Abraham, Roshini S.

ABSTRACTGenetic defects of the immune system are referred to as primary immunodeficiencies (PIDs). These immunodeficiencies are clinically and immunologically heterogeneous and, therefore, pose a challenge not only for the clinician but also for the diagnostic immunologist. There are several methodological tools available for evaluation and monitoring of patients with PIDs, and of these tools, flow cytometry has gained prominence, both for phenotyping and functional assays. Flow cytometry allows real-time analysis of cellular composition, cell signaling, and other relevant immunological pathways, providing an accessible tool for rapid diagnostic and prognostic assessment. This minireview provides an overview of the use of flow cytometry in disease-specific diagnosis of PIDs, in addition to other broader applications, which include immune phenotyping and cellular functional measurements.

Abstract Rationale Most immunocompetent patients diagnosed with latent tuberculosis infection (LTBI) will not progress to tuberculosis (TB) reactivation. However, current diagnostic tools cannot reliably distinguish nonprogressing from progressing patients a priori, and thus LTBI therapy must be prescribed with suboptimal patient specificity. We hypothesized that LTBI diagnostics could be improved by generating immunomarker profiles capable of categorizing distinct patient subsets by a combinatorial immunoassay approach. Objectives A combinatorial immunoassay analysis was applied to identify potential immunomarker combinations that distinguish among unexposed subjects, untreated patients with LTBI, and treated patients with LTBI and to differentiate risk of reactivation. Methods IFN-γ release assay (IGRA) was combined with a flow cytometric assay that detects induction of CD25+CD134+ coexpression on TB antigen–stimulated T cells from peripheral blood. The combinatorial immunoassay analysis was based on receiver operating characteristic curves, technical cut-offs, 95% bivariate normal density ellipse prediction, and statistical analysis. Risk of reactivation was estimated with a prediction formula. Measurements and Main Results Sixty-five out of 150 subjects were included. The combinatorial immunoassay approach identified at least four different T-cell subsets. The representation of these immune phenotypes was more heterogeneous in untreated patients with LTBI than in treated patients with LTBI or unexposed groups. Patients with IGRA(+) CD4+CD25+CD134+ T-cell phenotypes had the highest estimated reactivation risk (4.11 ± 2.11%). Conclusions These findings suggest that immune phenotypes defined by combinatorial assays may potentially have a role in identifying those at risk of developing TB; this potential role is supported by risk of reactivation modeling. Prospective studies will be needed to test this novel approach.

AbstractGuanine‐adenine‐thymine‐adenine 2 (GATA2) mutated disorders include the recently described MonoMAC syndrome (Monocytopenia and Mycobacterium avium complex infections), DCML (dendritic cell, monocyte, and lymphocyte deficiency), familial MDS/AML (myelodysplastic syndrome/acute myeloid leukemia) (myeloid neoplasms), congenital neutropenia, congenital lymphedema (Emberger's syndrome), sensorineural deafness, viral warts, and a spectrum of aggressive infections seen across all age groups. While considerable efforts have been made to identify the mutations that characterize this disorder, pathogenesis remains a work in progress with less than 100 patients described in current literature. Varying clinical presentations offer diagnostic challenges. Allogeneic stem cell transplant remains the treatment of choice. Morbidity, mortality, and social costs due to the familial nature of the disease are considerable. We describe our experience with the disorder in three affected families and a comprehensive review of current literature.

Abstract The genetic factors that contribute to the etiology of type 1 diabetes are still largely uncharacterized. However, the genes of the MHC (HLA in humans) have been consistently associated with susceptibility to disease. We have used several transgenic mice generated in our laboratory, bearing susceptible or resistant HLA alleles, in the absence of endogenous MHC class II (Aβo), to study immune responses to the autoantigen glutamic acid decarboxylase (GAD) 65 and its relevance in determining the association between autoreactivity and disease pathogenesis. Mice bearing diabetes-susceptible haplotypes, HLA DR3 (DRB1*0301) or DQ8 (DQB1*0302), singly or in combination showed spontaneous T cell reactivity to rat GAD 65, which is highly homologous to the self Ag, mouse GAD 65. The presence of diabetes-resistant or neutral alleles, such as HLA DQ6 (DQB1*0602) and DR2 (DRB1*1502) prevented the generation of any self-reactive responses to rat GAD. In addition, unmanipulated Aβo/DR3, Aβo/DQ8, and Aβo/DR3/DQ8 mice recognized specific peptides, mainly from the N-terminal region of the GAD 65 molecule. Most of these regions are conserved between human, mouse, and rat GAD 65. Further analysis revealed that the reactivity was mediated primarily by CD4+ T cells. Stimulation of these T cells by rat GAD 65 resulted in the generation of a mixed Th1/Th2 cytokine profile in the Aβo/DR3/DQ8, Aβo/DR3, and Aβo/DQ8 mice. Thus, the presence of diabetes-associated genes determines whether immune tolerance is maintained to islet autoantigens, but autoreactivity in itself is not sufficient to induce diabetes.

AbstractIntroductionSepsis biomarker research requires an infrastructure to identify septic patients efficiently and to collect and store specimens properly. We developed a novel infrastructure to study biomarkers of sepsis in children.MethodsPatients in pediatric and neonatal intensive care units were enrolled prospectively; enrollment information was stored in a secure, remotely accessible database. Researchers were notified of electronic medical record (EMR) orders for blood cultures (a surrogate for a diagnostic evaluation of suspected sepsis) by a page triggered by the order. Staff confirmed patient enrollment and remotely submitted an EMR order for collection of study specimens simultaneous with the blood culture. Specimens were processed and stored by a mobile clinical research unit.ResultsOver 2 years, 2029 patients were admitted; 138 were enrolled. Staff received pages for 95% of blood cultures collected from enrolled patients. The median time between the blood culture order and collection was 34 minutes (range 9–241). Study specimens were collected simultaneously with 41 blood cultures. The median times between specimen collection and storage for flow cytometry and cytokine analysis were 33 minutes (range 0–82) and 52 minutes (range 28–98), respectively.ConclusionThis novel infrastructure facilitated prompt, proper collection and storage of specimens for sepsis biomarker analysis. Clin Trans Sci 2012; Volume #: 1–5