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Data from: Examining the effects of the histone methyltransferase inhibitor BIX-01294 on histone modifications and gene expression in both a clinical population and mouse models
Schizophrenia has been consistently characterized by abnormal patterns of gene down-regulation, increased restrictive chromatin assemblies, and reduced transcriptional activity. Histone methyltransferase (HMT) mRNA and H3K9me2 levels are elevated in postmortem brain and peripheral blood cells of persons with schizophrenia. Moreover, this epigenomic state likely contributes to the disease, as HMT levels correlate with clinical symptomatology. This manuscript sought to establish the potential therapeutic value of the HMT inhibitor BIX-01294 (BIX). Human peripheral mononuclear cells (PBMC) from 24 individuals with schizophrenia and 24 healthy individuals were cultured in the presence of BIX (5uM or 10uM). Mice were given once daily intraperitoneal injections of BIX (0.5 or 1mg/kg) for one week. Cultured cells, mouse cortex, or striatum was harvested, RNA extracted and RT-PCR conducted for several schizophrenia candidate genes: IL-6, Gad1, Nanog, KLF4, Reln, and Bdnf9a. Total H3K9me2 levels were measured using western blot while H3K9me2 binding to selected genes of interest was conducted using chromatin immunoprecipitation (ChIP). Neuronal subtype-specific BDNF conditional knockdown was conducted using the cre/lox system of mutant animals. Treatment with BIX decreased H3K9me2 and increased selected mRNA levels in cultured PBMCs from both normal controls and participants with schizophrenia. In mice, peripheral administration of BIX decreased cortical H3K9me2 levels and increased schizophrenia candidate gene expression. In BDNF conditional knockdown animals, BIX administration was able to significantly rescue Bdnf9a mRNA levels in ChAT and D1 but not D2 Bdnf conditional knockdown mice. The results presented in this manuscript demonstrate a potential for further research into the clinical effectiveness of histone modifying pharmacology in the treatment of schizophrenia.
Data from: Diffusion tensor imaging in patients with glioblastoma multiforme using the supertoroidal model
Purpose: Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM). Materials and Methods: DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM). Results: The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts. Conclusions: Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.
Data from: Models of knot and stem development in black spruce trees indicate a shift in allocation priority to branches when growth is limited
The branch autonomy principle, which states that the growth of individual branches can be predicted from their morphology and position in the forest canopy irrespective of the characteristics of the tree, has been used to simplify models of branch growth in trees. However, observed changes in allocation priority within trees towards branches growing in light-favoured conditions, referred to as 'Milton's Law of resource availability and allocation,' have raised questions about the applicability of the branch autonomy principle. We present models linking knot ontogeny to the secondary growth of the main stem in black spruce (Picea mariana (Mill.) B.S.P.), which were used to assess the patterns of assimilate allocation over time, both within and between trees. Data describing the annual radial growth of 445 stem rings and the three-dimensional shape of 5,377 knots were extracted from optical scans and X-ray computed tomography images taken along the stems of 10 trees. Total knot to stem area increment ratios (KSR) were calculated for each year of growth, and statistical models were developed to describe the annual development of knot diameter and curvature as a function of stem radial increment, total tree height, stem diameter, and the position of knots along an annual growth unit. KSR varied as a function of tree age and of the height to diameter ratio of the stem, a variable indicative of the competitive status of the tree. Simulations of the development of an individual knot showed that an increase in the stem radial growth rate was associated with an increase in the initial growth of the knot, but also with a shorter lifespan. Our results provide support for 'Milton's Law,' since they indicate that allocation priority is given to locations where the potential return is the highest. The developed models provided realistic simulations of knot morphology within trees, which could be integrated into a functional-structural model of tree growth and above-ground resource partitioning.
Data from: Using a multi-stage hESC model to characterize BDE-47 toxicity during neurogenesis
While the ramifications associated with polybrominated diphenyl ethers (PBDE) exposures during human pregnancy have yet to be determined, increasing evidence in humans and animal models suggests that these compounds cause neurodevelopmental toxicity. Human embryonic stem cell models (hESCs) can be used to study the effects of environmental chemicals on the successive stages of neuronal development. Here, using a hESC differentiation model, we investigated the effects of common PBDE congeners (BDE-47 or -99) on the successive stages of early neuronal development. First, we determined the points of vulnerability to PBDEs across four stages of in vitro neural development by using assays to assess for cytotoxicity. Differentiated neural progenitors were identified to be more sensitive to PBDEs than their less differentiated counterparts. In follow-up investigations, we observed BDE-47 to inhibit functional processes critical for neurogenesis (e.g., proliferation, migration) in hESC-derived neural precursor cells (NPCs) at sub-lethal concentrations. Finally, to determine the mechanism(s) underlying PBDE-toxicity, we conducted global transcriptomic and methylomic analyses of BDE-47. We identified 589 genes to be differentially expressed (DE) due to BDE-47 exposure, including molecules involved in oxidative stress mediation, cell cycle, hormone signaling, steroid metabolism, and neurodevelopmental pathways. In parallel analyses, we identified a significant increase in CpG methylation. In summary, our results suggest, on a cellular level, PBDEs induce human neurodevelopmental toxicity in a concentration-dependent manner and sensitivity to these compounds is dependent on the developmental stage of exposure. Proposed mRNA and methylomic perturbations may underlie toxicity in early embryonic neuronal populations.
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.
Data from: Modelling dynamics in protein crystal structures by ensemble refinement
Single-structure models derived from X-ray data do not adequately account for the inherent, functionally important dynamics of protein molecules. We generated ensembles of structures by time-averaged refinement, where local molecular vibrations were sampled by molecular-dynamics (MD) simulation whilst global disorder was partitioned into an underlying overall translation–libration–screw (TLS) model. Modeling of 20 protein datasets at 1.1–3.1 Å resolution reduced cross-validated R_free values by 0.3–4.9%, indicating that ensemble models fit the X-ray data better than single structures. The ensembles revealed that, while most proteins display a well-ordered core, some proteins exhibit a 'molten core' likely supporting functionally important dynamics in ligand binding, enzyme activity and protomer assembly. Order–disorder changes in HIV protease indicate a mechanism of entropy compensation for ordering the catalytic residues upon ligand binding by disordering specific core residues. Thus, ensemble refinement extracts dynamical details from the X-ray data that allow a more comprehensive understanding of structure–dynamics–function relationships.
Data from: A resorbable antibiotic-eluting polymer composite bone void filler for perioperative infection prevention in a rabbit radial defect model
Nearly 1.3 million total joint replacement procedures are performed in the United States annually, with numbers projected to rise exponentially in the coming decades. Although finite infection rates for these procedures remain consistently low, device-related infections represent a significant cause of implant failure, requiring secondary or revision procedures. Revision procedures manifest several-fold higher infection recurrence rates. Importantly, many revision surgeries, infected or not, require bone void fillers to support the host bone and provide a sufficient tissue bed for new hardware placement. Antibiotic-eluting bone void fillers (ABVF), providing both osteoconductive and antimicrobial properties, represent one approach for reducing rates of orthopedic device-related infections. Using a solvent-free, molten-cast process, a polymer-controlled antibiotic-eluting calcium carbonate hydroxyapatite (HAP) ceramic composite BVF (ABVF) was fabricated, characterized, and evaluated in vivo using a bacterial challenge in a rabbit radial defect window model. ABVF loaded with tobramycin eliminated the infectious burden in rabbits challenged with a clinically relevant strain of Staphylococcus aureus (inoculum as high as 107 CFU). Histological, microbiological, and radiographic methods were used to detail the effects of ABVF on microbial challenge to host bone after 8 weeks in vivo. In contrast to the HAP/BVF controls, which provided no antibiotic protection and required euthanasia 3 weeks post-operatively, tobramycin-releasing ABVF animals showed no signs of infection (clinical, microbiological, or radiographic) when euthanized at the 8-week study endpoint. ABVF sites did exhibit fibrous encapsulation around the implant at 8 weeks. Local antibiotic release from ABVF to orthopedic sites requiring bone void fillers eliminated the periprosthetic bacterial challenge in this 8-week in vivo study, confirming previous in vitro results.
Data from: Quantifying post- laser ablation prostate therapy changes on MRI via a domain-specific biomechanical model: preliminary findings
Focal laser ablation destroys cancerous cells via thermal destruction of tissue by a laser. Heat is absorbed, causing thermal necrosis of the target region. It combines the aggressive benefits of radiation treatment (destroying cancer cells) without the harmful side effects (due to its precise localization). MRI is typically used pre-treatment to determine the targeted area, and post-treatment to determine efficacy by detecting necrotic tissue, or tumor recurrence. However, no system exists to quantitatively evaluate the post-treatment effects on the morphology and structure via MRI. To quantify these changes, the pre- and post-treatment MR images must first be spatially aligned. The goal is to quantify (a) laser-induced shape-based changes, and (b) changes in MRI parameters post-treatment. The shape-based changes may be correlated with treatment efficacy, and the quantitative effects of laser treatment over time is currently poorly understood. This work attempts to model changes in gland morphology following laser treatment due to (1) patient alignment, (2) changes due to surrounding organs such as the bladder and rectum, and (3) changes due to the treatment itself. To isolate the treatment-induced shape-based changes, the changes from (1) and (2) are first modeled and removed using a finite element model (FEM). A FEM models the physical properties of tissue. The use of a physical biomechanical model is important since a stated goal of this work is to determine the physical shape-based changes to the prostate from the treatment, and therefore only physical real deformations are to be allowed. A second FEM is then used to isolate the physical, shape-based, treatment-induced changes. We applied and evaluated our model in capturing the laser induced changes to the prostate morphology on eight patients with 3.0 Tesla, T2-weighted MRI, acquired approximately six months following treatment. Our results suggest the laser treatment causes a decrease in prostate volume, which appears to manifest predominantly at the site of ablation. After spatially aligning the images, changes to MRI intensity values are clearly visible at the site of ablation. Our results suggest that our new methodology is able to capture and quantify the degree of laser-induced changes to the prostate. The quantitative measurements reflecting of the deformation changes can be used to track treatment response over time.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Data from: Modeling of kidney hemodynamics: probability-based topology of an arterial network
Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (μCT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids.
Data from: The function(s) of the bone ornamentation in the crocodylomorph osteoderms: a biomechanical model based on a finite element analysis
This paper aims at assessing the influence of the bone ornamentation and, specifically, of the associated loss of bone mass on the mechanical response of the crocodylomorph osteoderms. To this aim, we have performed three dimensional modeling and finite element analyses on a sample which includes both extant dry bones and well-preserved fossils tracing back to the Late Triassic. We simulated an external attack under variable angles on the apical surface of each osteoderm and further repeated the simulation on an equivalent set of smoothed 3D-modeled osteoderms. The comparative results evidenced that the presence of an apical sculpture has no significant influence on the von Mises stress distribution in the osteoderm volume although it involves a slight increase in its numerical score. Moreover, performing parametric analyses, we evidenced that the Young's modulus in the osteoderm which may vary depending on the bone porosity, the collagen fiber orientation or the calcification density has no impact on the von Mises stress distribution inside the osteoderm volume. As the crocodylomorph bone ornamentation is continuously remodeled by pit resorption and secondary bone deposit, we assume that the apical sculpture may be the outcome of a "trade-off" between the bone mechanical resistance and the implication in physiological functions. These physiological functions are indeed based on the set-up of a bone superficial vessel network and/or on the recurrent release of mineral elements into the plasma: heat transfers when basking and respiratory acidosis buffering during prolonged apnea in neosuchians and teleosaurids; compensatory homeostasis in response to general calcium deficiencies. On a general morphological aspect, the osteoderm geometrical variability within our sample lead us to assess that the global osteoderm geometry (whether square or rectangular) does not influence the von Mises stress whereas the presence of a dorsal keel would rather reduce the stress along the vertical axis.
Data from: Beyond thermal performance curves: modeling time-dependent effects of thermal stress on ectotherm growth rates
Thermal performance curves have been widely used to model the ecological responses of ectotherms to variable thermal environments and climate change. Such models ignore the effects of time dependence—the temporal pattern and duration of temperature exposure—on performance. We developed and solved a simple mathematical model for growth rate of ectotherms, combining thermal performance curves for ingestion rate with the temporal dynamics of gene expression and protein production in response to high temperatures to predict temporal patterns of growth rate in constant and diurnally fluctuating temperatures. We used the model to explore the effects of heat shock proteins on larval growth rates of Manduca sexta. The model correctly captures two empirical patterns for larval growth rate: first, maximal growth rate and optimal temperature decline with increasing duration of temperature exposure; second, mean growth rates decline with time in diurnally fluctuating temperatures at higher mean temperatures. These qualitative results apply broadly to cases where proteins or other molecules produced in response to high temperatures reduce growth rates. We discuss some of the critical assumptions and predictions of the model and suggest potential extensions and alternatives. Incorporating time-dependent effects will be essential for making more realistic predictions about the physiological and ecological consequences of temperature fluctuations and climate change.
Data from: Empirical phylogenies and species abundance distributions are consistent with pre-equilibrium dynamics of neutral community models with gene flow
Community characteristics reflect past ecological and evolutionary dynamics. Here, we investigate whether it is possible to obtain realistically shaped modelled communities – i.e., with phylogenetic trees and species abundance distributions shaped similarly to typical empirical bird and mammal communities – from neutral community models. To test the effect of gene flow, we contrasted two spatially explicit individual-based neutral models: one with protracted speciation, delayed by gene flow, and one with point mutation speciation, unaffected by gene flow. The former produced more realistic communities (shape of phylogenetic tree and species-abundance distribution), consistent with gene flow being a key process in macro-evolutionary dynamics. Earlier models struggled to capture the empirically observed branching tempo in phylogenetic trees, as measured by the gamma statistic. We show that the low gamma values typical of empirical trees can be obtained in models with protracted speciation, in pre-equilibrium communities developing from an initially abundant and widespread species. This was even more so in communities sampled incompletely, particularly if the unknown species are the youngest. Overall, our results demonstrate that the characteristics of empirical communities that we have studied can, to a large extent, be explained through a purely neutral model under pre-equilibrium conditions.
Data from: Kinetic study of catalytic CO2 hydration by metal-substituted biomimetic carbonic anhydrase model complexes
The rapid rise of the CO2 level in the atmosphere has spurred the development of CO2 capture methods such as the use of biomimetic complexes that mimic carbonic anhydrase. In this study, model complexes with tris(2-pyridylmethyl)amine (TPA) were synthesized using various transition metals (Zn2+, Cu2+ and Ni2+) to control the intrinsic proton-donating ability. The pKa of the water coordinated to the metal, which indicates its proton-donating ability, was determined by potentiometric pH titration and found to increase in the order [(TPA)Cu(OH2)]2+ < [(TPA)Ni(OH2)]2+ < [(TPA)Zn(OH2)]2+. The effect of pKa on the CO2 hydration rate was investigated by stopped-flow spectrophotometry. Because the water ligand in [(TPA)Zn(OH2)]2+ had the highest pKa, it would be more difficult to deprotonate it than those coordinated to Cu2+ and Ni2+. It was therefore expected that the complex would have the slowest rate for the reaction of the deprotonated water with CO2 to form bicarbonate. However, it was confirmed that [(TPA)Zn(OH2)]2+ actually had the fastest CO2 hydration rate because the substitution of bicarbonate with water (bicarbonate release) occurred easily.
Data from: Big data analysis of genes associated with neuropsychiatric disorders in an Alzheimer's disease animal model
Alzheimer's disease is a neurodegenerative disease characterized by the impairment of cognitive function and loss of memory, affecting millions of individuals worldwide. With the dramatic increase in the prevalence of Alzheimer's disease, it is expected to impose extensive public health and economic burden. However, this burden is particularly heavy on the caregivers of Alzheimer's disease patients eliciting neuropsychiatric symptoms that include mood swings, hallucinations, and depression. Interestingly, these neuropsychiatric symptoms are shared across symptoms of bipolar disorder, schizophrenia, and major depression disorder. Despite the similarities in symptomatology, comorbidities of Alzheimer's disease and these neuropsychiatric disorders have not been studied in the Alzheimer's disease model. Here, we explore the comprehensive changes in gene expression of genes that are associated with bipolar disorder, schizophrenia, and major depression disorder through the microarray of an Alzheimer's disease animal model, the forebrain specific PSEN double knockout mouse. To analyze the genes related with these three neuropsychiatric disorders within the scope of our microarray data, we used selected 1207 of a total of 45,037 genes that satisfied our selection criteria. These genes were selected on the basis of 14 Gene Ontology terms significantly relevant with the three disorders which were identified by previous research conducted by the Psychiatric Genomics Consortium. Our study revealed that the forebrain specific deletion of Alzheimer's disease genes can significantly alter neuropsychiatric disorder associated genes. Most importantly, most of these significantly altered genes were found to be involved with schizophrenia. Taken together, we suggest that the synaptic dysfunction by mutation of Alzheimer's disease genes can lead to the manifestation of not only memory loss and impairments in cognition, but also neuropsychiatric symptoms.
Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model
Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The Environmental Threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We describe a new statistical model that can be applied in this common situation. The Latent Environmental Threshold Model (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. We show that coupling our model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies. We evaluate the performance of the LETM with a simulation study and illustrate its utility by applying it to empirical data on the size-dependent smolting process for stream-dwelling Atlantic salmon juveniles.
Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration itself captures relationships in the data that represent highly disease-relevant functions.
Data from: From the cover: three-dimensional (3d) heparg spheroid model with physiologically relevant xenobiotic metabolism competence and hepatocyte functionality for liver toxicity screening.
Effective prediction of human responses to chemical and drug exposure is of critical importance in environmental toxicology research and drug development. While significant progress has been made to address this challenge using invitro liver models, these approaches often fail due to inadequate tissue model functionality. Herein, we describe the development, optimization, and characterization of a novel three-dimensional (3D) spheroid model using differentiated HepaRG cells that achieve and maintain physiologically relevant levels of xenobiotic metabolism (CYP1A2, CYP2B6, and CYP3A4/5). This invitro model maintains a stable phenotype over multiple weeks in both 96- and 384-well formats, supports highly reproducible tissue-like architectures and models pharmacologically- and environmentally important hepatic receptor pathways (ie AhR, CAR, and PXR) analogous to primary human hepatocyte cultures. HepaRG spheroid cultures use 50–100× fewer cells than conventional two dimensional cultures, and enable the identification of metabolically activated toxicants. Spheroid size, time in culture and culture media composition were important factors affecting basal levels of xenobiotic metabolism and liver enzyme inducibility with activators of hepatic receptors AhR, CAR and PXR. Repeated exposure studies showed higher sensitivity than traditional 2D cultures in identifying compounds that cause liver injury and metabolism-dependent toxicity. This platform combines the well-documented impact of 3D culture configuration for improved tissue functionality and longevity with the requisite throughput and repeatability needed for year-over-year toxicology screening.
Data from: Monoamine abnormalities in the SAPAP3 knockout model of obsessive-compulsive disorder-related behaviour
Obsessive-compulsive disorder (OCD) is a leading cause of illness-related disability, but the neuralmechanisms underlying OCDsymptoms are unclear. One potential mechanism of OCD pathology is monoamine dysregulation. Because of the difficulty of studying monoamine signalling in patients, animal models offer a viable alternative to understanding this aspect of OCD pathophysiology. We used HPLC to characterize post-mortem monoamine levels in lateral orbitofrontal cortex (OFC), medial OFC, medial prefrontal cortex and dorsal and ventral striatum of SAPAP-3 knockout (KO) mice, a well-validated model of compulsive-like behaviours in OCD. As predicted from previous studies, excessive grooming was significantly increased in SAPAP-3 KO mice. Overall levels of the serotonin metabolite 5-hydroxyindoleacetic acid (HIAA) and the ratio of 5HIAA/serotonin (serotonin turnover) were increased in all cortical and striatal regions examined. In addition, dihydroxyphenylacetic acid/dopamine ratio was increased in lateral OFC, and HVA/dopamine ratio was increased in lateral and medial OFC. No baseline differences in serotonin or dopamine tissue content were observed.These data provide evidence of monoaminergic dysregulation in a translational model of OCD symptoms and are consistent with aberrant cortical and striatal serotonin and dopamine release/metabolism in SAPAP-3 KO mice. These results are guiding ongoing experiments using circuit and celltype specific manipulations of dopamine and serotonin to determine the contributions of these monoaminergic systems to compulsive behaviours, and serve here as a touchstone for an expanded discussion of these techniques for precise circuit dissection.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.