Identification of Stress-induced miRNA and their targets in Solanum lycopersicum

Background: MicroRNAs (miRNAs) are small endogenous non-coding RNAs with a length of roughly 18– 22 nucleotides long. They play important roles in different natural procedures. As of recently, little is known about their role in plant stress. The use of computational homology-based techniques for expressed sequence tags (ESTs) with the Ambros exact method and other structural component criteria is a rational approach for the disclosure and confinement of conserved miRNAs from several species. Aim: The study aimed to identify novel stress-induced miRNAs in tomatoes, using a computational approach. Methods: We used previously known sequences of mature miRNAs of different plants, like Vitis, Oryza, Triticum, Sorghum for the prediction of potential novel miRNAs in tomato. The hairpin structures of miRNAs were predicted, their functional annotations were performed, and the targeted genes were identified. Results: Only two miRNAs were predicted and validated to be novel belonging to the family of miRNA1301. The expression analysis of the novel miRNAs showed their significant role in the growth and development of the respective tissues. We have found that the miRNAs in the leaf are highly conserved related to the seed. This discovery significantly broadens the understanding of the functions of miRNA in tomatoes. MiR1301 was found to play role in transcriptional activation and transporter activity, and also involve in ubiquitin-protein ligase translation and transcription. Total 7 potential targets were predicted for the two identified miRNAs. Conclusion: Identification of new miRNAs and their target genes will establish the potential roadmap for the understanding of the core regulatory interactions during abiotic stress in S. Lycopersicum.

Structural changes during glass formation extracted by computational homology with machine learning

The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states.

A mass spectrometry guided approach for the identification of novel vaccine candidates in gram-negative pathogens

Vaccination is the most effective method to prevent infectious diseases. However, approaches to identify novel vaccine candidates are commonly laborious and protracted. While surface proteins are suitable vaccine candidates and can elicit antibacterial antibody responses, systematic approaches to define surfomes from gram-negatives have rarely been successful. Here we developed a combined discovery-driven mass spectrometry and computational strategy to identify bacterial vaccine candidates and validate their immunogenicity using a highly prevalent gram-negative pathogen, Helicobacter pylori, as a model organism. We efficiently isolated surface antigens by enzymatic cleavage, with a design of experiment based strategy to experimentally dissect cell surface-exposed from cytosolic proteins. From a total of 1,153 quantified bacterial proteins, we thereby identified 72 surface exposed antigens and further prioritized candidates by computational homology inference within and across species. We next tested candidate-specific immune responses. All candidates were recognized in sera from infected patients, and readily induced antibody responses after vaccination of mice. The candidate jhp_0775 induced specific B and T cell responses and significantly reduced colonization levels in mouse therapeutic vaccination studies. In infected humans, we further show that jhp_0775 is immunogenic and activates IFNγ secretion from peripheral CD4+ and CD8+ T cells. Our strategy provides a generic preclinical screening, selection and validation process for novel vaccine candidates against gram-negative bacteria, which could be employed to other gram-negative pathogens.

Modeling Associations

In this work, the authors develop signaling models based on ideas from homology and discuss how to design a model of signal space which decomposes the incoming signals into classes of progressively higher levels of associative meaning. The tools needed are illustrated with a simple approach using standard linear algebra processing but this is just a simple point of departure into a more complex and potentially more useful signal processing toolbox involving computational homology. These ideas then lead to models of grammar for signals in terms of cascaded barcode signal representations.