Biology subjects

Lu, A.

Publications and source records attributed to Lu, A..

79 records · Page 5Linked to original sources

bioRxiv2019

Glycosylation of gigaxonin regulates intermediate filaments: Novel molecular insights into giant axonal neuropathy

Gigaxonin (also known as KLHL16) is an E3 ligase adaptor protein that promotes the ubiquitination and degradation of intermediate filament (IF) proteins. Mutations in human gigaxonin cause the fatal neurodegenerative disease giant axonal neuropathy (GAN), in which IF proteins accumulate and aggregate in axons throughout the nervous system, impairing neuronal function and viability. Despite this pathophysiological significance, the upstream regulation and downstream effects of normal and aberrant gigaxonin function remain incompletely understood. Here, we report that gigaxonin is modified by O-linked-beta-N-acetylglucosamine (O-GlcNAc), a prevalent form of intracellular glycosylation, in a nutrient- and growth factor-dependent manner. Mass spectrometry analyses of human gigaxonin revealed nine candidate sites of O-GlcNAcylation, two of which - serine 272 and threonine 277 - are required for its ability to mediate IF turnover in novel gigaxonin-deficient human cell models that we created. Taken together, these results suggest that nutrient-responsive gigaxonin O-GlcNAcylation forms a regulatory link between metabolism and IF proteostasis. Our work may have significant implications for understanding the non-genetic modifiers of GAN phenotypes and for the optimization of gene therapy for this disease.

cell biology↗

bioRxiv2018

Hyocholic acid species and the risk of type 2 diabetes

Hyocholic acid (HCA) and its derivatives are found in only trace amounts in human blood, but constitute approximately 76 % of the bile acid (BA) pool in the pig, a species known for its exceptional resistance to type 2 diabetes mellitus (T2DM). Here we show that HCA species play a crucial role in maintaining glucose homeostasis and preventing T2DM. We found that in two cohort studies (n=1,213), both obesity and diabetes were associated with lower serum concentrations of HCA species. Serum HCA levels in apparently healthy individuals (n=132) were found to be strong predictors for metabolic health 10 years later. Oral administration of HCA increased serum fasting GLP-1, to a greater extent than metformin, in healthy and diabetic mouse models. HCA upregulated GLP-1 secretion in intestinal enteroendocrine cells via simultaneously activating G-protein-coupled BA receptor, TGR5, and inhibiting farnesoid X receptor, a unique mechanism that is not found in other BA species.

systems biology↗

bioRxiv2018

Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology

BackgroundThousands of SNPs associated with risk of Alzheimers disease (AD) in genome-wide association studies (GWAS) do not reach genome-wide significance. When combined, they contribute however to a highly predictive polygenic risk score. The relevance of these subthreshold risk genes to disease, and how their combined predictive power translates into functionally relevant disease pathways, is unknown. We investigate here at the genome-wide level and in an unbiased way to what extent AD risk genes show altered gene expression in the context of increasing A{beta} or Tau pathology in mouse models of AD.\n\nMethodsWe used an existing GWAS data set to generate lists of candidate AD genes at different levels of significance. We performed transcriptomic analysis on wild-type and transgenic APP/PS1 (APPtg) and Thy-TAU22 (TAUtg) mouse models at early and late stage of disease. We used unbiased weighted gene co-expression network analysis (WGCNA) to identify clusters of co-regulated genes responsive to A{beta} or TAU pathology. Gene set enrichment was used to identify clusters that were enriched for AD risk genes.\n\nFindingsConsistent and significant enrichment of AD risk genes was found in only one out of 63 coexpression modules. This module is highly responsive to A{beta} but not to TAU pathology. We identify in this module 18 AD risk genes (p-value=6{middle dot}5e-11) including 11 new ones, GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN and BLNK. All are expressed in microglia, have a binding site for the transcription factor SPI1 (PU.1), and become significantly upregulated when exposed to A{beta}. A subset regulates FC-gamma receptor mediated phagocytosis.\n\nInterpretationGenetic risk of AD is functionally translated into a microglia pathway responsive to A{beta} pathology. This insight integrates aspects of the amyloid hypothesis with genetic risk associated to sporadic AD.

neuroscience↗

bioRxiv2018

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.\n\nAuthor SummaryTo understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

bioinformatics↗

bioRxiv2018

Osteoblastic PLEKHO1 contributes to joint inflammation in rheumatoid arthritis

Osteoblasts participating in the inflammation regulation gradually obtain concerns. However, its role in joint inflammation of rheumatoid arthritis (RA) is largely unknown. Pleckstrin homology domain-containing family O member 1 (PLEKHO1) was previously identified as a negative regulator of osteogenic lineage activity. Here we demonstrated that PLEKHO1 was highly expressed in osteoblasts of articular specimens from RA patients and inflammatory arthritis mice. Genetic deletion of osteoblastic Plekho1 ameliorated joint inflammation in mice with collagen-induced arthritis (CIA) and K/BxN serum-transfer arthritis (STA), whereas overexpressing Plekho1 only within osteoblasts in CIA and STA mice demonstrated exacerbated local inflammation. Further in vitro studies indicated that PLEKHO1 was required for TRAF2-mediated RIP1 ubiquitination to activate NF-kB for inducing inflammatory cytokines production in osteoblasts. Moreover, osteoblastic PLEKHO1 inhibition improved joint inflammation and attenuated bone formation reduction in CIA mice and non-human primate arthritis model. These data strongly suggest that highly expressed PLEKHO1 in osteoblast mediates joint inflammation in RA. Targeting osteoblastic PLEKHO1 may exert dual therapeutic action of alleviating joint inflammation and promoting bone formation in RA.

cell biology↗

bioRxiv2018

The Goldilocks Effect: Female geladas in mid-sized groups have higher fitness

The cost-benefit ratio of group-living is thought to vary with group size: individuals in "optimally-sized" groups should have higher fitness than individuals in groups that are either too large or too small. However, the relationship between group size and individual fitness has been difficult to establish for long-lived species where the number of groups studied is typically quite low. Here we present evidence for optimal group size that maximizes female fitness in a population of geladas (Theropithecus gelada). Drawing on 14 years of demographic data, we found that females in small groups experienced the highest death rates, while females in mid-sized units exhibited the highest reproductive performance. This group-size effect on female reproductive performance was largely explained by variation in infant mortality (and, in particular, by infanticide from immigrant males) but not by variation in reproductive rates. Taken together, females in mid-sized units are projected to attain optimal fitness due to conspecific infanticide and, potentially, predation. Our findings provide insight into how and why group size shapes fitness in long-lived species.

evolutionary biology↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗