Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing

BACKGROUND

Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads, and then, based on this LM, calculates a metric called the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces the highest alignment rate without using a reference genome. The fundamental intuition of our approach is that the perplexity metric is inversely correlated with the quality of the assembly after error correction. Therefore, Lerna leverages the perplexity metric for automated tuning of k-mer sizes without needing a reference genome.

RESULTS

First, we show that the best k-mer value can vary for different datasets, even for the same EC tool. This motivates our design that automates k-mer size selection without using a reference genome. Second, we show the gains of our LM using its component attention-based transformers. We show the model's estimation of the perplexity metric before and after error correction. The lower the perplexity after correction, the better the k-mer size. We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality. We validate our approach on both short and long reads. Additionally, we show that our attention-based models have significant runtime improvement for the entire pipeline-18[Formula: see text] faster than previous works, due to parallelizing the attention mechanism and the use of JIT compilation for GPU inferencing.

CONCLUSION

Lerna improves de novo genome assembly by optimizing EC tools. Our code is made available in a public repository at: https://github.com/icanforce/lerna-genomics .

A data-driven approach to increasing the lifetime of IoT sensor nodes

Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia, a lightweight protocol that utilizes a window-based timeseries forecasting mechanism for data reduction. Ambrosia employs a configurable error threshold to ensure that the accuracy of end applications is unaffected by the data transfer reduction. Experimental evaluations using LoRa and BLE on a real livestock monitoring deployment demonstrate 60% reduction in data transmission and a 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× increase in battery lifetime.

Combinatorial screening of biochemical and physical signals for phenotypic regulation of stem cell-based cartilage tissue engineering

Despite great progress in biomaterial design strategies for replacing damaged articular cartilage, prevention of stem cell-derived chondrocyte hypertrophy and resulting inferior tissue formation is still a critical challenge. Here, by using engineered biomaterials and a high-throughput system for screening of combinatorial cues in cartilage microenvironments, we demonstrate that biomaterial cross-linking density that regulates matrix degradation and stiffness-together with defined presentation of growth factors, mechanical stimulation, and arginine-glycine-aspartic acid (RGD) peptides-can guide human mesenchymal stem cell (hMSC) differentiation into articular or hypertrophic cartilage phenotypes. Faster-degrading, soft matrices promoted articular cartilage tissue formation of hMSCs by inducing their proliferation and maturation, while slower-degrading, stiff matrices promoted cells to differentiate into hypertrophic chondrocytes through Yes-associated protein (YAP)-dependent mechanotransduction. in vitro and in vivo chondrogenesis studies also suggest that down-regulation of the Wingless and INT-1 (WNT) signaling pathway is required for better quality articular cartilage-like tissue production.

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community's data analysis tasks.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

Background

The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our system Aikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).

Results

We develop a suite of ML models, under the banner Aikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.

Conclusions

Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our Aikyatan suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3049-1) contains supplementary material, which is available to authorized users.

Federation in genomics pipelines: techniques and challenges

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.

A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data

BACKGROUND

MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA.

RESULTS

We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75 percent, when keeping the false positive rate of 20 percent, for non-canonical human miRNA target sites. This is an improvement of over 150 percent in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction, and computing performance metrics are fully distributed and are scalable.

CONCLUSIONS

We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark.

AVAILABILITY

All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.

Syndecan-1 in mechanosensing of nanotopological cues in engineered materials

The cells of the vascular system are highly sensitive to biophysical cues from their local cellular microenvironment. To engineer improved materials for vascular devices and delivery of cell therapies, a key challenge is to understand the mechanisms that cells use to sense biophysical cues from their environment. Syndecans are heparan sulfate proteoglycans (HSPGs) that consist of a protein core modified with heparan sulfate glycosaminoglycan chains. Due to their presence on the cell surface and their interaction with cytoskeletal and focal adhesion associated molecules, cell surface proteoglycans are well poised to serve as mechanosensors of the cellular microenvironment. Nanotopological cues have become recognized as major regulators of cell growth, migration and phenotype. We hypothesized that syndecan-1 could serve as a mechanosensor for nanotopological cues and can mediate the responsiveness of vascular smooth muscle cells to nanoengineered materials. We created engineered substrates made of polyurethane acrylate with nanogrooves using ultraviolet-assisted capillary force lithography. We cultured vascular smooth muscle cells with knockout of syndecan-1 on engineered substrates with varying compliance and nanotopology. We found that knockout of syndecan-1 reduced alignment of vascular smooth muscle cells to the nanogrooves under inflammatory treatments. In addition, we found that loss of syndecan-1 increased nuclear localization of Yap/Taz and phospho-Smad2/3 in response to nanogrooves. Syndecan-1 knockout vascular smooth muscle cells also had elevated levels of Rho-associated protein kinase-1 (Rock1), leading to increased cell stiffness and an enhanced contractile state in the cells. Together, our findings support that syndecan-1 knockout leads to alterations in mechanosensing of nanotopographical cues through alterations of in rho-associated signaling pathways, cell mechanics and mediators of the Hippo and TGF-β signaling pathways.

Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions

MicroRNAs (miRNAs) are short non-coding RNAs that regulate expression of target messenger RNAs (mRNAs) post-transcriptionally. Understanding the precise regulatory role of miRNAs is of great interest since miRNAs have been shown to play an important role in development, diseases, and other biological processes. Early work on miRNA target prediction has focused on static sequence-driven miRNA-mRNA complementarity. However, recent research also utilizes expression-level data to study context-dependent regulation effects in a more dynamic, physiologically-relevant setting.

Methods: We propose a novel artificial neural network (ANN) based method, named Tiresias, to predict such targets in a context-dependent manner by combining sequence and expression data. In order to predict the interacting pairs among miRNAs and mRNAs and their regulatory weights, we develop a two-stage ANN and present how to train it appropriately. Tiresias is designed to study various regulation models, ranging from a simple linear model to a complex non-linear model. Tiresias has a single hyper-parameter to control the sparsity of miRNA-mRNA interactions, which we optimize using Bayesian optimization.

Results: Tiresias performs better than existing computational methods such as GenMiR++, Elastic Net, and PIMiM, achieving an F1 score of >0.8 for a certain level of regulation strength. For the TCGA breast invasive carcinoma dataset, Tiresias results in the rate of up to 82% in detecting the experimentally-validated interactions between miRNAs and mRNAs, even if we assume that true regulations may result in a low level of regulation strength.

Conclusion: Tiresias is a two-stage ANN, computational method that deciphers context-dependent microRNA regulatory interactions. Experiment results demonstrate that Tiresias outperforms existing solutions and can achieve a high F1 score. Source code of Tiresias is available at https://bitbucket.org/cellsandmachines/.

CRISPR Genome Engineering for Human Pluripotent Stem Cell Research

The emergence of targeted and efficient genome editing technologies, such as repurposed bacterial programmable nucleases (e.g., CRISPR-Cas systems), has abetted the development of cell engineering approaches. Lessons learned from the development of RNA-interference (RNA-i) therapies can spur the translation of genome editing, such as those enabling the translation of human pluripotent stem cell engineering. In this review, we discuss the opportunities and the challenges of repurposing bacterial nucleases for genome editing, while appreciating their roles, primarily at the epigenomic granularity. First, we discuss the evolution of high-precision, genome editing technologies, highlighting CRISPR-Cas9. They exist in the form of programmable nucleases, engineered with sequence-specific localizing domains, and with the ability to revolutionize human stem cell technologies through precision targeting with greater on-target activities. Next, we highlight the major challenges that need to be met prior to bench-to-bedside translation, often learning from the path-to-clinic of complementary technologies, such as RNA-i. Finally, we suggest potential bioinformatics developments and CRISPR delivery vehicles that can be deployed to circumvent some of the challenges confronting genome editing technologies en route to the clinic.

Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions

Background

Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications.

Methods

Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS.

Results

We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was important in cell type H1 but less so in HeLa S3, while H3K4me1 was relatively important in all four cell types. We finally use the feature importance analysis to reduce the number of input features needed to train the DNN, thus reducing training time, which is often the computational bottleneck in the use of a DNN.

Conclusions

In this paper, we developed EP-DNN, which has high accuracy of prediction, with validation rates above 90 % for the operational region of enhancer prediction for all four cell lines that we studied, outperforming DEEP-ENCODE and RFECS. Then, we developed a method to analyze a trained DNN and determine which histone modifications are important, and within that, which features proximal or distal to the enhancer site, are important.

The MG-RAST metagenomics database and portal in 2015

MG-RAST (http://metagenomics.anl.gov) is an open-submission data portal for processing, analyzing, sharing and disseminating metagenomic datasets. The system currently hosts over 200 000 datasets and is continuously updated. The volume of submissions has increased 4-fold over the past 24 months, now averaging 4 terabasepairs per month. In addition to several new features, we report changes to the analysis workflow and the technologies used to scale the pipeline up to the required throughput levels. To show possible uses for the data from MG-RAST, we present several examples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence alignment tools.

MicroRNA target prediction using thermodynamic and sequence curves

BACKGROUND

MicroRNAs (miRNAs) are small regulatory RNA that mediate RNA interference by binding to various mRNA target regions. There have been several computational methods for the identification of target mRNAs for miRNAs. However, these have considered all contributory features as scalar representations, primarily, as thermodynamic or sequence-based features. Further, a majority of these methods solely target canonical sites, which are sites with "seed" complementarity. Here, we present a machine-learning classification scheme, titled Avishkar, which captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves, separately for various input features, such as thermodynamic and sequence features. Further, we use a principled approach to uniformly model canonical and non-canonical seed matches, using a novel seed enrichment metric.

RESULTS

We demonstrate that large number of seed-match patterns have high enrichment values, conserved across species, and that majority of miRNA binding sites involve non-canonical matches, corroborating recent findings. Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data. Our model significantly outperforms all established methods, for both canonical and non-canonical sites. We achieve this while using a much larger candidate miRNA-mRNA interaction set than prior work.

CONCLUSIONS

We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves, specifically about 20% better than the state-of-the-art, for different species (human or mouse), or different target types (canonical or non-canonical). To the best of our knowledge we provide the first distributed framework for microRNA target prediction based on Apache Hadoop and Spark.

AVAILABILITY

All source code and data is publicly available at https://bitbucket.org/cellsandmachines/avishkar.

Synergistic effects of matrix nanotopography and stiffness on vascular smooth muscle cell function

Vascular smooth muscle cells (vSMCs) retain the ability to undergo modulation in their phenotypic continuum, ranging from a mature contractile state to a proliferative, secretory state. vSMC differentiation is modulated by a complex array of microenvironmental cues, which include the biochemical milieu of the cells and the architecture and stiffness of the extracellular matrix. In this study, we demonstrate that by using UV-assisted capillary force lithography (CFL) to engineer a polyurethane substratum of defined nanotopography and stiffness, we can facilitate the differentiation of cultured vSMCs, reduce their inflammatory signature, and potentially promote the optimal functioning of the vSMC contractile and cytoskeletal machinery. Specifically, we found that the combination of medial tissue-like stiffness (11 MPa) and anisotropic nanotopography (ridge width_groove width_ridge height of 800_800_600 nm) resulted in significant upregulation of calponin, desmin, and smoothelin, in addition to the downregulation of intercellular adhesion molecule-1, tissue factor, interleukin-6, and monocyte chemoattractant protein-1. Further, our results allude to the mechanistic role of the RhoA/ROCK pathway and caveolin-1 in altered cellular mechanotransduction pathways via differential matrix nanotopography and stiffness. Notably, the nanopatterning of the stiffer substrata (1.1 GPa) resulted in the significant upregulation of RhoA, ROCK1, and ROCK2. This indicates that nanopatterning an 800_800_600 nm pattern on a stiff substratum may trigger the mechanical plasticity of vSMCs resulting in a hypercontractile vSMC phenotype, as observed in diabetes or hypertension. Given that matrix stiffness is an independent risk factor for cardiovascular disease and that CFL can create different matrix nanotopographic patterns with high pattern fidelity, we are poised to create a combinatorial library of arterial test beds, whether they are healthy, diseased, injured, or aged. Such high-throughput testing environments will pave the way for the evolution of the next generation of vascular scaffolds that can effectively crosstalk with the scaffold microenvironment and result in improved clinical outcomes.

Syndecan-1 regulates vascular smooth muscle cell phenotype

OBJECTIVE

We examined the role of syndecan-1 in modulating the phenotype of vascular smooth muscle cells in the context of endogenous inflammatory factors and altered microenvironments that occur in disease or injury-induced vascular remodeling.

METHODS AND RESULTS

Vascular smooth muscle cells (vSMCs) display a continuum of phenotypes that can be altered during vascular remodeling. While the syndecans have emerged as powerful and complex regulators of cell function, their role in controlling vSMC phenotype is unknown. Here, we isolated vSMCs from wild type (WT) and syndecan-1 knockout (S1KO) mice. Gene expression and western blotting studies indicated decreased levels of α-smooth muscle actin (α-SMA), calponin, and other vSMC-specific differentiation markers in S1KO relative to WT cells. The spread area of the S1KO cells was found to be greater than WT cells, with a corresponding increase in focal adhesion formation, Src phosphorylation, and alterations in actin cytoskeletal arrangement. In addition, S1KO led to increased S6RP phosphorylation and decreased AKT and PKC-α phosphorylation. To examine whether these changes were present in vivo, isolated aortae from aged WT and S1KO mice were stained for calponin. Consistent with our in-vitro findings, the WT mice aortae stained higher for calponin relative to S1KO. When exposed to the inflammatory cytokine TNF-α, WT vSMCs had an 80% reduction in syndecan-1 expression. Further, with TNF-α, S1KO vSMCs produced increased pro-inflammatory cytokines relative to WT. Finally, inhibition of interactions between syndecan-1 and integrins αvβ3 and αvβ5 using the inhibitory peptide synstatin appeared to have similar effects on vSMCs as knocking out syndecan-1, with decreased expression of vSMC differentiation markers and increased expression of inflammatory cytokines, receptors, and osteopontin.

CONCLUSIONS

Taken together, our results support that syndecan-1 promotes vSMC differentiation and quiescence. Thus, the presence of syndecan-1 would have a protective effect against vSMC dedifferentiation and this activity is linked to interactions with integrins αvβ3 and αvβ5.

Abstract 176: The Edge Effect: How Syndecan-1 Can Increase the Therapeutic Efficacy of Vasoregulatory Drugs and Growth Factors

Rationale

Vascular smooth muscle cells (vSMCs) in mature animals normally reside in a quiescent state, expressing a distinct combination of contractile proteins, ion channels, and signaling molecules. However, in cases of vascular disease and injury, vSMCs are capable of being activated, shifting their phenotype through a continuum of cell fates. While the heparan sulfate proteoglycans syndecans, residing at cellular edges as transmembrane receptors, have emerged as powerful and complex regulators of cell function, their role in intercepting growth factors and therapeutic molecules modulating vSMC phenotype is unknown.

Objective

The purpose of this study was to examine the role of syndecan-1 in modulating the vSMC phenotype in the context of endogenous factors and drug treatments used for vascular disease.

Methods and Results

vSMCs were isolated from wild type (WT) and syndecan-1 knockout (S1KO) mice and cultured in different biochemical environments. Gene and protein expression studies indicated decreased α-smooth muscle actin (α-SMA) and calponin levels in S1KO cells relative to WT under simulated inflammatory conditions. Rapamycin treatment reversed the reduction in expression caused by S1KO, suggesting that the effects were, in part, mediated through mTORC1 signaling. In addition, S1KO vSMCs expressed higher levels of tissue factor, IL-6, and VCAM-1. These differences were maintained under treatment with heparin and TGF-β1, PDGF-BB, or atorvastatin. In-vivo studies with aortae from aged WT and S1KO mice indicated that S1KO arteries stained significantly lower for calponin.

Conclusions

Our findings not only provide insights into the fundamental cell surface receptors responsible for vSMC differentiation but also identify that the loss of syndecan-1 can make vSMCs more vulnerable to inflammatory molecular signals and potentially cause resistance to treatment with rapamycin and atorvastatin. Thus, preservation or enhancement of syndecan-1 expression may be a potential therapeutic target for altering the course of in-stent restenosis and atherosclerosis.

Development of a probucol-releasing antithrombogenic drug eluting stent

The success of drug eluting stents (DESs) has been challenged by the manifestation of late stent thrombosis after DES implantation. The incomplete regeneration of the endothelial layer poststenting triggers adverse signaling processes precipitating in thrombosis. Various approaches have been attempted to prevent thrombosis, including the delivery of biological agents, such as estradiol, that promote endothelialization, and the use of natural polymers as coating materials. The underlying challenge has been the inability to release the biological agent in synchronization with the temporal sequence of vascular wound healing in vivo. The natural healing process of the endothelium after an injury starts after a week and may take up to a month in humans. This article presents a novel DES formulation using a hemocompatible polyurethane (PU) matrix to sustain the release of probucol (PB), an endothelial agonist, by exploiting the greater difference in the solubility parameters of PB and PU. This results in the formation of crystalline PB aggregates retarding drug release from PU. The physicochemical properties of PB in PU were confirmed using differential scanning calorimetry and X-ray diffraction. Drug-polymer compatibility was examined using infrared spectral analysis. Also, in vitro studies using primary human aortic endothelial cells resulted in the selection of 5% w/w PB as the optimal dose, to be further tested in vitro and in vivo. This work develops and tests a promising new DES formulation to enable faster endothelial cell proliferation poststenting, potentially minimizing the incidence and severity of thrombotic events after DES implantation.

Scaffold-free in vitro arterial mimetics: the importance of smooth muscle-endothelium contact

We have developed an in vitro endothelial cell (EC)-smooth muscle cell (SMC) coculture platform that can mimic either the healthy or diseased state of blood vessels. Transforming growth factor-beta1 (TGF-beta1) and heparin were introduced to the SMC cultures to upregulate the SMC differentiation markers, alpha-smooth muscle actin (alpha-SMA) and calponin (homotypic model). Interestingly, seeding of near-confluent concentrations of ECs on the SMCs (heterotypic model) induced higher levels of alpha-SMA and calponin expression in the SMC cultures than did the addition of heparin and TGF-beta1 alone. The expression levels increased further on pretreating the SMCs with TGF-beta1 and heparin before adding a near-confluent monolayer of ECs. In contrast, seeding of sparse concentrations of ECs forced the SMCs into a more hyperplastic state as determined by alpha-SMA and calponin expression. This study highlights the importance of both soluble factors and EC seeding densities when considering culture conditions; in vivo SMCs are in close proximity with and interact with a monolayer of ECs. Our study suggests that this architecture is important for healthy vascular tissue function. In addition, it shows that disruption of this architecture can be used to mimic diseased states. As the EC-SMC coculture model can mimic either a diseased or a healthy blood vessel it may be useful as a test bed for evaluating cardiovascular therapeutics.

Smart Polymeric Gels: Redefining the Limits of Biomedical Devices

This review describes recent progresses in the development and applications of smart polymeric gels, especially in the context of biomedical devices. The review has been organized into three separate sections: defining the basis of smart properties in polymeric gels; describing representative stimuli to which these gels respond; and illustrating a sample application area, namely, microfluidics. One of the major limitations in the use of hydrogels in stimuli-responsive applications is the diffusion rate limited transduction of signals. This can be obviated by engineering interconnected pores in the polymer structure to form capillary networks in the matrix and by downscaling the size of hydrogels to significantly decrease diffusion paths. Reducing the lag time in the induction of smart responses can be highly useful in biomedical devices, such as sensors and actuators. This review also describes molecular imprinting techniques to fabricate hydrogels for specific molecular recognition of target analytes. Additionally, it describes the significant advances in bottom-up nanofabrication strategies, involving supramolecular chemistry. Learning to assemble supramolecular structures from nature has led to the rapid prototyping of functional supramolecular devices. In essence, the barriers in the current performance potential of biomedical devices can be lowered or removed by the rapid convergence of interdisciplinary technologies.

Enhanced osteoblast‐like cell adhesion and proliferation using sulfonate‐bearing polymeric scaffolds

Orthopedic malfunction, degeneration, or damage remains a serious healthcare issue despite advances in medical technology. Proactive extracellular matrix (ECM)-mimetic scaffolds are being researched to orchestrate the activation of diverse osteogenic signaling cascades, facilitating osteointegration. We hypothesized that sulfonated functionalities incorporated into synthetic hydrogels would simulate anionic, sulfate-bearing proteoglycans, abundant in the ECM. Using this rationale, we successfully developed differentially sulfonated hydrogels, polymerizing a range of sulfopropyl acrylate potassium-acrylamide (SPAK-AM) mole ratios as monomer feeds under room temperature conditions. For anchorage-dependent cells, such as osteoblasts, adhesion is a critical prerequisite for subsequent osteointegration and cell specialization. The introduction of the sulfonated monomer, SPAK, resulted in favorable uptake of serum proteins with proportional increase in adhesion and proliferation rates of model cell lines, which included NIH/3T3 fibroblasts, MG-63 osteoblasts, and MC3T3-E1 subclone 4 preosteoblasts. In fact, higher proportions of sulfonate content (pSPAK75, pSPAK100) exhibited comparable or even higher degrees of adhesion and proliferation, relative to commercial grade tissue culture polystyrene in vitro. These results indicate promising potentials of sulfonated ECM-mimetic hydrogels as potential osteogenic tissue engineering scaffolds.