Kohlschütter–Tönz Syndrome: Mutations in ROGDI and Evidence of Genetic Heterogeneity

Kohlschütter–Tönz syndrome (KTS) is a rare autosomal recessive disorder characterized by amelogenesis imperfecta, psychomotor delay or regression and seizures starting early in childhood. KTS was established as a distinct clinical entity after the first report by Kohlschütter in 1974, and to date, only a total of 20 pedigrees have been reported. The genetic etiology of KTS remained elusive until recently when mutations in ROGDI were independently identified in three unrelated families and in five likely related Druze families. Herein, we report a clinical and genetic study of 10 KTS families. By using a combination of whole exome sequencing, linkage analysis, and Sanger sequencing, we identify novel homozygous or compound heterozygous ROGDI mutations in five families, all presenting with a typical KTS phenotype. The other families, mostly presenting with additional atypical features, were negative for ROGDI mutations, suggesting genetic heterogeneity of atypical forms of the disease.     

Towards a consensus for classification of mandibular condyle fractures

The merits of surgical treatment of fractures of the mandibular condyle versus non-surgical management remains highly controversial, despite a large volume of literature dedicated to this topic. One reason the controversy remains, is because most of the outcomes in the literature are not directly comparable. The disparate range of condylar fracture classifications used is one of the reasons that studies are not comparable. We sought to review classification systems for condylar fractures used in the recent scientific literature.Review of the literature from 2016 to 2019, looking for papers relating to fractures of the mandibular condyle. Papers identified were assessed for type of study, focus of study, classification system used.88 studies were identified, including prospective and retrospective cohort studies, randomised and non-randomised prospective studies, randomised controlled trials and case series. More studies focussed on epidemiological factors than surgical access, fixation or outcomes. 31 used no classification system, whilst 17 used unique classification systems and 40 used previously referenced classification systems.Classification systems are used to help separate clinical problems into distinguishable groups, where there is a difference in management or outcome depending on the distinguishing features.There is currently a wide diversity of classification systems used for condyle fractures, and as a result, comparisons of surgical access, fixation and outcomes are difficult to make. Having a single classification system across the published literature would allow easier comparison and the classification proposed by the AO group is recommended for future use.     

Combined NGS Approaches Identify Mutations in the Intraflagellar Transport Gene IFT140 in Skeletal Ciliopathies with Early Progressive Kidney Disease

Ciliopathies are genetically heterogeneous disorders characterized by variable expressivity and overlaps between different disease entities. This is exemplified by the short rib‐polydactyly syndromes, Jeune, Sensenbrenner, and Mainzer‐Saldino chondrodysplasia syndromes. These three syndromes are frequently caused by mutations in intraflagellar transport (IFT) genes affecting the primary cilia, which play a crucial role in skeletal and chondral development. Here, we identified mutations in IFT140, an IFT complex A gene, in five Jeune asphyxiating thoracic dystrophy (JATD) and two Mainzer‐Saldino syndrome (MSS) families, by screening a cohort of 66 JATD/MSS patients using whole exome sequencing and targeted resequencing of a customized ciliopathy gene panel. We also found an enrichment of rare IFT140 alleles in JATD compared with nonciliopathy diseases, implying putative modifier effects for certain alleles. IFT140 patients presented with mild chest narrowing, but all had end‐stage renal failure under 13 years of age and retinal dystrophy when examined for ocular dysfunction. This is consistent with the severe cystic phenotype of Ift140 conditional knockout mice, and the higher level of Ift140 expression in kidney and retina compared with the skeleton at E15.5 in the mouse. IFT140 is therefore a major cause of cono‐renal syndromes (JATD and MSS). The present study strengthens the rationale for IFT140 screening in skeletal ciliopathy spectrum patients that have kidney disease and/or retinal dystrophy.     

Mutation Detection by Clonal Sequencing of PCR Amplicons and Grouped Read Typing is Applicable to Clinical Diagnostics

We describe a sensitive technique for mutation detection using clonal sequencing. We analyzed DNA extracted from 13 cancer cell lines and 35 tumor samples and applied a novel approach to identify disease‐associated somatic mutations. By matching reads against an index of known variants, noise can be dramatically reduced, enabling the detection and quantification of those variants, even when they are present at less than 1% of the total sequenced population; this is comparable to, or better than, current diagnostic methods. Following the identification or exclusion of known variants, unmatched reads are grouped for BLAST searching to identify novel variants or contaminants. Known variants, novel variants, and contaminants were readily identified in tumor tissue using this approach. Our approach also enables an estimation of the per‐base sequencing error rate, providing a confidence threshold for interpretation of the results in the clinic. This novel approach has immediate applicability to clinical testing for disease‐associated genetic variants.     

Pathoscope: Species identification and strain attribution with unassembled sequencing data

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly—which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico “environmental” samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.The accurate and rapid identification of species and strains of pathogens is an essential component of biosurveillance from both human health and biodefense perspectives (Vaidyanathan 2011). For example, misidentification was among the issues that resulted in a 3-wk delay in accurate diagnosis of the recent outbreak of hemorrhagic Escherichia coli being due to strain O104:H4, resulting in over 3800 infections across 13 countries in Europe with 54 deaths (Frank et al. 2011). The most accurate diagnostic information, necessary for species identification and strain attribution, comes from the most refined level of biological data—genomic DNA sequences (Eppinger et al. 2011). Advances in DNA-sequencing technologies allows for the rapid collection of extraordinary amounts of genomic data, yet robust approaches to analyze this volume of data are just developing, from both statistical and algorithmic perspectives.Next-generation sequencing approaches have revolutionized the way we collect DNA sequence data, including for applications in pathology, bioforensics, and biosurveillance. Given a particular clinical or metagenomic sample, our goal is to identify the specific species, strains, or substrains present in the sample, as well as accurately estimate the proportions of DNA originating from each source genome in the sample. Current approaches for next-gen sequencing usually have read lengths between 25 and 1000 bp; however, these sequencing technologies include error rates that vary by approach and by samples. Such variation is typically less important for species identification given the relatively larger genetic divergences among species than among individuals within species. But for strain attribution, sequencing error has the potential to swamp out discriminatory signal in a data set, necessitating highly sensitive and refined computational models and a robust database for both species identification and strain attribution.Current methods for classifying metagenomic samples rely on one or more of three general approaches: composition or pattern matching (McHardy et al. 2007; Brady and Salzberg 2009; Segata et al. 2012), taxonomic mapping (Huson et al. 2007; Meyer et al. 2008; Monzoorul Haque et al. 2009; Gerlach and Stoye 2011; Patil et al. 2012; Segata et al. 2012), and whole-genome assembly (Kostic et al. 2011; Bhaduri et al. 2012). Composition and pattern-matching algorithms use predetermined patterns in the data, such as taxonomic clade markers (Segata et al. 2012), k-mer frequency, or GC content, often coupled with sophisticated classification algorithms such as support vector machines (McHardy et al. 2007; Patil et al. 2012) or interpolated Markov Models (Brady and Salzberg 2009) to classify reads to the species of interest. These approaches require intensive preprocessing of the genomic database before application. In addition, the classification rule and results can often change dramatically depending on the size and composition of the genome database.Taxonomy-based approaches typically rely on a “lowest common ancestor” approach (Huson et al. 2007), meaning that they identify the most specific taxonomic group for each read. If a read originates from a genomic region that shares homology with other organisms in the database, the read is assigned to the lowest taxonomic group that contains all of the genomes that share the homologous region. These methods are typically highly accurate for higher-level taxonomic levels (e.g., phylum and family), but experience reduced accuracy at lower levels (e.g., species and strain) (Gerlach and Stoye 2011). Furthermore, these approaches are not informative when the reads originate from one or more species or strains that are closely related to each other or different organisms in the database. In these cases, all of the reads can be reassigned to higher-level taxonomies, thus failing to identify the specific species or strains contained in the sample.Assembly-based algorithms can often lead to the most accurate strain identification. However, these methods also require the assembly of a whole genome from a sample, which is a computationally difficult and time-consuming process that requires large numbers of reads to achieve an adequate accuracy—often on the order of 50–100× coverage of the target genome (Schatz et al. 2010). Given current sequencing depths, obtaining this level of coverage is usually possible for purified samples, but coverage levels may not be sufficient for mixed samples or in multiplexed sequencing runs. Assembly approaches are further complicated by the fact that data collection at a crime scene or hospital might include additional environmental components in the biological sample (host genome or naturally occurring bacterial and viral species), thus requiring multiple filtering and alignment steps in order to obtain reads specific to the pathogen of interest.Here we describe an accurate and efficient approach to analyze next-generation sequence data for species identification and strain attribution that capitalizes on a Bayesian statistical framework implemented in the new software package Pathoscope v1.0. Our approach accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of reference genomes. Importantly, our approach incorporates the possibility that multiple species can be present in the sample or that the target strain is not even contained within the reference database. It also accurately discriminates between very closely related strains of the same species with much less than 1× coverage of the genome and without the need for sequence assembly or complex preprocessing of the database or taxonomy. No other method in the literature can identify species or substrains in such a direct and automatic manner and without the need for large numbers of reads. We demonstrate our approach through application to next-generation DNA sequence data from a recent outbreak of the hemorrhagic E. coli (O104:H4) strain in Europe (Frank et al. 2011; Rohde et al. 2011; Turner 2011) and on purified and in silico mixed samples from several other known bacterial agents that impact human health. Software and data examples for our approach are freely available for download at https://sourceforge.net/projects/pathoscope/.     

Segment-embedded frame definition affects the hip joint centre precision during walking

Due to marker-specific soft tissue artefacts, the choice of the markers defining the segment-embedded frame affects the functional joint centre location, with subsequent error propagation to joint kinematics and kinetics in gait analysis. Our aim was to assess the effect of the number and placement of markers on the precision of the hip joint centre (HJC) location during walking.Twelve markers (2 x 6) were attached to the pelvis and left thigh of 15 young male subjects. Set-up movements were collected to locate an optimised functional HJC. For all permutations of three from six markers, a HJC was located and subsequently reconstructed in a static trial and during walking. Precision measures with two different definitions of the origin, namely a single maker or their mean-point, and using three, four, five and six were calculated. Finally, marker triads that reduced the variability of the HJC location were determined. Both the number of markers and method for defining the origin significantly affected the HJC precision during static and walking trials. For walking, precision of 39 mm using three markers improved to 5 mm using redundant markers and the mean marker position as the segment origin. Markers placed close to the joint gave more consistent results.