The Genetic Basis of Thermal Reaction Norm Evolution in Lab and Natural Phage Populations

Two major goals of laboratory evolution experiments are to integrate from genotype to phenotype to fitness, and to understand the genetic basis of adaptation in natural populations. Here we demonstrate that both goals are possible by re-examining the outcome of a previous laboratory evolution experiment in which the bacteriophage G4 was adapted to high temperatures. We quantified the evolutionary changes in the thermal reaction norms—the curves that describe the effect of temperature on the growth rate of the phages—and decomposed the changes into modes of biological interest. Our analysis indicated that changes in optimal temperature accounted for almost half of the evolutionary changes in thermal reaction norm shape, and made the largest contribution toward adaptation at high temperatures. Genome sequencing allowed us to associate reaction norm shape changes with particular nucleotide mutations, and several of the identified mutations were found to be polymorphic in natural populations. Growth rate measures of natural phage that differed at a site that contributed substantially to adaptation in the lab indicated that this mutation also underlies thermal reaction norm shape variation in nature. In combination, our results suggest that laboratory evolution experiments may successfully predict the genetic bases of evolutionary responses to temperature in nature. The implications of this work for viral evolution arise from the fact that shifts in the thermal optimum are characterized by tradeoffs in performance between high and low temperatures. Optimum shifts, if characteristic of viral adaptation to novel temperatures, would ensure the success of vaccine development strategies that adapt viruses to low temperatures in an attempt to reduce virulence at higher (body) temperatures

Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.Comment: 61 pages double spaced, 8 figures, accepted for publication in Ap

ASL expression in ALDH1A1+ neurons in the substantia nigra metabolically contributes to neurodegenerative phenotype

Argininosuccinate lyase (ASL) is essential for the NO-dependent regulation of tyrosine hydroxylase (TH) and thus for catecholamine production. Using a conditional mouse model with loss of ASL in catecholamine neurons, we demonstrate that ASL is expressed in dopaminergic neurons in the substantia nigra pars compacta, including the ALDH1A1 + subpopulation that is pivotal for the pathogenesis of Parkinson disease (PD). Neuronal loss of ASL results in catecholamine deficiency, in accumulation and formation of tyrosine aggregates, in elevation of α-synuclein, and phenotypically in motor and cognitive deficits. NO supplementation rescues the formation of aggregates as well as the motor deficiencies. Our data point to a potential metabolic link between accumulations of tyrosine and seeding of pathological aggregates in neurons as initiators for the pathological processes involved in neurodegeneration. Hence, interventions in tyrosine metabolism via regulation of NO levels may be therapeutic beneficial for the treatment of catecholamine-related neurodegenerative disorders

The Endocrine Disruptor Mono-(2-Ethylhexyl) Phthalate Affects the Differentiation of Human Liposarcoma Cells (SW 872)

Esters of phthalic acid (phthalates) are largely used in industrial plastics, medical devices, and pharmaceutical formulations. They are easily released from plastics into the environment and can be found in measurable levels in human fluids. Phthalates are agonists for peroxisome proliferator-activated receptors (PPARs), through which they regulate translocator protein (TSPO; 18 kDa) transcription in a tissue-specific manner. TSPO is a drug- and cholesterol-binding protein involved in mitochondrial respiration, steroid formation, and cell proliferation. TSPO has been shown to increase during differentiation and decrease during maturation in mouse adipocytes. The purpose of this study was to establish the effect of mono-(2-ethylhexyl) phthalate (MEHP) on the differentiation of human SW 872 preadipocyte cells, and examine the role of TSPO in the process. After 4 days of treatment with 10 µM MEHP, we observed changes in the transcription of acetyl-CoA carboxylase alpha, adenosine triphosphate citrate lyase, glucose transporters 1 and 4, and the S100 calcium binding protein B, all of which are markers of preadipocyte differentiation. These observed gene expression changes coincided with a decrease in cellular proliferation without affecting cellular triglyceride content. Taken together, these data suggest that MEHP exerts a differentiating effect on human preadipocytes. Interestingly, MEHP was able to temporarily increase TSPO mRNA levels through the PPAR-α and β/δ pathways. These results suggest that TSPO can be considered an important player in the differentiation process itself, or alternatively a factor whose presence is essential for adipocyte development

Swimming with Predators and Pesticides: How Environmental Stressors Affect the Thermal Physiology of Tadpoles

To forecast biological responses to changing environments, we need to understand how a species’s physiology varies through space and time and assess how changes in physiological function due to environmental changes may interact with phenotypic changes caused by other types of environmental variation. Amphibian larvae are well known for expressing environmentally induced phenotypes, but relatively little is known about how these responses might interact with changing temperatures and their thermal physiology. To address this question, we studied the thermal physiology of grey treefrog tadpoles (Hyla versicolor) by determining whether exposures to predator cues and an herbicide (Roundup) can alter their critical maximum temperature (CTmax) and their swimming speed across a range of temperatures, which provides estimates of optimal temperature (Topt) for swimming speed and the shape of the thermal performance curve (TPC). We discovered that predator cues induced a 0.4uC higher CTmax value, whereas the herbicide had no effect. Tadpoles exposed to predator cues or the herbicide swam faster than control tadpoles and the increase in burst speed was higher near Topt. In regard to the shape of the TPC, exposure to predator cues increased Topt by 1.5uC, while exposure to the herbicide marginally lowered Topt by 0.4uC. Combining predator cues and the herbicide produced an intermediate Topt that was 0.5uC higher than the control. To our knowledge this is the first study to demonstrate a predator altering the thermal physiology of amphibian larvae (prey) by increasing CTmax, increasing the optimum temperature, and producing changes in the thermal performance curves. Furthermore, these plastic responses of CTmax and TPC to different inducing environments should be considered when forecasting biological responses to global warming.Peer reviewe

The Effects of Apolipoprotein F Deficiency on High Density Lipoprotein Cholesterol Metabolism in Mice

Apolipoprotein F (apoF) is 29 kilodalton secreted sialoglycoprotein that resides on the HDL and LDL fractions of human plasma. Human ApoF is also known as Lipid Transfer Inhibitor protein (LTIP) based on its ability to inhibit cholesteryl ester transfer protein (CETP)-mediated transfer events between lipoproteins. In contrast to other apolipoproteins, ApoF is predicted to lack strong amphipathic alpha helices and its true physiological function remains unknown. We previously showed that overexpression of Apolipoprotein F in mice reduced HDL cholesterol levels by 20–25% by accelerating clearance from the circulation. In order to investigate the effect of physiological levels of ApoF expression on HDL cholesterol metabolism, we generated ApoF deficient mice. Unexpectedly, deletion of ApoF had no substantial impact on plasma lipid concentrations, HDL size, lipid or protein composition. Sex-specific differences were observed in hepatic cholesterol content as well as serum cholesterol efflux capacity. Female ApoF KO mice had increased liver cholesteryl ester content relative to wild type controls on a chow diet (KO: 3.4+/−0.9 mg/dl vs. WT: 1.2+/−0.3 mg/dl, p<0.05). No differences were observed in ABCG1-mediated cholesterol efflux capacity in either sex. Interestingly, ApoB-depleted serum from male KO mice was less effective at promoting ABCA1-mediated cholesterol efflux from J774 macrophages relative to WT controls

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe

Solving unsolved rare neurological diseases-a Solve-RD viewpoint.

Funder: Durch Princess Beatrix Muscle Fund Durch Speeren voor Spieren Muscle FundFunder: University of Tübingen Medical Faculty PATE programFunder: European Reference Network for Rare Neurological Diseases | 739510Funder: European Joint Program on Rare Diseases (EJP-RD COFUND-EJP) | 44140962

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics