Proteomics reveals multiple routes to the osteogenic phenotype in mesenchymal stem cells

<p>Abstract</p> <p>Background</p> <p>Recently, we demonstrated that human mesenchymal stem cells (hMSC) stimulated with dexamethazone undergo gene focusing during osteogenic differentiation (<it>Stem Cells Dev </it>14(6): 1608–20, 2005). Here, we examine the protein expression profiles of three additional populations of hMSC stimulated to undergo osteogenic differentiation via either contact with pro-osteogenic extracellular matrix (ECM) proteins (collagen I, vitronectin, or laminin-5) or osteogenic media supplements (OS media). Specifically, we annotate these four protein expression profiles, as well as profiles from naïve hMSC and differentiated human osteoblasts (hOST), with known gene ontologies and analyze them as a tensor with modes for the expressed proteins, gene ontologies, and stimulants.</p> <p>Results</p> <p>Direct component analysis in the gene ontology space identifies three components that account for 90% of the variance between hMSC, osteoblasts, and the four stimulated hMSC populations. The directed component maps the differentiation stages of the stimulated stem cell populations along the differentiation axis created by the difference in the expression profiles of hMSC and hOST. Surprisingly, hMSC treated with ECM proteins lie closer to osteoblasts than do hMSC treated with OS media. Additionally, the second component demonstrates that proteomic profiles of collagen I- and vitronectin-stimulated hMSC are distinct from those of OS-stimulated cells. A three-mode tensor analysis reveals additional focus proteins critical for characterizing the phenotypic variations between naïve hMSC, partially differentiated hMSC, and hOST.</p> <p>Conclusion</p> <p>The differences between the proteomic profiles of OS-stimulated hMSC and ECM-hMSC characterize different transitional phenotypes en route to becoming osteoblasts. This conclusion is arrived at via a three-mode tensor analysis validated using hMSC plated on laminin-5.</p

Cupid: Automatic Fuzzer Selection for Collaborative Fuzzing

Combining the strengths of individual fuzzing methods is an appealing idea to find software faults more efficiently, especially when the computing budget is limited. In prior work, EnFuzz introduced the idea of ensemble fuzzing and devised three heuristics to classify properties of fuzzers in terms of diversity. Based on these heuristics, the authors manually picked a combination of different fuzzers that collaborate. In this paper, we generalize this idea by collecting and applying empirical data from single, isolated fuzzer runs to automatically identify a set of fuzzers that complement each other when executed collaboratively. To this end, we present Cupid, a collaborative fuzzing framework allowing automated, data-driven selection of multiple complementary fuzzers for parallelized and distributed fuzzing. We evaluate the automatically selected target-independent combination of fuzzers by Cupid on Google's fuzzer-test-suite, a collection of real-world binaries, as well as on the synthetic Lava-M dataset. We find that Cupid outperforms two expert-guided, target-specific and hand-picked combinations on Google's fuzzer-test-suite in terms of branch coverage, and improves bug finding on Lava-M by 10%. Most importantly, we improve the latency for obtaining 95% and 99% of the coverage by 90% and 64%, respectively. Furthermore, Cupid reduces the amount of CPU hours needed to find a high-performing combination of fuzzers by multiple orders of magnitude compared to an exhaustive evaluation

Apocynin Derivatives Interrupt Intracellular Signaling Resulting in Decreased Migration in Breast Cancer Cells

Cancer cells are defined by their ability to divide uncontrollably and metastasize to secondary sites in the body. Consequently, tumor cell migration represents a promising target for anticancer drug development. Using our high-throughput cell migration assay, we have screened several classes of compounds for noncytotoxic tumor cell migration inhibiting activity. One such compound, apocynin (4-acetovanillone), is oxidized by peroxidases to yield a variety of oligophenolic and quinone-type compounds that are recognized inhibitors of NADPH oxidase and may be inhibitors of the small G protein Rac1 that controls cell migration. We report here that while apocynin itself is not effective, apocynin derivatives inhibit migration of the breast cancer cell line MDA-MB-435 at subtoxic concentrations; the migration of nonmalignant MCF10A breast cells is unaffected. These compounds also cause a significant rearrangement of the actin cytoskeleton, cell rounding, and decreased levels of active Rac1 and its related G protein Cdc42. These results may suggest a promising new route to the development of novel anticancer therapeutics

Harvey: A Greybox Fuzzer for Smart Contracts

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Laminin-5 Induces Osteogenic Gene Expression in Human Mesenchymal Stem Cells through an ERK-dependent Pathway

The laminin family of proteins is critical for managing a variety of cellular activities including migration, adhesion, and differentiation. In bone, the roles of laminins in controlling osteogenic differentiation of human mesenchymal stem cells (hMSC) are unknown. We report here that laminin-5 is found in bone and expressed by hMSC. hMSC isolated from bone synthesize laminin-5 and adhere to exogenous laminin-5 through α3β1 integrin. Adhesion to laminin-5 activates extracellular signal-related kinase (ERK) within 30 min and leads to phosphorylation of the osteogenic transcription factor Runx2/CBFA-1 within 8 d. Cells plated on laminin-5 for 16 d express increased levels of osteogenic marker genes, and those plated for 21 d deposit a mineralized matrix, indicative of osteogenic differentiation. Addition of the ERK inhibitor PD98059 mitigates these effects. We conclude that contact with laminin-5 is sufficient to activate ERK and to stimulate osteogenic differentiation in hMSC

Fibronectin and stem cell differentiation – lessons from chondrogenesis

The extracellular matrix (ECM) is an intricate network of proteins that surrounds cells and has a central role in establishing an environment that is conducive to tissue-specific cell functions. In the case of stem cells, this environment is the stem cell niche, where ECM signals participate in cell fate decisions. In this Commentary, we describe how changes in ECM composition and mechanical properties can affect cell shape and stem cell differentiation. Using chondrogenic differentiation as a model, we examine the changes in the ECM that occur before and during mesenchymal stem cell differentiation. In particular, we focus on the main ECM protein fibronectin, its temporal expression pattern during chondrogenic differentiation, its potential effects on functions of differentiating chondrocytes, and how its interactions with other ECM components might affect cartilage development. Finally, we discuss data that support the possibility that the fibronectin matrix has an instructive role in directing cells through the condensation, proliferation and/or differentiation stages of cartilage formation