Biology subjects

Lu, B.

Publications and source records attributed to Lu, B..

6 recordsLinked to original sources

bioRxiv2018

Differential Susceptibility of Rat Retinal Ganglion Cell Subtypes Following Optic Nerve Crush

Retinal ganglion cells (RGCs) are a heterogeneous group of cells, comprised of numerous subpopulations, that work together to send visual information to the brain. In numerous blinding disorders termed optic neuropathies, RGCs are the main cell type affected leading to degeneration of these cells and eventual loss of vision. Previous studies have identified and characterized RGC subtypes in numerous animal systems, with only a handful of studies demonstrating their differential loss in response to disease and injury. Thus, efforts of the current study utilized an optic nerve crush (ONC) model to characterize the loss of RGCs and disease phenotypes associated with this injury. Additionally, the loss of RGC subtypes including direction selective-, alpha-, and ip-RGCs following ONC was explored. Results of this study demonstrated the differential loss of RGC subtypes with a high susceptibility for loss of alpha- and direction selective-RGCs and the preferential survival of ip-RGCs following ONC and allows for the establishment of additional studies focused on mechanisms and loss of these cells in optic neuropathies. Additionally, these results put important emphasis on the development of therapeutics targeted at the loss of specific subtypes as well as cellular replacement following injury and disease.

neuroscience↗

bioRxiv2018

High glucose-induced ubiquitylation of G6PD leads to the injury of podocyte

Oxidative stress contributes substantially to podocyte injury in diabetic kidney disease. The mechanism of hyperglycemia-induced oxidative stress in podocytes is not fully understood. Glucose-6-phosphate dehydrogenase is critical in maintaining NADPH, an important cofactor for antioxidant system. Here, we hypothesized that high glucose induces ubiquitylation and degradation of G6PD, which injures podocytes by reactive oxygen species (ROS) accumulation. We found that both G6PD protein expression and G6PD activity was decreased in kidneys of both diabetic patients and diabetic rodents. Overexpressing G6PD reversed redox imbalance and podocyte apoptosis induced by high glucose and palmitate. Inhibition of G6PD induced podocyte apoptosis. In G6PD deficient mice, podocyte apoptosis was also largely increased. High glucose had no effect on G6PD mRNA level but it caused decreased G6PD protein expression, which was mediated by the ubiquitin proteasome pathway. Furthermore, von Hippel-Lindau (VHL), an E3 ubiquitin ligase subunit, directly bound to G6PD and degraded G6PD through ubiquitylating G6PD on lysine residues 366/403. Our data suggest that high glucose induces ubiquitylation of G6PD by VHL, which leads to ROS accumulation and podocyte injury.

cell biology↗

bioRxiv2018

CLADES: A Classification-based Machine Learning Method for Species Delimitation from Population Genetic Data

Species are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are becoming increasingly available, there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species delimitation. There exists no species delimitation methods that are based on machine learning. Our method treats the species delimitation problem as a classification problem. It is a problem of identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purpose. Each pair of known populations are combined to form training samples with a label of \"same species\" or \"different species\". We use Support Vector Machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: \"same species\" or \"different species\". Given multi-locus genomic data of multiple related organisms or populations, our method (called CLADES) performs species delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in delimitation, e.g. with short species divergence time and gene flow.

evolutionary biology↗

bioRxiv2018

Physiological Significance of R-fMRI Indices in Detecting Structural Brain Lesions

Resting-state functional MRI (R-fMRI) research has recently entered the era of \"big data\", however, few studies have provided a rigorous validation of the physiological underpinnings of R-fMRI indices. Although studies have reported that various neuropsychiatric disorders exhibit abnormalities in R-fMRI measures, these \"biomarkers\" have not been validated in differentiating structural lesions (brain tumors) as a concept proof. We enrolled 60 patients with intracranial tumors located in the unilateral cranial cavity and 60 matched normal controls to test whether R-fMRI indices can differentiate tumors, which represents a prerequisite for adapting such indices as biomarkers for neuropsychiatric disorders. Common R-fMRI indices of tumors and their counterpart control regions, which were defined as the contralateral normal areas (for amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and degree centrality (DC)) and ipsilateral regions surrounding the tumors (for voxel-mirrored homotopic connectivity (VMHC)), were comprehensively assessed. According to paired t-tests with a Bonferroni correction, only ALFF (both with and without Z-standardization) and VMHC (Fishers r-to-z transformed) could successfully differentiate substantial tumors from their counterpart normal regions in patients. And DC was not able to differentiate tumor from normal unless employed Z-standardization. To validate the lower power in the between-subject design than in the within-subject design, each metric was calculated in a matched control group, and two-sample t-tests were used to compare the patient tumors and the normal controls at the same area. Only ALFF (and that with Z-standardization) along with VMHC succeeded in differentiating significant differences between tumors and the sham tumors areas of normal controls. This study tested the premise of R-fMRI biomarkers for differentiating lesions, and brings a new understanding to physical significance of the Z-standardization.

neuroscience↗

bioRxiv2017

Dynamics of a Form-Fitting Protein in a Nanopore: Avidin in ClyA

We probe the molecular dynamics of a protein, avidin, as it is captured and trapped in a nanopore, ClyA, with time-resolved single-molecule electrical conductance measurements, and we present a method for visualizing this process from the data. The case of avidin in ClyA has rich time-dependent conductance spectra of discrete levels that correlate with different configurations of the protein in the pore. One is very long-lasting, stable and noise-free, and portends the use of this system as a platform for more general studies of proteins and other molecules, where avidin acts as a shuttle that ferries analytes into the pore for probing. We demonstrate this by the sensitive detection of a biotin molecule attached to avidin captured by the pore. We also present an approach to determining the nanopore size based on a 3D printed model of the pore.

biophysics↗

bioRxiv2017

A Comprehensive Assessment Of Reproducibility Of R-fMRI Metrics On The Impact Of Different Strategies For Multiple Comparison Correction And Small Sample Size

Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability / replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 (40 per group)) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect \"true\" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility.

neuroscience↗