INTRODUCTION .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 DOMAIN ORGANIZATION: The Typical ABC Transporter . . . . . . . . . . . . . . . . . 73 THE TRANSMEMBRANE DOMAINS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 The "Two-Times-Six" Helix Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Sequence Similarities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 THE ATP-BINDING DOMAINS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 PERIPLASMIC-BINDING PROTEINS ... . 84 SUBSTRATE SPECIFICITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 THE ROLE OF ATP : Coupling Energy to Transport . . . . . . . . . . . . . . . . . . . " . . . . . 88 COVALENT MODIFICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 CELLULAR FUNCTIONS OF ABC TRANSPORTERS . . . . . . . . . . . . . . . . . . . . . . . 9 1 Nutrient Uptake . . . . . . . . . . . . ....... . 9 1 Protein Export ....... . .... . . . . . . . . . . . . ... . ......... . ...... . ... . .. 93 Intracellular Membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Regulation of ABC Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Regulation by ABC Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Drug and Antibiotic Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Channel Functions: CFTR and P-glycoprotein . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 MECHANISMS OF SOLUTE TRANSLOCATION . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Structure of the Transmembrane Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Channels and Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0 I Energy Coupling andlor Gating . 102 CONCLUDING REMARKS . 103

ABC Transporters: From Microorganisms to Man

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Summary
Microbial community analysis via high-throughput sequencing of amplified 16S rRNA genes is an essential microbiology tool. We found the popular primer pair 515F (515F-C) and 806R greatly underestimated (e.g. SAR11) or overestimated (e.g. Gammaproteobacteria) common marine taxa. We evaluated marine samples and mock communities (containing 11 or 27 marine 16S clones), showing alternative primers 515F-Y (5′-GTGYCAGCMGCCGCGGTAA) and 926R (5′-CCGYCAATTYMTTTRAGTTT) yield more accurate estimates of mock community abundances, produce longer amplicons that can differentiate taxa unresolvable with 515F-C/806R, and amplify eukaryotic 18S rRNA. Mock communities amplified with 515F-Y/926R yielded closer observed community composition versus expected (r2 = 0.95) compared with 515F-Y/806R (r2 ∼ 0.5). Unexpectedly, biases with 515F-Y/806R against SAR11 in field samples (∼4–10-fold) were stronger than in mock communities (∼2-fold). Correcting a mismatch to Thaumarchaea in the 515F-C increased their apparent abundance in field samples, but not as much as using 926R rather than 806R. With plankton samples rich in eukaryotic DNA (> 1 μm size fraction), 18S sequences averaged ∼17% of all sequences. A single mismatch can strongly bias amplification, but even perfectly matched primers can exhibit preferential amplification. We show that beyond in silico predictions, testing with mock communities and field samples is important in primer selection.

Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples

The recent sequencing of the entire genomes of Mycoplasma genitalium and M. pneumoniae has attracted considerable attention to the molecular biology of mycoplasmas, the smallest self-replicating organisms. It appears that we are now much closer to the goal of defining, in molecular terms, the entire machinery of a self-replicating cell. Comparative genomics based on comparison of the genomic makeup of mycoplasmal genomes with those of other bacteria, has opened new ways of looking at the evolutionary history of the mycoplasmas. There is now solid genetic support for the hypothesis that mycoplasmas have evolved as a branch of gram-positive bacteria by a process of reductive evolution. During this process, the mycoplasmas lost considerable portions of their ancestors’ chromosomes but retained the genes essential for life. Thus, the mycoplasmal genomes carry a high percentage of conserved genes, greatly facilitating gene annotation. The significant genome compaction that occurred in mycoplasmas was made possible by adopting a parasitic mode of life. The supply of nutrients from their hosts apparently enabled mycoplasmas to lose, during evolution, the genes for many assimilative processes. During their evolution and adaptation to a parasitic mode of life, the mycoplasmas have developed various genetic systems providing a highly plastic set of variable surface proteins to evade the host immune system. The uniqueness of the mycoplasmal systems is manifested by the presence of highly mutable modules combined with an ability to expand the antigenic repertoire by generating structural alternatives, all compressed into limited genomic sequences. In the absence of a cell wall and a periplasmic space, the majority of surface variable antigens in mycoplasmas are lipoproteins. Apart from providing specific antimycoplasmal defense, the host immune system is also involved in the development of pathogenic lesions and exacerbation of mycoplasma induced diseases. Mycoplasmas are able to stimulate as well as suppress lymphocytes in a nonspecific, polyclonal manner, both in vitro and in vivo. As well as to affecting various subsets of lymphocytes, mycoplasmas and mycoplasma-derived cell components modulate the activities of monocytes/macrophages and NK cells and trigger the production of a wide variety of up-regulating and down-regulating cytokines and chemokines. Mycoplasma-mediated secretion of proinflammatory cytokines, such as tumor necrosis factor alpha, interleukin-1 (IL-1), and IL-6, by macrophages and of up-regulating cytokines by mitogenically stimulated lymphocytes plays a major role in mycoplasma-induced immune system modulation and inflammatory responses.

Molecular Biology and Pathogenicity of Mycoplasmas

THE ATP-binding cassette (ABC) superfamily of transport systems now includes over thirty proteins that share extensive sequence similarity and domain organization (reviewed in refs 1–3). This superfamily includes the well characterized periplasmic binding protein-dependent uptake systems of prokaryotes, bacterial exporters, and eukaryotic proteins including the P-glycoprotein associated with multidrug resistance in tumours (MDR), the STE6 gene product that mediates export of yeast a-factor mating pheromone, pfMDR that is implicated in chloroquine resistance of the malarial parasite, and the product of the cystic fibrosis gene (CFTR). Here we present a tertiary structure model of the ATP-binding cassettes characteristic of this class of transport system, based on similarities between the predicted secondary structures of members of this family and the previously determined structure of adenylate kinase. This model has implications for both the molecular basis of transport and cystic fibrosis and provides a framework for further experimentation.

Structural model of ATP-binding proteins associated with cystic fibrosis, multidrug resistance and bacterial transport.

Metagenomic sequencing has become crucial to studying microbial communities, but meaningful taxonomic analysis and inter-comparison of such data are still hampered by technical limitations, between-study design variability and inconsistencies between taxonomies used. Here we present MAPseq, a framework for reference-based rRNA metagenomic analysis that is up to 30% more accurate (F1/2 score) and up to one hundred times faster than existing solutions, providing in a single run multiple taxonomy classifications and hierarchical OTU mappings, for both amplicon and shotgun sequencing strategies, and for datasets of virtually any size. Availability: Source code and binaries are freely available at http://meringlab.org/software/mapseq/

http://www.biorxiv.org/content/early/2017/04/12/126953.full.pdf

MAPseq: improved speed, accuracy and consistency in ribosomal RNA sequence analysis

Protected areas can make an important contribution to the conservation of genetic diversity in the current biodiversity crisis. We have examined two representative freshwater fish taxa, Cottus spp. and Salmo trutta, in 15 midmountain headwaters of the Franco-German Palatinate Forest-North Vosges Biosphere Reserve in Central Europe to facilitate freshwater genetic diversity protection. Population genetic analyses of microsatellites and mtDNA showed lower genetic diversity, but distinctly differentiated genetic structure in Cottus spp., and higher diversity, but less differentiated structure in Salmo trutta. Phylogenetic analyses of mtDNA designated most sculpin to Cottus gobio, but also identified the first known population of Cottus rhenanus in the region. In addition to species-specific recommendations, we derived stream-specific guidance in an attempt to make optimal use of the combined genetic information on both taxa for habitat-oriented management prioritization and improved conservation of freshwater genetic diversity.

Thomas Schmidt

Papers

MAPseq: improved speed, accuracy and consistency in ribosomal RNA sequence analysis

Genetic diversity management of sculpin (Cottus spp.) and brown trout (Salmo trutta) in the Palatinate Forest-North Vosges Biosphere Reserve