Giri Narasimhan

Dynamics of Pseudomonas aeruginosa genome evolution

One of the hallmarks of the Gram-negative bacterium Pseudomonas aeruginosa is its ability to thrive in diverse environments that includes humans with a variety of debilitating diseases or immune deficiencies. Here we report the complete sequence and comparative analysis of the genomes of two representative P. aeruginosa strains isolated from cystic fibrosis (CF) patients whose genetic disorder predisposes them to infections by this pathogen. The comparison of the genomes of the two CF strains with those of other P. aeruginosa presents a picture of a mosaic genome, consisting of a conserved core component, interrupted in each strain by combinations of specific blocks of genes. These strain-specific segments of the genome are found in limited chromosomal locations, referred to as regions of genomic plasticity. The ability of P. aeruginosa to shape its genomic composition to favor survival in the widest range of environmental reservoirs, with corresponding enhancement of its metabolic capacity is supported by the identification of a genomic island in one of the sequenced CF isolates, encoding enzymes capable of degrading terpenoids produced by trees. This work suggests that niche adaptation is a major evolutionary force influencing the composition of bacterial genomes. Unlike genome reduction seen in host-adapted bacterial pathogens, the genetic capacity of P. aeruginosa is determined by the ability of individual strains to acquire or discard genomic segments, giving rise to strains with customized genomic repertoires. Consequently, this organism can survive in a wide range of environmental reservoirs that can serve as sources of the infecting organisms.

Geometric Spanner Networks

Part I. Introduction: 1. Introduction 2. Algorithms and graphs 3. The algebraic computation-tree model Part II. Spanners Based on Simplical Cones: 4. Spanners based on the Q-graph 5. Cones in higher dimensional space and Q-graphs 6. Geometric analysis: the gap property 7. The gap-greedy algorithm 8. Enumerating distances using spanners of bounded degree Part III. The Well Separated Pair Decomposition and its Applications: 9. The well-separated pair decomposition 10. Applications of well-separated pairs 11. The Dumbbell theorem 12. Shortcutting trees and spanners with low spanner diameter 13. Approximating the stretch factor of Euclidean graphs Part IV. The Path Greedy Algorithm: 14. Geometric analysis: the leapfrog property 15. The path-greedy algorithm Part V. Further Results and Applications: 16. The distance range hierarchy 17. Approximating shortest paths in spanners 18. Fault-tolerant spanners 19. Designing approximation algorithms with spanners 20. Further results and open problems.

New sparseness results on graph spanners

Let <italic>G</italic>=(<italic>V,E</italic>) be an <italic>n</italic>-vertex connected graph with positive edge weights. A subgraph <italic>G</italic>′ = (<italic>V,E</italic>′) is a <italic>t-spanner</italic> of <italic>G</italic> if for all <italic>u, v ε V</italic>,the weighted distance between <italic>u</italic> and <italic>v</italic> in <italic>G</italic>′ is at most <italic>t</italic> times the weighted distance between <italic>u</italic> and <italic>v</italic> in <italic>G</italic>. We consider the problem of constructing sparse spanners, and the <italic>weight</italic>, defined as the sum of the edge weights in the spanner. In this paper, we concentrate on constructing spanners of small weight. For an arbitrary positive edge-weighted graph <italic>G</italic>, for any <italic>t</italic>> 1, and any ε>0, we show that a <italic>t</italic>-spanner of <italic>G</italic> with weight <italic>O</italic>(<italic>n</italic><supscrpt>(2+ε)/(t-1></supscrpt>) •<italic>wt</italic>(<italic>MST</italic>) can be constructed in polynomial time. We also show that (log<supscrpt>2</supscrpt><italic>n</italic>)-spanners of weight <italic>O</italic>(log <italic>n</italic>)•<italic>wt</italic>(<italic>MST</italic>) can be constructed. We then consider spanners for complete graphs induced by a set of points in <italic>d</italic>-dimensional real normed space. The weight of an edge <italic>xy</italic> is the norm of the <italic>xy</italic> vector. We show that for these graphs, <italic>t</italic>-spanners with total weight <italic>O</italic>(log <italic>n</italic>)•<italic>wt</italic>(<italic>MST</italic>) can be constructed in polynomial time.

Distinct transcriptional profiles characterize oral epithelium‐microbiota interactions

Transcriptional profiling, bioinformatics, statistical and ontology tools were used to uncover and dissect genes and pathways of human gingival epithelial cells that are modulated upon interaction with the periodontal pathogens Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis. Consistent with their biological and clinical differences, the common core transcriptional response of epithelial cells to both organisms was very limited, and organism‐specific responses predominated. A large number of differentially regulated genes linked to the P53 apoptotic network were found with both organisms, which was consistent with the pro‐apoptotic phenotype observed with A. actinomycetemcomitans and anti‐apoptotic phenotype of P. gingivalis. Furthermore, with A. actinomycetemcomitans, the induction of apoptosis did not appear to be Fas‐ or TNFα‐mediated. Linkage of specific bacterial components to host pathways and networks provided additional insight into the pathogenic process. Comparison of the transcriptional responses of epithelial cells challenged with parental P. gingivalis or with a mutant of P. gingivalis deficient in production of major fimbriae, which are required for optimal invasion, showed major expression differences that reverberated throughout the host cell transcriptome. In contrast, gene ORF859 in A. actinomycetemcomitans, which may play a role in intracellular homeostasis, had a more subtle effect on the transcriptome. These studies help unravel the complex and dynamic interactions between host epithelial cells and endogenous bacteria that can cause opportunistic infections.

Fast Greedy Algorithms for Constructing Sparse Geometric Spanners

Given a set V of n points in

Resource-constrained geometric network optimization

\IR^d

The Regulatory Repertoire of Pseudomonas aeruginosa AmpC ß-Lactamase Regulator AmpR Includes Virulence Genes

and a real constant t>1, we present the first O(nlog n)-time algorithm to compute a geometric t-spanner on V. A geometric t-spanner on V is a connected graph G = (V,E) with edge weights equal to the Euclidean distances between the endpoints, and with the property that, for all

NEW SPARSENESS RESULTS ON GRAPH SPANNERS

u,v\in V

Optimally sparse spanners in 3-dimensional Euclidean space

, the distance between u and v in G is at most t times the Euclidean distance between u and v. The spanner output by the algorithm has O(n) edges and weight

Approximate distance oracles for geometric graphs

O(1)\cdot wt(MST)