Relevance of laboratory testing for the diagnosis of primary immunodeficiencies: a review of case-based examples of selected immunodeficiencies

The field of primary immunodeficiencies (PIDs) is one of several in the area of clinical immunology that has not been static, but rather has shown exponential growth due to enhanced physician, scientist and patient education and awareness, leading to identification of new diseases, new molecular diagnoses of existing clinical phenotypes, broadening of the spectrum of clinical and phenotypic presentations associated with a single or related gene defects, increased bioinformatics resources, and utilization of advanced diagnostic technology and methodology for disease diagnosis and management resulting in improved outcomes and survival. There are currently over 200 PIDs with at least 170 associated genetic defects identified, with several of these being reported in recent years. The enormous clinical and immunological heterogeneity in the PIDs makes diagnosis challenging, but there is no doubt that early and accurate diagnosis facilitates prompt intervention leading to decreased morbidity and mortality. Diagnosis of PIDs often requires correlation of data obtained from clinical and radiological findings with laboratory immunological analyses and genetic testing. The field of laboratory diagnostic immunology is also rapidly burgeoning, both in terms of novel technologies and applications, and knowledge of human immunology. Over the years, the classification of PIDs has been primarily based on the immunological defect(s) ("immunophenotype") with the relatively recent addition of genotype, though there are clinical classifications as well. There can be substantial overlap in terms of the broad immunophenotype and clinical features between PIDs, and therefore, it is relevant to refine, at a cellular and molecular level, unique immunological defects that allow for a specific and accurate diagnosis. The diagnostic testing armamentarium for PID includes flow cytometry - phenotyping and functional, cellular and molecular assays, protein analysis, and mutation identification by gene sequencing. The complexity and diversity of the laboratory diagnosis of PIDs necessitates many of the above-mentioned tests being performed in highly specialized reference laboratories. Despite these restrictions, there remains an urgent need for improved standardization and optimization of phenotypic and functional flow cytometry and protein-specific assays. A key component in the interpretation of immunological assays is the comparison of patient data to that obtained in a statistically-robust manner from age and gender-matched healthy donors. This review highlights a few of the laboratory assays available for the diagnostic work-up of broad categories of PIDs, based on immunophenotyping, followed by examples of disease-specific testing

Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis

Background Rare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes. Methods We did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses. Findings A locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55–2·08], p=5·13×10– ¹⁵) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42–1·71], p=7·65×10– ²⁰) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25–1·48], p=1·69×10– ¹²; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-mediated inhibition of the enhancer reduced SOX17 expression. The HLA-DPA1/DPB1 rs2856830 genotype was strongly associated with survival. Median survival from diagnosis in patients with pulmonary arterial hypertension with the C/C homozygous genotype was double (13·50 years [95% CI 12·07 to >13·50]) that of those with the T/T genotype (6·97 years [6·02–8·05]), despite similar baseline disease severity. Interpretation This is the first study to report that common genetic variation at loci in an enhancer near SOX17 and in HLA-DPA1/DPB1 is associated with pulmonary arterial hypertension. Impairment of SOX17 function might be more common in pulmonary arterial hypertension than suggested by rare mutations in SOX17. Further studies are needed to confirm the association between HLA typing or rs2856830 genotyping and survival, and to determine whether HLA typing or rs2856830 genotyping improves risk stratification in clinical practice or trials. Funding UK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, INSERM, Université Paris-Sud, and French ANR

Thirst neurons anticipate the homeostatic consequences of eating and drinking

Thirst motivates animals to drink in order to maintain fluid balance. Traditionally, thirst has been viewed as a homeostatic response to changes in the blood volume or tonicity(1–3). However, most drinking behavior is regulated too rapidly to be controlled by blood composition directly and instead appears to anticipate homeostatic imbalances before they arise(4–11). How this is achieved remains unknown. Here we reveal an unexpected role for the subfornical organ (SFO) in the anticipatory regulation of thirst. We show by monitoring deep-brain calcium dynamics that thirst-promoting SFO neurons respond to inputs from the oral cavity during eating and drinking, which they then integrate with information about the composition of the blood. This integration allows SFO neurons to predict how ongoing food and water consumption will alter fluid balance in the future and then adjust behavior preemptively. Complementary optogenetic manipulations show that this anticipatory modulation is necessary for drinking in multiple contexts. These findings provide a neural mechanism to explain longstanding behavioral observations, including the prevalence of drinking during meals(10,11), the rapid satiation of thirst(7–9), and the fact that oral cooling is thirst-quenching(12–14)

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies