This researcher was invited to give the talk at IWMEDIC 2016, IV International Workshop. Please, follow the link to see the video presentation uploaded (or to be uploaded soon) at the workshop official site: https://sciforum.net/conference/mol2net-02/iwmedic-04

Pathogenic V. parahaemolyticus strains are able to adapt from environmental to laboratory isolation conditions that simulate some conditions upon infection in humans. To explore this adaptation, we determined the differential expression by RNAseq when growing in conditions for clinical isolation “I” (LB, NaCl 0.9% and 37 ºC plus bile acid) referred to those in their natural environment “E” (LB, NaCl 3% and 12 ºC). Analysis or the reads obtained after sequencing the RNA showed that 77% of the annotated genome was expressed in isolation (3841 genes) condition and 84% (4143 genes) in environmental condition.

Our transcriptome analysis revealed that among the 50 genes expressed in higher amount in each conditions, 21 were differentially expressed; 4 were downregulated and 17 upregulated in isolation condition; 14 corresponded to coding sequences (CDS), 5 to small-RNA and 3 to tRNA.

The development of reactivity descriptor based on DFT has provided the scientific community with a formal framework for the understanding of many empirical chemical concepts. The Fukui function is a local reactivity descriptor that supplies information about the reactive sites of a molecule, thus predicting the region where a molecule is more prone to suffering an electrophilic, nucleophilic or radical attack. TAFF is a program that carries out the topological analysis of the Fukui function in a fast, simple and efficient way; acting as a pipeline between the user and the external Multiwfn software, with the aim of making easy the analysis of a selected molecule.

Lignin peroxidase (LiP), a fungal heme-containing peroxidase, first discovered in the basidiomycete Phanerochaete chrysosporium, plays an important role in the degradation of lignin and lignin model compounds^1-3 due to its high redox potential. Veratryl alcohol (VA), is a secondary metabolite of the fungus P. chrysosporium and is the main substrate of LiP. Also, VA acts as a redox mediator in the oxidation of lignin and other phenolic and non-phenolic compounds, after being oxidized to a radical species (VA^•+) by Trp171, a catalytic residue located at the protein surface.^{4, 5}  In a previous report, we explored through molecular docking, MD and MM-GBSA simulations the way VA (in its neutral state) interacted with Trp171 and how it was stabilized by other residues at the protein surface.⁶ Furthermore, VA in a neutral and cationic state, was used to run long molecular dynamics of 1µs for LiP-VA^•+ and LiP-VA complexes with the Desmond software. Interaction profiles for each state of VA were obtained showing that there exists a clear difference in the interaction dynamics of both species. For a further understanding of the stabilization mechanism of VA^•+ at the protein surface, in this work are reported new MD simulations of 1 µs that take into account a higher substrate concentration and explore its affinity by WT LiP and the E168Q and D264N mutants.

Nowadays, the temporal regularity of bloom phenology of Chilean native species of interest in beekeeping has been affected by abiotic stress situations such as extemporaneous rainfall and irregular temperature. A predictive system for diverse blooming states of these species, it would reduce significantly the uncertainty of beekeepers, which will use this information to optimize their production decisions such as disease control and hive development in specific periods.

Here, we present a first approach for prediction of bloom phenology of Quillaja saponaria, a native Chilean tree commonly exploited by beekeepers. The system considers three states, the beginning of flowering, peak flowering, and terminal state.

Artificial Neural Networks (ANNs) coupled with Genetic Algorithms (GA) were trained with historical information of three farms of honey production in central area of Chile. The data gathered includes: i) Meteorological data such as temperature, humidity, radiation, and precipitations, and ii) blooming records of previous seasons in the studied areas, sampled weekly considering the previously defined three blooming states. Genetic algorithms were used to adjust the initial training parameters of the ANNs.

The results are quite promising. The predictive models exhibited average errors lower than 7 days in most of the evaluated situations, measured using Root Mean Square Error in each case. This is similar to the sampling frequency of blooming stages on the historical records, which validates the quality of the obtained predictions.

Chargaff’s second parity rule holds that for each of the two DNA strands in a genome, the %A is similar to %T and %G is similar to %C. Although the validity of the second rule is still in debate and the biological cause is unknown, a generalized form of the second parity rule has already been proposed. The generalization states that the frequency of a string of a particular length is similar to the frequency of its reverse complement in the same strand. In a previous work, we have developed a statistical hypothesis test for the generalized second Chargaff parity rule for any particular string in a genome. One obstacle to test all available genomes with this statistical test was the efficiency of the computational implementation. In this work, we circumvent this issue, implementing our statistical test in an efficient and multi-processing computer program. The development was carried out in Python, using packets for handling sequences in FASTA format and the required calculations. For each input genome, a database SQLite is generated holding the absolute frequencies of all existing strings in the genome up to a user defined length, and their reverse complements. Multiple sequences are accepted as input, each sequence being analyzed in one CPU. Thus, a bacterial genome, ~4M characters, with 4 sequences takes about 14 seconds to process entirely in 4 CPUs. This computer program will allow our test to be carried out in all available completely sequenced genomes and assess the validity of Chargaff’s second parity rule and its generalized version.

Nitrogen is an essential macronutrient for plants and other living organisms. The main source of nitrogen for plants in agricultural soils is nitrate (NO₃^-). This ion is transported through specific nitrate transporters located in the plant cell plasma membrane. In addition to its role in metabolism, NO₃^- can also act as a signal to regulate many biological processes in plants. The Nitrate Transporter 1.1 (NRT1.1) protein is a dual-affinity transporter that has been shown to act as a nitrate sensor. Recent studies have shown that NRT1.1 can also transport the phytohormone auxin. Using molecular modelling techniques, we intend to determine the molecular binding sites of nitrate and auxin in the Arabidopsis NRT1.1 transporter. We aim to search for residues that differentiate the functionality of NRT1.1-nitrate and NRT1.1-auxin. To achieve our goal we performed molecular docking of auxin in the Arabidopsis NRT1.1 crystallographic structure and molecular dynamics simulations of NRT1.1-nitrate and NRT1.1-auxin complexes. Additionally, the same studies were performed over the nitrate transporter NRT1.2 with the aim to compare both transporters and begin to understand the transport mechanism of both nutrients.

Hidden Markov Modeling (HMM) is a statistical technique to represent and predict a sequence of hidden features in a sequence of observed data. For example, a HMM could distinguish words in a stream of sounds. This technique can be very useful in Bioinformatic Sequence Analysis as public databases count with an ever growing number of sequences of nucleotides and aminoacids. In those sequences we would like to predict “hidden” functional, structural and evolutionary features. For example, we would like to predict functional motives in a gene sequence. Or, we would like to predict the secondary structure of a protein sequence.

In order to teach and learn this technique, we developed a Java based software that solves HMM algorithms once the user inputs the model. The graphical interface guides the learner through five steps: Steps 1 to 3, requesting the main elements of a HMM; 4. Asking the user which one of the three questions (related to algorithms Forward, Backward and Viterbi) is to be answered and; 5. Reporting the solutions step-by-step in PDF format document. In the case the user doesn’t know the information the software is requesting, it provides an option in which the program fills the unknown box with random generated numbers. This way, the student can go on and learn by examining the solution of the model.

This first version allows the Bioinformatics teacher to focus the HMM classes on modeling the biology and in the problem solving practice instead of on the mathematical theory that leads to the algorithm formulae.

 This researcher was invited to give the talk at IWMEDIC 2016, IV International Workshop. Please, follow the link to see the video presentation uploaded (or to be uploaded soon) at the workshop official site: https://sciforum.net/conference/mol2net-02/iwmedic-04

This researcher was invited to give the talk at IWMEDIC 2016, IV International Workshop. Please, follow the link to see the video presentation uploaded (or to be uploaded soon) at the workshop official site: https://sciforum.net/conference/mol2net-02/iwmedic-04