Integrating Signals from the T-Cell Receptor and the Interleukin-2 Receptor

T cells orchestrate the adaptive immune response, making them targets for immunotherapy. Although immunosuppressive therapies prevent disease progression, they also leave patients susceptible to opportunistic infections. To identify novel drug targets, we established a logical model describing T-cell receptor (TCR) signaling. However, to have a model that is able to predict new therapeutic approaches, the current drug targets must be included. Therefore, as a next step we generated the interleukin-2 receptor (IL-2R) signaling network and developed a tool to merge logical models. For IL-2R signaling, we show that STAT activation is independent of both Src- and PI3-kinases, while ERK activation depends upon both kinases and additionally requires novel PKCs. In addition, our merged model correctly predicted TCR-induced STAT activation. The combined network also allows information transfer from one receptor to add detail to another, thereby predicting that LAT mediates JNK activation in IL-2R signaling. In summary, the merged model not only enables us to unravel potential cross-talk, but it also suggests new experimental designs and provides a critical step towards designing strategies to reprogram T cells

A Smartphone-Based Intervention as an Adjunct to Standard-of-Care Treatment for Schizophrenia: Randomized Controlled Trial

BackgroundAntipsychotic medications have limited benefits in schizophrenia, and cognitive behavioral therapy may be beneficial as an adjunct. There may be potential for implementing mobile cognitive behavioral therapy–based treatment for schizophrenia in addition to standard antipsychotic medications. ObjectiveThis study aims to determine whether PEAR-004, a smartphone-based investigational digital therapeutic, improves the symptoms of an acute psychotic exacerbation of schizophrenia when it is added to standard treatments. MethodsThis was a 12-week, multicenter, randomized, sham-controlled, rater-blinded, parallel group proof‑of‑concept study of 112 participants with moderate acute psychotic exacerbation in schizophrenia. This study was conducted in 6 clinical trial research sites in the United States from December 2018 to September 2019. The primary outcome, change in Positive and Negative Syndrome Scale (PANSS) from baseline to week 12 or the last available visit, was analyzed using the mixed-effects regression model for repeated measures, applied to an intent-to-treat sample. ResultsThe total PANSS scores slightly decreased from baseline over the study period in both groups; the treatment difference at day 85 between PEAR-004 and sham was 2.7 points, in favor of the sham (2-sided P=.09). The secondary scales found no benefit, except for transient improvement in depressive symptoms with PEAR-004. Application engagement was good, and patient and clinical investigator satisfaction was high. No safety concerns were observed. There was some evidence of study site heterogeneity for the onboarding processes and directions on PEAR-004 product use at baseline and throughout the study. However, these differences did not affect the efficacy results. ConclusionsIn the largest-to-date randomized, sham-controlled study of a digital therapeutic in schizophrenia, PEAR-004 did not demonstrate an effect on the primary outcome—total PANSS scores—when compared with a nonspecific digital sham control. The secondary and exploratory results also did not demonstrate any notable benefits, except for possible temporary improvement in depressive symptoms. This study provided many useful scientific and operational insights that can be used in the further clinical development of PEAR-004 and other investigational digital therapeutics. Trial RegistrationClinicalTrials.gov NCT03751280; https://clinicaltrials.gov/ct2/show/NCT0375128

Data from: An immunosignature test distinguishes Trypanosoma cruzi, hepatitis B, hepatitis C and West Nile Virus seropositivity among asymptomatic blood donors

Background: The complexity of the eukaryotic parasite Trypanosoma (T.) cruzi manifests in its highly dynamic genome, multi-host life cycle, progressive morphologies and immune-evasion mechanisms. Accurate determination of infection or Chagas’ disease activity and prognosis continues to challenge researchers. We hypothesized that a diagnostic platform with higher ligand complexity than previously employed may hold value. Methodology: We applied the ImmunoSignature Technology (IST) for the detection of T. cruzi-specific antibodies among healthy blood donors. IST is based on capturing the information in an individual’s antibody repertoire by exposing their peripheral blood to a library of >100,000 position-addressable, chemically-diverse peptides. Principal findings: Initially, samples from two Chagas cohorts declared positive or negative by bank testing were studied. With the first cohort, library-peptides displaying differential binding signals between T. cruzi sero-states were used to train an algorithm. A classifier was fixed and tested against the training-independent second cohort to determine assay performance. Next, samples from a mixed cohort of donors declared positive for Chagas, hepatitis B, hepatitis C or West Nile virus were assayed on the same library. Signals were used to train a single algorithm that distinguished all four disease states. As a binary test, the accuracy of predicting T. cruzi seropositivity by IST was similar, perhaps modestly reduced, relative to conventional ELISAs. However, the results indicate that information beyond determination of seropositivity may have been captured. These include the identification of cohort subclasses, the simultaneous detection and discerning of other diseases, and the discovery of putative new antigens. Conclusions & significance: The central outcome of this study established IST as a reliable approach for specific determination of T. cruzi seropositivity versus disease-free individuals or those with other diseases. Its potential contribution for monitoring and controlling Chagas lies in IST’s delivery of higher resolution immune-state readouts than obtained with currently-used technologies. Despite the complexity of the ligand presentation and large quantitative readouts, performing an IST test is simple, scalable and reproducible

A Prescription Digital Therapeutic to Support Unsupervised Buprenorphine Initiation for Patients With Opioid Use Disorder: Protocol for a Proof-of-Concept Study

BackgroundHome-based (unsupervised) buprenorphine initiation is considered safe and effective, yet many patients report barriers to successful treatment initiation. Prescription digital therapeutics (PDTs) are software-based disease treatments regulated by the US Food and Drug Administration (FDA). The reSET-O PDT was authorized by the FDA in 2018 and delivers behavioral treatment for individuals receiving buprenorphine for opioid use disorder (OUD). A prototype PDT (PEAR-002b) designed for use with reSET-O was developed to assist in unsupervised buprenorphine initiation. ObjectiveThe primary objective of this pilot study is to evaluate the acceptability of PEAR-002b in individuals with OUD who use it to support buprenorphine initiation, their unsupervised buprenorphine initiation success rate, and their medication adherence. MethodsTen adults with OUD will be recruited for acceptability and feasibility testing. Outcomes will be assessed using week-1 visit attendance, participant interviews and satisfaction surveys, and urine drug screening (UDS). Three tools will be used in the study: PEAR-002b, reSET-O, and EmbracePlus. PEAR-002b includes a new set of features designed for use with reSET-O. The mechanism of action for the combined PEAR-002b and reSET-O treatment is a program of medication dosing support during week 1 of the initiation phase, cognitive behavioral therapy, and contingency management. During the medication initiation phase, participants are guided through a process to support proper medication use. PEAR-002b advises them when to take their buprenorphine based on provider inputs (eg, starting dose), self-reported substance use, and self-reported withdrawal symptoms. This study also administers the EmbracePlus device, a medical-grade smartwatch, to pilot methods for collecting physiologic data (eg, heart rate and skin conductance) and evaluate the device’s potential for use along with PDTs that are designed to improve OUD treatment initiation. Home buprenorphine initiation success will be summarized as the proportion of participants attending the post–buprenorphine initiation visit (week 1) and the proportion of participants who experience buprenorphine initiation–related adverse events (eg, precipitated withdrawal). Acceptability of PEAR-002b will be evaluated based on individual participants’ ratings of ease of use, satisfaction, perceived helpfulness, and likelihood of recommending PEAR-002b. Medication adherence will be evaluated by participant self-report data and confirmed by UDS. UDS data will be summarized as the mean of individual participants’ proportion of total urine samples testing positive for buprenorphine or norbuprenorphine over the 4-week study. ResultsThis project was funded in September 2019. As of September 2022, participant enrollment is ongoing. ConclusionsThis is the first study to our knowledge to develop a PDT that assists with unsupervised buprenorphine initiation with the intent to better support patients and prescribers during this early phase of treatment. This pilot study will assess the acceptability and utility of a digital therapeutic to assist individuals with OUD with unsupervised buprenorphine initiation. Trial RegistrationClinicalTrials.gov NCT05412966; https://clinicaltrials.gov/ct2/show/NCT05412966 International Registered Report Identifier (IRRID)PRR1-10.2196/4312

Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99)

BACKGROUND: Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx™ Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. METHODS: Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. RESULTS: The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. CONCLUSIONS: The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be “at risk” using the clinical factors

Derivation of a Three Biomarker Panel to Improve Diagnosis in Patients with Mild Traumatic Brain Injury

BackgroundNearly 5 million emergency department (ED) visits for head injury occur each year in the United States, of which <10% of patients show abnormal computed tomography (CT) findings. CT negative patients frequently suffer protracted somatic, behavioral, and neurocognitive dysfunction. Our goal was to evaluate biomarkers to identify mild TBI (mTBI) in patients with suspected head injury.MethodsAn observational ED study of head-injured and control patients was conducted at Johns Hopkins University (HeadSMART). Head CT was obtained (ACEP criteria) in patients with Glasgow Coma Scale scores of 13–15 and aged 18–80. Three candidate biomarker proteins, neurogranin (NRGN), neuron-specific enolase (NSE), and metallothionein 3 (MT3), were evaluated by immunoassay (samples <24 h from injury). American Congress of Rehabilitation Medicine (ACRM) criteria were used for diagnosis of mTBI patients for model building. Univariate analysis, logistic regression, and random forest (RF) algorithms were used for data analysis in R. Overall, 662 patients were studied. Statistical models were built using 328 healthy controls and 179 mTBI patients.ResultsMedian time from injury was 5.9 h (IQR, 4.0; range 0.8–24 h). mTBI patients had elevated NSE, but decreased MT3 versus controls (p < 0.01 for each). NRGN was also elevated but within 2–6 h after injury. In the derivation set, the best model to distinguish mTBI from healthy controls used three markers, age, and sex as covariates (C-statistic = 0.91, sensitivity 98%, specificity 72%). Panel test accuracy was validated with the 155 remaining ACRM+ mTBI patients. Applying the RF model to the ACRM+ mTBI validation set resulted in 78% correctly classified as mTBI (119/153). CT positive and CT negative validation subsets were 91% and 75% correctly classified. In samples taken <2 h from injury, 100% (10/10) samples classified correctly, indicating that hyperacute testing is possible with these biomarker assays. The model accuracy varied from 72–100% overall, and had greater accuracy with increasing severity, as shown by comparing CT+ with CT− (91% versus 75%), and Injury Severity Score ≥16 versus <16 (88% versus 72%, respectively). Objective blood tests, detecting NRGN, NSE, and MT3, can be used to identify mTBI, irrespective of neuroimaging findings

Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus: Combined results of the Inter99 and Botnia studies.

Purpose: To assess performance of a biomarker-based score that predicts the five-year risk of diabetes (Diabetes Risk Score, DRS) in an independent cohort that included 15-year follow-up. Method: DRS was developed on the Inter99 cohort, and validated on the Botnia cohort. Performance was benchmarked against other risk-assessment tools comparing calibration, time to event analysis, and net reclassification. Results: The area under the receiver-operating characteristic curve (AUC) was 0.84 for the Inter99 cohort and 0.78 for the Botnia cohort. In the Botnia cohort, DRS provided better discrimination than fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance, oral glucose tolerance test or risk scores derived from Framingham or San Antonio Study cohorts. Overall reclassification with DRS was significantly better than using FPG and glucose tolerance status (p < 0.0001). In time to event analysis, rates of conversion to diabetes in low, moderate, and high DRS groups were significantly different (p < 0.001). Conclusion: This study validates DRS performance in an independent population, and provides a more accurate assessment of T2DM risk than other methods