alphabeta sequence of F is IS31

Previous studies have shown that there is a deoxyribonucleic acid (DNA) segment, of length 1.3 kb and denoted as the alphabeta sequence, which occurs twice on the F plasmid at corrdinates 93.2 to 94.5/OF kb and 13.7 to 15.0F kb. In the present investigation, heteroduplexes were prepared between a phage DNA carrying the insertion sequence IS3 and suitable F-prime DNAs. The hybrids formed show that IS3 is the same as alphabeta. This result plus previous studies support the view that: (i) the insertion sequence IS2 and IS3 occur on F and, in multiple copies, on the main bacterial chromosome of Escherichia coli K-12; and (ii)these IS sequences on the main bacterial chromosomes are hot spots for Hfr formation by reciprocal recombination with the corresponding sequences of F

tsg101: A Novel Tumor Susceptibility Gene Isolated by Controlled Homozygous Functional Knockout of Allelic Loci in Mammalian Cells

AbstractUsing a novel strategy that enables the isolation of previously unknown genes encoding selectable recessive phenotypes, we identified a gene (tsg101) whose homozygous functional disruption produces cell transformation. Antisense RNA from a transactivated promoter introduced randomly into transcribed genes throughout the genome of mouse 3T3 fibroblasts was used to knock out alleles of chromosomal genes adjacent to promoter inserts, generating clones that grew in 0.5% agar and formed metastatic tumors in nude mice. Removal of the transactivator restored normal growth. The protein encoded by tsg101 cDNA encodes a coiled–coil domain that interacts with stathmin, a cytosolic phosphoprotein implicated previously in tumorigenesis. Overexpression of tsg101 antisense transcripts in naive 3T3 cells resulted in cell transformation and increased stathmin-specific mRNA

Optimization of Network Robustness to Waves of Targeted and Random Attack

We study the robustness of complex networks to multiple waves of simultaneous (i) targeted attacks in which the highest degree nodes are removed and (ii) random attacks (or failures) in which fractions $p_t$ and $p_r$ respectively of the nodes are removed until the network collapses. We find that the network design which optimizes network robustness has a bimodal degree distribution, with a fraction $r$ of the nodes having degree k_2= (\kav - 1 +r)/r and the remainder of the nodes having degree $k_1=1$, where \kav is the average degree of all the nodes. We find that the optimal value of $r$ is of the order of $p_t/p_r$ for $p_t/p_r\ll 1$

Numerical evaluation of the upper critical dimension of percolation in scale-free networks

We propose a numerical method to evaluate the upper critical dimension $d_c$ of random percolation clusters in Erd\H{o}s-R\'{e}nyi networks and in scale-free networks with degree distribution ${\cal P}(k) \sim k^{-\lambda}$, where $k$ is the degree of a node and $\lambda$ is the broadness of the degree distribution. Our results report the theoretical prediction, $d_c = 2(\lambda - 1)/(\lambda - 3)$ for scale-free networks with $3 < \lambda < 4$ and $d_c = 6$ for Erd\H{o}s-R\'{e}nyi networks and scale-free networks with $\lambda > 4$. When the removal of nodes is not random but targeted on removing the highest degree nodes we obtain $d_c = 6$ for all $\lambda > 2$. Our method also yields a better numerical evaluation of the critical percolation threshold, $p_c$, for scale-free networks. Our results suggest that the finite size effects increases when $\lambda$ approaches 3 from above.Comment: 10 pages, 5 figure

Percolation theory applied to measures of fragmentation in social networks

We apply percolation theory to a recently proposed measure of fragmentation $F$ for social networks. The measure $F$ is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction $q$ of nodes and the total number of pairs in the original fully connected network. We compare $F$ with the traditional measure used in percolation theory, $P_{\infty}$, the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erd\H{o}s-R\'{e}nyi (ER) and scale-free (SF) networks under various types of node removal strategies. The removal strategies are: random removal, high degree removal and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction $q$ of nodes above percolation threshold, $P_{\infty}\approx (1-F)^{1/2}$. For fixed $P_{\infty}$ and close to percolation threshold ($q=q_c$), we show that $1-F$ better reflects the actual fragmentation. Close to $q_c$, for a given $P_{\infty}$, $1-F$ has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure $F$ and the percolation measure $P_{\infty}$ for a real social network of workplaces linked by the households of the employees and find similar results.Comment: submitted to PR

Reversible antibiotic tolerance induced in <i>Staphylococcus aureus</i> by concurrent drug exposure

ABSTRACT   Resistance of Staphylococcus aureus to beta-lactam antibiotics has led to increasing use of the glycopeptide antibiotic vancomycin as a life-saving treatment for major S. aureus infections. Coinfection by an unrelated bacterial species may necessitate concurrent treatment with a second antibiotic that targets the coinfecting pathogen. While investigating factors that affect bacterial antibiotic sensitivity, we discovered that susceptibility of S. aureus to vancomycin is reduced by concurrent exposure to colistin, a cationic peptide antimicrobial employed to treat infections by Gram-negative pathogens. We show that colistin-induced vancomycin tolerance persists only as long as the inducer is present and is accompanied by gene expression changes similar to those resulting from mutations that produce stably inherited reduction of vancomycin sensitivity (vancomycin-intermediate S. aureus [VISA] strains). As colistin-induced vancomycin tolerance is reversible, it may not be detected by routine sensitivity testing and may be responsible for treatment failure at vancomycin doses expected to be clinically effective based on such routine testing. IMPORTANCE   Commonly, antibiotic resistance is associated with permanent genetic changes, such as point mutations or acquisition of resistance genes. We show that phenotypic resistance can arise where changes in gene expression result in tolerance to an antibiotic without any accompanying genetic changes. Specifically, methicillin-resistant Staphylococcus aureus (MRSA) behaves like vancomycin-intermediate S. aureus (VISA) upon exposure to colistin, which is currently used against infections by Gram-negative bacteria. Vancomycin is a last-resort drug for treatment of serious S. aureus infections, and VISA is associated with poor clinical prognosis. Phenotypic and reversible resistance will not be revealed by standard susceptibility testing and may underlie treatment failure

Robustness of onion-like correlated networks against targeted attacks

Recently, it was found by Schneider et al. [Proc. Natl. Acad. Sci. USA, 108, 3838 (2011)], using simulations, that scale-free networks with "onion structure" are very robust against targeted high degree attacks. The onion structure is a network where nodes with almost the same degree are connected. Motivated by this work, we propose and analyze, based on analytical considerations, an onion-like candidate for a nearly optimal structure against simultaneous random and targeted high degree node attacks. The nearly optimal structure can be viewed as a hierarchically interconnected random regular graphs, the degrees and populations of which are specified by the degree distribution. This network structure exhibits an extremely assortative degree-degree correlation and has a close relationship to the "onion structure." After deriving a set of exact expressions that enable us to calculate the critical percolation threshold and the giant component of a correlated network for an arbitrary type of node removal, we apply the theory to the cases of random scale-free networks that are highly vulnerable against targeted high degree node removal. Our results show that this vulnerability can be significantly reduced by implementing this onion-like type of degree-degree correlation without much undermining the almost complete robustness against random node removal. We also investigate in detail the robustness enhancement due to assortative degree-degree correlation by introducing a joint degree-degree probability matrix that interpolates between an uncorrelated network structure and the onion-like structure proposed here by tuning a single control parameter. The optimal values of the control parameter that maximize the robustness against simultaneous random and targeted attacks are also determined. Our analytical calculations are supported by numerical simulations.Comment: 12 pages, 8 figure

Optimal Paths in Complex Networks with Correlated Weights: The World-wide Airport Network

We study complex networks with weights, $w_{ij}$, associated with each link connecting node $i$ and $j$. The weights are chosen to be correlated with the network topology in the form found in two real world examples, (a) the world-wide airport network, and (b) the {\it E. Coli} metabolic network. Here $w_{ij} \sim x_{ij} (k_i k_j)^\alpha$, where $k_i$ and $k_j$ are the degrees of nodes $i$ and $j$, $x_{ij}$ is a random number and $\alpha$ represents the strength of the correlations. The case $\alpha > 0$ represents correlation between weights and degree, while $\alpha < 0$ represents anti-correlation and the case $\alpha = 0$ reduces to the case of no correlations. We study the scaling of the lengths of the optimal paths, $\ell_{\rm opt}$, with the system size $N$ in strong disorder for scale-free networks for different $\alpha$. We calculate the robustness of correlated scale-free networks with different $\alpha$, and find the networks with $\alpha < 0$ to be the most robust networks when compared to the other values of $\alpha$. We propose an analytical method to study percolation phenomena on networks with this kind of correlation. We compare our simulation results with the real world-wide airport network, and we find good agreement

Structure of shells in complex networks

In a network, we define shell $\ell$ as the set of nodes at distance $\ell$ with respect to a given node and define $r_\ell$ as the fraction of nodes outside shell $\ell$. In a transport process, information or disease usually diffuses from a random node and reach nodes shell after shell. Thus, understanding the shell structure is crucial for the study of the transport property of networks. For a randomly connected network with given degree distribution, we derive analytically the degree distribution and average degree of the nodes residing outside shell $\ell$ as a function of $r_\ell$. Further, we find that $r_\ell$ follows an iterative functional form $r_\ell=\phi(r_{\ell-1})$, where $\phi$ is expressed in terms of the generating function of the original degree distribution of the network. Our results can explain the power-law distribution of the number of nodes $B_\ell$ found in shells with $\ell$ larger than the network diameter $d$, which is the average distance between all pairs of nodes. For real world networks the theoretical prediction of $r_\ell$ deviates from the empirical $r_\ell$. We introduce a network correlation function $c(r_\ell)\equiv r_{\ell+1}/\phi(r_\ell)$ to characterize the correlations in the network, where $r_{\ell+1}$ is the empirical value and $\phi(r_\ell)$ is the theoretical prediction. $c(r_\ell)=1$ indicates perfect agreement between empirical results and theory. We apply $c(r_\ell)$ to several model and real world networks. We find that the networks fall into two distinct classes: (i) a class of {\it poorly-connected} networks with $c(r_\ell)>1$, which have larger average distances compared with randomly connected networks with the same degree distributions; and (ii) a class of {\it well-connected} networks with $c(r_\ell)<1$