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Dao, C.

Publications and source records attributed to Dao, C..

4 recordsLinked to original sources

Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program

Smoking behaviors and alcohol use disorder (AUD), moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to comorbid smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N=318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 18 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly comorbid phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

genetics↗

Visual Perception of 3D Space and Shape in Time - Part I: 2D Space Perception by 2D Linear Translation

Visual perception plays a critical role in navigating space and extracting useful semantic information crucial to survival. To identify distant landmarks, we constantly shift gaze vectors through saccades, while still maintaining the visual perception of stable allocentric space. How can we sustain stable allocentric space so effortlessly? To solve this question, we have developed a new concept of NHT (Neural Holography Tomography). This model states that retinotopy is invisible (not available to consciousness) and must be converted to a time code by traveling alpha brainwaves to perceive objects consciously. According to this framework, if identical alpha phases are continually assigned to a landmark, we perceive its exact and consistent allocentric location. To test this hypothesis, we designed reaction time (RT) experiments to observe evidence of the predicted space-to-time conversion. Various visual stimuli were generated at a wide range of eccentricities either on a large TV (up to 40{degrees}) or by LED strips on a hemispherical dome (up to 60{degrees}). Participants were instructed to report the observed patterns promptly under either covert (no eye movement) or overt (with eye movement) conditions. As predicted, stimuli presented at the center of fixation always produced the fastest RTs. The additional RT delay was precisely proportional to the eccentricity of the peripheral stimulus presentation. Furthermore, both covert and overt attention protocols created the same RT delays, and trajectories of saccadic eye motions were in parallel to the overt RT vs. eccentricity. These findings strongly support our NHT model, in which the observed RT-eccentricity dependence is indicative of the spatiotemporal conversion required for maintaining a stable allocentric frame of reference. That is, we perceive space by time.

neuroscience↗

Identification of a fatty acid-sensitive interaction between mitochondrial uncoupling protein 3 and enoyl-CoA hydratase 1 in skeletal muscle

Skeletal muscle mitochondrial fatty acid (FA) overload in response to chronic overnutrition is a prominent pathophysiological mechanism in obesity-induced metabolic disease. Increased disposal of FAs is therefore an attractive strategy for intervening in obesity and related disorders. Skeletal muscle uncoupling protein 3 (UCP3) activity is associated with increased FA oxidation and antagonizes weight gain in mice on obesogenic diets, but the mechanisms involved are not clear. Here, we show that UCP3 forms a direct, FA-stimulated, mitochondrial matrix-localized complex with the auxiliary unsaturated FA-metabolizing enzyme, {Delta}3,5-{Delta}2,4dienoyl-CoA-isomerase (ECH1). Expression studies in C2C12 myoblasts that functionally augments state 4 (uncoupled) respiration and FA oxidation in skeletal myocytes.\n\nMechanistic studies indicate that ECH1:UCP3 complex formation is likely stimulated by FA import into the mitochondria to enhance uncoupled respiration and unsaturated FA oxidation in mouse skeletal myocytes. In order to characterize the contribution of ECH1-dependent FA metabolism in NST, we generated an ECH1 knockout mouse and found that these mice were severely cold intolerant, despite an up-regulation of UCP3 expression in SKM. These findings illuminate a novel mechanism that links unsaturated FA metabolism with mitochondrial uncoupling and non-shivering thermogenesis in SKM.

biochemistry↗

PUMA: A tool for processing 16S rRNA taxonomy data for analysis and visualization

Microbial community profiling and functional inference via 16S rRNA analysis is quickly expanding across various areas of microbiology due to improvements to technology. There are numerous platforms for producing 16S rRNA taxonomic data which often vary in file and sequence formatting, creating a common barrier in microbiome studies. Additionally, many of the methods for analyzing and visualizing this sequencing data each require their own specific formatting. As a result, efficient and reproducible comparative analysis of taxonomic data and corresponding metadata in multiple programs remains a challenge in the investigation of microbial communities. PUMA, the Program for Unifying Microbiome Analysis, alleviates this problem in microbiome studies by allowing users to take advantage of numerous 16S rRNA taxonomic identification platforms and analysis tools in an efficient manner. PUMA accepts sequencing results from several taxonomic identification platforms and then automates configuration of data and file types for analysis and visualization via many popular tools. The protocol accomplishes this by producing a variety of properly configured, annotated, and altered files for both analysis and visualization of taxonomic community profiles and inferred functional profiles. PUMA provides an easy and flexible interface to accommodate for a variety of users to produce all files needed for all-inclusive analysis of targeted amplicon sequencing studies. PUMA is an unprecedented open-source solution for unifying multiple microbiome analysis softwares and uses an adaptable implementation with the potential to improve and consolidate the state of microbiome research.\n\nBody/Findings

microbiology↗