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Background Pediatric nonmalignant lymphoproliferative disorders (PLPDs) are clinically and genetically heterogeneous. Long-standing immune dysregulation and lymphoproliferation in children may be life-threatening, and a paucity of data exists to guide evaluation and treatment of children with PLPD. Objective The primary objective of this study was to ascertain the spectrum of genomic immunologic defects in PLPD. Secondary objectives included characterization of clinical outcomes and associations between genetic diagnoses and those outcomes. Methods PLPD was defined by persistent lymphadenopathy, lymph organ involvement, or lymphocytic infiltration for more than 3 months, with or without chronic or significant Epstein-Barr virus (EBV) infection. Fifty-one subjects from 47 different families with PLPD were analyzed using whole exome sequencing. Results Whole exome sequencing identified likely genetic errors of immunity in 51% to 62% of families (53% to 65% of affected children). Presence of a genetic etiology was associated with younger age and hemophagocytic lymphohistiocytosis. Ten-year survival for the cohort was 72.4%, and patients with viable genetic diagnoses had a higher survival rate (82%) compared to children without a genetic explanation (48%, P = .03). Survival outcomes for individuals with EBV-associated disease and no genetic explanation were particularly worse than outcomes for subjects with EBV-associated disease and a genetic explanation (17% vs 90%; P = .002). Ascertainment of a molecular diagnosis provided targetable treatment options for up to 18 individuals and led to active management changes for 12 patients. Conclusions PLPD defines children at high risk for mortality, and whole exome sequencing informs clinical risks and therapeutic opportunities for this diagnosis.

A population-level study is essential for understanding treatment effects, epidemiologic phenomena, and health care best practices. Evaluating large populations and associated data requires an analytic framework, which is commonly used by statisticians, epidemiologists, and data scientists. This document will serve to provide an overview of these commonly employed methods in allergy and immunology research. We will draw upon recent examples from the allergy-immunology literature to contextualize discrete principles of relevance to population-level analysis that include statistical features of a study population, elements of statistical inference, regression analysis, and an overview of machine learning practices. Our intent is to guide the reader through a practical description of this important quantitative discipline and facilitate greater understanding about data and result display in the medical literature.

Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptimal outcomes, underscoring a need to develop systematic methods for improving diagnostic rates.The principal aim of this study is to build and validate a generalizable analytical pipeline for population-wide detection of infection susceptibility and risk of primary immunodeficiency.This prospective, longitudinal cohort study coupled weighted rules with a machine learning classifier for risk stratification. Claims data were analyzed from a diverse population (n = 427,110) iteratively over 30 months. Cohort outcomes were enumerated for new diagnoses, hospitalizations, and acute care visits. This study followed TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) standards.Cohort members initially identified as high risk were proportionally more likely to receive a diagnosis of primary immunodeficiency compared to those at low-medium risk or those without claims of interest respectively (9% vs 1.5% vs 0.2%; P .001, chi-square test). Subsequent machine learning stratification enabled an annualized individual snapshot of complexity for triaging referrals. This study's top-performing machine learning model for visit-level prediction used a single dense layer neural network architecture (area under the receiver-operator characteristic curve = 0.98; F1 score = 0.98).A 2-step analytical pipeline can facilitate identification of individuals with primary immunodeficiency and accurately quantify clinical risk.

Individuals with primary immunodeficiency (PIDD) experience not only infectious complications but also immune dysregulation leading to autoimmunity, inflammation, and lymphoproliferative manifestations. Management of these complications often requires treatment with additional immunosuppressive medications, which pose an additional risk of infectious complications. Immunosuppression in individuals with PIDD therefore requires careful assessment and consideration of risks and benefits. Medications should be closely monitored, and strategies for risk mitigation of adverse events considered, such as exposure reduction, appropriate vaccination, use of antibiotics/antivirals, and optimization of immunoglobulin replacement therapy. In a subset of individuals who are not tolerating immune modulation or experiencing disease progression despite appropriate interventions, hematopoietic stem-cell transplantation is a management option.

Primary immunodeficiency disorders (PIDs) affect immune system development and/or function, increase infection susceptibility, and cause dysregulation or both. Recognition of PID requires assessment about the normal state of infection frequency and microbiology. To help clarify infection characteristics, we use data mined from the US Immunodeficiency Network (USIDNET) registry among primary antibody deficiency (PAD) patients before diagnosis. We analyzed PAD patient data from the USIDNET registry prior to ultimate diagnosis. Our analysis included basic descriptive statistics for 8 major infection subtypes and significance testing for comparing infection rate by specific organisms across 7 distinct PAD subtypes. Of 2038 patients reviewed, 1259 (61.8%) had infections reported prior to diagnosis. Most (77.4%) had four or less reported infections prior to diagnosis; however, some suffered up to 16 infections. Infection patterns differed across the PAD subtypes. Patients with agammaglobulinemia differed significantly from patients with all other forms of PAD studied in at least one infection category, whereas patients with CVID differed from 3 other PAD categories in at least one infection category. Patterns of infections in patients with hypogammaglobulinemia, specific antibody deficiency, and transient hypogammaglobulinemia were less unique. For each of the infection types, bacteria were the most prevalent cause of disease. Our data shows that distinct subtypes of PAD display unique infection patterns. We also show that patients with agammaglobulinemia suffer more invasive infections and differ most significantly from all other forms of PAD studied. Our analysis has broad implications about infection surveillance, progression, and vulnerability by PAD subtype.

PURPOSE OF REVIEW Healthcare has already been impacted by the fourth industrial revolution exemplified by tip of spear technology, such as artificial intelligence and quantum computing. Yet, there is much to be accomplished as systems remain suboptimal, and full interoperability of digital records is not realized. Given the footprint of technology in healthcare, the field of clinical immunology will certainly see improvements related to these tools. RECENT FINDINGS Biomedical informatics spans the gamut of technology in biomedicine. Within this distinct field, advances are being made, which allow for engineering of systems to automate disease detection, create computable phenotypes and improve record portability. Within clinical immunology, technologies are emerging along these lines and are expected to continue. SUMMARY This review highlights advancements in digital health including learning health systems, electronic phenotyping, artificial intelligence and use of registries. Technological advancements for improving diagnosis and care of patients with primary immunodeficiency diseases is also highlighted.

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

Knowledge related to the biology of inborn errors of immunity and associated laboratory testing methods continues to expand at a tremendous rate. Despite this, many patients with inborn errors of immunity suffer for prolonged periods of time before identification of their underlying condition, thereby delaying appropriate care. Understanding that test selection and optimal evaluation for patients with recurrent infections or unusual patterns of inflammation can be unclear, we present a document that distills relevant clinical features of immunologic disease due to inborn errors of immunity and related appropriate and available test options. This document is intended to serve the practicing clinical immunologist and, in turn, patients by describing best available test options for initial and expanded immunologic evaluations across the disease spectrum. Our goal is to demystify the process of evaluating patients with suspected immune dysfunction and to enable more rapid and accurate diagnosis of such individuals.

Background Primary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection. Objective We built a Bayesian network to provide real-time risk assessment about primary immunodeficiency and to facilitate prescriptive analytics for initiating the most appropriate diagnostic work up. Our goal is to improve diagnostic rates for primary immunodeficiency and shorten time to diagnosis. We aimed to use readily available health record data and a small training dataset to prove utility in diagnosing patients with relatively rare features. Methods We extracted data from the Texas Children’s Hospital electronic health record on a large population of primary immunodeficiency patients (n = 1762) and appropriately-matched set of controls (n = 1698). From the cohorts, clinically relevant prior probabilities were calculated enabling construction of a Bayesian network probabilistic model(PI Prob). Our model was constructed with clinical-immunology domain expertise, trained on a balanced cohort of 100 cases-controls and validated on an unseen balanced cohort of 150 cases-controls. Performance was measured by area under the receiver operator characteristic curve (AUROC). We also compared our network performance to classic machine learning model performance on the same dataset. Results PI Prob was accurate in classifying immunodeficiency patients from controls (AUROC = 0.945; p Conclusion Artificial intelligence methods can classify risk for primary immunodeficiency and guide management. PI Prob enables accurate, objective decision making about risk and guides the user towards the appropriate diagnostic evaluation for patients with recurrent infections. Probabilistic models can be trained with small datasets underscoring their utility for rare disease detection given appropriate domain expertise for feature selection and network construction.

Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare X-linked disorder caused by a loss of function mutation in the FOXP3 gene. It manifests early in infancy with clinical symptoms including autoimmune enteropathy, type 1 diabetes mellitus, and eczema. While aberrant FOXP3 expression has been associated with several types of cancer, little is known regarding the risk of cancer in patients with IPEX harboring the characteristic FOXP3 mutation. Here, we present a unique case of a primary signet ring gastric adenocarcinoma in a pediatric patient with IPEX syndrome.

Chronic granulomatous disease (CGD) is a primary immune deficiency due to defects in phagocyte respiratory burst leading to severe and life-threatening infections. Patients with CGD also suffer from disorders of inflammation and immune dysregulation including colitis and granulomatous lung disease, among others. Additionally, patients with CGD may be at increased risk of systemic inflammatory disorders such as hemophagocytic lymphohistiocytosis (HLH). The presentation of HLH often overlaps with symptoms of systemic inflammatory response syndrome (SIRS) or sepsis and therefore can be difficult to identify, especially in patients with a primary immune deficiency in which incidence of infection is increased. Thorough evaluation and empiric treatment for bacterial and fungal infections is necessary as HLH in CGD is almost always secondary to infection. Simultaneous treatment of infection with anti-microbials and inflammation with immunosuppression may be needed to blunt the hyperinflammatory response in secondary HLH. Herein, we present a series of X-linked CGD patients who developed HLH secondary to or with concurrent disseminated CGD-related infection. In two patients, CGD was a known diagnosis prior to development of HLH and in the other two CGD was diagnosed as part of the evaluation for HLH. Concurrent infection and HLH were fatal in three; one case was successfully treated, ultimately receiving hematopoietic stem cell transplantation. The current literature on presentation, diagnosis, and treatment of HLH in CGD is reviewed.

Purpose of review Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential. Recent findings Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement. Summary This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.

Highlights • Unique effects of the COVID19 pandemic are felt at the county level. • Combining information visualization techniques and publicly available datasets can yield insights about pandemic spread and community vulnerability. • Determinants of COVID19 county-level vulnerability are incompletely understood and additional study is warranted., Background Like most of the world, the United States’ public health and economy are impacted by the COVID19 pandemic. However, discrete pandemic effects may not be fully realized on the macro-scale. With this perspective, our goal is to visualize spread of the pandemic and measure county-level features which may portend vulnerability. Methods We accessed the New York Times GitHub repository COVID19 data and 2018 United States Census data for all United States Counties. The disparate datasets were merged and filtered to allow for visualization and assessments about case fatality rate (CFR%) and associated demographic, ethnic and economic features. Results Our results suggest that county-level COVID19 fatality rates are related to advanced population age (P < .001) and less diversity as evidenced by higher proportion of Caucasians in High CFR% counties (P < .001). Also, lower CFR% counties had a greater proportion of the population reporting has having 2 or more races (P < .001). We noted no significant differences between High and Low CFR% counties with respect to mean income or poverty rate. Conclusions Unique COVID19 impacts are realized at the county level. Use of public datasets, data science skills and information visualization can yield helpful insights to drive understanding about community-level vulnerability.

In early 2020, the first US and Canadian cases of the novel severe acute respiratory syndrome coronavirus 2 infection were detected. In the ensuing months, there has been rapid spread of the infection. In March 2020, in response to the virus, state/provincial and local governments instituted shelter-in-place orders, and nonessential ambulatory care was significantly curtailed, including allergy/immunology services. With rates of new infections and fatalities potentially reaching a plateau and/or declining, restrictions on provision of routine ambulatory care are lifting, and there is a need to help guide the allergy/immunology clinician on how to reinitiate services. Given the fact that coronavirus disease 2019 will circulate within our communities for months or longer, we present a flexible, algorithmic best-practices planning approach on how to prioritize services, in 4 stratified phases of reopening according to community risk level, as well as highlight key considerations for how to safely do so. The decisions on what services to offer and how fast to proceed are left to the discretion of the individual clinician and practice, operating in accordance with state and local ordinances with respect to the level of nonessential ambulatory care that can be provided. Clear communication with staff and patients before and after all changes should be incorporated into this new paradigm on continual change, given the movement may be forward and even backward through the phases because this is an evolving situation.

BackgroundPrimary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection.ObjectiveWe built an artificial intelligence model to provide real-time risk assessment about primary immunodeficiency and to facilitate prescriptive analytics for initiating the most appropriate diagnostic work up. Our goal is to improve diagnostic rates for primary immunodeficiency and shorten time to diagnosis.MethodsWe extracted data from the Texas Children’s Hospital electronic health record on a large population of primary immunodeficiency patients (n = 1762) and age-matched set of controls (n = 1698). From the cohorts, clinically relevant prior probabilities were calculated enabling construction of a Bayesian network probabilistic model. Our model was constructed with clinical-immunology domain expertise, trained on a balanced cohort of 100 cases-controls and validated on an unseen balanced cohort of 150 cases-controls. Performance was measured by area under the receiver operator characteristic curve (AUROC).ResultsOur Bayesian network was accurate in classifying immunodeficiency patients from controls (AUROC = 0.945; pConclusionArtificial intelligence methods can classify risk for primary immunodeficiency and guide management. Our Bayesian network enables highly accurate, objective decision making about risk and guides the user towards the appropriate diagnostic evaluation for patients with recurrent infections.

There is a lack of consensus regarding the role and method of hematopoietic stem cell transplantation (HSCT) on patients with chronic granulomatous disease (CGD). Long-term follow-up after HSCT in these patient population is essential to know its potential complications and decide who will benefit the most from HSCT. We report the outcome of HSCT and long-term follow-up in 24 patients with CGD, transplanted in our center from either related (n = 6) or unrelated (n = 18) donors, over a 12-year period (2003 to 2015), using high-dose alemtuzumab in the preparative regimen. We evaluated the incidence and timing of adverse events and potential risk factors. We described in detailed the novel finding of increased autoimmunity after HSCT in patients with CGD. At a median follow-up of 1460 days, 22 patients were full donor chimeras, and 2 patients had stable mixed chimerism. All assessable patients showed normalization of their neutrophil oxidative burst test. None of the patients developed grades II to IV acute graft-versus-host disease, and no patient had chronic graft-versus-host disease. Twelve of 24 patients developed 17 autoimmune diseases (ADs). Severe ADs (cytopenia and neuropathy) occurred exclusively in the unrelated donor setting and mainly in the first year after HSCT, whereas thyroid AD occurred in the related donor setting as well and more than 3 years after HSCT. Two patients died due to infectious complications after developing autoimmune cytopenias. One additional patient suffered severe brain injury. The remaining 21 patients have long-term Lansky scores ≥ 80. The outcome of HSCT from unrelated donors is comparable with related donors but might carry an increased risk of developing severe AD. A lower dose of alemtuzumab may reduce this risk and should be tested in further studies.

Wiskott-Aldrich syndrome (WAS) is a rare X-linked disorder characterized by a triad of immunodeficiency, eczema, and thrombocytopenia. Currently, hematopoietic stem cell transplantation (HSCT) is the most reliable curative treatment with excellent results for patients with HLA-matched family or unrelated donors. However, even after fully myeloablative preparative regimens, mixed donor chimerism is a potential concern. We performed a retrospective chart review of 12 children who underwent allogeneic HSCT for WAS to report our experience. The median age at transplant was 10.5 months (range, 3 to 39). The median nucleated cell dose from the marrow was 4.55 × 109/kg (range, .3 to 7.9). The median times to neutrophil and platelet engraftment were 19 days (range, 13 to 27) and 18.5 days (range, 12 to 31), respectively. The rate of overall survival was 92% with median follow-up of 67 months (range, 3 to 146). Two patients developed grade IV acute graft-versus-host disease, and 1 died on day +99. Five of 12 patient's (42%) had mixed donor chimerism (range, 12% to 85%) at day +180. None of the pretransplant patient parameters was predictive of mixed chimerism. Nonetheless, of these 5 patients, 2 had normalization of the platelet count despite the mixed chimerism, 2 had full donor chimerism after receiving a second transplant with the same donor, and 1 remains transfusion dependent awaiting a second transplant. Hence, even with a significant rate of mixed chimerism, HSCT provides substantial benefit to WAS patients, with excellent overall survival.

Purpose Common variable immunodeficiency disorder (CVID) is a primary immunodeficiency disease (PIDD) often associated with severe and chronic infections. Patients commonly receive immunoglobulin (Ig) treatment to reduce the cycle of recurrent infection and improve physical functioning. However, how Ig treatment in CVID affects quality of life (QOL) has not been thoroughly evaluated. The purpose of a recent Immune Deficiency Foundation (IDF) mail survey was to assess the factors that are associated with QOL in patients with CVID receiving Ig treatment. Methods A 75-question survey developed by the IDF and a 12-item Short Form Health Survey (SF-12) to assess QOL were mailed to adults with CVID. Mean SF-12 scores were compared between patients with CVID and the general US adult population normative sample. Results Overall, 945 patients with CVID completed the surveys. More than half of the patients (54.9%) received intravenous Ig and 44.9% received subcutaneous Ig treatment. Patients with CVID had significantly lower SF-12 scores compared with the general US population regardless of sex or age (p

Background: Early diagnosis of primary immunodeficiency disease leads to reductions in illness and decreased healthcare costs. Analysis of electronic health record data may allow for identification of persons at risk of host-defense impairments from within the general population. Our hypothesis was that coded infection history would inform individual risk of disease and ultimately lead to diagnosis. Methods: In this study we assessed individual risk for primary immunodeficiency by analyzing diagnostic codes and pharmacy records from members (n = 185,892) of a large pediatric health network. Relevant infection-associated diagnostic codes were weighted and enumerated for individual members allowing for risk score calculations ("Risk Vital Sign"). At-risk individuals underwent further assessment by chart review and re-analysis of diagnostic codes 12 months later. Results: Of the original cohort, 2188 (1.2%) individuals were identified as medium-high-risk for having a primary immunodeficiency. This group included 41 subjects who were ultimately diagnosed with primary immunodeficiency. An additional 57 medium-high risk patients had coded diagnoses worthy of referral. Conclusions: Population-wide informatics approaches can facilitate disease detection and improve outcomes. Early identification of the 98 patients with confirmed or suspected primary immunodeficiency described here could represent an annual cost savings of up to $7.7 million US Dollars.

In the event of a global infectious pandemic, drastic measures may be needed that limit or require adjustment of ambulatory allergy services. However, no rationale for how to prioritize service shut down and patient care exists. A consensus-based ad-hoc expert panel of allergy/immunology specialists from the United States and Canada developed a service and patient prioritization schematic to temporarily triage allergy/immunology services. Recommendations and feedback were developed iteratively, using an adapted modified Delphi methodology to achieve consensus. During the ongoing pandemic while social distancing is being encouraged, most allergy/immunology care could be postponed/delayed or handled through virtual care. With the exception of many patients with primary immunodeficiency, patients on venom immunotherapy, and patients with asthma of a certain severity, there is limited need for face-to-face visits under such conditions. These suggestions are intended to help provide a logical approach to quickly adjust service to mitigate risk to both medical staff and patients. Importantly, individual community circumstances may be unique and require contextual consideration. The decision to enact any of these measures rests with the judgment of each clinician and individual health care system. Pandemics are unanticipated, and enforced social distancing/quarantining is highly unusual. This expert panel consensus document offers a prioritization rational to help guide decision making when such situations arise and an allergist/immunologist is forced to reduce services or makes the decision on his or her own to do so.

Summary Patients with common variable immunodeficiency (CVID) have increased fatigue compared with the general population. Fatigue is associated with lower quality of life (QoL), which is associated with higher mortality in CVID. This study aimed to determine the prevalence of self-reported fatigue for patients with CVID and to identify its possible drivers and burden on QoL. We analysed data from the 2013 Immune Deficiency Foundation (IDF) treatment survey. Answers were included from 873 CVID patients who responded (respondents). Of the 873 respondents included in the analysis, 671 (76·9%) reported fatigue, of whom 400 (83·7%) were receiving intravenous (i.v.) immunoglobulins (IVIG) and 271 (68·6%) were receiving subcutaneous (s.c.) immunoglobulins. This difference in fatigue between patients receiving IVIG and SCIG was statistically significant (P

Chronic idiopathic urticaria (CIU), also known as chronic spontaneous urticaria, is characterized by the presence of hives on most days of the week, for 6 weeks or longer, and without an identifiable or consistent cause. Evaluation is clinical and based on the presence of episodic urticarial lesions. Although patients are subject to overtesting during the diagnosis of CIU, guidelines suggest starting with three basic laboratory tests. Treatment is a stepwise approach, involving second-generation antihistamines, histamine2 antagonists, leukotriene receptor antagonists, first-generation antihistamines, and potent antihistamines. Refractory CIU requires adding alternative agents such as omalizumab, anti-inflammatory agents, and immunosuppressants.

There are now 354 inborn errors of immunity (primary immunodeficiency diseases (PIDDs)) with 344 distinct molecular etiologies reported according to the International Union of Immunological Sciences (IUIS) (Clin Gastroenterol Hepatol 11: p. 1050-63, 2013, Semin Gastrointest Dis 8: p. 22-32, 1997, J Clin Immunol 38: p. 96-128, 2018). Using the IUIS document as a reference and cross-checking PubMed ( www.ncbi.nlm.nih.pubmed.gov ), we found that approximately one third of the 354 diseases of impaired immunity have a gastrointestinal component [J Clin Immunol 38: p. 96-128, 2018]. Often, the gastrointestinal symptomatology and pathology is the heralding sign of a PIDD; therefore, it is important to recognize patterns of disease which may manifest along the gastrointestinal tract as a more global derangement of immune function. As such, holistic consideration of immunity is warranted in patients with clinically significant gastrointestinal disease. Here, we discuss the manifold presentations and GI-specific complications of PIDDs which could lead patients to seek advice from a variety of clinician specialists. Often, patients with these medical problems will engage general pediatricians, surgeons, gastroenterologists, rheumatologists, and clinical immunologists among others. Following delineation of the presenting concern, accurate and often molecular diagnosis is imperative and a multi-disciplinary approach warranted for optimal management. In this review, we will summarize the current state of understanding of PIDD gastrointestinal disease involvement. We will do so by focusing upon gastrointestinal disease categories (i.e., inflammatory, diarrhea, nodular lymphoid hyperplasia, liver/biliary tract, structural disease, and oncologic disease) with an intent to aid the healthcare provider who may encounter a patient with an as-yet undiagnosed PIDD who presents initially with a gastrointestinal symptom, sign, or problem.

Newborn screening for Severe Combined Immunodeficiency has proven successful in identifying infants with T-cell deficiencies before they become severely ill. Additionally, the newborn screen can detect subtle early phenotypes that may become severe later in life. We present the case of siblings with features suggestive of T-cell lymphopenia identified as having low T-cell receptor excision circles counts by newborn screening. Expanded immune testing showed robust lymphocyte mitogen and antigen responses with normal vaccine responses and immunoglobulin levels for both boys over time. Genetic analysis revealed an Xq13.1 duplication in each child not found in the mother. The variant is downstream of the IL2RG gene with potential regulatory significance, suggesting a mechanism for the T-cell lymphopenia. The newborn screen provided these patients heightened surveillance and patient-specific management, including delayed live vaccines and Pneumocystis jiroveci pneumonia (PJP) prophylaxis. Fortunately, the brothers have not suffered invasive or opportunistic infections and are well at ages three and four. In this report, we illustrate the challenges of managing seemingly asymptomatic immunodeficient patients without a definitive genetic diagnosis and show how unbiased genetic analysis can expand understanding about primary immunodeficiency phenotypes.

BackgroundNatural Killer (NK) cells are critical innate effector cells whose development is dependent on the JAK-STAT pathway. NK deficiency can result in severe or refractory viral infections. Patients with Signal Transducer and Activator of Transcription (STAT)1 gain of function (GOF) mutations have increased viral susceptibility.ObjectiveWe sought to investigate NK cell function in STAT1 GOF patients. Methods: NK cell phenotype and function were determined in 16 STAT1 GOF patients.MethodsNK cell phenotype and function were determined in 16 STAT1 GOF patients.NK cell lines expressing patient mutations were generated with CRISPR-Cas9 mediated gene editing. STAT1 GOF NK cells were treated in vitro with ruxolitinib.ResultsPeripheral blood NK cells from of STAT1 GOF patients had impaired terminal maturation. Specifically, patients withSTAT1 GOFmutations have immature CD56dimNK cells with decreased expression of CD16, perforin, CD57 and impaired cytolytic function. STAT1 phosphorylation was elevated but STAT5 was aberrantly phosphorylated in response to IL-2 stimulation. Upstream inhibition of STAT signaling with the small molecule JAK1/2 inhibitor ruxolitinibin vitroandin vivorestored perforin expression in CD56dimNK cells and partially restored NK cell cytotoxic function.ConclusionsProperly regulated STAT1 signaling is critical for NK cell maturation and function. Modulation of elevated STAT1 phosphorylation with ruxolitinib is an important option for therapeutic intervention in patients withSTAT1 GOFmutations.

WOS: 000393996800025, PubMed: 27577878, Background: Primary immunodeficiency diseases (PIDDs) are clinically and genetically heterogeneous disorders thus far associated with mutations in more than 300 genes. The clinical phenotypes derived from distinct genotypes can overlap. Genetic etiology can be a prognostic indicator of disease severity and can influence treatment decisions. Objective: We sought to investigate the ability of whole-exome screening methods to detect disease-causing variants in patients with PIDDs. Methods: Patients with PIDDs from 278 families from 22 countries were investigated by using whole-exome sequencing. Computational copy number variant (CNV) prediction pipelines and an exome-tiling chromosomal microarray were also applied to identify intragenic CNVs. Analytic approaches initially focused on 475 known or candidate PIDD genes but were nonexclusive and further tailored based on clinical data, family history, and immunophenotyping. Results: A likely molecular diagnosis was achieved in 110 (40%) unrelated probands. Clinical diagnosis was revised in about half (60/ 110) and management was directly altered in nearly a quarter (26/ 110) of families based on molecular findings. Twelve PIDD-causing CNVs were detected, including 7 smaller than 30 Kb that would not have been detected with conventional diagnostic CNV arrays. Conclusion: This high-throughput genomic approach enabled detection of disease-related variants in unexpected genes; permitted detection of low-grade constitutional, somatic, and revertant mosaicism; and provided evidence of a mutational burden in mixed PIDD immunophenotypes., South-Eastern Norway Health Authority; American Women's club of Oslo; National Human Genome Research InstituteUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Human Genome Research Institute (NHGRI); National Heart, Lung, and BloodUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung & Blood Institute (NHLBI) [U54HG006542]; Jeffrey Modell Foundation; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [AI-120989], Funding for the work performed in Oslo was provided by the South-Eastern Norway Health Authority, and A. S.-P. received research scholarship from the American Women's club of Oslo. The BHCMG is supported by the National Human Genome Research Institute and the National Heart, Lung, and Blood (U54HG006542). Funding was also provided by the Jeffrey Modell Foundation and NIH AI-120989 (to J.S.O.).

Striatal degeneration from glutaryl-CoA dehydrogenase deficiency (glutaric aciduria type 1, GA1) is associated with cerebral formation and entrapment of glutaryl-CoA and its derivatives that depend on cerebral lysine influx. In 2006 we designed a lysine-free study formula enriched with arginine to selectively block lysine transport across cerebral endothelia and thereby limit glutaryl-CoA production by brain. Between 2006 and present, we treated twelve consecutive children with study formula (LYSx group) while holding all other treatment practices constant. Clinical and biochemical outcomes were compared to 25 GA1 patients (PROx group) treated between 1995 and 2005 with natural protein restriction (dietary lysine/arginine ratio of 1.7±0.3 mg:mg). We used published kinetic parameters of the y+and LAT1 blood-brain barrier transporters to model the influx of amino acids into the brain. Arginine fortification to achieve a mean dietary lysine/arginine ratio of 0.7±0.2 mg:mg was neuroprotective. All 12 LYSx patients are physically and neurologically healthy after 28 aggregate patient-years of follow up (current ages 28±21 months) and there were no adverse events related to formula use. This represents a 36% reduction of neurological risk (95% confidence interval 14-52%, p=0.018) that we can directly attribute to altered amino acid intake. During the first year of life, 20% lower lysine intake and two-fold higher arginine intake by LYSx patients were associated with 50% lower plasma lysine, 3-fold lower plasma lysine/arginine concentration ratio, 42% lower mean calculated cerebral lysine influx, 54% higher calculated cerebral arginine influx, 15-26% higher calculated cerebral influx of several anaplerotic precursors (isoleucine, threonine, methionine, and leucine), 50% less 3-hydroxyglutarate excretion, and a 3-fold lower hospitalization rate (0.8 versus 2.3 hospitalizations per patient per year). The relationship between arginine fortification and plasma lysine indicates that transport competition exists at both cerebrovascular and gastrointestinal barriers, suggesting their co-administration is key to efficacy. Monitoring the ratio between lysine and arginine in diet and plasma may prove a useful strategy for treating children with GA1.