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Rise in global population has increased the food demands and thus the competition among farmers to produce more and more. In the race to obtain higher productivity, farmers have resorted to injudicious farming practices that include the reckless use of nitrogenous fertilizers and intensive cropping on farmlands. Such practices have paved the path for large scale infestations of crops and plants by pests thus affecting the plant productivity and crop vigour. There are several traditional techniques to control pest infestations in plants such as the use of chemical or bio-pesticides, and integrated pest management practices which face several drawbacks. Delivery of gene/nucleic acid in plants through genetic engineering approaches is a more sustainable and effective method of protection against pests. The technology of RNA interference (RNAi) provides a sustainable solution to counter pest control problems faced by other traditional techniques. The RNAi technique involves delivery of dsDNA/dsRNA or other forms of nucleic acids into target organisms thereby bringing about gene silencing. However, RNAi is also limited to its use because of their susceptibility to degradation wherein the use of cationic polymers can provide a tangible solution. Cationic polymers form stable complexes with the nucleic acids known as "polyplexes", which may be attributed to their high positive charge densities thus protecting the exogenous nucleic acids from extracellular degradation. The current paper focuses on the utility of nucleic acids as a sustainable tool for pest control in crops and the use of cationic polymers for the efficient delivery of nucleic acids in pests thus protecting the plant from infestations.

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MOTIVATION: Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. RESULTS: We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. AVAILABILITY AND IMPLEMENTATION: Software implementation is available from https://github.com/jttoivon/moder2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Most transcription factors (TFs) can bind to a population of sequences closely related to a single optimal site. However, some TFs can bind to two distinct sequences that represent two local optima in the Gibbs free energy of binding (ΔG). To determine the molecular mechanism behind this effect, we solved the structures of human HOXB13 and CDX2 bound to their two optimal DNA sequences, CAATAAA and TCGTAAA. Thermodynamic analyses by isothermal titration calorimetry revealed that both sites were bound with similar ΔG. However, the interaction with the CAA sequence was driven by change in enthalpy (ΔH), whereas the TCG site was bound with similar affinity due to smaller loss of entropy (ΔS). This thermodynamic mechanism that leads to at least two local optima likely affects many macromolecular interactions, as ΔG depends on two partially independent variables ΔH and ΔS according to the central equation of thermodynamics, ΔG = ΔH - TΔS.

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Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.

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The majority of CpG dinucleotides in the human genome are methylated at cytosine bases. However, active gene regulatory elements are generally hypomethylated relative to their flanking regions, and the binding of some transcription factors (TFs) is diminished by methylation of their target sequences. By analysis of 542 human TFs with methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment), we found that there are also many TFs that prefer CpG-methylated sequences. Most of these are in the extended homeodomain family. Structural analysis showed that homeodomain specificity for methylcytosine depends on direct hydrophobic interactions with the methylcytosine 5-methyl group. This study provides a systematic examination of the effect of an epigenetic DNA modification on human TF binding specificity and reveals that many developmentally important proteins display preference for mCpG-containing sequences.

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The global emergence of Chikungunya virus (CHIKV) infection is alarming and currently there is no licensed vaccine or antiviral treatment available to mitigate this disease. CHIKV infection typically results in high viral load with an outcome of high fever, skin rashes, muscle pain, and sequelae of prolonged arthritis, which occurs in >90% of the infected cases. In this study, using biochemical pull-downs, mass-spectrometry, and microscopic imaging techniques, we have identified novel interactions between CHIKV nsP3 or nsP4 proteins with the host stress-pathway chaperone HSP-90 protein. Indeed, silencing of HSP-90 transcripts using siRNA disrupts CHIKV replication in cultured cells. Furthermore, drugs targeting HSP-90, such as commercially available geldanamycin, as well as other specific HSP-90 inhibitor drugs that had been obtained from a purinome mining approach (HS-10 and SNX-2112) showed dramatic reduction in viral titers and reduced inflammation in a CHIKV mouse model of severe infection and musculopathy. The detailed study of the underlying molecular mechanism of these viral and host protein interactions may provide a platform to develop novel therapeutics against CHIKV infection.

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