Prevalence of the E321G MYH1 variant for immune-mediated myositis and nonexertional rhabdomyolysis in performance subgroups of American Quarter Horses.

BackgroundImmune-mediated myositis (IMM) in American Quarter Horses (QHs) causes acute muscle atrophy and lymphocytic infiltration of myofibers. Recently, an E321G mutation in a highly conserved region of the myosin heavy chain 1 (MYH1) gene was associated with susceptibility to IMM and nonexertional rhabdomyolysis.ObjectivesTo estimate prevalence of the E321G MYH1 variant in the QH breed and performance subgroups.AnimalsThree-hundred seven elite performance QHs and 146 random registered QH controls.MethodsProspective genetic survey. Elite QHs from barrel racing, cutting, halter, racing, reining, Western Pleasure, and working cow disciplines and randomly selected registered QHs were genotyped for the E321G MYH1 variant and allele frequencies were calculated.ResultsThe E321G MYH1 variant allele frequency was 0.034 ± 0.011 in the general QH population (6.8% of individuals in the breed) and the highest among the reining (0.135 ± 0.040; 24.3% of reiners), working cow (0.085 ± 0.031), and halter (0.080 ± 0.027) performance subgroups. The E321G MYH1 variant was present in cutting (0.044 ± 0.022) and Western Pleasure (0.021 ± 0.015) QHs at lower frequency and was not observed in barrel racing or racing QHs.Conclusions and clinical importanceKnowing that reining and working cow QHs have the highest prevalence of the E321G MYH1 variant and that the variant is more prevalent than the alleles for hereditary equine regional dermal asthenia and hyperkalemic periodic paralysis in the general QH population will guide the use of genetic testing for diagnostic and breeding purposes

Draft versus finished sequence data for DNA and protein diagnostic signature development

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10(−3)–10(−5) (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures

Type 2 polysaccharide storage myopathy in Quarter Horses is a novel glycogen storage disease causing exertional rhabdomyolysis

Background: Both type 1 (PSSM1) and type 2 polysaccharide storage myopathy (PSSM2) are characterised by aggregates of abnormal polysaccharide in skeletal muscle. Whereas the genetic basis for PSSM1 is known (R309H GYS1), the cause of PSSM2 in Quarter Horses (PSSM2-QH) is unknown and glycogen concentrations not defined. Objectives: To characterise the histopathological and biochemical features of PSSM2-QH and determine if an associated monogenic variant exists in genes known to cause glycogenosis. Study design: Retrospective case control. Methods: Sixty-four PSSM2-QH, 30 PSSM1-QH and 185 control-QH were identified from a biopsy repository and clinical data, histopathology scores (0–3), glycogen concentrations and selected glycolytic enzyme activities compared. Coding sequences of 12 genes associated with muscle glycogenoses were identified from whole genome sequences and compared between seven PSSM2-QH and five control-QH. Results: Exertional rhabdomyolysis in PSSM2-QH occurred predominantly in barrel racing and working cow/roping performance types and improved with regular exercise and a low starch/fat-supplemented diet. Histopathological scores, including the amount of amylase-resistant polysaccharide (PSSM2-QH 1.4 ± 0.6, PSSM1-QH 2.1 ± 0.3, control-QH 0 ± 0, p \u3c 0.001), and glycogen concentrations (PSSM2-QH 129 ± 62, PSSM1-QH 175 ± 9, control-QH 80 ± 27 mmol/kg, p \u3c 0.0001) were intermediate in PSSM2-QH with significant differences among groups. In PSSM2-QH, abnormal polysaccharide had a less filamentous ultrastructure than PSSM1-QH and phosphorylase and phosphofructokinase activities were normal. Seventeen of 30 PSSM2-QH with available pedigrees descended from one of three stallions within four generations. Of the 29 predicted high or moderate impact genetic variants identified in candidate genes, none were present in only PSSM2-QH and absent in control-QH

CTBP1/CYP19A1/Estradiol axis together with adipose tissue impacts over prostate cancer growth associated to metabolic syndrome

Metabolic syndrome (MeS) increases prostate cancer (PCa) risk and aggressiveness. Cterminal binding protein 1 (CTBP1) is a transcriptional co-repressor of tumor suppressor genes that is activated by low NAD+/NADH ratio. Previously, our group established a MeS and PCa mice model that identified CTBP1 as a novel link associating both diseases. We found that CTBP1 controls the transcription of aromatase (CYP19A1), a key enzyme that converts androgens to estrogens. The aim of this work was to investigate the mechanism that explains CTBP1 as a link between MeS and PCa based on CYP19A1 and estrogen synthesis regulation using PCa cell lines, MeS/PCa mice and adipose co-culture systems. We found that CTBP1 and E1A binding protein p300 (EP300) bind to CYP19A1 promoter and downregulate its expression in PC3 cells. Estradiol, through the estrogen receptor beta, released CTBP1 from CYP19A1 promoter triggering its transcription and modulating PCa cell proliferation. We generated NSG and C57BL/6J MeS mice by chronically feeding animals with high fat diet. In the NSG model, CTBP1 depleted PCa xenografts showed an increase in the CYP19A1 expression with the subsequent increment in intratumor estradiol concentrations. Additionally, in C57BL/6J mice, MeS induces hypertrophy, hyperplasia and inflammation of the white adipose tissue, which leads to a proinflammatory phenotype and increases serum estradiol concentration. Thus, MeS increased PCa growth and Ctbp1, Fabp4 and IL-6 expression levels. These results describe, for the first time, a novel CTBP1/CYP19A1/Estradiol axis that explains, in part, the mechanism for prostate tumor growth increase by MeS.Fil: Massillo, Cintia Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Dalton, Guillermo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Porretti, Juliana Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Scalise, Georgina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Farré, Paula Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Piccioni, Flavia Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Secchiari, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Pascuali, Natalia Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Clyne, Colin. Hudson Institute Of Medical Research; AustraliaFil: Gardner, Kevin. Columbia University Medical Center; Estados UnidosFil: de Luca, Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentin

LAVA: An Open-Source Approach To Designing LAMP (Loop-Mediated Isothermal Amplification) DNA Signatures

<p>Abstract</p> <p>Background</p> <p>We developed an extendable open-source Loop-mediated isothermal AMPlification (LAMP) signature design program called LAVA (LAMP Assay Versatile Analysis). LAVA was created in response to limitations of existing LAMP signature programs.</p> <p>Results</p> <p>LAVA identifies combinations of six primer regions for basic LAMP signatures, or combinations of eight primer regions for LAMP signatures with loop primers, which can be used as LAMP signatures. The identified primers are conserved among target organism sequences. Primer combinations are optimized based on lengths, melting temperatures, and spacing among primer sites. We compare LAMP signature candidates for <it>Staphylococcus aureus </it>created both by LAVA and by PrimerExplorer. We also include signatures from a sample run targeting all strains of <it>Mycobacterium tuberculosis</it>.</p> <p>Conclusions</p> <p>We have designed and demonstrated new software for identifying signature candidates appropriate for LAMP assays. The software is available for download at <url>http://lava-dna.googlecode.com/</url>.</p

Draft versus finished sequence data for DNA and protein diagnostic signature development

Abstract Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop highquality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors, or NNs) to sequence. We use SAP to assess whether draft data is sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high quality draft with error rates of 10 -3 -10 -5 (~8x coverage) of target organisms is suitable for DNA signature prediction. Low quality draft with error rates of ~1% (3x to 6x coverage) of target isolates is inadequate for DNA signature prediction, although low quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high quality draft of target and low quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. 3 Introduction Draft sequencing requires that the order of base pairs in cloned fragments of a genome be determined usually at least 4 times (4x depth of coverage) at each position for a minimum degree of draft accuracy. This information is assembled into contigs, or fragments of the genome that cannot be joined further due to lack of sequence information across gaps between the contigs. To generate high-quality draft, usually about 8x coverage is optimal (1). Finished sequence, without gaps or ambiguous base calls, usually requires 8x to 10x coverage, along with additional analyses, often manual, to orient the contigs relative to one another and to close the gaps between them in a process called finishing. In fact, it has been stated that &quot;the defining distinction of draft sequencing is the avoidance of significant human intervention&quot; (1), although there are computational tools that may also be capable of automated finishing in some circumstances (2). While some tabulate the cost differential between high quality draft versus finished sequences to be 3-to 4-fold, and the speed differential to be over 10-fold (1), others state that the cost differential is a more modest 1.3-to 1.5-fold (3). In either case, draft sequencing is cheaper and faster. Experts have debated whether finished sequencing is always necessary, considering the higher costs (1,3,4). Thus, here we set out to determine whether draft sequence data is adequate for the computational prediction of DNA and protein diagnostic signatures. By a &quot;signature&quot; we mean a short region of sequence that is sufficient to uniquely identify an organism down to the species level, without false negatives due to strain variation or false positives due to cross reaction with close phylogenetic relatives. In addition, for DNA signatures, we require that the signature be suitable for a TaqMan reaction (e.g. composed of two primers and a probe of the desired T m &apos;s). Limited funds and facilities in which to sequence biothreat pathogens mean that decision makers must choose wisely which and how many organisms to sequence. Money and time saved as a result of draft rather than finished sequencing enables more target organisms, more isolates of the target, and more NN&apos;s of the target to be sequenced. However, if draft data does not facilitate the generation of high quality signatures for detection, the tradeoff of quantity over quality will not be worth it. We used the Sequencing Analysis Pipeline (SAP) (5,6) to compare the value of finished sequence, real draft sequence, and simulated draft sequence of different qualities for the computational prediction of DNA and protein signatures for pathogen detection/diagnostics. Marburg and variola viruses were used as model organisms for these analyses, due to the availability of multiple genomes for these organisms. We hope that variola may serve as a guide for making predictions about bacteria, in which the genomes are substantially larger, and thus the cost of sequencing is much higher than for viruses. Variola was selected as the best available surrogate for bacteria at the time we began these analyses because: 1) it is double-stranded DNA 2) it has a relatively low mutation rate, more like bacteria than like the RNA or shorter DNA viruses that have higher mutation rates and thus higher levels of variation 3) it is very long for a virus, albeit shorter than a bacterial genom

Toxic effects multi-walled carbon nanotubes on bivalves: comparison between of functionalized and non-functionalized nanoparticles

Despite of the large array of available carbon nanotube (CNT) configurations that allow different industrial and scientific applications of these nanoparticles, their impacts on aquatic organisms, especially on invertebrate species, are still limited. To our knowledge, no information is available on how surface chemistry alteration (functionalization) of CNTs may impact the toxicity of these NPs to bivalve species after a chronic exposure. For this reason, the impacts induced by chronic exposure (28 days) to unfunctionalized MWCNTs (Nf-MWCNTs) in comparison with functionalized MWCNTs (f-MWCNTs), were evaluated in R. philippinarum, by measuring alterations induced in clams' oxidative status, neurotoxicity and metabolic capacity. The results obtained revealed that exposure to both MWCNT materials altered energy-related responses, with higher metabolic capacity and lower glycogen, protein and lipid concentrations in clams exposed to these CNTs. Moreover, R. philippinarum exposed to Nf-MWCNTs and f-MWCNTs showed oxidative stress expressed in higher lipid peroxidation and lower ratio between reduced and oxidized glutathione, despite the activation of defense mechanisms (superoxide-dismutase, glutathione peroxidase and glutathione S-transferases) in exposed clams. Additionally, neurotoxicity was observed by inhibition of Cholinesterases activity in organisms exposed to both MWCNTs.publishe

Global Analysis of Extracytoplasmic Stress Signaling in Escherichia coli

The Bae, Cpx, Psp, Rcs, and σE pathways constitute the Escherichia coli signaling systems that detect and respond to alterations of the bacterial envelope. Contributions of these systems to stress response have previously been examined individually; however, the possible interconnections between these pathways are unknown. Here we investigate the dynamics between the five stress response pathways by determining the specificities of each system with respect to signal-inducing conditions, and monitoring global transcriptional changes in response to transient overexpression of each of the effectors. Our studies show that different extracytoplasmic stress conditions elicit a combined response of these pathways. Involvement of the five pathways in the various tested stress conditions is explained by our unexpected finding that transcriptional responses induced by the individual systems show little overlap. The extracytoplasmic stress signaling pathways in E. coli thus regulate mainly complementary functions whose discrete contributions are integrated to mount the full adaptive response

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages