Video-Assisted Mini-Thoracotomy Versus Anterior Thoracotomy Mitral Valve Replacement: Intraoperative Time and Hospitalization

Objectives: Minimally invasive mitral valve surgery (MIMVS) was introduced to avoid a full sternotomy through smaller or alternative chest wall incisions to reduce complications. We present our experience with MIMVS through two of its techniques. Methods: This prospective single-centre study was conducted on a total of 34 cases, divided into two groups: Group A (VAMVR) included 17 patients who underwent video-assisted mitral valve replacement. Group B (ATMVR) included 17 patients who underwent right anterior thoracotomy mitral valve replacement, comparing intraoperative procedures and the results of both techniques .Results: In the studied cases, the mean intraoperative time was 4.38 ± 0.69 hours, which widely ranged from 3 to 6 hours, with no significant difference between both techniques. It was 4.35 ± 0.7 hours in VAMVR and 4.41 ± 0.7 in ATMVR. mean ventilation time of 3.96 ± 1.08 hours. The mechanical ventilation time was 4.24 ± 1.1 hours in VAMVR cases and 3.68 ±1.1 hours in the ATMVR group. The mean overall ICU stay duration was 1.75 ± 0.33 days, with no impact of the technique used on this time, as it was 1.71 ± 0.25 days in VAMVR patients and 1.79 ± 0.4 in ATMVR patients. The total hospital stay time was about 5.71 ± 0.91 days, ranging from 4 to 8 days, with no impact of the procedure used on this time as it was 5.6 ± 0.94 days in VAMVR cases and 5.8 ± 0.88 days in ATMVR cases. Conclusions: There was no impact of the technique used in MIMVS, whether video-assisted or right anterior thoracotomy mitral valve replacement, on intraoperative time and ICU and hospital stays

CLP1 Founder Mutation Links tRNA Splicing and Maturation to Cerebellar Development and Neurodegeneration

SummaryNeurodegenerative diseases can occur so early as to affect neurodevelopment. From a cohort of more than 2,000 consanguineous families with childhood neurological disease, we identified a founder mutation in four independent pedigrees in cleavage and polyadenylation factor I subunit 1 (CLP1). CLP1 is a multifunctional kinase implicated in tRNA, mRNA, and siRNA maturation. Kinase activity of the CLP1 mutant protein was defective, and the tRNA endonuclease complex (TSEN) was destabilized, resulting in impaired pre-tRNA cleavage. Germline clp1 null zebrafish showed cerebellar neurodegeneration that was rescued by wild-type, but not mutant, human CLP1 expression. Patient-derived induced neurons displayed both depletion of mature tRNAs and accumulation of unspliced pre-tRNAs. Transfection of partially processed tRNA fragments into patient cells exacerbated an oxidative stress-induced reduction in cell survival. Our data link tRNA maturation to neuronal development and neurodegeneration through defective CLP1 function in humans

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

BACKGROUND: Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. RESULTS: SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. CONCLUSIONS: SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/

Clinical Pertinence Metric Enables Hypothesis-Independent Genome-Phenome Analysis for Neurologic Diagnosis

We describe an “integrated genome-phenome analysis” that combines both genomic sequence data and clinical information for genomic diagnosis. It is novel in that it uses robust diagnostic decision support and combines the clinical differential diagnosis and the genomic variants using a “pertinence” metric. This allows the analysis to be hypothesis-independent, not requiring assumptions about mode of inheritance, number of genes involved, or which clinical findings are most relevant. Using 20 genomic trios with neurologic disease, we find that pertinence scores averaging 99.9% identify the causative variant under conditions in which a genomic trio is analyzed and family-aware variant calling is done. The analysis takes seconds, and pertinence scores can be improved by clinicians adding more findings. The core conclusion is that automated genome-phenome analysis can be accurate, rapid, and efficient. We also conclude that an automated process offers a methodology for quality improvement of many components of genomic analysis

Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders

Hereditary spastic paraplegias (HSPs) are neurodegenerative motor neuron diseases characterized by progressive age-dependent loss of corticospinal motor tract function. Although the genetic basis is partly understood, only a fraction of cases can receive a genetic diagnosis, and a global view of HSP is lacking. By using whole-exome sequencing in combination with network analysis, we identified 18 previously unknown putative HSP genes and validated nearly all of these genes functionally or genetically. The pathways highlighted by these mutations link HSP to cellular transport, nucleotide metabolism, and synapse and axon development. Network analysis revealed a host of further candidate genes, of which three were mutated in our cohort. Our analysis links HSP to other neurodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease