Comparative analysis of the genome sequences of Bordetella pertussis,Bordetella parapertussis and Bordetella bronchiseptica

Bordetella pertussis, Bordetella parapertussis and Bordetella bronchiseptica are closely related Gram-negative beta-proteobacteria that colonize the respiratory tracts of mammals. B. pertussis is a strict human pathogen of recent evolutionary origin and is the primary etiologic agent of whooping cough. B. parapertussis can also cause whooping cough, and B. bronchiseptica causes chronic respiratory infections in a wide range of animals. We sequenced the genomes of B. bronchiseptica RB50 (5,338,400 bp; 5,007 predicted genes), B. parapertussis 12822 (4,773,551 bp; 4,404 genes) and B. pertussis Tohama I (4,086,186 bp; 3,816 genes). Our analysis indicates that B. parapertussis and B. pertussis are independent derivatives of B. bronchiseptica-like ancestors. During the evolution of these two host-restricted species there was large-scale gene loss and inactivation; host adaptation seems to be a consequence of loss, not gain, of function, and differences in virulence may be related to loss of regulatory or control functions.     

The immunoglobulin mu constant region gene is expressed in mouse thymocytes

It has been a matter of controversy whether the functional capacity of T cells to discriminate between antigens is mediated via immunoglobulin, an immunoglobulin-like molecule, or by the product(s) of unrelated genes. The progenitors of immunoglobulin-secreting cells, B cells, express membrane-bound immunoglobulin as the antigen-specific receptor on their surface. For T cells, although products of immunoglobulin heavy chain variable region genes are implicated as receptor components, there has been no compelling immunochemical evidence for participation of either immunoglobulin light chains or heavy chain constant regions (see refs 2-6 for the disparate views). Recently, using cloned immunoglobulin DNA sequences as hybridization probes, we have demonstrated that the immunoglobulin Cmu gene, but not the Cmu gene, is expressed as polyadenylated RNA in some T cell tumour (T lymphoma) cell lines. Individual T lymphoma lines yielded up to three discrete mu RNA species of different sizes (1.9, 2.2 and 3.0 kilobases), each species being different in size from the major mu RNA species present in B lymphoma cells (2.4 and 2.7 kilobases). We show here that cells from the normal mouse thymus contain mu RNA species, indistinguishable in size from those in T lymphoma cells, but contain little if any kappa RNA.     

Quantum annealing with manufactured spins

Many interesting but practically intractable problems can be reduced to that of finding the ground state of a system of interacting spins; however, finding such a ground state remains computationally difficult. It is believed that the ground state of some naturally occurring spin systems can be effectively attained through a process called quantum annealing. If it could be harnessed, quantum annealing might improve on known methods for solving certain types of problem. However, physical investigation of quantum annealing has been largely confined to microscopic spins in condensed-matter systems. Here we use quantum annealing to find the ground state of an artificial Ising spin system comprising an array of eight superconducting flux quantum bits with programmable spin-spin couplings. We observe a clear signature of quantum annealing, distinguishable from classical thermal annealing through the temperature dependence of the time at which the system dynamics freezes. Our implementation can be configured in situ to realize a wide variety of different spin networks, each of which can be monitored as it moves towards a low-energy configuration. This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples. Moreover, with an increased number of spins, such a system may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.