Pressure Driven Flow of Polymer Solutions in Nanoscale Slit Pores

Polymer solutions subject to pressure driven flow and in nanoscale slit pores are systematically investigated using the dissipative particle dynamics approach. We investigated the effect of molecular weight, polymer concentration and flow rate on the profiles across the channel of the fluid and polymer velocities, polymers density, and the three components of the polymers radius of gyration. We found that the mean streaming fluid velocity decreases as the polymer molecular weight or/and polymer concentration is increased, and that the deviation of the velocity profile from the parabolic profile is accentuated with increase in polymer molecular weight or concentration. We also found that the distribution of polymers conformation is highly anisotropic and non-uniform across the channel. The polymer density profile is also found to be non-uniform, exhibiting a local minimum in the center-plane followed by two symmetric peaks. We found a migration of the polymer chains either from or towards the walls. For relatively long chains, as compared to the thickness of the slit, a migration towards the walls is observed. However, for relatively short chains, a migration away from the walls is observed.Comment: 11 pages, 13 figure

Shotgun ion mobility mass spectrometry sequencing of heparan sulfate saccharides

Despite evident regulatory roles of heparan sulfate (HS) saccharides in numerous biological processes, definitive information on the bioactive sequences of these polymers is lacking, with only a handful of natural structures sequenced to date. Here, we develop a “Shotgun” Ion Mobility Mass Spectrometry Sequencing (SIMMS2) method in which intact HS saccharides are dissociated in an ion mobility mass spectrometer and collision cross section values of fragments measured. Matching of data for intact and fragment ions against known values for 36 fully defined HS saccharide structures (from di- to decasaccharides) permits unambiguous sequence determination of validated standards and unknown natural saccharides, notably including variants with 3O-sulfate groups. SIMMS2 analysis of two fibroblast growth factor-inhibiting hexasaccharides identified from a HS oligosaccharide library screen demonstrates that the approach allows elucidation of structure-activity relationships. SIMMS2 thus overcomes the bottleneck for decoding the informational content of functional HS motifs which is crucial for their future biomedical exploitation

Myeloid cells, BAFF, and IFN-γ establish an inflammatory loop that exacerbates autoimmunity in Lyn-deficient mice

Autoimmunity is traditionally attributed to altered lymphoid cell selection and/or tolerance, whereas the contribution of innate immune cells is less well understood. Autoimmunity is also associated with increased levels of B cell–activating factor of the TNF family (BAFF; also known as B lymphocyte stimulator), a cytokine that promotes survival of self-reactive B cell clones. We describe an important role for myeloid cells in autoimmune disease progression. Using Lyn-deficient mice, we show that overproduction of BAFF by hyperactive myeloid cells contributes to inflammation and autoimmunity in part by acting directly on T cells to induce the release of IFN-γ. Genetic deletion of IFN-γ or reduction of BAFF activity, achieved by either reducing myeloid cell hyperproduction or by treating with an anti-BAFF monoclonal antibody, reduced disease development in lyn−/− mice. The increased production of IFN-γ in lyn−/− mice feeds back on the myeloid cells to further stimulate BAFF release. Expression of BAFF receptor on T cells was required for their full activation and IFN-γ release. Overall, our data suggest that the reciprocal production of BAFF and IFN-γ establishes an inflammatory loop between myeloid cells and T cells that exacerbates autoimmunity in this model. Our findings uncover an important pathological role of BAFF in autoimmune disorders

Investigation of a dual siRNA/chemotherapy delivery system for breast cancer therapy

Multidrug resistance (MDR) is a problem that is often associated with a poor clinical outcome in chemotherapeutic cancer treatment. MDR may potentially be overcome by utilizing synergistic approaches, such as combining siRNA gene therapy and chemotherapy to target different mechanisms of apoptosis. In this study, a strategy is presented for developing multicomponent nanomedicines using orthogonal and compatible chemistries that lead to effective nanotherapeutics. Hyperbranched polymers were used as drug carriers that contained doxorubicin (DOX), attached via a pH-sensitive hydrazone linkage, and ataxia-telangiectasia mutated (ATM) siRNA, attached via a redox-sensitive disulfide group. This nanomedicine also contained cyanine 5 (Cy5) as a diagnostic tracer as well as in-house developed bispecific antibodies that allowed targeting of the epidermal growth factor receptor (EGFR) present on tumor tissue. Highly efficient coupling of siRNA was achieved with 80% of thiol end-groups on the hyperbranched polymer coupling with siRNA. This attachment was reversible, with the majority of siRNA released in vitro under reducing conditions as desired. In cellular studies, the nanomedicine exhibited increased DNA damage and cancer cell inhibition compared to the individual treatments. Moreover, the nanomedicine has great potential to suppress the metabolism of cancer cells including both mitochondrial respiration and glycolytic activity, with enhanced efficacy observed when targeted to the cell surface protein EGFR. Our findings indicated that co-delivery of ATM siRNA and DOX serves as a more efficient therapeutic avenue in cancer treatment than delivery of the single species and offers a potential route for synergistically enhanced gene therapy

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high‐throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of ‐omics data, joint analysis of molecular‐level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high‐dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high‐dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi‐Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/3/biom13189.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/2/biom13189-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/1/biom13189_am.pd

Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms

Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators

We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be sub-optimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, one with a Gaussian mixture prior that enables correlated mediator selection and the other with a Potts mixture prior that accounts for the correlation among active mediators in mediation analysis. We develop efficient sampling algorithms for both methods. Various simulations demonstrate that our methods enable effective identification of correlated active mediators, which could be missed by using existing methods that assume prior independence among active mediators. The proposed methods are applied to the LIFECODES birth cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) and identified new active mediators with important biological implications

Heparan Sulfate Domains Required for Fibroblast Growth Factor 1 and 2 Signaling through Fibroblast Growth Factor Receptor 1c

A small library of well defined heparan sulfate (HS) polysaccharides was chemoenzymatically synthesized and used for a detailed structure-activity study of fibroblast growth factor (FGF) 1 and FGF2 signaling through FGF receptor (FGFR) 1c. The HS polysaccharide tested contained both undersulfated (NA) domains and highly sulfated (NS) domains as well as very well defined non-reducing termini. This study examines differences in the HS selectivity of the positive canyons of the FGF12-FGFR1c2 and FGF22-FGFR1c2 HS binding sites of the symmetric FGF2-FGFR2-HS2 signal transduction complex. The results suggest that FGF12-FGFR1c2 binding site prefers a longer NS domain at the non-reducing terminus than FGF22-FGFR1c2. In addition, FGF22-FGFR1c2 can tolerate an HS chain having an N-acetylglucosamine residue at its non-reducing end. These results clearly demonstrate the different specificity of FGF12-FGFR1c2 and FGF22-FGFR1c2 for well defined HS structures and suggest that it is now possible to chemoenzymatically synthesize precise HS polysaccharides that can selectively mediate growth factor signaling. These HS polysaccharides might be useful in both understanding and controlling the growth, proliferation, and differentiation of cells in stem cell therapies, wound healing, and the treatment of cancer