Does toll-like receptor-3 (TLR-3) have any role in Indian AMD phenotype?

In our previous study, the mouse double minute 2 (MDM2) was identified as one of the leading genes that promote the metastasis of pancreatic cancer (PC). However, the mechanism by which MDM2 promotes metastasis of PC is not understood. In this study, we show that down-regulation of MDM2 through lentivirus-mediated RNA interference could also suppress in vitro proliferation and in vivo tumor growth, and led to an obvious inhibition of both in vitro invasion and in vivo live metastases of SW1990HM cells which had an over-expression of MDM2 and a higher metastatic potential. Moreover, we also show that the down-regulation of MDM2 induced a significant decrease in MMP9, Ki-67 and increase in P53, E-Cadherin expression, and results in an altered expression of genes involved in metastasis, apoptosis, and cell proliferation. Our results suggest that MDM2 plays an important role in metastasis as well as tumor growth of PC. MDM2 could be a hopeful target for the control of PC.     

Data Mining Strategies to Improve Multiplex Microbead Immunoassay Tolerance in a Mouse Model of Infectious Diseases

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.     

Influence of Adhesion Force on icaA and cidA Gene Expression and Production of Matrix Components in Staphylococcus aureus Biofilms

The majority of human infections are caused by biofilms. The biofilm mode of growth enhances the pathogenicity of Staphylococcus spp. considerably, because once they adhere, staphylococci embed themselves in a protective, self-produced matrix of extracellular polymeric substances (EPSs). The aim of this study was to investigate the influence of forces of staphylococcal adhesion to different biomaterials on icaA (which regulates the production of EPS matrix components) and cidA (which is associated with cell lysis and extracellular DNA [eDNA] release) gene expression in Staphylococcus aureus biofilms. Experiments were performed with S. aureus ATCC 12600 and its isogenic mutant, S. aureus ATCC 12600 Δpbp4, deficient in peptidoglycan cross-linking. Deletion of pbp4 was associated with greater cell wall deformability, while it did not affect the planktonic growth rate, biofilm formation, cell surface hydrophobicity, or zeta potential of the strains. The adhesion forces of S. aureus ATCC 12600 were the strongest on polyethylene (4.9 ± 0.5 nN), intermediate on polymethylmethacrylate (3.1 ± 0.7 nN), and the weakest on stainless steel (1.3 ± 0.2 nN). The production of poly-N-acetylglucosamine, eDNA presence, and expression of icaA genes decreased with increasing adhesion forces. However, no relation between adhesion forces and cidA expression was observed. The adhesion forces of the isogenic mutant S. aureus ATCC 12600 Δpbp4 (deficient in peptidoglycan cross-linking) were much weaker than those of the parent strain and did not show any correlation with the production of poly-N-acetylglucosamine, eDNA presence, or expression of the icaA and cidA genes. This suggests that adhesion forces modulate the production of the matrix molecule poly-N-acetylglucosamine, eDNA presence, and icaA gene expression by inducing nanoscale cell wall deformation, with cross-linked peptidoglycan layers playing a pivotal role in this adhesion force sensing.     

Metagenomics of petroleum muck: revealing microbial diversity and depicting microbial syntrophy

Present study attempts in revealing taxonomic and functional diversity of microorganism from petroleum muck using metagenomics approach. Using Ion Torrent Personal Genome Machine, total of 249 Mb raw data were obtained which was analysed using MG-RAST platform. The taxonomic analysis revealed predominance of Proteobacteria with Gammaproteobacteria as major class and Pseudomonas stutzeri as most abundant organism. Several enzymes involved in aliphatic and aromatic hydrocarbon degradation through both aerobic and anaerobic routes and proteins related to stress response were also present. Comparison of our metagenome with the existing metagenomes from oil-contaminated sites and wastewater treatment plant indicated uniqueness of this metagenome taxonomically and functionally. Based on these results a hypothetical community model showing survival and syntrophy of microorganisms in hydrocarbon-rich environment is proposed. Validation of the metagenome data was done in three tiers by validating major OTUs by isolating oil-degrading microbes, confirmation of key genes responsible for hydrocarbon degradation by Sanger sequencing and studying functional dynamics for degradation of the hydrocarbons by the muck meta-community using GC–MS.     

Perturbation of the interaction between Gal4p and Gal80p of the Saccharomyces cerevisiae GAL switch results in altered responses to galactose and glucose

In S. cerevisiae, following the Whole Genome Duplication (WGD), GAL1‐encoded galactokinase retained its signal transduction function but lost basal expression. On the other hand, its paralogue GAL3, lost kinase activity but retained its signalling function and basal expression, thus making it indispensable for the rapid induction of the S. cerevisiae GAL switch. However, a gal3Δ strain exhibits delayed growth kinetics due to the redundant signalling function of GAL1. The subfunctionalization between the paralogues GAL1 and GAL3 is due to expression divergence and is proposed to be due to the alteration in the Upstream Activating Sequences (UAS_G). We demonstrate that the GAL switch becomes independent of GAL3 by altering the interaction between Gal4p and Gal80p without altering the configuration of UAS_G. In addition to the above, the altered switch of S. cerevisiae loses ultrasensitivity and stringent glucose repression. These changes caused an increase in fitness in the disaccharide melibiose at the expense of a decrease in fitness in galactose. The above altered features of the ScGAL switch are similar to the features of the GAL switch of K. lactis that diverged from S. cerevisiae before the WGD.     

Population growth potential of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) on tomato,potato, and eggplant

The influence of host plant on population dynamics of an invasive pest, Tuta absoluta was studied on three economically important solanaceous crops. Experiments were conducted in laboratory (29 ± 0.5°C, 75 ± 5% RH and a photoperiod of 14:10 hr [L:D]) using tomato (Solanum lycopersicum L.), potato (Solanum tuberosum L.) and eggplant (Solanum melongena L.). Results indicated that intrinsic rate of increase (r), finite rate of increase (λ) and net reproductive rate (R₀) were higher, and mean generation time (T) was the shortest on tomato. Results suggested that T. absoluta developed on all the three plants, and tomato plant was most preferred one. Results suggested that T. absoluta has a potential to become a serious pest on potato and even on eggplant under favourable conditions. We used the life tables of 0.025th and 0.975th percentiles of bootstraps to project the uncertainty of population growth, a new concept.     

Identification of Optimum Sequencing Depth Especially for De Novo Genome Assembly of Small Genomes Using Next Generation Sequencing Data

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6–40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.