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BackgroundMitochondrial presequence proteases perform fundamental functions as they process about 70 % of all mitochondrial preproteins that are encoded in the nucleus and imported posttranslationally. The mitochondrial intermediate presequence protease MIP/Oct1, which carries out precursor processing, has not yet been established to have a role in human disease.MethodsWhole exome sequencing was performed on four unrelated probands with left ventricular non-compaction (LVNC), developmental delay (DD), seizures, and severe hypotonia. Proposed pathogenic variants were confirmed by Sanger sequencing or array comparative genomic hybridization. Functional analysis of the identified MIP variants was performed using the model organism Saccharomyces cerevisiae as the protein and its functions are highly conserved from yeast to human.ResultsBiallelic single nucleotide variants (SNVs) or copy number variants (CNVs) in MIPEP, which encodes MIP, were present in all four probands, three of whom had infantile/childhood death. Two patients had compound heterozygous SNVs (p.L582R/p.L71Q and p.E602*/p.L306F) and one patient from a consanguineous family had a homozygous SNV (p.K343E). The fourth patient, identified through the GeneMatcher tool, a part of the Matchmaker Exchange Project, was found to have inherited a paternal SNV (p.H512D) and a maternal CNV (1.4-Mb deletion of 13q12.12) that includes MIPEP. All amino acids affected in the patients’ missense variants are highly conserved from yeast to human and therefore S. cerevisiae was employed for functional analysis (for p.L71Q, p.L306F, and p.K343E). The mutations p.L339F (human p.L306F) and p.K376E (human p.K343E) resulted in a severe decrease of Oct1 protease activity and accumulation of non-processed Oct1 substrates and consequently impaired viability under respiratory growth conditions. The p.L83Q (human p.L71Q) failed to localize to the mitochondria.ConclusionsOur findings reveal for the first time the role of the mitochondrial intermediate peptidase in human disease. Loss of MIP function results in a syndrome which consists of LVNC, DD, seizures, hypotonia, and cataracts. Our approach highlights the power of data exchange and the importance of an interrelationship between clinical and research efforts for disease gene discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0360-6) contains supplementary material, which is available to authorized users.

OBJECTIVEIncidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP).METHODSThe authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online.RESULTSOf the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97).CONCLUSIONSUsing multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.