Deetect: A Deep Learning-Based Image Analysis Tool for Quantification of Adherent Cell Populations on Oxygenator Membranes after Extracorporeal Membrane Oxygenation Therapy.

The strong interaction of blood with the foreign surface of membrane oxygenators during ECMO therapy leads to adhesion of immune cells on the oxygenator membranes, which can be visualized in the form of image sequences using confocal laser scanning microscopy. The segmentation and quantification of these image sequences is a demanding task, but it is essential to understanding the significance of adhering cells during extracorporeal circulation. The aim of this work was to develop and test a deep learning-supported image processing tool (Deetect), suitable for the analysis of confocal image sequences of cell deposits on oxygenator membranes at certain predilection sites. Deetect was tested using confocal image sequences of stained (DAPI) blood cells that adhered to specific predilection sites (junctional warps and hollow fibers) of a phosphorylcholine-coated polymethylpentene membrane oxygenator after patient support (>24 h). Deetect comprises various functions to overcome difficulties that occur during quantification (segmentation, elimination of artifacts). To evaluate Deetects performance, images were counted and segmented manually as a reference and compared with the analysis by a traditional segmentation approach in Fiji and the newly developed tool. Deetect outperformed conventional segmentation in clustered areas. In sections where cell boundaries were difficult to distinguish visually, previously defined post-processing steps of Deetect were applied, resulting in a more objective approach for the resolution of these areas.

Defining Molecular Treatment Targets for Bladder Pain Syndrome/Interstitial Cystitis: Uncovering Adhesion Molecules.

Bladder pain syndrome/interstitial cystitis (BPS/IC) is a debilitating pain syndrome of unknown etiology that predominantly affects females. Clinically, BPS/IC presents in a wide spectrum where all patients report severe bladder pain together with one or more urinary tract symptoms. On bladder examination, some have normal-appearing bladders on cystoscopy, whereas others may have severely inflamed bladder walls with easily bleeding areas (glomerulations) and ulcerations (Hunner's lesion). Thus, the reported prevalence of BPS/IC is also highly variable, between 0.06% and 30%. Nevertheless, it is rightly defined as a rare disease (ORPHA:37202). The aetiopathogenesis of BPS/IC remains largely unknown. Current treatment is mainly symptomatic and palliative, which certainly adds to the suffering of patients. BPS/IC is known to have a genetic component. However, the genes responsible are not defined yet. In addition to traditional genetic approaches, novel research methodologies involving bioinformatics are evaluated to elucidate the genetic basis of BPS/IC. This article aims to review the current evidence on the genetic basis of BPS/IC to determine the most promising targets for possible novel treatments.

A Novel and Mosaic WDR45 Nonsense Variant Causes Beta-Propeller Protein-Associated Neurodegeneration Identified Through Whole Exome Sequencing and X chromosome Heterozygosity Analysis.

Beta-propeller protein-associated neurodegeneration (BPAN) is an X-linked rare dominant disorder of autophagy. The role of WDR45 has been implicated in BPAN almost exclusively in females possibly due to male lethality. Characterization of distinctive clinical manifestations and potentially the complex genetic determinants in rare male patients remain crucial for deciphering BPAN and other X-linked dominant diseases. We performed whole exome sequencing (WES) followed by segregation analysis and identified a novel nonsense and mosaic variant in WDR45, namely NM_007075.3:c.873C>G; p.(Tyr291*) in an affected male at the age of 34. His biphasic medical history was compatible with BPAN, which was characterized by delayed psychomotor development, intellectual disability, and progression into dystonia parkinsonism in his twenties. The variant had an apparently mosaic pattern both in whole exome and Sanger sequencing findings. In order to figure out if mosaicism was restricted to this variant or related to a chromosomal level mosaicism, we used our in-house WES data from 129 unrelated individuals to calculate the threshold values of male and female X chromosome heterozygosity (XcHet) in WES data for our pipeline. A background level of heterozygous variants on X chromosome excluding the pseudoautosomal loci is an observed phenomenon in WES analysis and this level has been used as a quality measure. Herein, we suggest utilization of this measure for detection of digital anomalies of the X chromosome in males by potentially observing a higher XcHet value than the threshold value. This approach has revealed a variant level mosaicism in the affected male, which was further supported with cytogenetic analyses.

Myophosphorylase (PYGM) mutations determined by next generation sequencing in a cohort from Turkey with McArdle disease.

This study aimed to identify PYGM mutations in patients with McArdle disease from Turkey by next generation sequencing (NGS). Genomic DNA was extracted from the blood of the McArdle patients (n = 67) and unrelated healthy volunteers (n = 53). The PYGM gene was sequenced with NGS and the observed mutations were validated by direct Sanger sequencing. A diagnostic algorithm was developed for patients with suspected McArdle disease. A total of 16 deleterious PYGM mutations were identified, of which 5 were novel, including 1 splice-site donor, 1 frame-shift, and 3 non-synonymous variants. The p.Met1Val (27-patients/11-families) was the most common PYGM mutation, followed by p.Arg576* (6/4), c.1827+7A>G (5/4), c.772+2_3delTG (5/3), p.Phe710del (4/2), p.Lys754Asnfs (2/1), and p.Arg50* (1/1). A molecular diagnostic flowchart is proposed for the McArdle patients in Turkey, covering the 6 most common PYGM mutations found in Turkey as well as the most common mutation in Europe. The diagnostic algorithm may alleviate the need for muscle biopsies in 77.6% of future patients. A prevalence of any of the mutations to a geographical region in Turkey was not identified. Furthermore, the NGS approach to sequence the entire PYGM gene was successful in detecting a common missense mutation and discovering novel mutations in this population study.

A further family of Stromme syndrome carrying CENPF mutation.

Stromme syndrome is a rare genetic disorder characterized by microcephaly, anterior ocular chamber anomalies, and "apple peel" type jejunal atresia. Here, we report a Stromme syndrome family with two affected siblings with a homozygous truncating frameshift mutation in CENPF. A 3-month-old girl was hospitalized due to prenatally diagnosed microcephaly, microphthalmia, and dysmorphological features. The history of a previous child with the same findings in addition to "apple peel" intestinal atresia had been noted. Regarding the clinical features of both affected siblings, a diagnosis of Stromme syndrome was established. Exome-sequencing of these two cases showed the homozygous mutation (c.5912_5913insA)/(p.T1974Nfs*9) in CENPF. While confirmation of this gene being responsible for Stromme syndrome was pending our results, Filges et al. reported that CENPF was indeed underlying the reason for Stromme syndrome. This is the second case report identifying CENPF mutation as the cause of Stromme syndrome.

Exome sequencing identifies a novel homozygous CLN8 mutation in a Turkish family with Northern epilepsy.

Neuronal ceroid lipofuscinosis (NCL), one of the most common neurodegenerative childhood-onset disorders, is characterized by autosomal-recessive inheritance, epileptic seizures, progressive psychomotor deterioration, visual impairment, and premature death. Based on the country of origin of the patients, the clinical features/courses, and the molecular genetics background of the disorder, 14 distinct NCL subtypes have been described to date. CLN8 mutation was first identified in Finnish patients, and the condition was named Northern Epilepsy (NE); however, the severe phenotype of the CLN8 gene was subsequently found outside Finland and named 'variant late-infantile' NCL. In this study, five patients and their six healthy relatives from a large Turkish consanguineous family were enrolled. The study involved detailed clinical, radiological and molecular genetic evaluations. Whole-exome sequencing and homozygosity mapping revealed a novel homozygous CLN8 mutation, c.677T>C (p.Leu226Pro). We defined NE cases in Turkey, caused by a novel mutation in CLN8. WES can be an important diagnostic method in rare cases with atypical courses.

Coffin-Siris syndrome with café-au-lait spots, obesity and hyperinsulinism caused by a mutation in the ARID1B gene.

Coffin-Siris syndrome (CSS) (MIM 135900) is characterized by developmental delay, severe speech impairment, distinctive facial features, hypertrichosis, aplasia or hypoplasia of the distal phalanx or nail of the fifth digit and agenesis of the corpus callosum. Recently, it was shown that mutations in the ARID1B gene are the main cause of CSS, accounting for 76% of identified mutations. Here, we report a 15 year-old female patient who was admitted to our clinic with seizures, speech problems, dysmorphic features, bilaterally big, large thumb, café-au-lait (CAL) spots, obesity and hyperinsulinism. First, the patient was thought to have an association of neurofibromatosis and Rubinstein Taybi syndrome. Because of the large size of the NF1 gene for neurofibromatosis and CREBBP gene for Rubinstein Taybi syndrome, whole exome sequence analysis (WES) was conducted and a novel ARID1B mutation was identified. The proband WES test identified a novel heterozygous frameshift mutation c.3394_3395insTA in exon 13 of ARID1B (NM_017519.2) predicting a premature stop codon p.(Tyr1132Leufs*67). Sanger sequencing confirmed the heterozygous c.3394_3395insTA mutation in the proband and that it was not present in her parents indicating de novo mutation. Further investigation and new cases will help to understand this phenomenon better.

Loss-of-Function Mutations in ELMO2 Cause Intraosseous Vascular Malformation by Impeding RAC1 Signaling.

Vascular malformations are non-neoplastic expansions of blood vessels that arise due to errors during angiogenesis. They are a heterogeneous group of sporadic or inherited vascular disorders characterized by localized lesions of arteriovenous, capillary, or lymphatic origin. Vascular malformations that occur inside bone tissue are rare. Herein, we report loss-of-function mutations in ELMO2 (which translates extracellular signals into cellular movements) that are causative for autosomal-recessive intraosseous vascular malformation (VMOS) in five different families. Individuals with VMOS suffer from life-threatening progressive expansion of the jaw, craniofacial, and other intramembranous bones caused by malformed blood vessels that lack a mature vascular smooth muscle layer. Analysis of primary fibroblasts from an affected individual showed that absence of ELMO2 correlated with a significant downregulation of binding partner DOCK1, resulting in deficient RAC1-dependent cell migration. Unexpectedly, elmo2-knockout zebrafish appeared phenotypically normal, suggesting that there might be human-specific ELMO2 requirements in bone vasculature homeostasis or genetic compensation by related genes. Comparative phylogenetic analysis indicated that elmo2 originated upon the appearance of intramembranous bones and the jaw in ancestral vertebrates, implying that elmo2 might have been involved in the evolution of these novel traits. The present findings highlight the necessity of ELMO2 for maintaining vascular integrity, specifically in intramembranous bones.

FMFilter: A fast model based variant filtering tool.

The availability of whole exome and genome sequencing has completely changed the structure of genetic disease studies. It is now possible to solve the disease causing mechanisms within shorter time and budgets. For this reason, mining out the valuable information from the huge amount of data produced by next generation techniques becomes a challenging task. Current tools analyze sequencing data in various methods. However, there is still need for fast, easy to use and efficacious tools. Considering genetic disease studies, there is a lack of publicly available tools which support compound heterozygous and de novo models. Also, existing tools either require advanced IT expertise or are inefficient for handling large variant files. In this work, we provide FMFilter, an efficient sieving tool for next generation sequencing data produced by genetic disease studies. We develop a software which allows to choose the inheritance model (recessive, dominant, compound heterozygous and de novo), the affected and control individuals. The program provides a user friendly Graphical User Interface which eliminates the requirement of advanced computer techniques. It has various filtering options which enable to eliminate the majority of the false alarms. FMFilter requires negligible memory, therefore it can easily handle very large variant files like multiple whole genomes with ordinary computers. We demonstrate the variant reduction capability and effectiveness of the proposed tool with public and in-house data for different inheritance models. We also compare FMFilter with the existing filtering software. We conclude that FMFilter provides an effective and easy to use environment for analyzing next generation sequencing data from Mendelian diseases.

A clinical variant in SCN1A inherited from a mosaic father cosegregates with a novel variant to cause Dravet syndrome in a consanguineous family.

A consanguineous family from Turkey having two children with intellectual disability exhibiting myoclonic, febrile and other generalized seizures was recruited to identify the genetic origin of these phenotypes. A combined approach of SNP genotyping and exome sequencing was employed both to screen genes associated with Dravet syndrome and to detect homozygous variants. Analysis of exome data was extended further to identify compound heterozygosity. Herein, we report identification of two paternally inherited genetic variants in SCN1A (rs121917918; p.R101Q and p.I1576T), one of which was previously implicated in Dravet syndrome. Interestingly, the previously reported clinical variant (rs121917918; p.R101Q) displayed mosaicism in the blood and saliva of the father. The study supported the genetic diagnosis of affected children as Dravet syndrome possibly due to the combined effect of one clinically associated (rs121917918; p.R101Q) and one novel (p.I1576T) variants in SCN1A gene. This finding is important given that heterozygous variants may be overlooked in standard exome scans of consanguineous families. Thus, we are presenting an interesting example, where the inheritance of the condition may be misinterpreted as recessive and identical by descent due to consanguinity and mosaicism in one of the parents.

HomSI: a homozygous stretch identifier from next-generation sequencing data.

UNLABELLED: In consanguineous families, as a result of inheriting the same genomic segments through both parents, the individuals have stretches of their genomes that are homozygous. This situation leads to the prevalence of recessive diseases among the members of these families. Homozygosity mapping is based on this observation, and in consanguineous families, several recessive disease genes have been discovered with the help of this technique. The researchers typically use single nucleotide polymorphism arrays to determine the homozygous regions and then search for the disease gene by sequencing the genes within this candidate disease loci. Recently, the advent of next-generation sequencing enables the concurrent identification of homozygous regions and the detection of mutations relevant for diagnosis, using data from a single sequencing experiment. In this respect, we have developed a novel tool that identifies homozygous regions using deep sequence data. Using *.vcf (variant call format) files as an input file, our program identifies the majority of homozygous regions found by microarray single nucleotide polymorphism genotype data. AVAILABILITY AND IMPLEMENTATION: HomSI software is freely available at www.igbam.bilgem.tubitak.gov.tr/softwares/HomSI, with an online manual.

Comparison of aggregators for multi-objective SNP selection.

SNPs (Single Nucleotide Polymorphisms) are genomic variants that associate with many genetic characteristics. These variants can also be utilized to track the on-going mutation in population genetics. The goal of this study was to select the most relevant SNP subsets for discriminating ethnic groups. Each SNP was evaluated by its: i) Mutual information, ii) Relief-F score, iii) Loadings of the first principal component, iv) Loadings of the second principal component. Combining these four feature ranking criteria in different ways, three different aggregation methods (Pareto Optimal, Condorcet, MC4) were compared with respect to their SNP selection accuracies. Results showed that SNP subsets chosen with Pareto Optimal yielded better classification accuracy.

Multi objective SNP selection using pareto optimality.

Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.