Biology subjects

Lu, D.

Publications and source records attributed to Lu, D..

6 recordsLinked to original sources

bioRxiv2018

A single cell transcriptomic analysis of human neocortical development

Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.\n\nOne Sentence SummaryComprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk.

neuroscience↗

bioRxiv2018

Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms

In this paper, we evaluated the power of metagenome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of metagenomic information taken at week 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back-fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting metagenomic information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of microbiome on feed efficiency as well as carcass and meat quality should be investigated.

genetics↗

bioRxiv2018

Recombinant expression of Proteorhodopsin and biofilm regulators in Escherichia coli for nanoparticle binding and removal in a wastewater treatment model

The small size of nanoparticles is both an advantage and a problem. Their high surface-area-to-volume ratio enables novel medical, industrial, and commercial applications. However, their small size also allows them to evade conventional filtration during water treatment, posing health risks to humans, plants, and aquatic life. This project aims to remove nanoparticles during wastewater treatment using genetically modified Escherichia coli in two ways: 1) binding citrate-capped nanoparticles with the membrane protein Proteorhodopsin, and 2) trapping nanoparticles using Escherichia coli biofilm produced by overexpressing two regulators: OmpR234 and CsgD. We demonstrate experimentally that Escherichia coli expressing Proteorhodopsin binds to 60 nm citrate-capped silver nanoparticles. We also successfully upregulate biofilm production and show that Escherichia coli biofilms are able to trap 30 nm gold particles. Finally, both Proteorhodopsin and biofilm approaches are able to bind and remove nanoparticles in simulated wastewater treatment tanks. We envision integrating our trapping system in both rural and urban wastewater treatment plants to efficiently capture all nanoparticles before treated water is released into the environment.\n\nFinancial DisclosureThis work was funded by the Taipei American School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting InterestsThe authors have declared that no competing interests exist.\n\nEthics StatementN/A\n\nData AvailabilityYes - all data are fully available without restriction. Sequences for the plasmids used in this study are available through the Registry of Standard Biological Parts. Links to raw data are included in Supplementary Information.

synthetic biology↗

bioRxiv2017

Unconsciously Implanted Visuoauditory Memory in the Presence of Cholecystokinin Retrieved in Behavioral Contexts

We investigated whether visuoauditory association can be artificially implanted in rodents and then retrieved in a behaviorally relevant context. Rats were trained to approach the left or right hole of a behavioral apparatus to retrieve a reward depending on the side of electrical stimulation of the auditory cortex (EAC) they received and mice were fear-conditioned to EAC. Next, an irrelevant visual stimulus (VS) was repeatedly paired with EAC in the presence of cholecystokinin (CCK) or with activation of terminals of entorhinal CCK neurons in the auditory cortex. In subsequent behavioral testing with VS, rats approached the hole associated with reward availability and mice showed a freezing response to the VS. A CCK antagonist blocked the establishment of visuoauditory association, whereas a CCK agonist rescued the deficit of association. Our findings provide a scientific foundation for \"memory implantation\" and indicate that CCK is the switching chemical for formation of visuoauditory association.

neuroscience↗

bioRxiv2017

Genome-wide Variants of Eurasian Facial Shape Differentiation and a prospective model of DNA based Face Prediction

It is a long standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association analyses were conducted on a discovery panel of Uyghurs. Six significant loci were identified four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals, and tested in hypothetic forensic scenarios.

genetics↗

bioRxiv2016

Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation

Both genetic variants and brain region abnormalities are recognized to play a role in cognitive decline. We explore the association between singlenucleotide polymorphisms (SNPs) in linkage regions for Alzheimers disease and rates of decline in brain structure using data from the Alzheimers Disease Neuroimaging Initiative (ADNI).\n\nIn an initial discovery stage, we assessed the presence of linear association between the minor allele counts of 75,845 SNPs in the Alzgene linkage regions and predicted rates of change in structural MRI measurements for 56 brain regions using an RV test. In a second, refinement stage, we reduced the number of SNPs using a bootstrap-enhanced sparse canonical correlation analysis (SCCA) with a fixed tuning parameter. Each SNP was assigned an importance measure proportional to the number of times it was estimated to have a nonzero coefficient in repeated re-sampling from the ADNI-1 sample. We created refined lists of SNPs based on importance probabilities greater than 50% and 90%, respectively. In a third, validation stage, we assessed the multivariate association between these refined lists of SNPs and the rates of structural change in the independent ADNI-2 study dataset.\n\nThere was strong statistical evidence for linear association between the SNPs in the Alzgene linkage regions and the 56 imaging phenotypes in both the ADNI-1 and ADNI-2 samples (p < 0.0001). The bootstrap-enhanced SCCA identified 1,694 priority SNPs with importance probabilities > 50% and 22 SNPs with importance probabilities > 90%. The 1,694 prioritized SNPs in the ADNI-1 data were associated with imaging phenotypes in the ADNI-2 data (p = 0.0021).\n\nThis manuscript presents an analysis that addresses challenges in current imaging genetics studies such as biased sampling designs and highdimensional data with low-signal. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. We hypothesize that the effect sizes of the 1,694 SNPs in the priority set are likely small, but further investigation within this set may advance understanding of the missing heritability in late-onset Alzheimers disease. Multivariate analysis; Linkage regions; Imaging genetics; Endophenotypes; Inverse probability weighting; Variable importance probabilities

neuroscience↗