Nuclear accumulation of multiple protein kinases during prolactin-induced proliferation of Nb2 rat lymphoma cells

Intracellular kinases play important roles in signal transduction and are involved in the surface receptor-mediated regulation of cellular functions, including mitogenesis. In the present study, we examined the possible involvement of various protein kinases in the passage of a mitogenic signal from the cell surface to the nucleus of Nb₂ cells, a rat nodal lymphoma cell line in which prolactin is a mitogen. Following a prolactin challenge, various kinase activities were monitored at short intervals in different cellular fractions over a 60 min period. Protein kinase C (PKC) activity in the cytosolic fraction rapidly declined to 50% of its original activity within the first 30 min, while PKC activity in the nuclear fractions increased sharply, reaching its highest level by 30 min following a prolactin challenge. There were also increases in both casein kinase and protein tyrosine kinase (PTK) activities in the nuclear fractions during the first 30 min following a prolactin challenge that paralleled PKC activity. The activities of all three kinases declined thereafter, reaching levels close to their respective basal values by 60 min following initiation of prolactin treatment. These observations suggest the possibility that multiple protein kinases may be involved in mitogenic signal transduction for prolactin in Nb₂ cells. © 1996 Wiley-Liss, Inc.     

Prevalence and molecular characterization of alpha-thalassemia syndromes among Indians

OBJECTIVE: This study was undertaken to determine the prevalence and molecular basis of alpha-thalassemia in the Indian population and its implications in genetic counseling and prenatal diagnosis. METHODS: 1253 random samples were screened for hemoglobinopathies. Red cell indices were measured on the Sysmex K 1000 cell counter; HbA2 and HbF levels were quantitated using high performance liquid chromotography (HPLC). Cellulose acetate electrophoresis and isoelectric focusing (IEF) was done to detect the presence of Hb Bart's in cord blood samples. alpha-Globin gene mapping was done using Southern blot hybridization of BamHI and BglII digests. RESULTS: Of the 1253 subjects, 132 had a single alpha-gene deletion (10.5%) while 29 had two alpha-gene deletions (2.31%). Fifteen cases showed the presence of alpha-gene triplication (1.1%). A single case showed the presence of one alpha-gene deletion as well as alpha-gene triplication (-alpha/alphaalphaalpha). Overall, the prevalence of alpha-thalassemia was 12.9%. Region-wise and caste-wise analysis showed the highest prevalence of alpha-thalassemia among the Punjabi population originating from the northern region of India. CONCLUSION: alpha-Thalassemia is by far the commonest hemoglobinopathy in India, but it is not a cause of serious genetic risk is the milder form (-alpha/alphaalpha) of alpha-thalassemia, which is predominant. Knowing the alpha-genotype is useful for genetic counseling for prenatal diagnosis in couples where one of the parents may have reduced indices coupled with a raised RBC count and normal HbA2 levels.     

Adaptive evolution of newly emerged micro-RNA genes in Drosophila

How often micro-RNA (miRNA) genes emerged and how fast theyevolved soon after their emergence are some of the central questionsin the evolution of miRNAs. Because most known miRNA genes areancient and highly conserved, these questions can be best answeredby identifying newly emerged miRNA genes. Among the 78 miRNAgenes in Drosophila reported before 2007, only 5 are confirmedto be newly emerged in the genus (although many more can befound in the newly reported data set; e.g., Ruby et al. 2007;Stark et al. 2007; Lu et al. 2008). These new miRNA genes haveundergone numerous changes, even in the normally invariant maturesequences. Four of them (the miR-310/311/312/313 cluster, denotedmiR-310s) were duplicated from other conserved miRNA genes.The fifth one (miR-303) appears to be a very young gene, originatingde novo from a non-miRNA sequence recently. We sequenced these5 miRNA genes and their neighboring regions from a worldwidecollection of Drosophila melanogaster lines. The levels of divergenceand polymorphism in these miRNA genes, vis-à-vis thoseof the neighboring DNA sequences, suggest that these 5 genesare evolving adaptively. Furthermore, the polymorphism patternof miR-310s in D. melanogaster is indicative of hitchhikingunder positive selection. Thus, a large number of adaptive changesover a long period of time may be essential for the evolutionof newly emerged miRNA genes.     

Structural and Enzymatic Characterization of Os3BGlu6, a Rice β-Glucosidase Hydrolyzing Hydrophobic Glycosides and (1→3)- and (1→2)-Linked Disaccharides

Glycoside hydrolase family 1 (GH1) β-glucosidases play roles in many processes in plants, such as chemical defense, alkaloid metabolism, hydrolysis of cell wall-derived oligosaccharides, phytohormone regulation, and lignification. However, the functions of most of the 34 GH1 gene products in rice (Oryza sativa) are unknown. Os3BGlu6, a rice β-glucosidase representing a previously uncharacterized phylogenetic cluster of GH1, was produced in recombinant Escherichia coli. Os3BGlu6 hydrolyzed p-nitrophenyl (pNP)-β-d-fucoside (k_cat/K_m = 67 mm⁻¹ s⁻¹), pNP-β-d-glucoside (k_cat/K_m = 6.2 mm⁻¹ s⁻¹), and pNP-β-d-galactoside (k_cat/K_m = 1.6 mm⁻¹s⁻¹) efficiently but had little activity toward other pNP glycosides. It also had high activity toward n-octyl-β-d-glucoside and β-(1→3)- and β-(1→2)-linked disaccharides and was able to hydrolyze apigenin β-glucoside and several other natural glycosides. Crystal structures of Os3BGlu6 and its complexes with a covalent intermediate, 2-deoxy-2-fluoroglucoside, and a nonhydrolyzable substrate analog, n-octyl-β-d-thioglucopyranoside, were solved at 1.83, 1.81, and 1.80 Å resolution, respectively. The position of the covalently trapped 2-F-glucosyl residue in the enzyme was similar to that in a 2-F-glucosyl intermediate complex of Os3BGlu7 (rice BGlu1). The side chain of methionine-251 in the mouth of the active site appeared to block the binding of extended β-(1→4)-linked oligosaccharides and interact with the hydrophobic aglycone of n-octyl-β-d-thioglucopyranoside. This correlates with the preference of Os3BGlu6 for short oligosaccharides and hydrophobic glycosides.β-Glucosidases (EC 3.2.1.21) have a wide range of functions in plants, including acting in cell wall remodeling, lignification, chemical defense, plant-microbe interactions, phytohormone activation, activation of metabolic intermediates, and release of volatiles from their glycosides (Esen, 1993). They fulfill these roles by hydrolyzing the glycosidic bond at the nonreducing terminal glucosyl residue of a glycoside or an oligosaccharide, thereby releasing Glc and an aglycone or a shortened carbohydrate. The aglycone released from the glycoside may be a monolignol, a toxic compound, or a compound that further reacts to release a toxic component, an active phytohormone, a reactive metabolic intermediate, or a volatile scent compound (Brzobohatý et al., 1993; Dharmawardhama et al., 1995; Reuveni et al., 1999; Lee et al., 2006; Barleben et al., 2007; Morant et al., 2008). Indeed, the wide range of glucosides of undocumented functions found in plants suggests that many β-glucosidase functions may remain to be discovered.Plant β-glucosidases fall into related families that have been classified as glycosyl hydrolase (GH) families GH1, GH3, and GH5 (Henrissat, 1991; Coutinho and Henrissat, 1998, 1999). Of these, GH1 has been most thoroughly documented and shown to comprise a gene family encoding 40 putative functional GHs in Arabidopsis (Arabidopsis thaliana) and 34 in rice (Oryza sativa) in addition to a few pseudogenes (Xu et al., 2004; Opassiri et al., 2006). In addition to β-glucosidases, plant GH1 members include β-mannosidases (Mo and Bewley, 2002), β-thioglucosidases (Burmeister et al., 1997), and disaccharidases such as primeverosidase (Mizutani et al., 2002) as well as hydroxyisourate hydrolase, which hydrolyzes the internal bond in a purine ring rather than a glycosidic bond (Raychaudhuri and Tipton, 2002). The specificity for the glycone in GH1 enzymes varies. Some enzymes are quite specific for β-d-glucosides or β-d-mannosides, while many accept either β-d-glucosides or β-d-fucosides, and some also hydrolyze β-d-galactosides, β-d-xylosides, and α-l-arabinoside (Esen, 1993). However, most GH1 enzymes are thought to hydrolyze glucosides in the plant, and it is the aglycone specificity that determines the functions of most GH1 enzymes.Aglycone specificity of GH1 β-glucosidases ranges from rather broad to absolutely specific for one substrate and is not obvious from sequence similarity. For instance, maize (Zea mays) ZmGlu1 β-glucosidase hydrolyzes a range of glycosides, including its natural substrate, 2-O-β-d-glucopyranosyl-4-dihydroxy-1,4-benzoxazin-3-one (DIMBOAGlc), but not dhurrin, whereas sorghum (Sorghum bicolor) Dhr1, which is 72% identical to ZmGlu1, only hydrolyzes its natural cyanogenic substrate dhurrin (Verdoucq et al., 2003). Similarly, despite sharing over 80% amino acid sequence identity, the legume isoflavonoid β-glucosidases dalcochinase from Dalbergia cochinchinensis and Dnbglu2 from Dalbergia nigrescens hydrolyze each other''s natural substrate very poorly (Chuankhayan et al., 2007). Thus, small differences in the amino acid sequence surrounding the active site may be expected to account for significant differences in substrate specificity.GH1 is classified in GH clan A, which consists of GH families whose members have a (β/α)₈-barrel structure with the catalytic acid/base on strand 4 of the β-barrel and the catalytic nucleophile on strand 7 (Henrissat et al., 1995; Jenkins et al., 1995). As such, all GH1 enzymes have similar overall structures, but it has been noted that four variable loops at the C-terminal end of the β-barrel strands, designated A, B, C, and D, account for much of the differences in the active site architecture (Sanz-Aparicio et al., 1998). The similar structures with great diversity in substrate specificity make plant GH1 enzymes an ideal model system to investigate the structural basis of substrate specificity. To date, seven plant β-glucosidase structures have been reported, including three closely related chloroplastic enzymes from maize (Czjzek et al., 2000, 2001), sorghum (Verdoucq et al., 2004), and wheat (Triticum aestivum; Sue et al., 2006), the cytoplasmic strictosidine β-glucosidase from Rauvolfia serpentine (Barleben et al., 2007), and the secreted enzymes white clover (Trifolium repens) cyanogenic β-glucosidase (Barrett et al., 1995), white mustard (Sinapsis alba) myrosinase (thioglucosidase; Burmeister et al., 1997), and rice Os3BGlu7 (BGlu1; Chuenchor et al., 2008). These enzymes hydrolyze substrates with a range of structures, but they cannot account for the full range of β-glucosidase substrates available in plants, and determining the structural differences that bring about substrate specificity differences in even closely related GH1 enzymes has proven tricky (Verdoucq et al., 2003, 2004; Sue et al., 2006; Chuenchor et al., 2008).Amino acid sequence-based phylogenetic analysis of GH1 enzymes encoded by the rice genome showed that there are eight clusters containing both rice and Arabidopsis proteins that are more closely related to each other than they are to enzymes from the same plants outside the clusters (Fig. 1; Opassiri et al., 2006). In addition, there are a cluster of sixteen putative β-glucosidases and a cluster of myrosinases in Arabidopsis without any closely related rice counterparts. Comparison with characterized GH1 enzymes from other plants reveals other clusters of related enzymes not found in rice or Arabidopsis, including the chloroplastic enzymes, from which the maize, sorghum, and wheat structures are derived, and the cytoplasmic metabolic enzymes, from with the strictosidine hydrolase structure is derived (Fig. 1). Therefore, although the known structures provide good tools for molecular modeling of plant enzymes, most rice and Arabidopsis GH1 enzymes lack a close correspondence in sequence and functional evolution to these structures, suggesting that the variable loops that determine the active site may be different. It would be useful, therefore, to know the structures and substrate specificities of representative members of each of the eight clusters seen in rice and Arabidopsis. To begin to acquire this information, we have expressed Os3BGlu6, a member of cluster At/Os 1 in Figure 1, characterized its substrate specificity, and determined its structure alone and in complex with a glycosyl intermediate and a nonhydrolyzable substrate analog.Open in a separate windowFigure 1.Simplified phylogenetic tree of the amino acid sequences of eukaryotic GH1 proteins with known structures and those of rice and Arabidopsis GH1 gene products. The protein sequences of the eukaryotic proteins with known structures are marked with four-character PDB codes for one of their structures, including Trifolium repens cyanogenic β-glucosidase (1CBG; Barrett et al., 1995), Sinapsis alba myrosinase (1MYR; Burmeister et al., 1997), Zea mays ZmGlu1 β-glucosidase (1E1F; Czjzek et al., 2000), Sorghum bicolor Dhr1 dhurrinase (1V02; Verdoucq et al., 2004), Triticum aestivum β-glucosidase (2DGA; Sue et al., 2006), Rauvolfia serpentina strictosidine β-glucosidase (2JF6; Barleben et al., 2007), and Oryza sativa Os3BGlu7 (BGlu1) β-glucosidase (2RGL; Chuenchor et al., 2008) from plants, along with Brevicoryne brassicae myrosinase (1WCG; Husebye et al., 2005), Homo sapiens cytoplasmic (Klotho) β-glucosidase (2E9M; Hayashi et al., 2007), and Phanerochaete chrysosporium (2E3Z; Nijikken et al., 2007), while those encoded in the Arabidopsis and rice genomes are labeled with the systematic names given by Xu et al. (2004) and Opassiri et al. (2006), respectively. One or two example proteins from each plant are given for each of the eight clusters of genes shared by Arabidopsis (At) and rice (Os) and the Arabidopsis-specific clusters At I (β-glucosidases) and At II (myrosinases), with the number of Arabidopsis or rice enzymes in each cluster given in parentheses. These sequences were aligned with all of the Arabidopsis and rice sequences in ClustalX (Thompson et al., 1997), the alignment was manually edited, all but representative sequences were removed, and the tree was calculated by the neighbor-joining method, bootstrapped with 1,000 trials, and then drawn with TreeView (Page, 1996). The grass plastid β-glucosidases, which are not represented in Arabidopsis and rice, are shown in the group marked “Plastid.” Percentage bootstrap reproducibility values are shown on internal branches where they are greater than 60%. Except those marked by asterisks, all external branches represent groups with 100% bootstrap reproducibility. To avoid excess complexity, those groups of sequences marked with asterisks are not monophyletic and represent more branches within the designated cluster than are shown. For a complete phylogenetic analysis of Arabidopsis and rice GH1 proteins, see Opassiri et al. (2006).     

SNP discovery from next-generation transcriptome sequencing data and their validation using KASP assay in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Single nucleotide polymorphisms (SNPs) are becoming the most amenable form of DNA-based molecular markers for genetic analysis. In hexaploid bread wheat (Triticum aestivum L.), it is difficult to discern true polymorphic SNPs due to homoeologous and paralogous genes. Two serial analysis of gene expression (SAGE) libraries were developed utilizing leaves from resistant plants carrying leaf rust resistance gene Lr28; one library was derived from leaves that were mock inoculated and the other was derived from leaves inoculated with the urediniospores of the leaf rust pathogen Puccinia triticina. Next-generation sequencing reads, after quality trimming and removal of fungal sequences, were mapped to wheat reference sequences at Ensembl Plants. CLC Genomics Workbench and Freebayes softwares were employed for SNP calling. A total of 611 SNPs were predicted to be common by both softwares, of which 207 varietal SNPs were identified by ConservedPrimer software. A subset of 100 SNPs was used for validation across 47 wheat genotypes using Kompetitive Allele Specific PCR (KASP) assay; 83 SNPs could be successfully validated. These SNPs were positioned on wheat subgenomes and chromosome arms. When functionally annotated, many sequences harboring SNPs showed homology to resistance and resistance-like genes listed in Plant Resistance Gene database (PRGdb) as well as pathogenesis-related (PR) and stress-responsive genes. The results of the present study involving discovery of SNPs associated with resistance to leaf rust, a major threat to wheat production worldwide, will be valuable for molecular breeding for rust resistance.     

Multifarious plant growth promoting characteristics of chickpea rhizosphere associated Bacilli help to suppress soil-borne pathogens

Wilt and root rot are the major constraints in chickpea production and very difficult to manage through agrochemicals. Hence, for an ecofriendly and biological management, 240 strains of Bacillus and Bacillus derived genera were isolated from chickpea rhizosphere, further narrowed down to 14 strains on the basis of in vitro production of indole acetic acid, siderophore, phosphate solubilization, hydrolytic enzymes and were evaluated for antagonism against chickpea pathogens (Fusarium oxysporum f. sp. ciceri race 1, F. solani and Macrophomina phaseolina). The strains were identified on the basis of physiological characters and 16S RNA gene sequencing. The genotypic comparisons of strains were determined by BOX-polymerase chain reaction profiles and amplified rDNA restriction analysis. These isolates were evaluated in greenhouse assay in which B. subtilis (B-CM191, B-CV235, B-CL-122) proved to be effective in reducing wilt incidence and significant enhancement in growth (root and shoot length) and dry matter of chickpea plants. PCR amplification of bacillomycin (bmyB) and β-glucanase genes suggests that amplified genes from the Bacillus could have a role to further define the diversity, ecology, and biocontrol activities in the suppression of soil-borne pathogens.     

Identification of Novel Regulatory Small RNAs in Acinetobacter baumannii

Small RNA (sRNA) molecules are non-coding RNAs that have been implicated in regulation of various cellular processes in living systems, allowing them to adapt to changing environmental conditions. Till date, sRNAs have not been reported in Acinetobacter baumannii (A. baumannii), which has emerged as a significant multiple drug resistant nosocomial pathogen. In the present study, a combination of bioinformatic and experimental approach was used for identification of novel sRNAs. A total of 31 putative sRNAs were predicted by a combination of two algorithms, sRNAPredict and QRNA. Initially 10 sRNAs were chosen on the basis of lower E- value and three sRNAs (designated as AbsR11, 25 and 28) showed positive signal on Northern blot. These sRNAs are novel in nature as they do not have homologous sequences in other bacterial species. Expression of the three sRNAs was examined in various phases of bacterial growth. Further, the effect of various stress conditions on sRNA gene expression was determined. A detailed investigation revealed differential expression profile of AbsR25 in presence of varying amounts of ethidium bromide (EtBr), suggesting that its expression is influenced by environmental or internal signals such as stress response. A decrease in expression of AbsR25 and concomitant increase in the expression of bioinformatically predicted targets in presence of high EtBr was reverberated by the decrease in target gene expression when AbsR25 was overexpressed. This hints at the negative regulation of target genes by AbsR25. Interestingly, the putative targets include transporter genes and the degree of variation in expression of one of them (A1S_1331) suggests that AbsR25 is involved in regulation of a transporter. This study provides a perspective for future studies of sRNAs and their possible involvement in regulation of antibiotic resistance in bacteria specifically in cryptic A. baumannii.