Getting its feet on the ground : elucidating Paralouatta's semi-terrestriality using the virtual morpho-functional toolbox

Altres ajuts: CERCA Programme/Generalitat de CatalunyaCurrently, there are no living platyrrhine primates inhabiting the main Caribbean islands. Nevertheless, the fossil record of this area has provided outstanding findings of different New World monkeys that were part of a diverse radiation exhibiting remarkably unusual morphologies. Among these, the Cuban genus Paralouatta corresponds to one of the most enigmatic primates ever found in the Greater Antilles. Some researchers have argued that Paralouatta's post-cranium shows evidence of semi-terrestriality, a locomotor adaptation that is unusual, if not unique, in platyrrhine evolutionary history. Whether or not Paralouatta was truly semi-terrestrial remains uncertain, however, due to a lack of more sophisticated functional analyses on its morphology. Using novel virtual morpho-functional techniques on a comparative sample of 3D talar models belonging to diverse primate species representing three substrate preferences, this study aims to further evaluate whether Paralouatta was a semi-terrestrial genus or not. Geometric morphometrics and finite element analysis were used to empirically assess shape and biomechanical performance, respectively, and then several machine-learning (ML) classification algorithms were trained using both morphometric and biomechanical data to elucidate the substrate preference of the fossils. The ML algorithms categorized the Paralouatta specimens as either arboreal or as species commonly active on both ground and in trees. These mixed results are suggestive of some level of semi-terrestriality, thus representing the only known example of this locomotor behavior in platyrrhine evolutionary history

Convex hull estimation of mammalian body segment parameters

From The Royal Society via Jisc Publications RouterHistory: received 2021-05-18, collection 2021-06, accepted 2021-06-14, pub-electronic 2021-06-30Article version: VoRPublication status: PublishedFunder: Manchester Environmental Research InstituteFunder: Natural Environment Research Council; Id: http://dx.doi.org/10.13039/501100000270; Grant(s): NE/R011168/1Obtaining accurate values for body segment parameters (BSPs) is fundamental in many biomechanical studies, particularly for gait analysis. Convex hulling, where the smallest-possible convex object that surrounds a set of points is calculated, has been suggested as an effective and time-efficient method to estimate these parameters in extinct animals, where soft tissues are rarely preserved. We investigated the effectiveness of convex hull BSP estimation in a range of extant mammals, to inform the potential future usage of this technique with extinct taxa. Computed tomography scans of both the skeleton and skin of every species investigated were virtually segmented. BSPs (the mass, position of the centre of mass and inertial tensors of each segment) were calculated from the resultant soft tissue segments, while the bone segments were used as the basis for convex hull reconstructions. We performed phylogenetic generalized least squares and ordinary least squares regressions to compare the BSPs calculated from soft tissue segments with those estimated using convex hulls, finding consistent predictive relationships for each body segment. The resultant regression equations can, therefore, be used with confidence in future volumetric reconstruction and biomechanical analyses of mammals, in both extinct and extant species where such data may not be available

Allele-Specific Amplification in Cancer Revealed by SNP Array Analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ

A Latent Model for Prioritization of SNPs for Functional Studies

One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial/time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model (BLVM) for incorporating “features” about a SNP to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study (GWAS). In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases/traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2nd and 7th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value (which ranked 197th for invasive case analysis), was ranked 8th based on the posterior probability of being in the top 5 markers (0.13). In summary, the application of the BLVM allows for the systematic integration of multiple SNP “features” for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking

Diabetes Susceptibility in the Canadian Oji-Cree Population Is Moderated by Abnormal mRNA Processing of HNF1A G319S Transcripts

OBJECTIVE—The G319S HNF1A variant is associated with an increased risk of type 2 diabetes in the Canadian Oji-Cree population. We hypothesized that the variant site at the 3′ end of exon 4 might influence splicing and characterized mRNA transcripts to investigate the mutational mechanism underlying this susceptibility to diabetes

Environmental DNA metabarcoding for fish diversity assessment in a macrotidal estuary: A comparison with established fish survey methods

Fishes are a dominant component of the macrofauna in estuaries and are important for assessing the health of these threatened ecosystems. Several studies have applied environmental DNA (eDNA) metabarcoding to assess the biodiversity of fishes in estuaries. However, none have combined measurement of physicochemical variables with a spatially extensive sampling design across the full salinity gradient. This study aimed to compare spatial fish assemblage composition detected via eDNA metabarcoding of surface water samples with conventional fishing gear surveys in a macrotidal estuary (river Dee, North Wales, UK). In addition, eDNA assemblage composition across seasons was investigated. In autumn 2018, triplicate eDNA samples were taken at 13 stations in a spatially systematic design alongside seine, fyke and beam trawl sampling. In summer 2019, eDNA samples from eight of the 13 original stations were collected again in the upper and lower estuary. DNA was extracted from samples and subjected to metabarcoding analysis using an established assay targeting teleost fishes. The key findings were that in autumn, eDNA detected 17 of the 26 (71%) species caught by fishing gears, which included the most abundant species. Overall, eDNA detected a greater species richness, per 30 samples, than seine or fyke nets (but not beam trawling). Additionally, there was a clear correlation between salinity and assemblage composition, which was consistent across seasons. Overall, the study indicates that eDNA metabarcoding could enhance existing fish sampling methods, by generating a more comprehensive picture of estuarine fish biodiversity and providing additional information for ecological inference and management actions

GluN2A NMDA Receptor Enhancement Improves Brain Oscillations, Synchrony, and Cognitive Functions in Dravet Syndrome and Alzheimer's Disease Models.

NMDA receptors (NMDARs) play subunit-specific roles in synaptic function and are implicated in neuropsychiatric and neurodegenerative disorders. However, the in vivo consequences and therapeutic potential of pharmacologically enhancing NMDAR function via allosteric modulation are largely unknown. We examine the in vivo effects of GNE-0723, a positive allosteric modulator of GluN2A-subunit-containing NMDARs, on brain network and cognitive functions in mouse models of Dravet syndrome (DS) and Alzheimer's disease (AD). GNE-0723 use dependently potentiates synaptic NMDA receptor currents and reduces brain oscillation power with a predominant effect on low-frequency (12-20 Hz) oscillations. Interestingly, DS and AD mouse models display aberrant low-frequency oscillatory power that is tightly correlated with network hypersynchrony. GNE-0723 treatment reduces aberrant low-frequency oscillations and epileptiform discharges and improves cognitive functions in DS and AD mouse models. GluN2A-subunit-containing NMDAR enhancers may have therapeutic benefits in brain disorders with network hypersynchrony and cognitive impairments

Mediterranean Diet and Breast Density in the Minnesota Breast Cancer Family Study

Mediterranean populations’ lower breast cancer incidence has been attributed to a traditional Mediterranean diet, but few studies have quantified Mediterranean dietary pattern intake in relation to breast cancer. We examined the association of a Mediterranean diet scale (MDS) with mammographic breast density as a surrogate marker for breast cancer risk. Participants completed a dietary questionnaire and provided screening mammograms for breast density assessment using a computer-assisted method. Among 1,286 women, MDS was not clearly associated with percent density in multivariate linear regression analyses. Because of previous work suggesting dietary effects limited to smokers, we conducted stratified analyses and found MDS and percent density to be significantly, inversely associated among current smokers (β = –1.68, P = 0.002) but not among nonsmokers (β = –0.08, P = 0.72; P for interaction = 0.008). Our results confirm a previous suggestion that selected dietary patterns may be protective primarily in the presence of procarcinogenic compounds such as those found in tobacco smoke

Super-Enhancer-Associated LncRNA UCA1 Interacts Directly with AMOT to Activate YAP Target Genes in Epithelial Ovarian Cancer

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of tumorigenesis, and yet their mechanistic roles remain challenging to characterize. Here, we integrate functional proteomics with lncRNA-interactome profiling to characterize Urothelial Cancer Associated 1 (UCA1), a candidate driver of ovarian cancer development. Reverse phase protein array (RPPA) analysis indicates that UCA1 activates transcription coactivator YAP and its target genes. In vivo RNA antisense purification (iRAP) of UCA1 interacting proteins identified angiomotin (AMOT), a known YAP regulator, as a direct binding partner. Loss-of-function experiments show that AMOT mediates YAP activation by UCA1, as UCA1 enhances the AMOT-YAP interaction to promote YAP dephosphorylation and nuclear translocation. Together, we characterize UCA1 as a lncRNA regulator of Hippo-YAP signaling and highlight the UCA1-AMOT-YAP signaling axis in ovarian cancer development