Exome Sequencing of Index Patients with Retinal Dystrophies as a Tool for Molecular Diagnosis

Background

Retinal dystrophies (RD) are a group of hereditary diseases that lead to debilitating visual impairment and are usually transmitted as a Mendelian trait. Pathogenic mutations can occur in any of the 100 or more disease genes identified so far, making molecular diagnosis a rather laborious process. In this work we explored the use of whole exome sequencing (WES) as a tool for identification of RD mutations, with the aim of assessing its applicability in a diagnostic context.

Methodology/Principal Findings

We ascertained 12 Spanish families with seemingly recessive RD. All of the index patients underwent mutational pre-screening by chip-based sequence hybridization and resulted to be negative for known RD mutations. With the exception of one pedigree, to simulate a standard diagnostic scenario we processed by WES only the DNA from the index patient of each family, followed by in silico data analysis. We successfully identified causative mutations in patients from 10 different families, which were later verified by Sanger sequencing and co-segregation analyses. Specifically, we detected pathogenic DNA variants (∼50% novel mutations) in the genes RP1, USH2A, CNGB3, NMNAT1, CHM, and ABCA4, responsible for retinitis pigmentosa, Usher syndrome, achromatopsia, Leber congenital amaurosis, choroideremia, or recessive Stargardt/cone-rod dystrophy cases.

Conclusions/Significance

Despite the absence of genetic information from other family members that could help excluding nonpathogenic DNA variants, we could detect causative mutations in a variety of genes known to represent a wide spectrum of clinical phenotypes in 83% of the patients analyzed. Considering the constant drop in costs for human exome sequencing and the relative simplicity of the analyses made, this technique could represent a valuable tool for molecular diagnostics or genetic research, even in cases for which no genotypes from family members are available.     

Bioelectrical Impedance Vector Analysis (BIVA) in Slovak population: application in a clinical sample

The purpose of this study is to provide new data on body composition in the Slovak population, particularly impedance vector components according to sex and age, relevant for bioelectrical impedance vector analysis (BIVA) in a clinical sample. The reference sample consisted of 1543 apparently healthy individuals (1007 females and 536 males), aged from 18 to 92 years and of 60 patients with Parkinson’s disease (PD) (26 females and 34 males), aged from 40 to 81 years. Bioelectrical parameters of resistance (R) and reactance (Xc) were measured with a monofrequency analyser (BIA 101). BIVA was used to analyse tissue electric properties in control subjects and patients with PD. The mean vector position differed significantly between PD patients and healthy controls in males of age subgroups 60–69 years and 70–79 years, respectively. These results were conterminous with significant Hotelling’s T²-test; 60–69 y T²=7.8, P=0.024 and 70–79 y T²=7.6, P=0.026. In the RXc-score graph three patients had values outside the 95% ellipse. Altered tissue electric properties were present in 23.5% of males and 15.4% of females. Distribution of impedance vector components in different age categories of healthy Slovak subjects are relevant to comparative population studies and to clinical practice.     

Genetic Instability and Intratumoral Heterogeneity in Neuroblastoma with MYCN Amplification Plus 11q Deletion

Background/Aim

Genetic analysis in neuroblastoma has identified the profound influence of MYCN amplification and 11q deletion in patients’ prognosis. These two features of high-risk neuroblastoma usually occur as mutually exclusive genetic markers, although in rare cases both are present in the same tumor. The purpose of this study was to characterize the genetic profile of these uncommon neuroblastomas harboring both these high-risk features.

Methods

We selected 18 neuroblastomas with MNA plus 11q loss detected by FISH. Chromosomal aberrations were analyzed using Multiplex Ligation-dependent Probe Amplification and Single Nucleotide Polymorphism array techniques.

Results and Conclusion

This group of tumors has approximately the same high frequency of aberrations as found earlier for 11q deleted tumors. In some cases, DNA instability generates genetic heterogeneity, and must be taken into account in routine genetic diagnosis.     

Brain trauma and autophagy: What flies and mice can teach us about conserved responses

Drosophila models have been successfully used to identify many genetic components that affect neurodegenerative disorders. Recently, there has been a growing interest in identifying innate and environmental factors that influence the individual outcomes following traumatic brain injury (TBI). This includes both severe TBI and more subtle, mild TBI (mTBI), which is common in people playing contact sports. Autophagy, as a clearance pathway, exerts protective effects in multiple neurological disease models. In a recent publication, we highlighted the development of a novel repetitive mTBI system using Drosophila, which recapitulates several phenotypes associated with trauma in mammalian models. In particular, flies subjected to mTBI exhibit an acute impairment of the macroautophagy/autophagy pathway that is restored 1 wk following traumatic injury exposure. These phenotypes closely resemble temporary autophagy defects observed in a mouse TBI model. Through these studies, we also identified methods to directly assess autophagic responses in the fly nervous system and laid the groundwork for future studies designed to identify genetic, epigenetic and environmental factors that have an impact on TBI outcomes.     

Comparison of Chemical Composition in Tuber aestivum Vittad. of Different Geographical Origin

Truffles are prized and nutrition‐rich edible hypogeous fungi. The aim of this study was a comprehensive investigation of chemical composition of Burgundy truffle (Tuber aestivum Vittad .). We tried to answer the question: what is the impact of the environment on the truffle quality. To know the nutritional value of Burgundy truffle we compared lipids, proteins, saccharides, polyphenolics, flavonoids, total sterols, ergosterol, volatile flavour and aroma compounds content in fruit bodies of the fungus collected in three different geographical regions, i.e., Poland, Slovakia, and Italy. A comparison of the above mentioned compounds is especially interesting due to environmental and climatic differences among the studied geographical regions. Results revealed that fruit bodies of T. aestivum from Poland and Slovakia possessed nearly similar content of proteins, total sterols, and saccharides. The fruiting bodies from Italy contained significantly larger amounts of most of the investigated compounds. In turn, Polish specimens had higher content of lipids and polyphenolics than Slovak and Italian ones. We have found higher similarity of volatile compounds composition between Polish and Italian specimens than those of Polish and Slovak origin.     

Predicting microbial traits with phylogenies

Phylogeny reflects genetic and phenotypic traits in Bacteria and Archaea. The phylogenetic conservatism of microbial traits has prompted the application of phylogeny-based algorithms to predict unknown trait values of extant taxa based on the traits of their evolutionary relatives to estimate, for instance, rRNA gene copy numbers, gene contents or tolerance to abiotic conditions. Unlike the ‘macrobial'' world, microbial ecologists face scenarios potentially compromising the accuracy of trait reconstruction methods, as, for example, extremely large phylogenies and limited information on the traits of interest. We review 990 bacterial and archaeal traits from the literature and support that phylogenetic trait conservatism is widespread through the tree of life, while revealing that it is generally weak for ecologically relevant phenotypic traits and high for genetically complex traits. We then perform a simulation exercise to assess the accuracy of phylogeny-based trait predictions in common scenarios faced by microbial ecologists. Our simulations show that ca. 60% of the variation in phylogeny-based trait predictions depends on the magnitude of the trait conservatism, the number of species in the tree, the proportion of species with unknown trait values and the mean distance in the tree to the nearest neighbour with a known trait value. Results are similar for both binary and continuous traits. We discuss these results under the light of the reviewed traits and provide recommendations for the use of phylogeny-based trait predictions for microbial ecologists.Trait-based approaches in community ecology studies are becoming increasingly appealing for microbial ecologists partly because metagenomic sequencing allows surveying molecular functions (Green et al., 2008; Lauro et al., 2009; Burke et al., 2011; Raes et al., 2011; Brown et al., 2014; Fierer et al., 2014). Although genetic data can provide precise information on cellular processes or metabolic pathways, they are generally blind to other ecologically relevant phenotypic traits such as the tolerance to certain abiotic conditions or the specific growth rate (but see Vieira-Silva and Rocha, 2010). Unlike ‘macrobial'' ecologists, who can directly observe phenotypic characters of plants and animals, microbial ecologists usually face situations where most of the phenotypes of their study organisms are unknown. This difficulty relies on the fact that gathering phenotypic (physiological, morphological, biochemical) data requires culturing microbial species. The unbalanced growth of genotypic vs phenotypic information is currently challenging microbial ecologists to work with phylogenetic trees of increasing size (hundreds to thousands of species) in which the percentage of species with unknown traits becomes larger and larger.Recent evidence indicate that phylogeny reflects molecular functions and phenotypes in Bacteria and Archaea (Langille et al., 2013; Martiny et al., 2013). This is due to the phylogenetic conservatism of microbial traits (Martiny et al., 2013), which likely arises from microbial evolution mostly proceeding by vertical gene inheritance rather than horizontal gene transfer (Kurland et al., 2003, see Fraser et al., 2007 for theoretical models on the role of horizontal gene transfer in bacterial speciation). At present, the massive sequencing of microbes in the environment is providing a huge amount of genetic information that is extremely useful to reconstruct the phylogenetic relationships among microbial lineages. This fact has triggered the interest of microbial ecologists to apply the methods developed to predict unobserved trait values of extant taxa based on the traits observed in their evolutionary relatives (Kembel et al., 2012; Langille et al., 2013; Angly et al., 2014, see review in Zaneveld and Thurber, 2014). All these methods are based on the existence of a significant phylogenetic signal or, in other words, in the fact that close relatives have more similar traits than expected by chance. Phylogeny-based trait prediction procedures (PTP hereafter) in microbes have been mainly performed under the phylogenetic generalized least squares framework (Martins and Hansen, 1997; Garland and Ives, 2000). Specifically, the trait value (for continuous traits) or state (for binary traits) of the focal species have been reconstructed through ancestral state reconstructions after rerooting the phylogeny at the most recent common ancestor of the taxon with unobserved trait and the rest of the tree (Kembel et al., 2012). The accuracy of PTP methods has been typically assessed under ‘macrobial'' scenarios containing phylogenies of moderate size, with low-to-medium proportion of species with unknown traits and significant phylogenetic signals. For example, Fagan et al. (2013) predicted population growth rates of mammals in phylogenies of 42–65 species containing 54–64% of unknowns and a significant phylogenetic signal (Blomberg et al., 2003; Blomberg''s K) ranging from 0.68 to 1.42. However, the current microbial scenarios derived from high-throughput sequencing projects face large-sized phylogenies (hundreds to thousands tips) with a high number of species with unknown traits and varying phylogenetic signals jeopardizing the applicability of PTP methods (Zaneveld and Thurber, 2014).The extent to which phylogeny reflects phenotype is strongly dependent on the degree of conservatism with which the focal trait has evolved. For instance, complex traits that involve many genes (for example, photosynthesis or methanogenesis) show higher conservatism than simpler traits, such as the consumption of a specific carbon source (Martiny et al., 2013). Furthermore, certain traits such as those related to genes encoding antibiotic or metal resistance are particularly prone to be horizontally transferred (Bruins et al., 2000), a process that can blur their phylogenetic signal. Therefore, if phylogenetic relatedness is to be used to infer the phenotype, the phylogenetic conservatism of the target trait needs to be quantified in every case.Altogether, the abovementioned observations indicate that the possibility to estimate phenotypes from phylogenies depends on the amount of phylogenetic and phenotypic information available to predict the unobserved trait values. Here we provide a simulation exercise to test the accuracy of the most widely used PTP method in microbial ecology to predict continuous trait values and binary trait states of extant taxa with different amount of phenotypic and phylogenetic information. We simulated several situations faced by microbial ecologists, including phylogenies of different sizes in which a small (P=0.3), medium (P=0.6) or large (P=0.9) proportion of species have unknown trait values. The correlations between the actual and the predicted trait values were obtained for characters evolved under different degree of conservatism. Finally, we put these values in the context of the phylogenetic signals described in the literature for different continuous and binary microbial traits and provide some recommendations for future analyses aimed to predict microbial traits with the help of the phylogenetic information.     

Lipid remodelling is a widespread strategy in marine heterotrophic bacteria upon phosphorus deficiency

Upon phosphorus (P) deficiency, marine phytoplankton reduce their requirements for P by replacing membrane phospholipids with alternative non-phosphorus lipids. It was very recently demonstrated that a SAR11 isolate also shares this capability when phosphate starved in culture. Yet, the extent to which this process occurs in other marine heterotrophic bacteria and in the natural environment is unknown. Here, we demonstrate that the substitution of membrane phospholipids for a variety of non-phosphorus lipids is a conserved response to P deficiency among phylogenetically diverse marine heterotrophic bacteria, including members of the Alphaproteobacteria and Flavobacteria. By deletion mutagenesis and complementation in the model marine bacterium Phaeobacter sp. MED193 and heterologous expression in recombinant Escherichia coli, we confirm the roles of a phospholipase C (PlcP) and a glycosyltransferase in lipid remodelling. Analyses of the Global Ocean Sampling and Tara Oceans metagenome data sets demonstrate that PlcP is particularly abundant in areas characterized by low phosphate concentrations. Furthermore, we show that lipid remodelling occurs seasonally and responds to changing nutrient conditions in natural microbial communities from the Mediterranean Sea. Together, our results point to the key role of lipid substitution as an adaptive strategy enabling heterotrophic bacteria to thrive in the vast P-depleted areas of the ocean.