Biology subjects

Levine, B.

Publications and source records attributed to Levine, B..

4 recordsLinked to original sources

bioRxiv2022

Interaction of surface topography and taper mismatch on head-stem modular junction contact mechanics during assembly in modern total hip replacement

Implant failure due to fretting corrosion at the head-stem modular junction is an increasing problem in modular total hip arthroplasty. The effect of varying microgroove topography on modular junction contact mechanics has not been well characterized. The aim of this work was to employ a novel, microgrooved finite element (FEA) model of the hip taper interface and assess the role of microgroove geometry and taper mismatch angle on the modular junction mechanics during assembly. A two-dimensional, axisymmetric FEA model was created using a modern 12/14 taper design of a CoCrMo femoral head taper and Ti6Al4V stem taper. Microgrooves were modelled at the contacting interface of the tapers and varied based on height and spacing measurements obtained from a repository of measured retrievals. Additionally, taper angular mismatch between the head and stem was varied to simulate proximal- and distal-locked engagement. Forty simulations were conducted to parametrically evaluate the effects of microgroove surface topography and angular mismatch on predicted contact area, contact pressure, and equivalent plastic strain. Multiple linear regression analysis was highly significant (p < 0.001; R2 > 0.74) for all outcome variables. The regression analysis identified microgroove geometry on the head taper to have the greatest influence on modular junction contact mechanics. Additionally, there was a significant second order relationship between both peak contact pressure (p < 0.001) and plastic strain (p < 0.001) with taper mismatch angle. These modeling techniques will be used to identify the implant parameters that maximize taper interference strength via large in-silico parametric studies.

bioengineering↗

bioRxiv2021

Quantitative Phosphoproteomic Analyses Identify STK11IP as a Lysosome-Specific Substrate of mTORC1 that Regulates Lysosomal Acidification

The evolutionarily conserved serine/threonine kinase mTORC1 is a central regulator of cell growth and proliferation. mTORC1 is activated on the lysosome surface. However, once mTORC1 is activated, it is unclear whether mTORC1 phosphorylates local lysosomal proteins to regulate specific aspects of lysosomal biology. Through cross-reference analyses of lysosome proteomic with mTORC1-regulated phosphoproteomic, we identified STK11IP as a novel lysosome-specific substrate of mTORC1. mTORC1 directly phosphorylates STK11IP at S404. Knockout of STK11IP led to a robust increase of autophagosome-lysosome fusion and autophagy flux. Dephosphorylation of STK11IP at S404 represses the role of STK11IP as an autophagy inhibitor. Mechanistically, STK11IP binds to V-ATPase, and regulates the activity of V-ATPase. Knockout of STK11IP protects mice from fasting and Methionine and Choline-Deficient Diet (MCD) diet induced fatty liver. Thus, our study demonstrates that STK11IP phosphorylation represents a novel mechanism for mTORC1 to regulate lysosomal acidification, and points to STK11IP as a promising therapeutic target for the amelioration of diseases with aberrant autophagy signaling.

cell biology↗

bioRxiv2021

RNA sequencing on muscle biopsy from a 5-week bedrest study reveals the effect of exercise and potential interactions with dorsal root ganglion neurons

Lack of physical activity is a predictor of poor health outcomes that can be prevented or reversed by exercise. Sedentary lifestyle, chronic disease or microgravity can cause muscle deconditioning that then has an impact on other physiological systems. An example is the nervous system, which is adversely affected by decreased physical activity resulting in increased incidence of neurological problems such as chronic pain. We sought to better understand how this might occur by conducting RNA sequencing experiments on muscle biopsies from human volunteers in a 5-week bed-rest study with an exercise intervention arm. We also used a computational method for examining ligand-receptor interactions between muscle and human dorsal root ganglion (DRG) neurons, the latter of which play a key role in nociception and are generators of signals responsible for chronic pain. We identified 1352 differentially expressed genes (DEGs) in bed rest subjects without an exercise intervention but only 132 DEGs in subjects with the intervention. Thirty-six genes were shared between the exercise and no intervention groups. Among 591 upregulated muscle genes in the no intervention arm, 26 of these were ligands that have receptors that are expressed by human DRG neurons. We detected a specific splice variant of one of these ligands, placental growth factor (PGF), in deconditioned muscle that binds to neuropilin 1, a receptor that is highly expressed in DRG neurons and known to promote neuropathic pain. We conclude that exercise intervention protects muscle from deconditioning transcriptomic changes, and prevents changes in expression of ligands that might sensitize DRG neurons that promote pain. Our work creates a set of actionable hypotheses to better understand how deconditioned muscle may influence the function of sensory neurons that innervate the entire body.

neuroscience↗

bioRxiv2018

Generalization of the minimum covariance determinant algorithm for categorical and mixed data types

The minimum covariance determinant (MCD) algorithm is one of the most common techniques to detect anomalous or outlying observations. The MCD algorithm depends on two features of multivariate data: the determinant of a matrix (i.e., geometric mean of the eigenvalues) and Mahalanobis distances (MD). While the MCD algorithm is commonly used, and has many extensions, the MCD is limited to analyses of quantitative data and more specifically data assumed to be continuous. One reason why the MCD does not extend to other data types such as categorical or ordinal data is because there is not a well-defined MD for data types other than continuous data. To address the lack of MCD-like techniques for categorical or mixed data we present a generalization of the MCD. To do so, we rely on a multivariate technique called correspondence analysis (CA). Through CA we can define MD via singular vectors and also compute the determinant from CAs eigenvalues. Here we define and illustrate a generalized MCD on categorical data and then show how our generalized MCD extends beyond categorical data to accommodate mixed data types (e.g., categorical, ordinal, and continuous). We illustrate this generalized MCD on data from two large scale projects: the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Alzheimers Disease Neuroimaging Initiative (ADNI), with genetics (categorical), clinical instruments and surveys (categorical or ordinal), and neuroimaging (continuous) data. We also make R code and toy data available in order to illustrate our generalized MCD.

bioinformatics↗