Nuclear localization of HD-Zip IV transcription factor GLABRA2 is driven by Importin α.

GLABRA2 (GL2), a class IV homeodomain leucine-zipper (HD-Zip IV) transcription factor (TF) from Arabidopsis , is a developmental regulator of specialized cell types in the epidermis. GL2 contains a putative monopartite nuclear localization sequence (NLS) partially overlapping with its homeodomain (HD). We demonstrate that NLS deletion or alanine substitution of its basic residues (KRKRKK) affects nuclear localization and results in a loss-of-function phenotype. Fusion of the predicted NLS (GTNKRKRKKYHRH) to the fluorescent protein EYFP is sufficient for its nuclear localization in roots and trichomes. The functional NLS is evolutionarily conserved in a distinct subset of HD-Zip IV members including PROTODERMAL FACTOR2 (PDF2). Despite partial overlap of the NLS with the HD, genetic dissection of the NLS from PDF2 indicates that nuclear localization and DNA binding are separable functions. Affinity purification of GL2 from plant tissues followed by mass spectrometry-based proteomics identified Importin α (IMPα) isoforms as potential GL2 interactors. NLS structural prediction and molecular docking studies with IMPα-3 revealed major interacting residues. Split-ubiquitin cytosolic yeast two-hybrid assays suggest interaction between GL2 and four IMPα isoforms from Arabidopsis. Direct interactions were verified in vitro by co-immunoprecipitation with recombinant proteins. IMPα triple mutants ( impα- 1,2,3 ) exhibit defects in EYFP:GL2 nuclear localization in trichomes but not in roots, consistent with tissue-specific and redundant functions of IMPα isoforms in Arabidopsis . Taken together, our findings provide mechanistic evidence for IMPα-dependent nuclear localization of GL2 and other HD-Zip IV TFs in plants. One sentence summary: GLABRA2, a representative HD-Zip IV transcription factor from Arabidopsis , contains an evolutionarily conserved monopartite nuclear localization sequence that is recognized by Importin α for translocation to the nucleus, a process that is necessary for cell-type differentiation of the epidermis.

Potential Information Loss Due to Categorization of Minimum Inhibitory Concentration Frequency Distributions.

A bacterial isolate's susceptibility to antimicrobial is expressed as the lowest drug concentration inhibiting its visible growth, termed minimum inhibitory concentration (MIC). The susceptibilities of isolates from a host population at a particular time vary, with isolates with specific MICs present at different frequencies. Currently, for either clinical or monitoring purposes, an isolate is most often categorized as Susceptible, Intermediate, or Resistant to the antimicrobial by comparing its MIC to a breakpoint value. Such data categorizations are known in statistics to cause information loss compared to analyzing the underlying frequency distributions. The U.S. National Antimicrobial Resistance Monitoring System (NARMS) includes foodborne bacteria at the food animal processing and retail product points. The breakpoints used to interpret the MIC values for foodborne bacteria are those relevant to clinical treatments by the antimicrobials in humans in whom the isolates were to cause infection. However, conceptually different objectives arise when inference is sought concerning changes in susceptibility/resistance across isolates of a bacterial species in host populations among different sampling points or times. For the NARMS 1996-2013 data for animal processing and retail, we determined the fraction of comparisons of susceptibility/resistance to 44 antimicrobial drugs of twelve classes of a bacterial species in a given animal host or product population where there was a significant change in the MIC frequency distributions between consecutive years or the two sampling points, while the categorization-based analyses concluded no change. The categorization-based analyses missed significant changes in 54% of the year-to-year comparisons and in 71% of the slaughter-to-retail within-year comparisons. Hence, analyses using the breakpoint-based categorizations of the MIC data may miss significant developments in the resistance distributions between the sampling points or times. Methods considering the MIC frequency distributions in their entirety may be superior for epidemiological analyses of resistance dynamics in populations.

Modeling nanopores for sequencing DNA.

Using nanopores to sequence DNA rapidly and at a low cost has the potential to radically transform the field of genomic research. However, despite all the exciting developments in the field, sequencing DNA using a nanopore has yet to be demonstrated. Among the many problems that hinder development of the nanopore sequencing methods is the inability of current experimental techniques to visualize DNA conformations in a nanopore and directly relate the microscopic state of the system to the measured signal. We have recently shown that such tasks could be accomplished through computation. This chapter provides step-by-step instructions of how to build atomic scale models of biological and solid-state nanopore systems, use the molecular dynamics method to simulate the electric field-driven transport of ions and DNA through the nanopores, and analyze the results of such computational experiments.