Biallelic PI4KA variants cause neurological, intestinal and immunological disease.

Phosphatidylinositol 4-kinase IIIα (PI4KIIIα/PI4KA/OMIM:600286) is a lipid kinase generating phosphatidylinositol 4-phosphate (PI4P), a membrane phospholipid with critical roles in the physiology of multiple cell types. PI4KIIIα's role in PI4P generation requires its assembly into a heterotetrameric complex with EFR3, TTC7 and FAM126. Sequence alterations in two of these molecular partners, TTC7 (encoded by TTC7A or TCC7B) and FAM126, have been associated with a heterogeneous group of either neurological (FAM126A) or intestinal and immunological (TTC7A) conditions. Here we show that biallelic PI4KA sequence alterations in humans are associated with neurological disease, in particular hypomyelinating leukodystrophy. In addition, affected individuals may present with inflammatory bowel disease, multiple intestinal atresia and combined immunodeficiency. Our cellular, biochemical and structural modelling studies indicate that PI4KA-associated phenotypical outcomes probably stem from impairment of PI4KIIIα-TTC7-FAM126's organ-specific functions, due to defective catalytic activity or altered intra-complex functional interactions. Together, these data define PI4KA gene alteration as a cause of a variable phenotypical spectrum and provide fundamental new insight into the combinatorial biology of the PI4KIIIα-FAM126-TTC7-EFR3 molecular complex.

A Distinct Esophageal mRNA Pattern Identifies Eosinophilic Esophagitis Patients With Food Impactions.

Eosinophilic esophagitis (EoE), a Th2-type allergic immune disorder characterized by an eosinophil-rich esophageal immune infiltrate, is often associated with food impaction (FI) in pediatric patients but the molecular mechanisms underlying the development of this complication are not well understood. We aim to identify molecular pathways involved in the development of FI. Due to large variations in disease presentation, our analysis was further geared to find markers capable of distinguishing EoE patients that are prone to develop food impactions and thus expand an established medical algorithm for EoE by developing a secondary analysis that allows for the identification of patients with food impactions as a distinct patient population. To this end, mRNA patterns from esophageal biopsies of pediatric EoE patients presenting with and without food impactions were compared and machine learning techniques were employed to establish a diagnostic probability score to identify patients with food impactions (EoE+FI). Our analysis showed that EoE patients with food impaction were indistinguishable from other EoE patients based on their tissue eosinophil count, serum IgE levels, or the mRNA transcriptome-based p(EoE). Irrespectively, an additional analysis loop of the medical algorithm was able to separate EoE+FI patients and a composite FI-score was established that identified such patients with a sensitivity of 93% and a specificity of 100%. The esophageal mRNA pattern of EoE+FI patients was typified by lower expression levels of mast cell markers and Th2 associated transcripts, such as FCERIB, CPA3, CCL2, IL4, and IL5. Furthermore, lower expression levels of regulators of esophageal motility (NOS2 and HIF1A) were detected in EoE+FI. The EoE+FI -specific mRNA pattern indicates that impaired motility may be one underlying factor for the development of food impactions in pediatric patients. The availability of improved diagnostic tools such as a medical algorithm for EoE subpopulations will have a direct impact on clinical practice because such strategies can identify molecular inflammatory characteristics of individual EoE patients, which, in turn, will facilitate the development of individualized therapeutic approaches that target the relevant pathways affected in each patient.

An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

BACKGROUND: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status. OBJECTIVE: This study sought to establish an automated medical algorithm to assist in the evaluation of EoE. METHODS: Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease and controls. Dimensionality reduction in the training set established weighted factors, which were confirmed by immunohistochemistry. Following weighted factor analysis, p(EoE) was determined by random forest classification. Accuracy was tested in an external test set, and predictive power was assessed with equivocal patients. Esophageal IgE production was quantified with epsilon germ line (IGHE) transcripts and correlated with serum IgE and the Th2-type mRNA profile to establish an IGHE score for tissue allergy. RESULTS: In the primary analysis, a 3-class statistical model generated a p(EoE) score based on common characteristics of the inflammatory EoE profile. A p(EoE) ≥ 25 successfully identified EoE with high accuracy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of equivocal cases by 84.6%. The p(EoE) changed in response to therapy. A secondary analysis loop in EoE patients defined an IGHE score of ≥37.5 for a patient subpopulation with increased esophageal allergic inflammation. CONCLUSIONS: The development of intelligent data analysis from a machine learning perspective provides exciting opportunities to improve diagnostic precision and improve patient care in EoE. The p(EoE) and the IGHE score are steps toward the development of decision trees to define EoE subpopulations and, consequently, will facilitate individualized therapy.

Commensal bacterial modulation of the host immune response to ameliorate pain in a murine model of chronic prostatitis.

The human commensal microflora plays an essential role in modulating the immune response to control homeostasis. Staphylococcus epidermidis, a commensal bacterium most commonly associated with the skin exerts such effects locally, modulating local immune responses during inflammation and preventing superinfection by pathogens such as Staphylococcus aureus. Although the prostate is considered by many to be sterile, multiple investigations have shown that small numbers of gram-positive bacterial species such as S. epidermidis can be isolated from the expressed prostatic secretions of both healthy and diseased men. Chronic pelvic pain syndrome is a complex syndrome with symptoms including pain and lower urinary tract dysfunction. It has an unknown etiology and limited effective treatments but is associated with modulation of prostate immune responses. Chronic pelvic pain syndrome can be modeled using murine experimental prostatitis (EAP), where CD4+ve IL17A+ve T cells have been shown to play a critical role in disease orchestration and development of pelvic tactile allodynia. Here, we report that intraurethral instillation of a specific S. epidermidis strain (designated NPI [non-pain inducing]), isolated from the expressed prostatic secretion of a healthy human male, into EAP-treated mice reduced the pelvic tactile allodynia responses and increased CD4+ve IL17A+ve T-cell numbers associated with EAP. Furthermore, a cell wall constituent of NPI, lipoteichoic acid, specifically recapitulates these effects and mediates increased expression of CTLA4-like ligands PDL1 and PDL2 on prostatic CD11b+ve antigen-presenting cells. These results identify a new potential therapeutic role for commensal S. epidermidis NPI lipoteichoic acid in the treatment of prostatitis-associated pain.