Metabolic fate of L-lactaldehyde derived from an alternative L-rhamnose pathway

Fungal Pichia stipitis and bacterial Azotobacter vinelandii possess an alternative pathway of L-rhamnose metabolism, which is different from the known bacterial pathway. In a previous study (Watanabe S, Saimura M & Makino K (2008) Eukaryotic and bacterial gene clusters related to an alternative pathway of non-phosphorylated L-rhamnose metabolism. J Biol Chem283, 20372-20382), we identified and characterized the gene clusters encoding the four metabolic enzymes [L-rhamnose 1-dehydrogenase (LRA1), L-rhamnono-gamma-lactonase (LRA2), L-rhamnonate dehydratase (LRA3) and l-2-keto-3-deoxyrhamnonate aldolase (LRA4)]. In the known and alternative L-rhamnose pathways, L-lactaldehyde is commonly produced from l-2-keto-3-deoxyrhamnonate and L-rhamnulose 1-phosphate by each specific aldolase, respectively. To estimate the metabolic fate of L-lactaldehyde in fungi, we purified L-lactaldehyde dehydrogenase (LADH) from P. stipitis cells L-rhamnose-grown to homogeneity, and identified the gene encoding this enzyme (PsLADH) by matrix-assisted laser desorption ionization-quadruple ion trap-time of flight mass spectrometry. In contrast, LADH of A. vinelandii (AvLADH) was clustered with the LRA1-4 gene on the genome. Physiological characterization using recombinant enzymes revealed that, of the tested aldehyde substrates, L-lactaldehyde is the best substrate for both PsLADH and AvLADH, and that PsLADH shows broad substrate specificity and relaxed coenzyme specificity compared with AvLADH. In the phylogenetic tree of the aldehyde dehydrogenase superfamily, PsLADH is poorly related to the known bacterial LADHs, including that of Escherichia coli (EcLADH). However, despite its involvement in different L-rhamnose metabolism, AvLADH belongs to the same subfamily as EcLADH. This suggests that the substrate specificities for L-lactaldehyde between fungal and bacterial LADHs have been acquired independently.     

Ebola virus matrix protein VP40 uses the COPII transport system for its intracellular transport

The Ebola virus matrix protein VP40 plays an important role in virion formation and viral egress from cells. However, the host cell proteins and mechanisms responsible for intracellular transport of VP40 prior to its contribution to virion formation remain to be elucidated. Therefore we used coimmunoprecipitation and mass spectrometric analyses to identify host proteins interacting with VP40. We found that Sec24C, a component of the host COPII vesicular transport system, interacts specifically with VP40 via VP40 amino acids 303 to 307. Coimmunoprecipitation and dominant-negative mutant studies indicated that the COPII transport system plays a critical role in VP40 intracellular transport to the plasma membrane. Marburg virus VP40 was also shown to use the COPII transport system for intracellular transport. These findings identify a conserved intersection between a host pathway and filovirus replication, an intersection that can be targeted in the development of new antiviral drugs.     

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L ₁-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.     

Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker

The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc.) and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.     

Comparative analysis of microbial diversity and bacterial seedling disease‐suppressive activity in organic‐farmed and standardized commercial conventional soils for rice nursery cultivation

The outbreak of rice plant diseases can be effectively suppressed in organic farming systems. However, the mechanisms of disease suppression by organic farming systems are not well understood. When Burkholderia‐infected rice seeds were sown and cultivated on nine organic‐farmed soils which were supplied by nine independent organic rice farmers or standardized commercial conventional soils, the emergence of bacterial seedling diseases was suppressed to equivalent degrees in nine organic‐farmed soils, whereas the diseases occurred in two commercial conventional soils. In any organic or commercial conventional soil sown with healthy rice seeds as a control, the diseases did not appear. Upon physicochemical analysis of the nine organic‐farmed soils, component common to these organic‐farmed soils seemed to not be directly associated with disease‐suppressive activity. However, microbiome analyses indicated that the bacterial population in these nine organic‐farmed soils was more diverse than those in commercial conventional soils. Intriguingly, the diverse bacterial population structures of organic‐farmed soils were preserved after irrigating and sowing rice seeds, but that of commercial conventional soils was clearly changed by them. Thus, organic‐farmed soils seem to maintain robust bacterial populations despite the irrigation and seedling growth. Indeed, pathogenic Burkholderia in infected rice seeds also did not proliferate in the seedling grown on organic‐farmed soils. Taken together, the common feature of organic‐farmed soils might be the correlation between bacterial seedling disease‐suppressive activity and higher robustness of the diversified microbiome.     

P-loop Flexibility in Na+ Channel Pores Revealed by Single- and Double-cysteine Replacements

Replacement of individual P-loop residues with cysteines in rat skeletal muscle Na⁺ channels (SkM1) caused an increased sensitivity to current blockade by Cd²⁺ thus allowing detection of residues lining the pore. Simultaneous replacement of two residues in distinct P-loops created channels with enhanced and reduced sensitivity to Cd²⁺ block relative to the individual single mutants, suggesting coordinated Cd²⁺ binding and cross-linking by the inserted sulfhydryl pairs. Double-mutant channels with reduced sensitivity to Cd²⁺ block showed enhanced sensitivity after the application of sulfhydryl reducing agents. These results allow identification of residue pairs capable of approaching one another to within less than 3.5 Å. We often observed that multiple consecutive adjacent residues in one P-loop could coordinately bind Cd²⁺ with a single residue in another P-loop. These results suggest that, on the time-scale of Cd²⁺ binding to mutant Na⁺ channels, P-loops show a high degree of flexibility.     

Embryonic stem cells derived from C57BL/6J and C57BL/6N mice

Mouse embryonic stem (ES) cells with the C57BL/6 genetic background allow the generation of knockout mice without the need to backcross to C57BL/6. However, C57BL/6 ES cells whose pluripotency after homologous recombination has been confirmed are not yet available from public cell banks. To facilitate the use of ES cells derived from C57BL/6 sublines in both biologic and medical research, we demonstrated that the use of knockout serum replacement as a medium supplement and 8-cell blastomeres as recipient embryos allowed establishment of ES cells and production of germline chimeric mice, respectively. Under effective conditions, a large number of ES cell lines were established from C57BL/6J and C57BL/6N blastocysts. The majority of ES cells in many cell lines obtained from both strains showed a normal chromosome number. Germline chimeric mice were generated from C57BL/6J and C57BL/6N ES cells. Finally, the ES cell line B6J-S1UTR, derived from C57BL/6J, was used for successful production of gene knockout mice. C57BL/6J ES (B6J-S1UTR and B6J-23UTR) and C57BL/6N ES (B6N-22UTR) cells are available from the cell bank of the BioResource Center at RIKEN Tsukuba Institute (http://www.brc.riken.jp/lab/cell/english/).