An expressed sequence tag (EST) approach was used to investigate gene expression in the unicelluar marine alga Emiliania huxleyi. We randomly selected 3000 EST sequences from a cDNA library of transcripts expressed under conditions promoting coccolithogenesis. Cluster analysis and contig assembly resulted in a unigene set of approximately 1523 ESTs. Only 36% of the unique sequences exhibited significant homology to sequences in GenBank. Of particular interest were the numerous transcripts with homology to sequences associated with sexual reproduction and calcium homeostasis in other unicellular and multicellular organisms. The majority of ESTs (64%) had little or no significant sequence homology to entries in GenBank, suggesting a potential for further novel gene discovery. The catalog of ESTs reported herein represents a significant increase in the limited sequence information currently available for E. huxleyi and should make the coccolithophorid more accessible to powerful genomics and postgenomics technologies.
Ahstract-A hybrid differential evolution-binary particle swarm optimization (DE-BPSO) algorithm is proposed as a feature selection algorithm in the development of quantitative structure-activity relationship (QSAR) models. DE is used to evolve the velocities of the particle swarm from which a series of rules are used to determine the discrete values of the position vectors which form chemical descriptor subsets. These descriptor subsets are then used to develop models for QSAR analysis. DE BPSO was found to outperform the standalone BPSO algorithm. The DE-BPSO algorithm was then used to develop multiple linear regression models for the analysis of aryl ;3-diketo acid compounds for the inhibition of mV-l integrase. This model highlights the significance of hydrophobicity and partial positive charges of the hydrogen atoms on the molecular surface in influencing the biological activities of these compounds for the inhibition of mV-l integrase.
Small RNAs (smRNAs) control a variety of cellular processes by silencing target genes at the transcriptional or post-transcription level. While extensively studied in plants, relatively little is known about smRNAs and their targets in marine phytoplankton, such as Emiliania huxleyi (E. huxleyi). Deep sequencing was performed of smRNAs extracted at different time points as E. huxleyi cells transition from logarithmic to stationary phase growth in batch culture. Computational analyses predicted 18 E. huxleyi specific miRNAs. The 18 miRNA candidates and their precursors vary in length (18–24 nt and 71–252 nt, respectively), genome copy number (3–1,459), and the number of genes targeted (2–107). Stem-loop real time reverse transcriptase (RT) PCR was used to validate miRNA expression which varied by nearly three orders of magnitude when growth slows and cells enter stationary phase. Stem-loop RT PCR was also used to examine the expression profiles of miRNA in calcifying and non-calcifying cultures, and a small subset was found to be differentially expressed when nutrients become limiting and calcification is enhanced. In addition to miRNAs, endogenous small RNAs such as ra-siRNAs, ta-siRNAs, nat-siRNAs, and piwiRNAs were predicted along with the machinery for the biogenesis and processing of si-RNAs. This study is the first genome-wide investigation smRNAs pathways in E. huxleyi. Results provide new insights into the importance of smRNAs in regulating aspects of physiological growth and adaptation in marine phytoplankton and further challenge the notion that smRNAs evolved with multicellularity, expanding our perspective of these ancient regulatory pathways.
Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper we present a thorough sentiment analysis of tweets related to education available on twitter platform and deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months. Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the change in education system over the world. Using Natural language tool kit (NLTK) functionalities and Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment altered due to the changes with regard to the education system. Thus, we would like to present a better understanding of people’s sentiment on education while trying to cope with the pandemic in such unprecedented times.
The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.
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