Quiescin sulfhydryl oxidase 1 (QSOX1) is a highly conserved disulfide bond-generating enzyme that is overexpressed in diverse tumor types. Its enzymatic activity promotes the growth and invasion of tumor cells and alters extracellular matrix composition. In a nude mouse-human tumor xenograft model, tumors containing shRNA for QSOX1 grew significantly more slowly than controls, suggesting that QSOX1 supports a proliferative phenotype in vivo. High throughput screening experiments identified ebselen as an in vitro inhibitor of QSOX1 enzymatic activity. Ebselen treatment of pancreatic and renal cancer cell lines stalled tumor growth and inhibited invasion through Matrigel in vitro. Daily oral treatment with ebselen resulted in a 58% reduction in tumor growth in mice bearing human pancreatic tumor xenografts compared to controls. Mass spectrometric analysis of ebselen-treated QSOX1 mechanistically revealed that C165 and C237 of QSOX1 covalently bound to ebselen. This report details the anti-neoplastic properties of ebselen in pancreatic and renal cancer cell lines. The results here offer a “proof-of-principle” that enzymatic inhibition of QSOX1 may have clinical relevancy.

Clear cell renal cell carcinomas (ccRCCs) harbor frequent mutations in epigenetic modifiers including SETD2, the H3K36me3 writer. We profiled DNA methylation (5mC) across the genome in cell line-based models of SETD2 inactivation and SETD2 mutant primary tumors because 5mC has been linked to H3K36me3 and is therapeutically targetable. SETD2 depleted cell line models (long-term and acute) exhibited a DNA hypermethylation phenotype coinciding with ectopic gains in H3K36me3 centered across intergenic regions adjacent to low expressing genes, which became upregulated upon dysregulation of the epigenome. Poised enhancers of developmental genes were prominent hypermethylation targets. SETD2 mutant primary ccRCCs, papillary renal cell carcinomas, and lung adenocarcinomas all demonstrated a DNA hypermethylation phenotype that segregated tumors by SETD2 genotype and advanced grade. These findings collectively demonstrate that SETD2 mutations drive tumorigenesis by coordinated disruption of the epigenome and transcriptome,and they have important implications for future therapeutic strategies targeting chromatin regulator mutant tumors.

Significance: Modification of cysteine thiols dramatically affects protein function and stability. Hence, the abilities to quantify specific protein sulfhydryl groups within complex biological samples and map disulfide bond structures are crucial to gaining greater insights into how proteins operate in human health and disease. Recent Advances: Many different molecular probes are now commercially available to label and track cysteine residues at great sensitivity. Coupled with mass spectrometry, stable isotope-labeled sulfhydryl-specific reagents can provide previously unprecedented molecular insights into the dynamics of cysteine modification. Likewise, the combined application of modern mass spectrometers with improved sample preparation techniques and novel data mining algorithms is beginning to routinize the analysis of complex protein disulfide structures. Critical Issues: Proper application of these modern tools and techniques, however, still requires fundamental understanding of sulfhydryl chemistry as well as the assumptions that accompany sample preparation and underlie effective data interpretation. Future Directions: The continued development of tools, technical approaches, and corresponding data processing algorithms will, undoubtedly, facilitate site-specific protein sulfhydryl quantification and disulfide structure analysis from within complex biological mixtures with ever-improving accuracy and sensitivity. Fully routinizing disulfide structure analysis will require an equal but balanced focus on sample preparation and corresponding mass spectral dataset reproducibility.

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Background: The cytokine MIF (Macrophage Migration Inhibitory Factor) has diverse physiological roles and is present at elevated concentrations in numerous disease states. However, its molecular heterogeneity has not been previously investigated in biological samples. Mass Spectrometric Immunoassay (MSIA) may help elucidate MIF post-translational modifications existing in vivo and provide additional clarity regarding its relationship to diverse pathologies.

Results: In this work, we have developed and validated a fully quantitative MSIA assay for MIF, and used it in the discovery and quantification of different proteoforms of MIF in serum samples, including cysteinylated and glycated MIF. The MSIA assay had a linear range of 1.56-50 ng/mL, and exhibited good precision, linearity, and recovery characteristics. The new assay was applied to a small cohort of human serum samples, and benchmarked against an MIF ELISA assay.

Conclusions: The quantitative MIF MSIA assay provides a sensitive, precise and high throughput method to delineate and quantify MIF proteoforms in biological samples.

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.