Genetic variation and population structure of a threatened timber tree <Emphasis Type="Italic">Dalbergia cochinchinensis</Emphasis> in Cambodia

Dalbergia cochinchinensis Pierre ex Laness. (Fabaceae) is a commercially important tree in Southeast Asia. Although this species is under legal protections, illegal logging and disorderly developments have reduced its populations, and the conservation of this species is currently of much concern. In this study, we determined nucleotide sequences at six chloroplasts and ten nuclear loci in four populations of D. cochinchinensis in Cambodia, followed by population genetic analyses. The average silent nucleotide diversity over the nuclear loci, excluding one with an exceptionally high value, was 0.0057 in the entire population, and the mean F _ST across the nuclear loci between each population pair was between 0.135 and 0.467. Thus, the nucleotide diversity in the studied populations was not low compared with that in other tree species, and the level of population differentiation was high. Neutrality test statistics indicated a recent reduction of population size and a subdivision of the population within this species. The divergence times and migration rates were estimated with a likelihood-based method assuming the isolation with migration model. Based on the results, the three populations split 68,000–138,000 years ago, possibly corresponding to the start of the last glacial period, and the level of gene flow among the populations was very low thereafter. Moreover, after the split, population sizes were reduced considerably. Notably, the nucleotide diversity in an insertion sequence in a noncoding region of nuclear C4H was much higher than the mean nucleotide diversity in silent sites across other nuclear genes, indicating that the region was affected by selection.     

Probabilistic alignments with quality scores: an application to short-read mapping toward accurate SNP/indel detection

MOTIVATION: Recent studies have revealed the importance of considering quality scores of reads generated by next-generation sequence (NGS) platforms in various downstream analyses. It is also known that probabilistic alignments based on marginal probabilities (e.g. aligned-column and/or gap probabilities) provide more accurate alignment than conventional maximum score-based alignment. There exists, however, no study about probabilistic alignment that considers quality scores explicitly, although the method is expected to be useful in SNP/indel callers and bisulfite mapping, because accurate estimation of aligned columns or gaps is important in those analyses. RESULTS: In this study, we propose methods of probabilistic alignment that consider quality scores of (one of) the sequences as well as a usual score matrix. The method is based on posterior decoding techniques in which various marginal probabilities are computed from a probabilistic model of alignments with quality scores, and can arbitrarily trade-off sensitivity and positive predictive value (PPV) of prediction (aligned columns and gaps). The method is directly applicable to read mapping (alignment) toward accurate detection of SNPs and indels. Several computational experiments indicated that probabilistic alignments can estimate aligned columns and gaps accurately, compared with other mapping algorithms e.g. SHRiMP2, Stampy, BWA and Novoalign. The study also suggested that our approach yields favorable precision for SNP/indel calling.     

Cloning of a rat glia maturation factor-gamma (rGMFG) cDNA and expression of its mRNA and protein in rat organs

We isolated a rat glia maturation factor-gamma(rGMFG) cDNA and examined the tissue distribution of GMFG in rat by Northern and Western blot analyses. Sequence analysis of the entire cDNA revealed an open reading frame of 426 nucleotides with a deduced protein of 142 amino acid residues. The deduced amino acid sequence of the putative product is highly homologous (78.9%) to rat glia maturation factor-beta (rGMFB). Northern blot analysis indicated that a 0.9-kb mRNA is predominantly expressed in rat thymus, testis, and spleen. GMFG showed a different tissue distribution from GMFB, being present predominantly in proliferative and differentiative organs.     

Cartducin stimulates mesenchymal chondroprogenitor cell proliferation through both extracellular signal-regulated kinase and phosphatidylinositol 3-kinase/Akt pathways

Cartducin, a paralog of Acrp30/adiponectin, is a secretory protein produced by both chondrogenic precursors and proliferating chondrocytes, and belongs to a novel C1q family of proteins. We have recently shown that cartducin promotes the growth of both mesenchymal chondroprogenitor cells and chondrosarcoma-derived chondrocytic cells in vitro. However, the cartducin-signaling pathways responsible for the regulation of cell proliferation have not been documented. In this study, we examined whether cartducin exists in serum and further investigated the intracellular signaling pathways stimulated by cartducin in mesenchymal chondroprogenitor cells. Western blot analysis showed that, unlike Acrp30/adiponectin, cartducin was undetectable in mouse serum. Next, mesenchymal chondroprogenitor N1511 cells were stimulated with cartducin, and three major groups of mitogen-activated protein kinase (MAPK) pathways and the phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathway were examined. Cartducin activated extracellular signal-regulated kinase 1/2 (ERK1/2) and Akt, but not c-jun N-terminal kinase (JNK) nor p38 MAPK. The MEK1/2 inhibitor, U0126, blocked cartducin-stimulated ERK1/2 phosphorylation and suppressed the DNA synthesis induced by cartducin in N1511 cells. The PI3K inhibitor, LY294002, blocked cartducin-stimulated Akt phosphorylation and a decrease in cartducin-induced DNA synthesis in N1511 cells was also observed. These data suggest that cartducin is a peripheral skeletal growth factor, and that the proliferation of mesenchymal chondroprogenitor cells stimulated by cartducin is associated with activations of the ERK1/2 and PI3K/Akt signaling pathways.     

Peptide substrates for G protein-coupled receptor kinase 2

G protein-coupled receptor kinases (GRKs) control the signaling and activation of G protein-coupled receptors through phosphorylation. In this study, consensus substrate motifs for GRK2 were identified from the sequences of GRK2 protein substrates, and 17 candidate peptides were synthesized to identify peptide substrates with high affinity for GRK2. GRK2 appears to require an acidic amino acid at the −2, −3, or −4 positions and its consensus phosphorylation site motifs were identified as (D/E)X_1–3(S/T), (D/E)X_1–3(S/T)(D/E), or (D/E)X_0–2(D/E)(S/T). Among the 17 peptide substrates examined, a 13-amino-acid peptide fragment of β-tubulin (DEMEFTEAESNMN) showed the highest affinity for GRK2 (K_m, 33.9 μM; V_max, 0.35 pmol min⁻¹ mg⁻¹), but very low affinity for GRK5. This peptide may be a useful tool for investigating cellular signaling pathways regulated by GRK2.     

Accurate extraction of functional associations between proteins based on common interaction partners and common domains

MOTIVATION: Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction. RESULT: In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.