Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage.

Heart development is exquisitely sensitive to the precise temporal regulation of thousands of genes that govern developmental decisions during differentiation. However, we currently lack a detailed understanding of how chromatin and gene expression patterns are coordinated during developmental transitions in the cardiac lineage. Here, we interrogated the transcriptome and several histone modifications across the genome during defined stages of cardiac differentiation. We find distinct chromatin patterns that are coordinated with stage-specific expression of functionally related genes, including many human disease-associated genes. Moreover, we discover a novel preactivation chromatin pattern at the promoters of genes associated with heart development and cardiac function. We further identify stage-specific distal enhancer elements and find enriched DNA binding motifs within these regions that predict sets of transcription factors that orchestrate cardiac differentiation. Together, these findings form a basis for understanding developmentally regulated chromatin transitions during lineage commitment and the molecular etiology of congenital heart disease.

Tissue predictability of elastography is low in collagenase induced deep digital flexor tendinopathy.

Elastography is an emerging imaging modality for characterizing tendon injury in horses, but its ability to differentiate tissue deformability relative to treatment group and biochemical properties using a prospective, experimental study design remain unknown. Objectives of the current study were to (a) to investigate differences in glycosaminoglycan, DNA, and soluble collagen levels in mesenchymal stem cell (MSC) treated limbs compared to untreated control limbs utilizing a collagenase model of tendinopathy; (b) compare elastographic features between treatment groups; and (c) determine tissue-level predictive capabilities of elastography in relation to biochemical outcomes. Bone marrow was collected for MSC culture and expansion. Tendinopathy of both forelimb deep digital flexor tendons (DDFTs) was induced with collagenase under ultrasonographic guidance. One randomly assigned limb was treated with intra-lesional MSC injection with the opposite limb serving as an untreated control. Horses were placed into a controlled exercise program with elastographic evaluations performed baseline (0) and 14, 60, 90, and 214 days post-treatment. Postmortem biochemical analysis was performed. MSC-treated limbs demonstrated significantly less (42%) glycosaminoglycan (P = .006). Significant differences in elastographic region of interest (ROI) percent hardness, ROI color histogram, and subjective lesion stiffness were appreciated between treatment groups at various study time points. Elastographic outcome parameters were weak predictors of biochemical tissue analysis, with all R2 values ≤ 0.50. Within this range of differences in glycosaminoglycan content between treatment groups, elastography outcomes did not predict biochemical differences. Tissue-specific differences between DDFTs treated with MSCs compared to controls were apparent biochemically, but not predicted by elastography.

Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation.

Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State-space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state-space model of measles transmission and vaccine-based interventions at the country-level and a particle filter-based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age-structure to allow explicit representation of age-targeted vaccine interventions. We illustrate the performance of estimators of model parameters and predictions of unobserved states on simulated data from two dynamic models: one on the annual time-scale of observations and one on the biweekly time-scale of the epidemiological dynamics. We show that our model results in approximately unbiased estimates of unobserved burden and the underreporting rate. We further illustrate the performance of the fitted model for prediction of future disease burden in the next one to 15 years.

Changes in Shear Modulus of the Lumbar Multifidus Muscle During Different Body Positions.

Multifidus function is important for active stabilization of the spine, but it can be compromised in patients with chronic low back pain and other spine pathologies. Force production and strength of back muscles are often evaluated using isometric or isokinetic tests, which lack the ability to quantify multifidi contribution independent of the erector spinae and adjacent hip musculature. The objective of this study is to evaluate localized force production capability in multifidus muscle using ultrasound shear wave elastography (SWE) in healthy individuals. Three different body positions were considered: lying prone, sitting up, and sitting up with the right arm lifted. These positions were chosen to progressively increase multifidus contraction and to minimize body motion during measurements. Shear modulus was measured at the superficial and deeper layers of the multifidus. Repeatability and possible sources of error of the shear modulus measurements were analyzed. Multifidus shear modulus (median (interquartile range)) increased from prone, i.e., 16.15 (6.69) kPa, to sitting up, i.e., 27.28 (15.72) kPa, to sitting up with the right arm lifted position, i.e., 45.02 (25.27) kPa. Multifidi shear modulus in the deeper layer of the multifidi was lower than the superficial layer, suggesting lower muscle contraction. Intraclass correlation coefficients (ICCs) for evaluation of shear modulus by muscle layer were found to be excellent (ICC = 0.76-0.80). Results suggest that the proposed protocol could quantify local changes in spinal muscle function in healthy adults; further research in patients with spine pathology is warranted.

Nonoperative management of splenic injuries: significance of age.

BACKGROUND: In the nonoperative management (NOM) of blunt splenic injuries (BSI), the clinical relevance of age as a risk factor has not been well studied. METHODS: Using the 2011 National Trauma Data Bank data set, age was analyzed both as a continuous variable and a categorical variable (group 1 [13-54 y], group 2 [55-74 y], and group 3 [≥75 y]). BSI severity was stratified by abbreviated injury scale (AIS): group 1 (AIS ≤2), group 2 (AIS 3), and group 3 (AIS ≥4). A semiparametric proportional odds model was used to model NOM outcomes and effects due to age and BSI severity. RESULTS: Of 15,113 subjects, 15.3% failed NOM. The odds of failure increased by a factor of 1.014 for each year of age, or factor of 1.5 for groups 2 and 3 each. BSI severity groups 2 and 3 had increases in the odds of failure by factors of 3.9 and 13, respectively, compared with those of group 1. Most failures occurred by 48 h irrespective of age. The effect of age was most pronounced in age groups 2 and 3 with the most severe BSI, where a NOM failure rate of >50% was seen. Both age and failure of NOM were independent predictors of mortality. CONCLUSIONS: Age is associated with failure of NOM but its effect seems more clinically relevant only in high-grade BSI. Factors that could influence NOM success in elderly patients with high-grade injuries deserve further study.

SnIPRE: selection inference using a Poisson random effects model.

We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.

A bayesian mixture model for comparative spectral count data in shotgun proteomics.

Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.

Impact of Helminth Infections and Nutritional Constraints on the Small Intestine Microbiota.

Helminth infections and nutrition can independently alter the composition and abundance of the gastrointestinal microbiota, however, their combined effect is poorly understood. Here, we used the T. retortaeformis-rabbit system to examine how the helminth infection and host restriction from coprophagy/ready-to-absorb nutrients affected the duodenal microbiota, and how these changes related to the acquired immune response at the site of infection. A factorial experiment was performed where the bacterial community, its functionality and the immune response were examined in four treatments (Infect, Infect+Collar, Control+Collar and Control). Helminths reduced the diversity and abundance of the microbiota while the combination of parasites and coprophagic restriction led to a more diversified and abundant microbiota than infected cases, without significantly affecting the intensity of infection. Animals restricted from coprophagy and free from parasites exhibited the richest and most abundant bacterial community. By forcing the individuals to absorb nutrients from less digested food, the coprophagic restriction appears to have facilitated the diversity and proliferation of bacteria in the duodenum. Changes in the microbiota were more clearly associated with changes in the immune response for the infected than the nutrient restricted animals. The functional and metabolic characteristics of the duodenal microbiota were not significantly different between treatments. Overall, infection and diet affect the gut microbiota but their interactions and outcome can be complex. These findings can have important implications for the development of control measures to helminth infections where poor nutrition/malnutrition can also be a concern.

Life extension factor klotho enhances cognition.

Aging is the primary risk factor for cognitive decline, an emerging health threat to aging societies worldwide. Whether anti-aging factors such as klotho can counteract cognitive decline is unknown. We show that a lifespan-extending variant of the human KLOTHO gene, KL-VS, is associated with enhanced cognition in heterozygous carriers. Because this allele increased klotho levels in serum, we analyzed transgenic mice with systemic overexpression of klotho. They performed better than controls in multiple tests of learning and memory. Elevating klotho in mice also enhanced long-term potentiation, a form of synaptic plasticity, and enriched synaptic GluN2B, an N-methyl-D-aspartate receptor (NMDAR) subunit with key functions in learning and memory. Blockade of GluN2B abolished klotho-mediated effects. Surprisingly, klotho effects were evident also in young mice and did not correlate with age in humans, suggesting independence from the aging process. Augmenting klotho or its effects may enhance cognition and counteract cognitive deficits at different life stages.

Derivation conditions impact X-inactivation status in female human induced pluripotent stem cells.

Female human induced pluripotent stem cell (hiPSC) lines exhibit variability in X-inactivation status. The majority of hiPSC lines maintain one transcriptionally active X (Xa) and one inactive X (Xi) chromosome from donor cells. However, at low frequency, hiPSC lines with two Xas are produced, suggesting that epigenetic alterations of the Xi occur sporadically during reprogramming. We show here that X-inactivation status in female hiPSC lines depends on derivation conditions. hiPSC lines generated by the Kyoto method (retroviral or episomal reprogramming), which uses leukemia inhibitory factor (LIF)-expressing SNL feeders, frequently had two Xas. Early passage Xa/Xi hiPSC lines generated on non-SNL feeders were converted into Xa/Xa hiPSC lines after several passages on SNL feeders, and supplementation with recombinant LIF caused reactivation of some of X-linked genes. Thus, feeders are a significant factor affecting X-inactivation status. The efficient production of Xa/Xa hiPSC lines provides unprecedented opportunities to understand human X-reactivation and -inactivation.

Successful computational prediction of novel imprinted genes from epigenomic features.

Approximately 100 mouse genes undergo genomic imprinting, whereby one of the two parental alleles is epigenetically silenced. Imprinted genes influence processes including development, X chromosome inactivation, obesity, schizophrenia, and diabetes, motivating the identification of all imprinted loci. Local sequence features have been used to predict candidate imprinted genes, but rigorous testing using reciprocal crosses validated only three, one of which resided in previously identified imprinting clusters. Here we show that specific epigenetic features in mouse cells correlate with imprinting status in mice, and we identify hundreds of additional genes predicted to be imprinted in the mouse. We used a multitiered approach to validate imprinted expression, including use of a custom single nucleotide polymorphism array and traditional molecular methods. Of 65 candidates subjected to molecular assays for allele-specific expression, we found 10 novel imprinted genes that were maternally expressed in the placenta.