Biology subjects

Silva, R.

Publications and source records attributed to Silva, R..

24 records · Page 2Linked to original sources

bioRxiv2019

A meiosis-specific AAA+ assembly reveals repurposing of ORC during budding yeast gametogenesis

ORC (Orc1-6) is an AAA+ complex that loads the AAA+ MCM helicase to replication origins. Orc1, a subunit of ORC, functionally interacts with budding yeast Pch2, a meiosis-specific AAA+ protein. Pch2 regulates several chromosomal events of gametogenesis, but mechanisms that dictate Pch2 function remain poorly understood. We demonstrate that ORC directly interacts with an AAA+ Pch2 hexamer. The ORC-Pch2 assembly is established without Cdc6, a factor crucial for ORC-MCM binding. Biochemical analysis suggests that Pch2 utilizes ORCs Cdc6-binding interface and employs its non-enzymatic NH2-terminal domain and AAA+ core to engage ORC. In contrast to phenotypes observed upon Orc1 impairment, nuclear depletion of other subunits of ORC does not lead to Pch2-like phenotypes, indicating that ORC integrity per se is not required to support Pch2 function. We thus reveal functional interplay between Pch2 and ORC, and uncover the repurposing of ORC to establish a non-canonical and meiosis-specific AAA+ assembly.

biochemistry↗

bioRxiv2019

Parallel Group ICA + ICA: Joint Estimation of Linked Functional Network Variability and Structural Covariation with Application to Schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called \"parallel group ICA+ICA\" that incorporates temporal fMRI information from GICA into a parallel ICA framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g. structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate inter-modality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

bioinformatics↗

bioRxiv2019

A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that components forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

epidemiology↗

bioRxiv2019

Percentage of Foveal versus Total Macular Geographic Atrophy as a Predictor of Visual Acuity in Age-related Macular Degeneration

ObjectivesTo investigate the relationship between visual acuity (VA), total area of geographic atrophy (GA) and percentage of foveal GA. MethodsMulticenter, retrospective cross-sectional study of patients with GA due to age-related macular degeneration. Demographics, VA, fundus autofluorescence (FAF) and optical coherence tomography (OCT) images were collected. Using FAF images aided by OCT, foveal sparing status, GA pattern, total GA size, and percentage of GA covering the foveal area - area within a 1.5 mm diameter circle centered on the fovea centralis - were assessed. Univariable and multiple linear regression analyses were performed. Results54 eyes (mean age 78.7 {+/-}7.7 (SD), 60.0% female) were studied. Mean VA was 0.8 {+/-} 0.6 logMAR, mean total GA 8.8 {+/-} 6.7 mm2 and mean percentage of foveal GA was 71.5 {+/-} 30.9%. Of all assessed eyes, 48.2% (n = 26) presented with multifocal GA, and 18.5% (n = 10) had foveal sparing. Multiple regression analysis revealed that, controlling for age and GA pattern, the percentage of foveal GA presented a statistically significant association with VA ({beta} = 0.41, P = 0.004). No significant associations were observed with mean total GA size, while controlling for the same variables ({beta} = 0.010, P = 0.440). ConclusionPercentage of foveal GA was significantly associated with VA impairment, while the same was not verified for total GA area. These findings suggest that percentage of foveal GA may represent a more useful tool for assessing the impact of GA on VA. Further validation is needed in larger cohorts.

neuroscience↗

bioRxiv2017

Unbiased association and expression studies identify novel genes for tooth development

Previously reported co-occurrence of colorectal cancer (CRC) and tooth agenesis (TA) and the overlap in disease-associated gene variants suggest involvement of similar molecular pathways. In this study, we took an unbiased approach and tested genome-wide significant CRC-associated variants for association with isolated TA. Thirty single nucleotide variants (SNVs) in CRC-predisposing genes/loci were genotyped in a discovery dataset composed of 440 individuals with and without isolated TA. Genome-wide significant associations were found between TA and DUSP10 rs6687758 (P=1.25 x 10-9) and ATF1 rs11169552 (P=4.36 x 10-10), with strong association found with CASC8 rs10505477 (P=8.2 x 10-5). Additional CRC marker haplotypes were also significantly associated with TA (P<0.0002). Genotyping an independent dataset consisting of 52 cases with TA and 427 controls confirmed the association with CASC8.\n\nAtf1 and Dusp10 expression was detected in the mouse developing teeth from early bud stages to the formation of the complete tooth, suggesting a potential role for these genes and their encoded proteins in tooth development. Our findings suggest Atf1 and Dusp10 as new tooth development genes, while having a role in colorectal cancer. While their individual contributions in tooth development remain to be elucidated, these genes may be considered additional candidates to be tested in future human genetic studies.

genetics↗

bioRxiv2017

Global determinants of navigation ability

Countries vary in their geographical and cultural properties. Only a few studies have explored how such variations influence how humans navigate or reason about space [1-7]. We predicted that these variations impact human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.

neuroscience↗