Global Transcriptional Programs in Peripheral Nerve Endoneurium and DRG Are Resistant to the Onset of Type 1 Diabetic Neuropathy in Ins2Akita/+ Mice

While the morphological and electrophysiological changes underlying diabetic peripheral neuropathy (DPN) are relatively well described, the involved molecular mechanisms remain poorly understood. In this study, we investigated whether phenotypic changes associated with early DPN are correlated with transcriptional alterations in the neuronal (dorsal root ganglia [DRG]) or the glial (endoneurium) compartments of the peripheral nerve. We used Ins2Akita/+ mice to study transcriptional changes underlying the onset of DPN in type 1 diabetes mellitus (DM). Weight, blood glucose and motor nerve conduction velocity (MNCV) were measured in Ins2Akita/+ and control mice during the first three months of life in order to determine the onset of DPN. Based on this phenotypic characterization, we performed gene expression profiling using sciatic nerve endoneurium and DRG isolated from pre-symptomatic and early symptomatic Ins2Akita/+ mice and sex-matched littermate controls. Our phenotypic analysis of Ins2Akita/+ mice revealed that DPN, as measured by reduced MNCV, is detectable in affected animals already one week after the onset of hyperglycemia. Surprisingly, the onset of DPN was not associated with any major persistent changes in gene expression profiles in either sciatic nerve endoneurium or DRG. Our data thus demonstrated that the transcriptional programs in both endoneurial and neuronal compartments of the peripheral nerve are relatively resistant to the onset of hyperglycemia and hypoinsulinemia suggesting that either minor transcriptional alterations or changes on the proteomic level are responsible for the functional deficits associated with the onset of DPN in type 1 DM

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets

Cardiovascular Response to Beta-Adrenergic Blockade or Activation in 23 Inbred Mouse Strains

We report the characterisation of 27 cardiovascular-related traits in 23 inbred mouse strains. Mice were phenotyped either in response to chronic administration of a single dose of the β-adrenergic receptor blocker atenolol or under a low and a high dose of the β-agonist isoproterenol and compared to baseline condition. The robustness of our data is supported by high trait heritabilities (typically H2>0.7) and significant correlations of trait values measured in baseline condition with independent multistrain datasets of the Mouse Phenome Database. We then focused on the drug-, dose-, and strain-specific responses to β-stimulation and β-blockade of a selection of traits including heart rate, systolic blood pressure, cardiac weight indices, ECG parameters and body weight. Because of the wealth of data accumulated, we applied integrative analyses such as comprehensive bi-clustering to investigate the structure of the response across the different phenotypes, strains and experimental conditions. Information extracted from these analyses is discussed in terms of novelty and biological implications. For example, we observe that traits related to ventricular weight in most strains respond only to the high dose of isoproterenol, while heart rate and atrial weight are already affected by the low dose. Finally, we observe little concordance between strain similarity based on the phenotypes and genotypic relatedness computed from genomic SNP profiles. This indicates that cardiovascular phenotypes are unlikely to segregate according to global phylogeny, but rather be governed by smaller, local differences in the genetic architecture of the various strains

The Connectome Visualization Utility: Software for Visualization of Human Brain Networks

In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires sophisticated visualization and analysis software. The current availability of software packages to analyze the human connectome is limited. The Connectome Visualization Utility (CVU) is a new software package designed for the visualization and network analysis of human brain networks. CVU complements existing software packages by offering expanded interactive analysis and advanced visualization features, including the automated visualization of networks in three different complementary styles and features the special visualization of scalar graph theoretical properties and modular structure. By decoupling the process of network creation from network visualization and analysis, we ensure that CVU can visualize networks from any imaging modality. CVU offers a graphical user interface, interactive scripting, and represents data uses transparent neuroimaging and matrix-based file types rather than opaque application-specific file formats

An Atlas for Schistosoma mansoni Organs and Life-Cycle Stages Using Cell Type-Specific Markers and Confocal Microscopy

Schistosomiasis (bilharzia) is a tropical disease caused by trematode parasites (Schistosoma) that affects hundreds of millions of people in the developing world. Currently only a single drug (praziquantel) is available to treat this disease, highlighting the importance of developing new techniques to study Schistosoma. While molecular advances, including RNA interference and the availability of complete genome sequences for two Schistosoma species, will help to revolutionize studies of these animals, an array of tools for visualizing the consequences of experimental perturbations on tissue integrity and development needs to be made widely available. To this end, we screened a battery of commercially available stains, antibodies and fluorescently labeled lectins, many of which have not been described previously for analyzing schistosomes, for their ability to label various cell and tissue types in the cercarial stage of S. mansoni. This analysis uncovered more than 20 new markers that label most cercarial tissues, including the tegument, the musculature, the protonephridia, the secretory system and the nervous system. Using these markers we present a high-resolution visual depiction of cercarial anatomy. Examining the effectiveness of a subset of these markers in S. mansoni adults and miracidia, we demonstrate the value of these tools for labeling tissues in a variety of life-cycle stages. The methodologies described here will facilitate functional analyses aimed at understanding fundamental biological processes in these parasites

Regulatory RNAs and chromatin modification in dosage compensation: A continuous path from flies to humans?

Chromosomal sex determination is a widely distributed strategy in nature. In the most classic scenario, one sex is characterized by a homologue pair of sex chromosomes, while the other includes two morphologically and functionally distinct gonosomes. In mammalian diploid cells, the female is characterized by the presence of two identical X chromosomes, while the male features an XY pair, with the Y bearing the major genetic determinant of sex, i.e. the SRY gene. In other species, such as the fruitfly, sex is determined by the ratio of autosomes to X chromosomes. Regardless of the exact mechanism, however, all these animals would exhibit a sex-specific gene expression inequality, due to the different number of X chromosomes, a phenomenon inhibited by a series of genetic and epigenetic regulatory events described as "dosage compensation". Since adequate available data is currently restricted to worms, flies and mammals, while for other groups of animals, such as reptiles, fish and birds it is very limited, it is not yet clear whether this is an evolutionary conserved mechanism. However certain striking similarities have already been observed among evolutionary distant species, such as Drosophila melanogaster and Mus musculus. These mainly refer to a) the need for a counting mechanism, to determine the chromosomal content of the cell, i.e. the ratio of autosomes to gonosomes (a process well understood in flies, but still hypothesized in mammals), b) the implication of non-translated, sex-specific, regulatory RNAs (roX and Xist, respectively) as key elements in this process and the location of similar mediators in the Z chromosome of chicken c) the inclusion of a chromatin modification epigenetic final step, which ensures that gene expression remains stably regulated throughout the affected area of the gonosome. This review summarizes these points and proposes a possible role for comparative genetics, as they seem to constitute proof of maintained cell economy (by using the same basic regulatory elements in various different scenarios) throughout numerous centuries of evolutionary history

Genetic overlap between endometriosis and endometrial cancer: evidence from cross-disease genetic correlation and GWAS meta-analyses.

Epidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg  = 0.23, P = 9.3 × 10-3 ), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10-3 ) and concordance in effect direction (P = 2.0 × 10-3 ) between the two diseases. Cross-disease GWAS meta-analysis highlighted 13 distinct loci associated at P ≤ 10-5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10-8 , OR = 1.11, 95% CI = 1.07-1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross-disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases

Pharmacogenetics: data, concepts and tools to improve drug discovery and drug treatment

Variation in the human genome is a most important cause of variable response to drugs and other xenobiotics. Susceptibility to almost all diseases is determined to some extent by genetic variation. Driven by the advances in molecular biology, pharmacogenetics has evolved within the past 40 years from a niche discipline to a major driving force of clinical pharmacology, and it is currently one of the most actively pursued disciplines in applied biomedical research in general. Nowadays we can assess more than 1,000,000 polymorphisms or the expression of more than 25,000 genes in each participant of a clinical study – at affordable costs. This has not yet significantly changed common therapeutic practices, but a number of physicians are starting to consider polymorphisms, such as those in CYP2C9, CYP2C19, CYP2D6, TPMT and VKORC1, in daily medical practice. More obviously, pharmacogenetics has changed the practices and requirements in preclinical and clinical drug research; large clinical trials without a pharmacogenomic add-on appear to have become the minority. This review is about how the discipline of pharmacogenetics has evolved from the analysis of single proteins to current approaches involving the broad analyses of the entire genome and of all mRNA species or all metabolites and other approaches aimed at trying to understand the entire biological system. Pharmacogenetics and genomics are becoming substantially integrated fields of the profession of clinical pharmacology, and education in the relevant methods, knowledge and concepts form an indispensable part of the clinical pharmacology curriculum and the professional life of pharmacologists from early drug discovery to pharmacovigilance