Clinical prediction model: Multisystem inflammatory syndrome in children versus Kawasaki disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD). OBJECTIVE: The study objective was to develop a prediction model to assist with this diagnostic dilemma. METHODS: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients. RESULTS: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99). CONCLUSIONS: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records.

OBJECTIVE: Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. MATERIALS AND METHODS: We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. RESULTS: The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001). DISCUSSION: We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. CONCLUSION: Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records.

Objective: Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. Materials and Methods: We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. Results: The Peds-Phecodes aggregate 15,533 ICD-9-CM codes and 82,949 ICD-10-CM codes into 2,051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001). Discussion: We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. Conclusion: Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis.

BACKGROUND: The objective of this study was to develop an algorithm that accurately identifies juvenile idiopathic arthritis (JIA) patients in the electronic health record (EHR). METHODS: Algorithms were developed in a de-identified EHR by searching for a priori JIA ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes and JIA-related keywords. Exclusion criteria were selected to remove other autoimmune diseases. A training set of 200 patients was randomly selected from patients containing ≥1 occurrence of a JIA ICD-9 or ICD-10-CM code. Case status was determined by a rheumatology clinic note documenting a JIA diagnosis before age 20. For each algorithm, positive predictive value (PPV), sensitivity, and F-measure were determined using the training set. RESULTS: We developed 103 algorithms using combinations of ICD codes, keywords, and exclusion criteria. The algorithm requiring 4 or more counts of JIA ICD-9 or ICD-10-CM codes, keywords "enthesitis" and "uveitis", and exclusion of ICD-9 or ICD-10-CM codes for systemic lupus erythematosus, dermatomyositis, polymyositis, and dermatopolymyositis had the highest PPV of 97% in the training set with an F-measure of 87%. There were 1,131 JIA cases returned by this algorithm. We validated the highest performing algorithm in a separate cohort from the training set with a PPV of 92% and an F-measure of 75%. CONCLUSION: We developed and validated JIA EHR algorithms with ICD-9 and ICD-10-CM codes to accurately identify a JIA cohort. Three algorithms achieved PPVs of 97%, each with different algorithm criteria, allowing for users to select an algorithm to best fit their research needs.

Increased Development of Th1, Th17, and Th1.17 Cells Under T1 Polarizing Conditions in Juvenile Idiopathic Arthritis.

In juvenile idiopathic arthritis (JIA) inflammatory T cells and their produced cytokines are drug targets and play a role in disease pathogenesis. Despite their clinical importance, the sources and types of inflammatory T cells involved remain unclear. T cells respond to polarizing factors to initiate types of immunity to fight infections, which include immunity types 1 (T1), 2 (T2), and 3 (T17). Polarizing factors drive CD4+ T cells towards T helper (Th) cell subtypes and CD8+ T cells towards cytotoxic T cell (Tc) subtypes. T1 and T17 polarization are associated with autoimmunity and production of the cytokines IFNγ and IL-17 respectively. We show that JIA and child healthy control (HC) peripheral blood mononuclear cells are remarkably similar, with the same frequencies of CD4+ and CD8+ naïve and memory T cell subsets, T cell proliferation, and CD4+ and CD8+ T cell subsets upon T1, T2, and T17 polarization. Yet, under T1 polarizing conditions JIA cells produced increased IFNγ and inappropriately produced IL-17. Under T17 polarizing conditions JIA T cells produced increased IL-17. Gene expression of IFNγ, IL-17, Tbet, and RORγT by quantitative PCR and RNA sequencing revealed activation of immune responses and inappropriate activation of IL-17 signaling pathways in JIA polarized T1 cells. The polarized JIA T1 cells were comprised of Th and Tc cells, with Th cells producing IFNγ (Th1), IL-17 (Th17), and both IFNγ-IL-17 (Th1.17) and Tc cells producing IFNγ (Tc1). The JIA polarized CD4+ T1 cells expressed both Tbet and RORγT, with higher expression of the transcription factors associated with higher frequency of IL-17 producing cells. T1 polarized naïve CD4+ cells from JIA also produced more IFNγ and more IL-17 than HC. We show that in JIA T1 polarization inappropriately generates Th1, Th17, and Th1.17 cells. Our data provides a tool for studying the development of heterogeneous inflammatory T cells in JIA under T1 polarizing conditions and for identifying pathogenic immune cells that are important as drug targets and diagnostic markers.

Case Report: Infantile Urticaria as a Herald of Neonatal Onset Multisystem Inflammatory Disease With a Novel Mutation in NLRP3.

Neonatal multisystem onset inflammatory disorder (NOMID) is a severe autoinflammatory syndrome that can have an initial presentation as infantile urticaria. Thus, an immediate recognition of the clinical symptoms is essential for obtaining a genetic diagnosis and initiation of early therapies to prevent morbidity and mortality. Herein, we describe a neonate presenting with urticaria and systemic inflammation within hours after birth who developed arthropathy and neurologic findings. Pathologic evaluation of the skin revealed an infiltration of lymphocytes, eosinophils, and scattered neutrophils. Genetic analysis identified a novel heterozygous germline variant of unknown significance in the NLRP3 gene, causing the missense mutation M408T. Variants of unknown significance are common in genetic sequencing studies and are diagnostically challenging. Functional studies of the M408T variant demonstrated enhanced formation and activity of the NLRP3 inflammasome, with increased cleavage of the inflammatory cytokine IL-1ß. Upon initiation of IL-1 pathway blockade, the infant had a robust response and improvement in clinical and laboratory findings. Our experimental data support that this novel variant in NLRP3 is causal for this infant's diagnosis of NOMID. Rapid assessment of infantile urticaria with biopsy and genetic diagnosis led to early recognition and targeted anti-cytokine therapy. This observation expands the NOMID-causing variants in NLRP3 and underscores the role of genetic sequencing in rapidly identifying and treating autoinflammatory disease in infants. In addition, these findings highlight the importance of establishing the functional impact of variants of unknown significance, and the impact this knowledge may have on therapeutic decision making.

Juvenile idiopathic arthritis associated with a mutation in GATA3.

BACKGROUND: GATA3 is a transcription factor that is important during development and plays a role in differentiation and activity of immune cells, particularly T cells. Abnormal T cell function is found in autoimmune arthritis. We present the first known case of autoimmune arthritis associated with a novel GATA3 mutation. METHODS: Whole exome sequencing of the proband was performed on a clinical basis. Peripheral blood mononuclear cells (PBMCs) were collected from the proband, healthy sibling, and parent. cDNA prepared from RNA was analyzed with polymerase chain reaction and Sanger sequencing. Intracellular proteins were assessed by immunoblot of PBMC homogenates. GATA3 in vitro activity was measured in HeLa cell cultures expressing a mammalian expression vector containing GATA3 or mutants generated by site-directed mutagenesis. GATA3 transcriptional activity was examined using a luciferase reporter assay system. T helper cell ex vivo function was evaluated by stimulating PBMCs to differentiate into effector T cells along Th0, Th1, Th2, and Th17 lineages, and re-stimulating effector cells to secrete cytokines. Cytokine production was measured by enzyme-linked immunosorbent assay. RESULTS: The proband is the first known case of autoimmune arthritis associated with a mutation in GATA3. The proband M401VfsX106 protein is expressed and has a dominant negative function on GATA3 transcriptional activity. The proband PBMCs have markedly increased differentiation along the Th1 and Th17 pathways, with decreased differentiation along the Th2 pathway. Unexpectedly, Th0 cells from the proband express high levels of IFNγ. CONCLUSIONS: Our research presents the first known case of autoimmune arthritis associated with a mutation in GATA3. This work expands the phenotypic spectrum of GATA3 mutations. It reveals the novel insight that decreased and altered GATA3 activity coincides with autoimmune arthritis. This work suggests that modulation of GATA3 may be a therapeutic approach for patients with autoimmune arthritis.

N-Alpha-Acetyltransferases and Regulation of CFTR Expression.

The majority of cystic fibrosis (CF)-causing mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) lead to the misfolding, mistrafficking, and degradation of the mutant protein. Inhibition of degradation does not effectively increase the amount of trafficking competent CFTR, but typically leads to increased ER retention of misfolded forms. Thus, the initial off pathway steps occur early in the processing of the protein. To identify proteins that interact with these early forms of CFTR, in vitro crosslink experiments identified cotranslational partners of the nascent chain of the severe misfolded mutant, G85E CFTR. The mutant preferentially interacts with a subunit of an N-alpha-acetyltransferase A. Based on recent reports that acetylation of the N-termini of some N-end rule substrates control their ubiquitination and subsequent degradation, a potential role for this modification in regulation of CFTR expression was assessed. Knockdown experiments identified two complexes, which affect G85E CFTR proteins levels, NatA and NatB. Effects of the knockdowns on mRNA levels, translation rates, and degradation rates established that the two complexes regulate G85E CFTR through two separate mechanisms. NatA acts indirectly by regulating transcription levels and NatB acts through a previously identified, but incompletely understood posttranslational mechanism. This regulation did not effect trafficking of G85E CFTR, which remains retained in the ER, nor did it alter the degradation rate of CFTR. A mutation predicted to inhibit N-terminal acetylation of CFTR, Q2P, was without effect, suggesting neither system acts directly on CFTR. These results contradict the prediction that N-terminal acetylation of CFTR determines its fitness as a proteasome substrate, but rather NatB plays a role in the conformational maturation of CFTR in the ER through actions on an unidentified protein.

Inefficient SRP interaction with a nascent chain triggers a mRNA quality control pathway.

Misfolded proteins are often cytotoxic, unless cellular systems prevent their accumulation. Data presented here uncover a mechanism by which defects in secretory proteins lead to a dramatic reduction in their mRNAs and protein expression. When mutant signal sequences fail to bind to the signal recognition particle (SRP) at the ribosome exit site, the nascent chain instead contacts Argonaute2 (Ago2), and the mutant mRNAs are specifically degraded. Severity of signal sequence mutations correlated with increased proximity of Ago2 to nascent chain and mRNA degradation. Ago2 knockdown inhibited degradation of the mutant mRNA, while overexpression of Ago2 or knockdown of SRP54 promoted degradation of secretory protein mRNA. The results reveal a previously unappreciated general mechanism of translational quality control, in which specific mRNA degradation preemptively regulates aberrant protein production (RAPP).

Development of CFTR Structure.

Cystic fibrosis is a lethal genetic disease caused by lack of functional cystic fibrosis transmembrane conductance regulator (CFTR) proteins at the apical surface of secretory epithelia. CFTR is a multidomain protein, containing five domains, and its functional structure is attained in a hierarchical folding process. Most CF-causing mutations in CFTR, including the most common mutation, a deletion of phenylalanine at position 508 (ΔF508), are unable to properly fold into this functional native three dimensional structure. Currently, no high-resolution structural information about full length CFTR exists. However, insight has been gained through examining homologous ABC transporter structures, molecular modeling, and high-resolution structures of individual, isolated CFTR domains. Taken together, these studies indicate that the prevalent ΔF508 mutation disrupts two essential steps during the development of the native structure: folding of the first nucleotide binding domain (NBD1) and its later association with the fourth intracellular loop (ICL4) in the second transmembrane domain (TMD2). Therapeutics to rescue ΔF508 and other mutants in CFTR can be targeted to correct defects that occur during the complex folding process. This article reviews the structural relationships between CFTR and ABC transporters and current knowledge about how CFTR attains its structure-with a focus on how this process is altered by CF-causing mutations in a manner targetable by therapeutics.

Alteration of CFTR transmembrane span integration by disease-causing mutations.

Many missense mutations in the cystic fibrosis transmembrane conductance regulator protein (CFTR) result in its misfolding, endoplasmic reticulum (ER) accumulation, and, thus, cystic fibrosis. A number of these mutations are located in the predicted CFTR transmembrane (TM) spans and have been projected to alter span integration. However, the boundaries of the spans have not been precisely defined experimentally. In this study, the ER luminal integration profiles of TM1 and TM2 were determined using the ER glycosylation machinery, and the effects of the CF-causing mutations G85E and G91R thereon were assessed. The mutations either destabilize the integrated conformation or alter the TM1 ER integration profile. G85E misfolding is based in TM1 destabilization by glutamic acid and loss of glycine and correlates with the temperature-insensitive ER accumulation of immature full-length CFTR harboring the mutation. By contrast, temperature-dependent misfolding owing to the G91R mutation depends on the introduction of the basic side chain rather than the loss of the glycine. This work demonstrates that CF-causing mutations predicted to have similar effects on CFTR structure actually result in disparate molecular perturbations that underlie ER accumulation and the pathology of CF.