BackgroundDisorders of sex development (DSD) are congenital conditions in which chromosomal, gonadal, or phenotypic sex is atypical. Clinical management of DSD is often difficult and currently only 13% of patients receive an accurate clinical genetic diagnosis. To address this we have developed a massively parallel sequencing targeted DSD gene panel which allows us to sequence all 64 known diagnostic DSD genes and candidate genes simultaneously.ResultsWe analyzed DNA from the largest reported international cohort of patients with DSD (278 patients with 46,XY DSD and 48 with 46,XX DSD). Our targeted gene panel compares favorably with other sequencing platforms. We found a total of 28 diagnostic genes that are implicated in DSD, highlighting the genetic spectrum of this disorder. Sequencing revealed 93 previously unreported DSD gene variants. Overall, we identified a likely genetic diagnosis in 43% of patients with 46,XY DSD. In patients with 46,XY disorders of androgen synthesis and action the genetic diagnosis rate reached 60%. Surprisingly, little difference in diagnostic rate was observed between singletons and trios. In many cases our findings are informative as to the likely cause of the DSD, which will facilitate clinical management.ConclusionsOur massively parallel sequencing targeted DSD gene panel represents an economical means of improving the genetic diagnostic capability for patients affected by DSD. Implementation of this panel in a large cohort of patients has expanded our understanding of the underlying genetic etiology of DSD. The inclusion of research candidate genes also provides an invaluable resource for future identification of novel genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1105-y) contains supplementary material, which is available to authorized users.

Heterozygous coding mutations in the INS gene that encodes preproinsulin were recently shown to be an important cause of permanent neonatal diabetes. These dominantly acting mutations prevent normal folding of proinsulin, which leads to beta-cell death through endoplasmic reticulum stress and apoptosis. We now report 10 different recessive INS mutations in 15 probands with neonatal diabetes. Functional studies showed that recessive mutations resulted in diabetes because of decreased insulin biosynthesis through distinct mechanisms, including gene deletion, lack of the translation initiation signal, and altered mRNA stability because of the disruption of a polyadenylation signal. A subset of recessive mutations caused abnormal INS transcription, including the deletion of the C1 and E1 cis regulatory elements, or three different single base-pair substitutions in a CC dinucleotide sequence located between E1 and A1 elements. In keeping with an earlier and more severe beta-cell defect, patients with recessive INS mutations had a lower birth weight (−3.2 SD score vs. −2.0 SD score) and were diagnosed earlier (median 1 week vs. 10 weeks) compared to those with dominant INS mutations. Mutations in the insulin gene can therefore result in neonatal diabetes as a result of two contrasting pathogenic mechanisms. Moreover, the recessively inherited mutations provide a genetic demonstration of the essential role of multiple sequence elements that regulate the biosynthesis of insulin in man. (8-12). In contrast, abnormalities in chromosome 6q24 are the most common cause of TNDM (13), followed by mutations in the KCNJ11 and ABCC8 genes (14). Despite these advances, the etiology of neonatal diabetes is still not known in at least 30% of patients with PNDM, suggesting other genetic causes are still to be found (9).Insulin is secreted from islet beta cells of the pancreas. Insufficient secretion of insulin results in hyperglycemia and diabetes, whereas excessive secretion results in hypoglycemia. Insulin biosynthesis and secretion are therefore tightly regulated to maintain blood glucose levels within a narrow physiological range. Extensive studies have dissected an array of cis sequence elements in the INS promoter region and their cognate DNA binding factors, which together ensure the cellular specificity and rate of INS transcription (15)(16)(17)(18)(19)(20)(21)(22). In addition, insulin biosynthesis is strongly dependent on posttranscriptional regulatory mechanisms, including the modulation of translation and stability (23-25). The latter is largely mediated through sequences located in the untranslated regions of INS transcripts (26-28).

Aims/hypothesis The objective of this study was to assess the impact of patient-led sensor-guided pump management on glycaemic control, and compare the effect with that of standard insulin pump therapy. Methods An open multicentre parallel randomised controlled trial was conducted at five tertiary diabetes centres. Participants aged 13.0-40.0 years with well-controlled type 1 diabetes were randomised 1:1 to either study group for 3 months. Randomisation was carried out using a central computer-generated schedule. Participants in the intervention group used sensor-guided pump management; no instructive guidelines in interpreting real-time data were provided ('patient-led' use). Participants in the control group continued their original insulin pump regimen. Continuous glucose monitoring (CGM) and HbA 1c level were used to assess outcomes. The primary outcome was the difference in the proportion of time in the target glycaemic range during the 3 month study period (derived from CGM, target range 4-10 mmol/l). Secondary outcomes were difference in HbA 1c , time in hypoglycaemic (≤3.9 mmol/l) and hyperglycaemic (≥10.1 mmol/l) ranges and glycaemic variability. Results Sixty-two participants were recruited and randomised; 5/31 and 2/31 withdrew from intervention and control groups, respectively, leaving 26/31 and 29/31 for the intention-to-treat analyses. When adjusted for baseline values, the mean end-of-study HbA 1c was 0.43% lower in the intervention group compared with the control group (95% CI 0.19 to 0.75%; p=0.009). No difference was observed in CGM-derived time in target (measured difference 1.72; 95% CI −5.37 to 8.81), hypoglycaemic (0.54; 95% CI −3.48 to 4.55) or hyperglycaemic (−2.18; 95% CI −10.0 to 5.69) range or in glycaemic variability (−0.29; 95% CI −0.34 to 0.28). Within the intervention group, HbA 1c was 0.51% lower in participants with sensor use ≥70% compared with participants with sensor use <70% (95% CI −0.98 to −0.04, p=0.04). Five episodes of device malfunction occurred. Conclusions/interpretation Individuals established on insulin pump therapy can employ sensor-guided pump management to improve glycaemic control. An apparent dose-dependent effect of sensor usage was noted; however, Diabetologia