Tubulin Complexity in Cancer and Metastasis.

Tubulin plays a fundamental role in cellular function and as the subject for microtubule-active agents in the treatment of ovarian cancer. Microtubule-binding proteins (e.g., tau, MAP1/2/4, EB1, CLIP, TOG, survivin, stathmin) and posttranslational modifications (e.g., tyrosination, deglutamylation, acetylation, glycation, phosphorylation, polyamination) further diversify tubulin functionality and may permit additional opportunities to understand microtubule behavior in disease and to develop microtubule-modifying approaches to combat ovarian cancer. Tubulin-based structures that project from suspended ovarian cancer cells known as microtentacles may contribute to metastatic potential of ovarian cancer cells and could represent an exciting novel therapeutic target.

Microtubule-Targeting Agents: Disruption of the Cellular Cytoskeleton as a Backbone of Ovarian Cancer Therapy.

Microtubules are dynamic polymers composed of α- and ß-tubulin heterodimers. Microtubules are universally conserved among eukaryotes and participate in nearly every cellular process, including intracellular trafficking, replication, polarity, cytoskeletal shape, and motility. Due to their fundamental role in mitosis, they represent a classic target of anti-cancer therapy. Microtubule-stabilizing agents currently constitute a component of the most effective regimens for ovarian cancer therapy in both primary and recurrent settings. Unfortunately, the development of resistance continues to present a therapeutic challenge. An understanding of the underlying mechanisms of resistance to microtubule-active agents may facilitate the development of novel and improved approaches to this disease.

Fragmentation of outage clusters during the recovery of power distribution grids.

The understanding of recovery processes in power distribution grids is limited by the lack of realistic outage data, especially large-scale blackout datasets. By analyzing data from three electrical companies across the United States, we find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity (defined as the peak number of outages during a blackout), but is independent of the number of customers affected. We present a cluster-based recovery framework to analytically characterize the dependence between outages, and interpret the dominant role blackout intensity plays in recovery. The recovery of blackouts is not random and has a universal pattern that is independent of the disruption cause, the post-disaster network structure, and the detailed repair strategy. Our study reveals that suppressing blackout intensity is a promising way to speed up restoration.

Recovery coupling in multilayer networks.

The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks-communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming a hard interdependence, where a component failure in one network causes failures in the other network, resulting in a cascade of failures across multiple systems. While empirical evidence of such hard failures is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in documented interdependencies induced by the recovery process. In this work, we explore recovery coupling, capturing the dependence of the recovery of one system on the instantaneous functional state of another system. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system's functionality.

Viewing the US presidential electoral map through the lens of public health.

Health, disease, and mortality vary greatly at the county level, and there are strong geographical trends of disease in the United States. Healthcare is and has been a top priority for voters in the U.S., and an important political issue. Consequently, it is important to determine what relationship voting patterns have with health, disease, and mortality, as doing so may help guide appropriate policy. We performed a comprehensive analysis of the relationship between voting patterns and over 150 different public health and wellbeing variables at the county level, comparing all states, including counties in 2016 battleground states, and counties in states that flipped from majority Democrat to majority Republican from 2012 to 2016. We also investigated county-level health trends over the last 30+ years and find statistically significant relationships between a number of health measures and the voting patterns of counties in presidential elections. Collectively, these data exhibit a strong pattern: counties that voted Republican in the 2016 election had overall worse health outcomes than those that voted Democrat. We hope that this strong relationship can guide improvements in healthcare policy legislation at the county level.

Optimal resilience of modular interacting networks.

Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction [Formula: see text] of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal [Formula: see text] varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

Faster calculation of the percolation correlation length on spatial networks.

The divergence of the correlation length ξ at criticality is an important phenomenon of percolation in two-dimensional systems. Substantial speed-ups to the calculation of the percolation threshold and component distribution have been achieved by utilizing disjoint sets, but existing algorithms of this sort cannot measure the correlation length. Here we utilize the parallel axis theorem to track the correlation length as nodes are added to the system, allowing us to utilize disjoint sets to measure ξ for the entire percolation process with arbitrary precision in a single sweep. This algorithm enables direct measurement of the correlation length in lattices as well as spatial network topologies and provides an important tool for understanding critical phenomena in spatial systems.

Critical Stretching of Mean-Field Regimes in Spatial Networks.

We study a spatial network model with exponentially distributed link lengths on an underlying grid of points, undergoing a structural crossover from a random, Erdos-Rényi graph, to a d-dimensional lattice at the characteristic interaction range ζ. We find that, whilst far from the percolation threshold the random part of the giant component scales linearly with ζ, close to criticality it extends in space until the universal length scale ζ^{6/(6-d)}, for d<6, before crossing over to the spatial one. We demonstrate the universal behavior of the spatiotemporal scales characterizing this critical stretching phenomenon of mean-field regimes in percolation and in dynamical processes on d=2 networks, and we discuss its general implications to real-world phenomena, such as neural activation, traffic flows or epidemic spreading.

Rifamycin congeners kanglemycins are active against rifampicin-resistant bacteria via a distinct mechanism.

Rifamycin antibiotics (Rifs) target bacterial RNA polymerases (RNAPs) and are widely used to treat infections including tuberculosis. The utility of these compounds is threatened by the increasing incidence of resistance (RifR). As resistance mechanisms found in clinical settings may also occur in natural environments, here we postulated that bacteria could have evolved to produce rifamycin congeners active against clinically relevant resistance phenotypes. We survey soil metagenomes and identify a tailoring enzyme-rich family of gene clusters encoding biosynthesis of rifamycin congeners (kanglemycins, Kangs) with potent in vivo and in vitro activity against the most common clinically relevant RifR mutations. Our structural and mechanistic analyses reveal the basis for Kang inhibition of RifR RNAP. Unlike Rifs, Kangs function through a mechanism that includes interfering with 5'-initiating substrate binding. Our results suggest that examining soil microbiomes for new analogues of clinically used antibiotics may uncover metabolites capable of circumventing clinically important resistance mechanisms.

Color-avoiding percolation.

Many real world networks have groups of similar nodes which are vulnerable to the same failure or adversary. Nodes can be colored in such a way that colors encode the shared vulnerabilities. Using multiple paths to avoid these vulnerabilities can greatly improve network robustness, if such paths exist. Color-avoiding percolation provides a theoretical framework for analyzing this scenario, focusing on the maximal set of nodes which can be connected via multiple color-avoiding paths. In this paper we extend the basic theory of color-avoiding percolation that was published in S. M. Krause et al. [Phys. Rev. X 6, 041022 (2016)]2160-330810.1103/PhysRevX.6.041022. We explicitly account for the fact that the same particular link can be part of different paths avoiding different colors. This fact was previously accounted for with a heuristic approximation. Here we propose a better method for solving this problem which is substantially more accurate for many avoided colors. Further, we formulate our method with differentiated node functions, either as senders and receivers, or as transmitters. In both functions, nodes can be explicitly trusted or avoided. With only one avoided color we obtain standard percolation. Avoiding additional colors one by one, we can understand the critical behavior of color-avoiding percolation. For unequal color frequencies, we find that the colors with the largest frequencies control the critical threshold and exponent. Colors of small frequencies have only a minor influence on color-avoiding connectivity, thus allowing for approximations.

Explosive synchronization coexists with classical synchronization in the Kuramoto model.

Explosive synchronization has recently been reported in a system of adaptively coupled Kuramoto oscillators, without any conditions on the frequency or degree of the nodes. Here, we find that, in fact, the explosive phase coexists with the standard phase of the Kuramoto oscillators. We determine this by extending the mean-field theory of adaptively coupled oscillators with full coupling to the case with partial coupling of a fraction f. This analysis shows that a metastable region exists for all finite values of f > 0, and therefore explosive synchronization is expected for any perturbation of adaptively coupling added to the standard Kuramoto model. We verify this theory with GPU-accelerated simulations on very large networks (N ∼ 10(6)) and find that, in fact, an explosive transition with hysteresis is observed for all finite couplings. By demonstrating that explosive transitions coexist with standard transitions in the limit of f → 0, we show that this behavior is far more likely to occur naturally than was previously believed.

Localized attacks on spatially embedded networks with dependencies.

Many real world complex systems such as critical infrastructure networks are embedded in space and their components may depend on one another to function. They are also susceptible to geographically localized damage caused by malicious attacks or natural disasters. Here, we study a general model of spatially embedded networks with dependencies under localized attacks. We develop a theoretical and numerical approach to describe and predict the effects of localized attacks on spatially embedded systems with dependencies. Surprisingly, we find that a localized attack can cause substantially more damage than an equivalent random attack. Furthermore, we find that for a broad range of parameters, systems which appear stable are in fact metastable. Though robust to random failures-even of finite fraction-if subjected to a localized attack larger than a critical size which is independent of the system size (i.e., a zero fraction), a cascading failure emerges which leads to complete system collapse. Our results demonstrate the potential high risk of localized attacks on spatially embedded network systems with dependencies and may be useful for designing more resilient systems.

Engineering the genome of Thermus thermophilus using a counterselectable marker.

UNLABELLED: Thermus thermophilus is an extremely thermophilic bacterium that is widely used as a model thermophile, in large part due to its amenability to genetic manipulation. Here we describe a system for the introduction of genomic point mutations or deletions using a counterselectable marker consisting of a conditionally lethal mutant allele of pheS encoding the phenylalanyl-tRNA synthetase α-subunit. Mutant PheS with an A294G amino acid substitution renders cells sensitive to the phenylalanine analog p-chlorophenylalanine. Insertion of the mutant pheS allele via a linked kanamycin resistance gene into a chromosomal locus provides a gene replacement intermediate that can be removed by homologous recombination using p-chlorophenylalanine as a counterselective agent. This selection is suitable for the sequential introduction of multiple mutations to produce a final strain unmarked by an antibiotic resistance gene. We demonstrated the utility of this method by constructing strains bearing either a point mutation in or a precise deletion of the rrsB gene encoding 16S rRNA. We also used this selection to identify spontaneous, large-scale deletions in the pTT27 megaplasmid, apparently mediated by either of the T. thermophilus insertion elements ISTth7 and ISTth8. One such deletion removed 121 kb, including 118 genes, or over half of pTT27, including multiple sugar hydrolase genes, and facilitated the development of a plasmid-encoded reporter system based on ß-galactosidase. The ability to introduce mutations ranging from single base substitutions to large-scale deletions provides a potentially powerful tool for engineering the genome of T. thermophilus and possibly other thermophiles as well. IMPORTANCE: Thermus thermophilus is an extreme thermophile that has played an important part in the development of both biotechnology and basic biological research. Its suitability as a genetic model system is established by its natural competence for transformation, but the scarcity of genetic tools limits the kinds of manipulations that can currently be performed. We have developed a counterselectable marker that allows the introduction of unmarked deletions and point mutations into the T. thermophilus genome. We find that this marker can also be used to select large chromosomal deletions apparently resulting from aberrant transposition of endogenous insertion sequences. This system has the potential to advance the genetic manipulation of this important model organism.

Robustness of a network formed of spatially embedded networks.

We present analytic and numeric results for percolation in a network formed of interdependent spatially embedded networks. We show results for a treelike and a random regular network of networks each with (i) unconstrained dependency links and (ii) dependency links restricted to a maximum Euclidean length r. Analytic results are given for each network of networks with spatially unconstrained dependency links and compared to simulations. For the case of two fully interdependent spatially embedded networks it was found [Li et al., Phys. Rev. Lett. 108, 228702 (2012)] that the system undergoes a first-order phase transition only for r>r(c) ≈ 8. We find here that for treelike networks of networks (composed of n networks) r(c) significantly decreases as n increases and rapidly (n ≥ 11) reaches its limiting value of 1. For cases where the dependencies form loops, such as in random regular networks, we show analytically and confirm through simulations that there is a certain fraction of dependent nodes, q(max), above which the entire network structure collapses even if a single node is removed. The value of q(max) decreases quickly with m, the degree of the random regular network of networks. Our results show the extreme sensitivity of coupled spatial networks and emphasize the susceptibility of these networks to sudden collapse. The theory proposed here requires only numerical knowledge about the percolation behavior of a single network and therefore can be used to find the robustness of any network of networks where the profile of percolation of a singe network is known numerically.