Biology subjects

Anderson, M.

Publications and source records attributed to Anderson, M..

4 recordsLinked to original sources

bioRxiv2018

Large-Scale Genome-Wide Meta Analysis of Polycystic Ovary Syndrome Suggests Shared Genetic Architecture for Different Diagnosis Criteria.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed with different criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease indicate shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. Only one locus differed in its association by diagnostic criteria, otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or Rotterdam criteria across common variants at 13 loci.

genetics↗

bioRxiv2018

Candida albicans dispersed cells signify a developmental state distinct from biofilm and planktonic phases

Candida albicans surface-attached biofilms are sites of amplification of an infection through continuous discharge of cells capable of initiating new infectious foci. Yeast cells released from biofilms on intravenous catheters have direct access to the bloodstream. We previously reported that dispersed cells are largely lateral yeast cells that originate from the hyphal layers of the biofilm. Compared to their planktonic counterparts, these biofilm-dispersed yeast cells displayed enhanced virulence-associated gene expression and drug resistance. Little is known about the molecular properties of dispersed cells. We found that the inducer of dispersal, PES1, genetically interacts with the repressor of filamentation, NRG1, in a manner that supports a genetic definition of dispersed cells as yeast. We combined a flow biofilm model with RNA sequencing technology, to identify transcriptomic characteristics of freshly dispersed yeast cells versus biofilms or age-matched planktonic yeast cells growing in glucose-rich medium. Dispersed cells largely inherited a biofilm-like mRNA profile but with one stark difference: dispersed cells were transcriptionally reprogrammed to metabolize alternative carbon sources, while their sessile parents expressed glycolytic genes, despite exposure to the same nutritional signals. Our studies hence define dispersal cell production as an intrinsic step of biofilm development which generates propagules capable of colonizing distant host sites. This developmental step anticipates the need for virulence-associated gene expression before experiencing the associated external signals.

microbiology↗

bioRxiv2017

IMPROVEMENTS TO BAYESIAN GENE ACTIVITY STATE ESTIMATION FROM GENOME-WIDE TRANSCRIPTOMICS DATA

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

genomics↗

bioRxiv2017

Imaging specific cellular glycan structures using glycosyltransferases via click chemistry

Heparan sulfate (HS) is a polysaccharide fundamentally important for biologically activities. T/Tn antigens are universal carbohydrate cancer markers. Here, we report the specific imaging of these carbohydrates using a mesenchymal stem cell line and human umbilical vein endothelial cells (HUVEC). The staining specificities were demonstrated by comparing imaging of different glycans and validated by either removal of target glycans, which results in loss of signal, or installation of target glycans, which results in gain of signal. As controls, representative key glycans including O-GlcNAc, lactosaminyl glycans and hyaluronan were also imaged. HS staining revealed novel architectural features of the extracellular matrix (ECM) of HUVEC cells. Results from T/Tn antigen staining suggest that O-GalNAcylation is a rate-limiting step for O-glycan synthesis. Overall, these highly specific approaches for HS and T/Tn antigen imaging should greatly facilitate the detection and functional characterization of these biologically important glycans.

molecular biology↗