership in Scipher Medicine Corporation. Bergman has received consulting fees from AbbVie, Gilead, GlaxoSmithKline, Novartis, Pfizer, Regeneron, Sanofi, and Scipher Medicine and owns stock or stock options in Johnson & Johnson. Kenney, Logan, and Lim-Harashima are consultants for Scipher Medicine Corporation. Basu has nothing to disclose.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study.

INTRODUCTION: Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility. METHODS: The protein-protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI. RESULTS: The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0-8.3, p value 0.0001). Odds ratios (3.4-8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3-26.6 by ACR, DAS28-CRP, and CDAI metrics. CONCLUSION: The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists' confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4­8.8 for targeted therapy-naïve patients and 3.3­26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis­a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study.

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

Perceived clinical utility of a test for predicting inadequate response to TNF inhibitor therapies in rheumatoid arthritis: results from a decision impact study.

Tumor necrosis factor inhibitor (TNFi) therapies are often the first biologic therapy used to treat rheumatoid arthritis (RA) patients. However, a substantial fraction of patients do not respond adequately to TNFi therapies. A test with the ability to predict response would inform therapeutic decision-making and improve clinical and financial outcomes. A 32-question decision-impact survey was conducted with 248 rheumatologists to gauge the perceived clinical utility of a novel test that predicts inadequate response to TNFi therapies in RA patients. Participants were informed about the predictive characteristics of the test and asked to indicate prescribing decisions based on four result scenarios. Overall, rheumatologists had a favorable view of the test: 80.2% agreed that it would improve medical decision-making, 92.3% said it would increase their confidence when making prescribing decisions, and 81.5% said it would be useful when considering TNFi therapies. Rheumatologists would be more likely to prescribe a TNFi therapy when the test reported that no signal of non-response was detected (79.8%) and less likely to prescribe a TNFi therapy when a signal of non-response was detected (11.3%-25.4%). Rheumatologists (84.7%) agreed that payers should provide coverage for such a test. This study shows that rheumatologists support the clinical need for a test to predict inadequate response to TNFi therapies. Test results were perceived to lead to changes in prescribing behaviors as results instill confidence in the ordering rheumatologist.

Clinical Utility and Cost Savings in Predicting Inadequate Response to Anti-TNF Therapies in Rheumatoid Arthritis.

INTRODUCTION: The PrismRA® test identifies rheumatoid arthritis (RA) patients who are unlikely to respond to anti-tumor necrosis factor (anti-TNF) therapies. This study evaluated the clinical and financial outcomes of incorporating PrismRA into routine clinical care of RA patients. METHODS: A decision-analytic model was created to evaluate clinical and economic outcomes in the 12-month period following first biologic treatment. Two treatment strategies were compared: (1) observed clinical decision-making based on a 175-patient cohort receiving an anti-TNF therapy as their first biologic after failure of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and (2) modeled clinical decision-making of the same population using PrismRA results to inform first-line biologic treatment choice. Modeled costs include biologic drug pharmacy, non-biologic pharmacy, and total medical costs. The odds of inadequate response to anti-TNF therapies and various components of patient care were calculated based on PrismRA results. RESULTS: Identifying predicted inadequate responders to anti-TNF therapies resulted in a modeled 38% increase in ACR50 response to first-line biologic therapies. The fraction of patients who achieved an ACR50 response to any therapy (TNFi and others) within the 12-month period was 33% higher in the PrismRA-stratified population than in the unstratified population (59 vs. 44%, respectively). When therapy prescriptions were modeled according to PrismRA results, cost savings were modeled for all financial variables: overall costs (4% decreased total, 19% decreased on ineffective treatments), total biologic drug pharmacy (4% total, 23% ineffective), non-biologic pharmacy (2% total, 19% ineffective), and medical costs (6% total, 19% ineffective). Female sex was the clinical metric that showed the greatest association with inadequate response to anti-TNF therapies (odds ratio 2.42, 95% confidence interval 1.20, 4.88). CONCLUSIONS: If PrismRA is implemented into routine clinical care as modeled, predicting which RA patients will have an inadequate response to anti-TNF therapies could save > $7 million in overall ineffective healthcare costs per 1000 patients tested and increase targeted DMARD response rates in RA.

Idiosyncrasies of Viral Noncoding RNAs Provide Insights into Host Cell Biology.

Like their host cells, many viruses express noncoding RNAs (ncRNAs). Despite the technical challenge of ascribing function to ncRNAs, diverse biological roles for virally expressed ncRNAs have been described, including regulation of viral replication, modulation of host gene expression, host immune evasion, cellular survival, and cellular transformation. Insights into conserved interactions between viral ncRNAs and host cell machinery frequently lead to novel findings concerning host cell biology. In this review, we discuss the functions and biogenesis of ncRNAs produced by animal viruses. Specifically, we describe noncanonical pathways of microRNA (miRNA) biogenesis and novel mechanisms used by viruses to manipulate miRNA and messenger RNA stability. We also highlight recent advances in understanding the function of viral long ncRNAs and circular RNAs.

Two herpesviral noncoding PAN RNAs are functionally homologous but do not associate with common chromatin loci.

During lytic replication of Kaposi's sarcoma-associated herpesvirus (KSHV), a nuclear viral long noncoding RNA known as PAN RNA becomes the most abundant polyadenylated transcript in the cell. Knockout or knockdown of KSHV PAN RNA results in loss of late lytic viral gene expression and, consequently, reduction of progeny virion release from the cell. Here, we demonstrate that knockdown of PAN RNA from the related Rhesus macaque rhadinovirus (RRV) phenocopies that of KSHV PAN RNA. These two PAN RNA homologs, although lacking significant nucleotide sequence conservation, can functionally substitute for each other to rescue phenotypes associated with the absence of PAN RNA expression. Because PAN RNA is exclusively nuclear, previous studies suggested that it directly interacts with host and viral chromatin to modulate gene expression. We studied KSHV and RRV PAN RNA homologs using capture hybridization analysis of RNA targets (CHART) and observed their association with host chromatin, but the loci differ between PAN RNA homologs. Accordingly, we find that KSHV PAN RNA is undetectable in chromatin following cell fractionation. Thus, modulation of gene expression at specific chromatin loci appears not to be the primary, nor the pertinent function of this viral long noncoding RNA. PAN RNA represents a cautionary tale for the investigation of RNA association with chromatin whereby cross-linking of DNA spatially adjacent to an abundant nuclear RNA gives the appearance of specific interactions. Similarly, PAN RNA expression does not affect viral transcription factor complex expression or activity, which is required for generation of the late lytic viral mRNAs. Rather, we provide evidence for an alternative model of PAN RNA function whereby knockdown of KSHV or RRV PAN RNA results in compromised nuclear mRNA export thereby reducing the cytoplasmic levels of viral mRNAs available for production of late lytic viral proteins.

Kaposi's Sarcoma-Associated Herpesvirus mRNA Accumulation in Nuclear Foci Is Influenced by Viral DNA Replication and Viral Noncoding Polyadenylated Nuclear RNA.

Kaposi's sarcoma-associated herpesvirus (KSHV), like other herpesviruses, replicates within the nuclei of its human cell host and hijacks host machinery for expression of its genes. The activities that culminate in viral DNA synthesis and assembly of viral proteins into capsids physically concentrate in nuclear areas termed viral replication compartments. We sought to better understand the spatiotemporal regulation of viral RNAs during the KSHV lytic phase by examining and quantifying the subcellular localization of select viral transcripts. We found that viral mRNAs, as expected, localized to the cytoplasm throughout the lytic phase. However, dependent on active viral DNA replication, viral transcripts also accumulated in the nucleus, often in foci in and around replication compartments, independent of the host shutoff effect. Our data point to involvement of the viral long noncoding polyadenylated nuclear (PAN) RNA in the localization of an early, intronless viral mRNA encoding ORF59-58 to nuclear foci that are associated with replication compartments.IMPORTANCE Late in the lytic phase, mRNAs from Kaposi's sarcoma-associated herpesvirus accumulate in the host cell nucleus near viral replication compartments, centers of viral DNA synthesis and virion production. This work contributes spatiotemporal data on herpesviral mRNAs within the lytic host cell and suggests a mechanism for viral RNA accumulation. Our findings indicate that the mechanism is independent of the host shutoff effect and splicing but dependent on active viral DNA synthesis and in part on the viral noncoding RNA, PAN RNA. PAN RNA is essential for the viral life cycle, and its contribution to the nuclear accumulation of viral messages may facilitate propagation of the virus.

Settling the m6A debate: methylation of mature mRNA is not dynamic but accelerates turnover.

Post-transcriptional modification of RNA nucleosides has been implicated as a pivotal regulator of mRNA biology. In this issue of Genes & Development, Ke and colleagues (pp. 990-1006) provide insights into the temporal and spatial distribution of N6-methyladenosine (m6A) in RNA transcripts by analyzing different subcellular fractions. Using a recently developed biochemical approach for detecting m6A, the researchers show that m6A methylations are enriched in exons and are added to transcripts prior to splicing. Although m6A addition is widely thought to be readily reversible, they demonstrate in HeLa cells that once RNA is released from chromatin, the modifications are surprisingly static. This study integrates data from previous publications to clarify conflicting conclusions regarding the role of m6A in mRNA biogenesis and function. Ke and colleagues found that m6A methylation levels negatively correlate with transcript half-life but are not required for most pre-mRNA splicing events.