The aacA-aphD gentamicin and kanamycin resistance determinant of Tn4001 from Staphylococcus aureus: expression and nucleotide sequence analysis

The aacA-aphD aminoglycoside resistance determinant of the Staphylococcus aureus transposon Tn4001, which specifies resistance to gentamicin, tobramycin and kanamycin, has been cloned and shown to express these resistances in Escherichia coli. The determinant encoded a single protein with an apparent size of 59 kDa which specified both aminoglycoside acetyltransferase [AAC(6')] and aminoglycoside phosphotransferase [APH(2")] activities. Nucleotide sequence analysis of the determinant showed it to be capable of encoding a 479-amino-acid protein of 56.9 kDa. analysis of Tn1725 insertion mutants of the determinant indicated that resistance to tobramycin and kanamycin is due to the AAC activity specified by, approximately, the first 170 amino acids of the predicted protein sequence and is consistent with the gentamicin resistance, specified by the APH activity, being encoded within the C-terminal region of the protein. Comparison of the C-terminal end of the predicted amino acid sequence with the reported sequences of 13 APHs and a viomycin phosphotransferase revealed a region which is highly conserved among these phosphotransferases.     

The novel peptide NbPPI1 identified from Nicotiana benthamiana triggers immune responses and enhances resistance against Phytophthora pathogens

In plants, recognition of small secreted peptides, such as damage/danger‐associated molecular patterns (DAMPs), regulates diverse processes, including stress and immune responses. Here, we identified an SGPS (Ser‐Gly‐Pro‐Ser) motif‐containing peptide, Nicotiana tabacum NtPROPPI, and its two homologs in Nicotiana benthamiana, NbPROPPI1 and NbPROPPI2. Phytophthora parasitica infection and salicylic acid (SA) treatment induced NbPROPPI1/2 expression. Moreover, SignalP predicted that the 89‐amino acid NtPROPPI includes a 24‐amino acid N‐terminal signal peptide and NbPROPPI1/2‐GFP fusion proteins were mainly localized to the periplasm. Transient expression of NbPROPPI1/2 inhibited P. parasitica colonization, and NbPROPPI1/2 knockdown rendered plants more susceptible to P. parasitica. An eight‐amino‐acid segment in the NbPROPPI1 C‐terminus was essential for its immune function and a synthetic 20‐residue peptide, NbPPI1, derived from the C‐terminus of NbPROPPI1 provoked significant immune responses in N. benthamiana. These responses led to enhanced accumulation of reactive oxygen species, activation of mitogen‐activated protein kinases, and up‐regulation of the defense genes Flg22‐induced receptor‐like kinase (FRK) and WRKY DNA‐binding protein 33 (WRKY33). The NbPPI1‐induced defense responses require Brassinosteroid insensitive 1‐associated receptor kinase 1 (BAK1). These results suggest that NbPPI1 functions as a DAMP in N. benthamiana; this novel DAMP provides a potentially useful target for improving plant resistance to Pytophthora pathogens.     

Experimental Murine Fascioliasis Derives Early Immune Suppression with Increased Levels of TGF-�� and IL-4

In fascioliasis, T-helper 2 (Th2) responses predominate, while little is known regarding early immune phenomenon. We herein analyzed early immunophenotype changes of BALB/c, C57BL/6, and C3H/He mice experimentally infected with 5 Fasciola hepatica metacercariae. A remarkable expansion of CD19⁺ B cells was observed as early as week 1 post-infection while CD4⁺/CD8⁺ T cells were down-regulated. Accumulation of Mac1⁺ cells with time after infection correlated well with splenomegaly of all mice strains tested. The expression of tumor necrosis factor (TNF)-α mRNA in splenocytes significantly decreased while that of IL-4 up-regulated. IL-1β expression was down-modulated in BALB/c and C57BL/6 mice, but not in C3H/He. Serum levels of transforming growth factor (TGF)-β were considerably elevated in all mice during 3 weeks of infection period. These collective results suggest that experimental murine fascioliasis might derive immune suppression with elevated levels of TGF-β and IL-4 during the early stages of infection.     

Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm

A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into -values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.     

Buckley–James boosting model based on extreme learning machine and random survival forests

Buckley–James (BJ) model is a typical semiparametric accelerated failure time model, which is closely related to the ordinary least squares method and easy to be constructed. However, traditional BJ model built on linearity assumption only captures simple linear relationships, while it has difficulty in processing nonlinear problems. To overcome this difficulty, in this paper, we develop a novel regression model for right-censored survival data within the learning framework of BJ model, basing on random survival forests (RSF), extreme learning machine (ELM), and L₂ boosting algorithm. The proposed method, referred to as ELM-based BJ boosting model, employs RSF for covariates imputation first, then develops a new ensemble of ELMs—ELM-based boosting algorithm for regression by ensemble scheme of L₂ boosting, and finally, uses the output function of the proposed ELM-based boosting model to replace the linear combination of covariates in BJ model. Due to fitting the logarithm of survival time with covariates by the nonparametric ELM-based boosting method instead of the least square method, the ELM-based BJ boosting model can capture both linear covariate effects and nonlinear covariate effects. In both simulation studies and real data applications, in terms of concordance index and integrated Brier sore, the proposed ELM-based BJ boosting model can outperform traditional BJ model, two kinds of BJ boosting models proposed by Wang et al., RSF, and Cox proportional hazards model.     

Chromatin Dynamics during DNA Repair Revealed by Pair Correlation Analysis of Molecular Flow in the Nucleus

Chromatin dynamics modulate DNA repair factor accessibility throughout the DNA damage response. The spatiotemporal scale upon which these dynamics occur render them invisible to live cell imaging. Here we present a believed novel assay to monitor the in vivo structural rearrangements of chromatin during DNA repair. By pair correlation analysis of EGFP molecular flow into chromatin before and after damage, this assay measures millisecond variations in chromatin compaction with submicron resolution. Combined with laser microirradiation we employ this assay to monitor the real-time accessibility of DNA at the damage site. We find from comparison of EGFP molecular flow with a molecule that has an affinity toward double-strand breaks (Ku-EGFP) that DNA damage induces a transient decrease in chromatin compaction at the damage site and an increase in compaction to adjacent regions, which together facilitate DNA repair factor recruitment to the lesion with high spatiotemporal control.