Motor-Substrate Interactions in Mycoplasma Motility Explains Non-Arrhenius Temperature Dependence

Mycoplasmas exhibit a novel, substrate-dependent gliding motility that is driven by ∼400 “leg” proteins. The legs interact with the substrate and transmit the forces generated by an assembly of ATPase motors. The velocity of the cell increases linearly by nearly 10-fold over a narrow temperature range of 10-40°C. This corresponds to an Arrhenius factor that decreases from ∼45 k_BT at 10°C to ∼10 k_BT at 40°C. On the other hand, load-velocity curves at different temperatures extrapolate to nearly the same stall force, suggesting a temperature-insensitive force-generation mechanism near stall. In this article, we propose a leg-substrate interaction mechanism that explains the intriguing temperature sensitivity of this motility. The large Arrhenius factor at low temperature comes about from the addition of many smaller energy barriers arising from many substrate-binding sites at the distal end of the leg protein. The Arrhenius dependence attenuates at high temperature due to two factors: 1), the reduced effective multiplicity of energy barriers intrinsic to the multiple-site binding mechanism; and 2), the temperature-sensitive weakly facilitated leg release that curtails the power stroke. The model suggests an explanation for the similar steep, sub-Arrhenius temperature-velocity curves observed in many molecular motors, such as kinesin and myosin, wherein the temperature behavior is dominated not by the catalytic biochemistry, but by the motor-substrate interaction.     

Genetic diversity and population structure of Swertia tetraptera (Gentianaceae), an endemic species of Qinghai-Tibetan Plateau

Swertia tetraptera Maxim is an annual alpine herb endemic to the Qinghai-Tibetan Plateau (QTP). Its populations are locally scattered as isolated patches throughout this region. Genetic variation within and among thirty-four populations of this species was assessed using ISSR fingerprinting with 10 primers. High levels of genetic diversity exist within species (P = 98.9%, I = 0.3475; He = 0.2227), while the within-population diversity is low (P = 32.7%, I = 0.177; He = 0.12). High levels of genetic differentiation were detected among populations based on various statistics, including Nei’s genetic diversity analysis (G_ST = 0.4608), Bayesian analysis (θ^B = 0.476) and AMOVA (F_ST = 0.57). That is, populations shared low levels of genetic identity (I = 0.2622–0.0966). This genetic structure was probably due to severe genetic drift, breeding system and limited gene flow. The observed genetic structure of the populations implies that different populations across the distribution range of the species should be sampled to maintain high genetic diversity when a conservation strategy is implemented.     

The physical mapping of bacteriophage T5 transfer tRNAs.

Transfer RNAs, isolated from Escherichia coli F cells infected with T5 bacteriophage, were charged with radioactive amino acids and used in RNA-DNA hybridization studies to detect and locate T5 tRNA cistrons in the T5 DNA chromosome. Hybridization of 14 3H-aminoacyl-tRNA species, including purified T5 [35S]Met-tRNAm and [35S]Met-tRNAf, to the separated strands of T5+ DNA indicates that most, if not all, of the T5 tRNAs are transcribed from the continuous heavy strand of T5 DNA. Heteroduplex mapping of eight mutant T5 DNA deletions has enabled us to locate and determine the size of these deleted segments. By correlating this information with the presence and absence of specific tDNA sequences in these mutants, as determined by tRNA-DNA hybridization, we were able to define the physical limits of four tDNA-containing loci along the T5 DNA molecule. A physical map for 15 tRNA species examined indicates that the structural genes for these tRNAs are clustered within a segment length of T5 DNA that represents approximately 11.2% of the total wild type T5 DNA. The existence of the deletion mutants indicates that T5 tRNAs are dispensable for T5 replication under the growth conditions and for the host employed.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.