Interleukin 2 Activates Brain Microvascular Endothelial Cells Resulting in Destabilization of Adherens Junctions.

The pleiotropic cytokine interleukin 2 (IL2) disrupts the blood-brain barrier and alters brain microcirculation, underlying vascular leak syndrome that complicates cancer immunotherapy with IL2. The microvascular effects of IL2 also play a role in the development of multiple sclerosis and other chronic neurological disorders. The mechanism of IL2-induced disruption of brain microcirculation has not been determined previously. We found that both human and murine brain microvascular endothelial cells express constituents of the IL2 receptor complex. Then we established that signaling through this receptor complex leads to activation of the transcription factor, nuclear factor κB, resulting in expression of proinflammatory interleukin 6 and monocyte chemoattractant protein 1. We also discovered that IL2 induces disruption of adherens junctions, concomitant with cytoskeletal reorganization, ultimately leading to increased endothelial cell permeability. IL2-induced phosphorylation of vascular endothelial cadherin (VE-cadherin), a constituent of adherens junctions, leads to dissociation of its stabilizing adaptor partners, p120-catenin and ß-catenin. Increased phosphorylation of VE-cadherin was also accompanied by a reduction of Src homology 2 domain-containing protein-tyrosine phosphatase 2, known to maintain vascular barrier function. These results unravel the mechanism of deleterious effects induced by IL2 on brain microvascular endothelial cells and may inform the development of new measures to improve IL2 cancer immunotherapy, as well as treatments for autoimmune diseases affecting the central nervous system.

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methods, interrogating total RNA-sequencing data sets from peripheral whole blood of treatment-naïve patients with RRMS and NMO and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between patients with NMO and RRMS, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors identified biological associations and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Mitoxantrone and vorinostat-two identified small molecules with previously reported use in patients with NMO and experimental autoimmune encephalomyelitis-reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA-sequencing data further enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding further enhances detection of disease-specific signatures and possible therapeutic targets.

Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections. METHODS: Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. RESULTS: The predictive models achieved a mean R2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality. CONCLUSION: The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks.

Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. METHODS: We combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. RESULTS: Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. CONCLUSION: Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia.

The COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future. Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R2) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.

Longitudinal assessment and stability of long non-coding RNA gene expression profiles measured in human peripheral whole blood collected into PAXgene blood RNA tubes.

OBJECTIVE: Long non-coding RNAs (lncRNAs) are emerging as novel biomarkers for a variety of chronic conditions including autoimmune disease. PAXgene Blood RNA tubes are routinely used in clinical research and molecular diagnostic development to capture RNA profiles in peripheral whole blood. While the stability of mRNA expression profiles captured using PAXgene tubes has been documented previously, no previous work has determined the stability of lncRNA expression profiles observed in PAXgene tubes stored at - 80 °C. Here we sought to determine the effects on lncRNA expression profiles following - 80 °C storage of total RNA templates, cDNA synthesized using fresh or frozen total RNA template, and the impact of freeze-thaw cycles on both total RNA and cDNA obtained from PAXgene tubes. RESULTS: We find that storage of whole blood in PAXgene tubes, total RNA and cDNA for up to 1 year at - 80 °C or up to ten total RNA or cDNA freeze-thaw cycles do not significantly alter lncRNA expression profiles compared to baseline. As monthly expression profiles were determined, some month to month lncRNA expression variability was observed. However, all monthly observations fell within the 95% confidence interval calculated at baseline.

Illuminating an Invisible Epidemic: A Systemic Review of the Clinical and Economic Benefits of Early Diagnosis and Treatment in Inflammatory Disease and Related Syndromes.

Healthcare expenditures in the United States are growing at an alarming level with the Centers for Medicare and Medicaid Services (CMS) projecting that they will reach $5.7 trillion per year by 2026. Inflammatory diseases and related syndromes are growing in prevalence among Western societies. This growing population that affects close to 60 million people in the U.S. places a significant burden on the healthcare system. Characterized by relatively slow development, these diseases and syndromes prove challenging to diagnose, leading to delayed treatment against the backdrop of inevitable disability progression. Patients require healthcare attention but are initially hidden from clinician's view by the seemingly generalized, non-specific symptoms. It is imperative to identify and manage these underlying conditions to slow disease progression and reduce the likelihood that costly comorbidities will develop. Enhanced diagnostic criteria coupled with additional technological innovation to identify inflammatory conditions earlier is necessary and in the best interest of all healthcare stakeholders. The current total cost to the U.S. healthcare system is at least $90B dollars annually. Through unique analysis of financial cost drivers, this review identifies opportunities to improve clinical outcomes and help control these disease-related costs by 20% or more.

Protection from Endotoxin Shock by Selective Targeting of Proinflammatory Signaling to the Nucleus Mediated by Importin Alpha 5.

Endotoxin shock is induced by LPS, one of the most potent virulence factors of the Gram-negative bacteria that cause sepsis. It remains unknown if either proinflammatory stress-responsive transcription factors (SRTFs), ferried to nucleus by importin α5, or lipid-regulating sterol regulatory element binding proteins (SREBPs), transported to the nucleus by importin ß1, mediate endotoxin shock. A novel cell-penetrating peptide targeting importin α5 while sparing importin ß1 protected 80% of animals from death in response to a high dose of LPS. This peptide suppresses inflammatory mediators, liver glycogen depletion, endothelial injury, neutrophil trafficking, and apoptosis caused by LPS. In d-galactosamine-pretreated mice challenged by 700-times lower dose of LPS, rapid death through massive apoptosis and hemorrhagic necrosis of the liver was also averted by the importin α5-selective peptide. Thus, using a new tool for selective suppression of nuclear transport, we demonstrate that SRTFs, rather than SREBPs, mediate endotoxin shock.

Survival, bacterial clearance and thrombocytopenia are improved in polymicrobial sepsis by targeting nuclear transport shuttles.

The rising tide of sepsis, a leading cause of death in the US and globally, is not adequately controlled by current antimicrobial therapies and supportive measures, thereby requiring new adjunctive treatments. Severe microvascular injury and multiple organ failure in sepsis are attributed to a "genomic storm" resulting from changes in microbial and host genomes encoding virulence factors and endogenous inflammatory mediators, respectively. This storm is mediated by stress-responsive transcription factors that are ferried to the nucleus by nuclear transport shuttles importins/karyopherins. We studied the impact of simultaneously targeting two of these shuttles, importin alpha 5 (Imp α5) and importin beta 1 (Imp ß1), with a cell-penetrating Nuclear Transport Modifier (NTM) in a mouse model of polymicrobial sepsis. NTM reduced nuclear import of stress-responsive transcription factors nuclear factor kappa B, signal transducer and activator of transcription 1 alpha, and activator protein 1 in liver, which was also protected from sepsis-associated metabolic changes. Strikingly, NTM without antimicrobial therapy improved bacterial clearance in blood, spleen, and lungs, wherein a 700-fold reduction in bacterial burden was achieved while production of proinflammatory cytokines and chemokines in blood plasma was suppressed. Furthermore, NTM significantly improved thrombocytopenia, a prominent sign of microvascular injury in sepsis, inhibited neutrophil infiltration in the liver, decreased L-selectin, and normalized plasma levels of E-selectin and P-selectin, indicating reduced microvascular injury. Importantly, NTM combined with antimicrobial therapy extended the median time to death from 42 to 83 hours and increased survival from 30% to 55% (p = 0.022) as compared to antimicrobial therapy alone. This study documents the fundamental role of nuclear signaling mediated by Imp α5 and Imp ß1 in the mechanism of polymicrobial sepsis and highlights the potential for targeting nuclear transport as an adjunctive therapy in sepsis management.

The "genomic storm" induced by bacterial endotoxin is calmed by a nuclear transport modifier that attenuates localized and systemic inflammation.

Lipopolysaccharide (LPS) is a potent microbial virulence factor that can trigger production of proinflammatory mediators involved in the pathogenesis of localized and systemic inflammation. Importantly, the role of nuclear transport of stress responsive transcription factors in this LPS-generated "genomic storm" remains largely undefined. We developed a new nuclear transport modifier (NTM) peptide, cell-penetrating cSN50.1, which targets nuclear transport shuttles importin α5 and importin ß1, to analyze its effect in LPS-induced localized (acute lung injury) and systemic (lethal endotoxic shock) murine inflammation models. We analyzed a human genome database to match 46 genes that encode cytokines, chemokines and their receptors with transcription factors whose nuclear transport is known to be modulated by NTM. We then tested the effect of cSN50.1 peptide on proinflammatory gene expression in murine bone marrow-derived macrophages stimulated with LPS. This NTM suppressed a proinflammatory transcriptome of 37 out of 84 genes analyzed, without altering expression of housekeeping genes or being cytotoxic. Consistent with gene expression analysis in primary macrophages, plasma levels of 23 out of 26 LPS-induced proinflammatory cytokines, chemokines, and growth factors were significantly attenuated in a murine model of LPS-induced systemic inflammation (lethal endotoxic shock) while the anti-inflammatory cytokine, interleukin 10, was enhanced. This anti-inflammatory reprogramming of the endotoxin-induced genomic response was accompanied by complete protection against lethal endotoxic shock with prophylactic NTM treatment, and 75% protection when NTM was first administered after LPS exposure. In a murine model of localized lung inflammation caused by direct airway exposure to LPS, expression of cytokines and chemokines in the bronchoalveolar space was suppressed with a concomitant reduction of neutrophil trafficking. Thus, calming the LPS-triggered "genomic storm" by modulating nuclear transport with cSN50.1 peptide attenuates the systemic inflammatory response associated with lethal shock as well as localized lung inflammation.

Intracellular delivery of a cell-penetrating SOCS1 that targets IFN-gamma signaling.

Suppressor of cytokine signaling-1 (SOCS1) is an intracellular inhibitor of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway that couples interferon-gamma (IFN-gamma) signaling to the nucleus. Because several inflammatory diseases are associated with uncontrolled IFN-gamma signaling, we engineered a recombinant cell-penetrating SOCS1 (CP-SOCS1) to target this pathway. Here, we show that CP-SOCS1, analogous to endogenous SOCS1, interacted with components of the IFN-gamma signaling complex and functionally attenuated the phosphorylation of STAT1, which resulted in the subsequent inhibition of the production of proinflammatory chemokines and cytokines. Thus, controlled, intracellular delivery of recombinant CP-SOCS1 boosted the anti-inflammatory potential of the cell by restoring the homeostatic balance between pro- and anti-inflammatory signaling. This approach to controlling signal transduction has potential use for therapeutic targeting of signaling pathways associated with inflammatory diseases.