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ISCB-LA SOIBIO EMBnet 2018 | Nov 5 – 9, 2018 | Viña del Mar, Chile | HOME

Oral Presentations

Schedule subject to change
Wednesday, November 7th
11:00 AM-11:15 AM
Structural aspects of antibody recognition: a moments-based approach to shape and electrostatics
  • Lorenzo Di Rienzo, Sapienza University of Rome, Italy
  • Edoardo Milanetti, Center for Life Nanoscience, Istituto Italiano di Tecnologia, Rome, Italy, Italy
  • Rosalba Lepore, Swiss Institute of Bioinformatics, Biozentrum,University of Basel, Klingelbergstrasse 50–70, CH-4056 Basel, Switzerland, Italy

Abstract: Show

The interaction between antibody and antigen is a central event during the immune response and its accurate description is crucial to our understanding of molecular recognition processes; indeed, given the very high affinity and specificity that antibodies exhibit towards their antigens, they offer a paradigmatic model of molecular binding. Antibody specificity arises from the sequence and structure variability of its binding site, which is composed by six regions, appropriately named “hypervariable loops” or Complementary Determining Regions (CDRs).
Here we present a novel superposition-free method, able to accurately compare antibody and antigen surfaces according to shape and physico-chemical characteristics of their binding sites. The method adopts a moments-based representation of the surface in which Zernike polynomials are used as the base of the expansion.
We show that using a Zernike-based classification of the binding sites we can predict, given the structure of an antibody, the nature of the recognized antigen with an overall accuracy of 78%. Moreover, based on the analysis of both paratope and epitope surfaces, we show that a maximum shape complementarity is reached considering short-range interactions while longer-range interactions account for optimal electrostatic complementarity. Based on these findings, we tested the possibility to predict epitope regions using Zernike-based complementarity measures with encouraging precision. These results represent a step towards the very elusive goal of predicting antibody specificity and further shed light on the well known, but still intriguing, ability of antibodies to find and bind their antigen.

11:15 AM-11:30 AM
Computational prediction of disease-causing variants from the molecular to the interactome scale
  • Fabrizio Pucci, Université Libre de Bruxelles, Belgium
  • François Ancien, Université Libre de Bruxelles, Belgium
  • Maxime Godfroid, Christian-Albrechts-Universität Kiel, Germany
  • Ibrahim Tanyalcin, Vrije Universiteit Brussel, Belgium
  • Wim Vranken, Vrije Universiteit Brussel, Belgium
  • Marianne Rooman, Université Libre de Bruxelles, Belgium

Abstract: Show

To understand why some missense mutations in the human exome cause disease phenotypes while others are neutral, their impact needs to be investigated at the molecular, macromolecular and biological network levels. We present a multi-scale approach for predicting variants that cause diseases due to their effect on protein stability, function or protein-protein interaction network. At the molecular level, the distance between mutated residues and annotated functional sites was used to set up a predictor identifying disease-causing mutations that affect function. At the protein level, statistical potentials were used to estimate the folding free energy of individual proteins and protein-protein complexes, and to predict the change in protein stability upon mutation and in binding affinity for an interacting partner. These free energies were used to develop a neural network-based classifier, called SNPMuSiC, that predicts with high accuracy the subset of mutations that are deleterious because of stability issues. At the interactome level, the protein-protein affinity predictor was applied to study the robustness of the network against mutations and to predict the variants’ edgotype, i.e. whether they induce a node removal, an edgetic perturbation or have no effect. The different predictions were successfully assessed on the basis of variant annotations and were shown to have particularly high positive predictive values. The systematic characterization of the impact of variants is a fundamental step in understanding the genotype-phenotype relationship and will be an invaluable asset for the choice of personalized therapeutic strategies.

11:30 AM-11:45 AM
Pharmacophore-based virtual screening of Peroxizome (Pex)14 inhibitors to combat Trypanosomiasis
  • Bruno Alejandro Del Carpio Martinez, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Eymi Gladys Cárcamo Rodriguez, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Nicol Flores-Mancilla, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Kiara Alejandra García Bustos, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Luis Alfredo Valencia Herencia, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Christian Zevallos-Delgado, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Diego E. Valencia, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru
  • Badhin Gomez Valdez, Centro de Investigación en Ingeniería Molecular - Universidad Católica de Santa Maria, Peru

Abstract: Show

Trypanosomiasis is one of the infectious diseases caused by parasites of the genus Trypanosoma. In humans, the main causes of this disease are due to the infection of the parasite Trypanosoma brucei and Trypanosoma Cruzi. Currently, a cure for the phases of parasitic development within human beings is being sought. The aim of this study was to determine the interaction capacity of inhibitors against the development of peroxisome by inhibiting the PEX 14 protein in both Trypanosoma species. The presence of Pex14 inhibitors was determined, according to the study carried out by Dawidowski, M. et al. (PDB ID: 5L87), where the presence of inhibitors of union between the PEX14 protein and the PEX5 was identified as 5-(1H-indol-3-ylmethyl)-1-methyl-N-(naphthalen-1-ylmethyl)-6,7-dihydro-4H-pyrazolo[4,3-c]pyridine-3-carboxamide. 117 analogs were obtained using ChemBL. The tertiary structure of the PEX14 protein carried out using I-Tasser in Trypanosoma cruzi (NCBI Accession: PBJ75185.1) and Trypanosoma brucei (NCBI Accession: CAD54628.1). Molecular coupling was performed using the Virtual Screening method with the IGemdock software. Protein stabilization was performed on a Dipalmitoylphosphatidylcholine (DPPC) membrane per 1000 ps. The results demonstrate a high stability of the membrane PEX14 proteins, in addition to their stability of the binding of the inhibitors by the active blocking site formed by the Phenylalanine residues, suggested by Dawidowski, M. et al. It is important to observe how this type of drugs can limit the interaction of PEX14 with other proteins that trigger the process of glycosoma formation.

11:45 AM-12:00 PM
New robust Bayesian methods detecting Allelic Specific Expression (ASE) and associating it with external phenotypes
  • Inti Pedroso, Universidad Mayor, Chile

Abstract: Show

We developed new statistical methodologies to identify Allelic Specific Expression (ASE) within a Bayesian framework. Current methodologies provide restrictive null distributions to test the significance of ASE, ignore differences in read depth and are generally restricted to comparing individuals alleles or haplotypes (F1 individuals). We tackle this three challenges and propose new empirical Bayes procedures using mixtures of Beta-Binomial distributions to estimate the null distributions that account for an individual-wise factors, such as read depth, while detecting ASE. Additionally, we employ exact equations to derive posterior distribution of ASE for each gene/transcript and to obtain robust probabilities of ASE. Our framework does not rely on knowledge of the specific alleles being analyzed and allows as to compare ASE patterns it across highly genetically divergent individuals. To that end, we develop methodologies to associated ASE with sample’s metadata. We showcase out new methodologies with applications to RNA-seq data associated with social behavior on the bumblebee Bombus terrestris, on human disease and mouse samples. Our new methodologies provide statistically sound and robust procedures to identify ASE patterns and associate them with individual’s phenotypes enabling systematic discovery of disease biomarkers and genes underlying complex traits on genetically diverse species. Our analysis framework is implemented on python package.

2:30 PM-3:00 PM
Genome-scale analysis of Wolbachia metabolism
  • Natalia Jimenez, University of Chile, Chile
  • Ziomara Gerdtzen, Universidad de Chile, Chile
  • Alvaro Olivera-Nappa, Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Chile
  • J. Cristian Salgado Herrera, University of Chile, Chile
  • C. Conca, Department of Mathematical Engineering, University of Chile, Chile

Abstract: Show

Background: Wolbachia are alpha-proteobacteria known to infect arthropods,
which are of interest for disease control since they have been associated with
improved resistance to viral infection. Although several genomes for different
strains have been sequenced, there is little knowledge regarding the relationship
between this bacterium and their hosts, particularly on their dependency for
survival. Motivated by the potential applications on disease control, we developed
genome-scale models of four Wolbachia strains known to infect arthropods:
wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop
(Drosophila melanogaster).
Results: The obtained metabolic reconstructions exhibit a metabolism relying
mainly on amino acids for energy production and biomass synthesis. A gap
analysis was performed to detect metabolic candidates which could explain the
endosymbiotic nature of this bacterium, finding that amino acids, requirements
for ubiquinone precursors and provisioning of metabolites such as riboflavin could
play a crucial role in this relationship.
Conclusions: This work provides the first Wolbachia metabolic reconstruction,
directed towards an improved understanding of the relationship with its host and
the development of new approaches for control of the spread of arboviral
diseases. This approach, where metabolic gaps are key objects of study instead of
just additions to complete a model, could be applied to other endosymbiotic
bacteria of interest

3:00 PM-3:15 PM
The site-specific amino acid preferences of homologous proteins depend on sequence divergence
  • Evandro Ferrada, Universidad Mayor, Chile

Abstract: Show

The propensity of protein sites to be occupied by any of the 20 amino acids is known as site-specific amino acid preferences (SSAP). Under the assumption that SSAP are conserved among homologs, they can be used to parameterize evolutionary models for the reconstruction of accurate phylogenetic trees. However, simulations and experimental studies have not been able to fully assess the relative conservation of SSAP as a function of sequence divergence between homologs. Here we implement a computational procedure to predict a protein's SSAP based on changes in thermodynamic stability upon mutation. An advantage of this computational approach is that it allows us to interrogate a large and unbiased sample of homologous structures, over the entire spectrum of sequence divergence, and under selection for the same molecular trait. We show that computational predictions have reproducibilities that resemble those obtained in experimental replicates, and can largely recapitulate the SSAP observed in a large-scale mutagenesis experiment. Our results support recent experimental reports on the conservation of SSAP of related homologs, with a slowly increasing fraction of up to 15% of different sites at sequence distances lower than 70%. However, our observations also suggest that even under the sole contribution of thermodynamic stability, highly divergent homologs can accumulate up to 30% of different sites. We show that this relation holds for homologs of diverse sizes and structural classes. Analyses of residue contact networks suggest that a major contributing factor to differences in SSAP is the increasing accumulation of structural deviations resulting from sequence divergence.

3:15 PM-3:30 PM
Implications of the Recent Explosion In Police Use Of Genealogy Databases To Open Cold Cases By Identifying Suspects From DNA Voluntarily Contributed By Their Distant Relatives
  • Howard Cash, Gene Codes Forensics, United States

Abstract: Show

Forensic DNA profiling is a cornerstone of modern criminalistics. Recent laboratory and computational advances increase the efficiency and accuracy of analyzing regional ancestry, and identifying more and more distant relatives. "Recreational genealogy" has been aggressively marketed in recent years, sometimes by commercial companies that sequence and analyze DNA for a below-cost price to participants, and then further analyze and aggregate data for license to third party researchers.

We know from history that genetic information can and has been abused. This is one reason why permissible uses of law enforcement's DNA databases have been carefully circumscribed by the legislation that created them. However, these protections are less efficacious than they were only a few years ago; Public and private genealogy databases are not controlled by the same national legislation, procedures and case law.

In April, 2018, a four-decade-old investigation into a serial murderer/rapist/burglar generated an arrest based on the genealogy site GEDMatch, where genome-based kinship analysis proposed as many as 20 individuals who could share the same great-great-great grandparents as the Golden State Killer. In a steady stream since then, cold cases have been reopened with suspects based on relationships such as "first cousin, once removed" and "two second cousins." As of this writing, it is not known if the relatives of the accused have been notified that their voluntary DNA contributions lead to the arrest of a family member. Professionals in genetics, genealogy and bioinformatics should join the debate over the ethics of this analysis of personal biological data.

3:30 PM-3:45 PM
Analysis of variations among Mycobacterium tuberculosis isolates with the MinHash algorithm
  • Leonardo Lucianna, Universidad de Buenos Aires. Maestría en Explotación de Datos y Descubrimiento de Conocimiento, Argentina
  • Marcelo Soria, Universidad de Buenos Aires. Facultad de Agronomía, Argentina

Abstract: Show

The accurate genotyping of Mycobacterium tuberculosis complex (MTC) isolates is of paramount importance in epidemiological studies. Several methodologies exist that differentiate between similar MTC strains based on variations in specific and/or repetitive genomic regions. Nonetheless, the ever-decreasing cost of high-throughput DNA sequencing opened the gates for alignment-free methods that take advantage of the whole genome sequence. In this work we assess the applicability of the MinHash algorithm to analyze whole genome sequences of MTC strains. MinHash is a local sensitivity hashing scheme originally designed for accurate and fast document comparison through calculation of the Jaccard distance, which has gained recent interest in bioinformatics. We analyzed the genomic similarities of 57 publicly available MTC strains with MinHash. The derived neighbor-joining trees reproduced the known relationships among MTC clades. Although the Jaccard metric is a poor choice for alignment-based methods, it was satisfactory with the MinHash on whole genomes. We extended the analysis to the sets of coding sequences and predicted proteins, but the phylogenetic trees showed a poor correlation to the previous trees. We found this was caused by variations in the start and end positions of loci introduced by different gene finders. When we re-annotated all the genomes with the same annotator, Prokka, the overall known relationships were retrieved, although they were not identical. This fact probably reflects the different selection pressures on coding and non-coding regions. In summary, MinHash is an efficient method to study phylogenetic relationships within the MTC at the genomic and proteomic levels.

3:45 PM-4:00 PM
Insights on protein thermal stability: a graph representation of molecular interactions
  • Mattia Miotto, Sapienza University of Rome, Italy
  • Pier Paolo Olimpieri, Sapienza University of Rome, Italy
  • Lorenzo Di Rienzo, Sapienza University of Rome, Italy
  • Francesco Ambrosetti, Sapienza University of Rome, Italy
  • Pietro Corsi, University Roma Tre, Rome, Italy
  • Rosalba Lepore, Biozentrum, University of Basel, Switzerland
  • Gian Gaetano Tartaglia, Centre for Genomic Regulation (CRG) and Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain
  • Edoardo Milanetti, Sapienza University of Rome, Italy

Abstract: Show

Understanding the molecular mechanisms of thermal stability is a still open challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity.
Although many steps have been made to distinguish thermophilic proteins from their mesophilic counterparts, the goal of knowing general rules that make a single protein thermally stable is still far away.
Predicting the stability of a protein ab initio using a structure based-approach has never been achieved so far. Lack of success in this area is mostly due to limitations in our knowledge about the relationship between thermal resistance and role of the interactions that stabilize a protein structure.
Here, it is presented a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. The approach combines energetic and structural organization of non-bonded interactions to predict the melting temperature of proteins, describing proteins as energy-weighted graphs and comparing them using ensembles of interaction networks.
Th method allows us to examine the structural properties of each protein at the amino acid level and to investigate the position of specific interactions within the 3D native structure, leading to the definition of a new parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and Area Under the Roc Curve of 78%.

Thursday, November 8th
10:30 AM-10:45 AM
Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers
  • Patricio Jeraldo, Mayo Clinic, United States
  • Vanessa Hale, The Ohio State University, United States
  • Donna Xia, University of Alabama, United States
  • Jun Chen, Mayo Clinic, United States
  • Michael Mundy, Mayo Clinic, United States
  • Janet Yao, Mayo Clinic, United States
  • Sambhawa Priya, University of Minnesota, United States
  • Gary Keeney, Mayo Clinic, United States
  • Kelly Lyke, Mayo Clinic, United States
  • Jason Ridlon, University of Illinois at Urbana-Champaign, United States
  • Bryan A White, University of Illinois at Urbana-Champaign, United States
  • Amy J French, Mayo Clinic, United States
  • Stephen Thibodeau, Mayo Clinic, United States
  • Christian Diener, Instituto Nacional de Medicina Genomica, Mexico
  • Osbaldo Resendis-Antonio, Instituto Nacional de Medicina Genomica, Mexico
  • Jaime Gransee, Mayo Clinic, United States
  • Tumpa Dutta, Mayo Clinic, United States
  • Xuan-Mai T Petterson, Mayo Clinic, United States
  • Ran Blekhman, University of Minnesota, United States
  • Lisa Boardman, Mayo Clinic, United States
  • David Larson, Mayo Clinic, United States
  • Heidi Nelson, Mayo Clinic, United States
  • Nicholas Chia, Mayo Clinic, United States

Abstract: Show

Background: The link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor genetic subtype, microbial community interactions, metabolism, and ecology.
Methods: We collected paired colon tumor and normal adjacent tissue and mucosa samples from patients with CRC. Mismatch repair (MMR) status was classified as either deficient (dMMR) or proficient (pMMR) MMR tumor subtypes. Samples underwent 16S rRNA gene sequencing and, for a subset of samples, targeted metabolomic analysis. dMMR and pMMR microbial communities were then analyzed using a generalized linear mixed effects model. Using genome-scale metabolic models to generate microbial interaction networks, we assessed global network properties and metabolic influence of specific microbes and metabolites within the dMMR and pMMR communities.
Results: We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR, but not in pMMR. Nitrites, nitrates and heme are predicted to be highly influential metabolites microbial composition, notably on dMMR communities. Finally, dMMR microbial communities are predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.
Conclusions: Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

10:45 AM-11:00 AM
Using large-scale shotgun proteomics and R Bioconductor packages to understand host-parasite interactions
  • Miguel De Jesús Cosenza Contreras, Federal University of Ouro Preto, Brazil
  • Renata Alves de Oliveira E Castro, Federal University of Ouro Preto, Brazil
  • Bruno Mattei, Federal University of Ouro Preto, Brazil
  • Jonatan Marques Campos, Federal University of Ouro Preto, Brazil
  • Gustavo Gonçalves, Federal University of Ouro Preto, Brazil
  • William de Castro Borges, Federal University of Ouro Preto, Brazil

Abstract: Show

Schistosomiasis is a neglected tropical disease caused by helminth parasites of the genus Schistosoma. When infecting mammals, the parasites cause an exaggerated immune response among host tissues that could lead to the development of a chronic hepatosplenic disease, with the establishment of liver fibrosis potentially leading to death of the infected individual. There is an important immune dynamic associated with the development of the parasites within their host and, until now, it has been poorly explored with a complex quantitative perspective. With this in mind, we used mass spectrometry-based label-free shotgun proteomics and R Bioconductor packages to evaluate the complex dynamic associated with parasitism and establishment of the disease. For this, we used a mice model to simulate a chronic infection by Schistosoma mansoni and analyzed the protein extracts from spleen and liver tissues. The ‘Patternlab for proteomics’ software was employed for the identities search and quantitative analysis of the spectral data. The ReactomePA, clusterprofiler and pathview packages from R Bioconductor were interrogated for protein categorization, enrichment analysis and visualization of the quantitative data. We found that most proteins upregulated during hepatosplenic schistosomiasis are associated with the development of the adaptive immune response, with particular enrichment of pathways related to antigen processing and presentation, cell cycle, translation and cellular response to stress. These findings should offer new interesting insights on the host-parasite interactions and the development of the immune pathology.

11:15 AM-11:30 AM
Copaifera langsdorffi novel putative lncRNA conservational analysis in adaptive response to different ecosystems.
  • Monica Danilevicz, Universidade Federal do Rio de Janeiro, Brazil
  • Kanhu Charan Moharana, Universidade Federal do Norte Fluminense, Brazil
  • Thiago Venancio, UENF, Brazil
  • Monica Cardoso, Instituto de Pesquisa do Jardim Botânico, Brazil
  • Flávia Thiebaut, Universidade Federal do Rio de Janeiro, Brazil
  • Sergio Ricardo Sodré Cardoso, Instituto de Pesquisa do Jardim Botânico, Brazil
  • Luciana Osorio Franco, Instituto de Pesquisa do Jardim Botânico, Brazil
  • Francisco Prosdocimi, Universidade Federal do Rio de Janeiro, Brazil
  • Adriana Hemerly, Universidade Federal do Rio de Janeiro, Brazil
  • Paulo Cavalcanti Gomes Ferreira, Universidade Federal do Rio de Janeiro, Brazil

Abstract: Show

The C. langsdorffii (copaíba) are widely spread in diverse biomes, to thrive in diverse ecosystems requires many adaptability traits, and genetic fine tunning in response to environmental stress. LncRNA are reported to be involved in the epigenetic regulation of several plants response to stress. Thus, we sequenced copaíba poly-A enriched RNA from dry and rainforest biomes – the lncRNA identification was performed using CPC and PLEK to assess coding capacity. We kept the transcripts present in both samples, with RPKM>1 and ORF>300bp. We used BLAST and Bowtie to assess the transcripts conservation across the Fabaceae using genome and transcriptome data. The secondary structure stability was assessed using ViennaRNA. We identified 8,020 putative lncRNA, of which 2,312 and 565 presented 2 and 5-fold regulation respectively. The comparison of lncRNA to Fabaceae genomes found 1,879 conserved transcripts, from which 156 are conserved in all genomes. Comparing the lncRNA to Fabaceae reference transcriptomes, 2,194 transcripts were mapped to at least one specie. There are 1,141 lncRNA present in both genome and transcriptome analyses, of which 254 are differentially regulated between copaíba populations. From this conserved subset of lncRNA, we selected 12 transcripts to assess in silico their secondary structure stability. The majority presented MFE>-80kcal/mol, thus regarded as stable transcripts. A single lncRNA matched to a known transcript at lncRNA database, it was conserved in the genome and regulated in the population from dry biome. Our results indicate the potential involvement of lncRNAs in the adaptation of C. langsdorffii in two different biomes.

11:30 AM-11:45 AM
Systems immunology to predict regulation factors on skin cutaneous melanoma
  • Mindy Muñoz, USP, Brazil
  • Helder Nakaya, USP, Brazil

Abstract: Show

Cutaneous melanoma is a melanocyte skin cancer and it is one of the most aggressive tumors in humans. It causes a great number of deaths worldwide, and in Brazil approximately 1,300 melanoma patients die each year. The Cancer Genome Atlas (TCGA) database contains genomics, epigenomics and transcriptomics data from 470 samples of skin cutaneous melanoma (SKCM). Few studies have applied systems biology approaches to investigate melanoma progression. We propose perform an integrative omics analysis with the SKCM data available in TCGA. We decide work out in a integration by steps, where we obtained the transcriptomics data with those epigenomics data finding genes that are already important in the immune system. Most of differentially expressed genes (68%) between metastatic and primary tumors were up-regulated. These up-regulated genes were found to be related with adaptive and innate immune systems through pathways analysis. Furthermore, the down-regulated genes in metastatic melanoma were related to Keratinization. We found interesting genes related to immune system highlighting some as MS4A1 (also known as CD20) and FCRL2 in the up-regulated and hypermethylated, LIFR in the up-regulated and hypomethylated genes, JUP in down-regulated and hypermethylated and PPL in the down-regulated and hypomethylated genes in the tumor progression. We use X2K-Web tool to infers upstream regulatory networks from signatures of methylated and differentially expressed genes from SKCM data, combining transcription factor enrichment analysis, protein-protein interaction with kinase enrichment analysis. The networks predict kinases to regulate the expression of immune-related genes that may act as regulators in melanoma progression.

11:45 AM-12:00 PM
Host-microbe interaction analysis of the Crohn’s disease metaproteome reveals potential differences in the microbiome-mediated autophagy modulation
  • Tahila Andrighetti, UNESP / Earlham Institute, Brazil
  • Padhmanand Sudhakar, Earlham Institute, United Kingdom
  • Leila Gul, Earlham Institute, United Kingdom
  • Balázs Bohár, Eotvos Lorand University, Hungary
  • Ney Lemke, São Paulo State University (UNESP), Brazil
  • Tamas Korcsmaros, Earlham Institute, United Kingdom

Abstract: Show

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with symptoms of diarrhea, abdominal pain and faecal bleeding. CD results from an abnormal inflammation caused by genetic and environmental factors such as a dysbiosed microbiome which results in altered microbial proteins on the mucosal luminal interface. Differences between healthy and CD patients in terms of their gut microbiome are supported by many meta-omic analysis. However, despite these advancements, there is no sufficient insight into the molecular mechanisms and host signalling pathways mediated by the bacterial community proteins. In this study, we performed an integrated computational approach to assess the potential impact of microbial proteins differentially present in CD and healthy subjects. The microbial protein dataset was derived from a Swedish twin cohort study identifying metaproteomic differences between the individuals of the same twin-pairs wherein one of the individuals was diagnosed with CD and the other not. By doing comparisons within twin-pairs, we were able to offset genetic variability as a foregrounded deterministic contributing factor to the disease. We used structural feature based interaction signatures to predict the human extracellular receptor proteins bound by the microbial proteins unique to CD and healthy individuals. We also determined signalling pathways downstream of host receptors bound by the microbial proteins and identified potential differences in terms of autophagy modulation mediated by the CD/healthy microbiome. Our approach incorporating heterogeneous data including CD metaproteomics, differential expression data, signalling pathways and interaction networks provides insights into the possible molecular mechanisms mediated by the dysbiotic microbiota in CD.

12:00 PM-12:30 PM
Mathematical Modeling of the Relocation of the Divalent Metal Transporter DMT1 in the Intestinal Iron Absorption Process
  • Layimar Cegarra, University of Chile, Chile
  • Andrea Colins, University of Chile, Chile
  • Ziomara P. Gerdtzen, University of Chile, Chile
  • Marco T. Nuñez, University of Chile, Chile
  • J. Cristian Salgado, University of Chile, Chile

Abstract: Show

Background: Iron is essential for the normal development of cellular processes. This metal has a high redox potential that can damage cells and its overload or deficiency is related to several diseases, therefore it is crucial for its absorption to be highly regulated. A fast-response regulatory mechanism has been reported known as mucosal block, which allows to regulate iron absorption after an initial iron challenge. In this mechanism, the internalization of the DMT1 transporters in enterocytes would be a key factor.
Results: Two phenomenological models were proposed for the iron absorption process: DMT1’s binary switching mechanism model and DMT1’s swinging-mechanism model, which represent the absorption mechanism for iron uptake in intestinal cells. The first model considers mutually excluding processes for endocytosis and exocytosis of DMT1. The second model considers a Ball’s oscillator to represent the oscillatory behavior of DMT1’s internalization. Both models are capable of capturing the kinetics of iron absorption and represent empirical observations, but the DMT1 swinging-mechanism model exhibits a better correlation with experimental data and is able to capture the regulatory phenomenon of mucosal block.
Conclusion: The DMT1 swinging-mechanism model is the first phenomenological model reported to effectively represent the complexity of the iron absorption process, as it can predict the behavior of iron absorption fluxes after challenging cells with an initial dose of iron, and the reduction in iron uptake observed as a result of mucosal block after a second iron dose.

2:30 PM-3:00 PM
A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer
  • Sarah Fischer, Rostock University Medical Center, Germany
  • Mohamed Tahoun, Suez canal University, Egypt
  • Bastian Klaan, Rostock University Medical Center, Germany
  • Kolja Thierfelder, Rostock University Medical Center, Germany
  • Marc-Andre Weber, Rostock University Medical Center, Germany
  • Bernd Krause, Rostock University Medical Center, Germany
  • Andreas Erbersdobler, Rostock University Medical Center, Germany
  • Oliver Hakenberg, Rostock University Medical Center, Germany
  • Georg Fuellen, Rostock University Medical Center, Germany
  • Mohamed Hamed, Rostock University Medical Center, Germany

Abstract: Show

Background
Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in
pre-treatment clinical evaluation such as the correct identification of the tumor stage.
Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen
(PSA) levels and Gleason score are not sufficiently accurate for stage prediction. We
hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative
analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa
stages.

Results
We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene
and miRNA expression) datasets for PCa. Comprehensive analysis of gene and miRNA
expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular
characteristics for each stage and the corresponding gene regulatory interaction network that
may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-
217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were
highly correlated (average r = ±0.75) with aggressiveness-related imaging features in both
tumor stages. When combined with related clinical features, these biomarkers markedly
improved the prediction accuracy for the pathological stage.
Conclusion
The integrated regulatory analysis of coding and non-coding RNAs helps to unravel the
molecular mechanisms behind PCa upstaging and to expand our knowledgebase of potential
stage-specific diagnostic factors, that are correlated with PCa aggressiveness- related imaging
features. Our prediction model based on the combined set of clinical parameters, and of
molecular features of genes and miRNAs has the potential to yield clinically relevant results
for characterizing PCa aggressiveness.

3:00 PM-3:15 PM
Modelling the regulatory network controlling dendritic cell differentiation from human monocytes
  • Karen J. Nuñez-Reza, International Laboratory for Human Genome Research, Mexico
  • Aurélien Naldi, Institut de Biologie de l'École Normale Supérieure, France
  • Paulina Pozos, International Laboratory for Human Genome Research, Mexico
  • Pablo M. González-De-la-Rosa, Laboratorio Nacional de Genómica para la Biodiversidad, Mexico
  • Mónica Padilla-Gálvez, International Laboratory for Human Genome Research, Mexico
  • Darely Y. Gutierrez-Reyna, Centro de Investigación en Dinámica Celular, Mexico
  • Christian Molina-Aguilar, International Laboratory for Human Genome Research, Mexico
  • Jesús Emiliano Sotelo Fonseca, Laboratorio Nacional de Genómica para la Biodiversidad, Mexico
  • Salvatore Spicuglia, TAGC Laboratory, Aix Marseille Université, France
  • Angelica Santana, Universidad Autonoma del Estado de Morelos (Facultad de Ciencias), Mexico
  • Morgane Thomas-Chollier, Institut de Biologie de l'École Normale Supérieure, France
  • Denis Thieffry, Institut de Biologie de l'École Normale Supérieure, France
  • Alejandra E. Medina-Rivera, Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Mexico

Abstract: Show

The aim of this project is to determine the regulatory mechanisms that govern the differentiation of monocytes to dendritic cells (DCs) and integrate the regulatory information in a logical model for DC differentiation. Albeit progresses in the field of DC differentiation, we still lack a comprehensive understanding of the signaling pathways and regulatory circuits involved.
We characterized the epigenome of DCs and monocytes based on bulk ChIP-seq and RNA-seq data made available by the BLUEPRINT consortium (Stunnenberg et al, 2016).
Using the software GINsim (Naldi et al, 2018), we built a logical model that includes the pathways previously described for CSF-2 and IL-4 signalling. CSF-2 and IL-4 activate the Jak/Stat pathway, leading to activation of the transcription factors STAT3, STAT5, STAT6, IRF4, NFKB2 and CEBAα, all reported to be involved in monocyte to dendritic cell differentiation.
The results of ChIP-seq and bulk RNA-seq analysis will enrich our model, considering novel transcription factors and regulatory interactions. Model analyses will serve as a screening platform to select components and interaction to assess experimentally (Collombet et al, 2017; Rodriguez et al, unpublished data).
DCs play an important role in the activation of the adaptive immune response, making them excellent candidates for cancer treatments and vaccines development. The construction of a predictive dynamical model of monocytes to dendritic cell differentiation enable a better understanding of this process, to identify clinically relevant intervention points and ultimately design novel therapeutic strategies.

3:15 PM-3:30 PM
Generalizing experimental evidence through Hidden Markovian Models for a transcriptome de novo assembly selection criteria.
  • Patricia Carvajal-Lopez, Centro de Investigaciones Biológicas del Noroeste, S.C., Mexico
  • Eduardo Romero-Vivas, Centro de Investigaciones Biológicas del Noroeste, S.C., Mexico
  • Fernando Von Borstel, Centro de Investigaciones Biológicas del Noroeste, S.C., Mexico
  • Joaquín Gutiérrez-Jagüey, Centro de Investigaciones Biológicas del Noroeste, S.C., Mexico

Abstract: Show

Transcriptome NGS data is assembled based on computational algorithms when lacking references. However, multiple biological and technical factors induce errors. In this context, it is even more critical to establish quality criteria for assembly selection because quantitative metrics have not shown correspondence to test references. Previous research by this group has proven genetic expression microarray information feasible to support assembly quality evaluation and selection. We propose that decisions on assemblies of thousands, or tenth of thousands reads based on smaller datasets could be better accomplished by using statistical models of the experimental evidence. Microarray and RNA-Seq data from one model organism Drosophila melanogaster, and UniGene and RNA-Seq for non-model organism Litopenaeus vannamei were used in this investigation. Results were verified mapping assemblies to transcriptome references and the UniProt/Swiss-Prot protein data base. Assemblies were generated through standard de novo procedures and parameter variation, permitting the evaluation of conventional quality metrics and reference mappings. Once the highest-quality assemblies were identified, the proposed strategy of generalizing microarray-probe and UniGenes through Hidden Markovian Models (HMM) for assembly selection was evaluated. The Microarray-based HMM criterion was applied in D. melanogaster, and the generalizations were extended to L. vannamei assembly selection through UniGene-Based models. In contrast to quantitative metrics, the proposed evaluation strategies, based on microarrays, UniGenes and HMMs usage, did indeed identify the assemblies with the highest reference mappings. Thus, the usage of HMMs to generalize experimental evidence for quality evaluation and selection of de novo assemblies in multiple assembly strategies is proposed.

3:30 PM-3:45 PM
The structure of a giant. Bioinformatics meets experimental techniques.
  • Marta Bunster, Universidad de Concepción, Chile
  • José Martínez-Oyanedel, Universidad de Concepción, Chile

Abstract: Show

The structure of a Phycobilisome is related to its function of light harvesting and energy transfer towards the photoreaction center. Phycobilisomes are macro complexes of phycobiliproteins and linker proteins. In Gracilaria chilensis, a red algae, they are formed by the phycobiliproteins, R-phycoerythrin , R-phycocyanin, allophycocyanin(APC) and linker proteins. Each phycobilisome is formed by a CORE of Allophycocyanin from which radiate RODS formed by Phycocyanin and Phycoerythrin A common feature of all phycobiliproteins is that they are formed by (alfa beta) heterodimers that oligomerize to trimers or hexamers originating ring structures that are piled up as in an antenna. Each phycobiliprotein contains chromophores bound to specific cysteine residues.

The work describes a combination of biophysical approaches such as X-ray crystallography, and spectroscopy, as well as molecular simulations in silico, in order to approach to the structure of the phycobilisome. Electron microscopy provided evidences for a three cylinders core of Allophycocyanin, 5 to 6 rods of Phycoerythrin and Phycocyanin in the PBS. The three dimensional structures of all the phycobiliproteins were determined by X ray diffraction, and their association to form rods and the core was approached by in silico and in vitro studies. Variation of subunits and the presence of linkers were also approached by transcriptomics, biochemical techniques and spectroscopy.
All this information is presented in a model for the structure and function of the phycobilisome of Gracilaria chilensis.

3:45 PM-4:00 PM
The use of metagenomics for environmental monitoring of altitude savannas in the Amazon
  • Guilherme Oliveira, Instituto Tecnológico Vale, Brazil
  • Mabel Ortiz, Instituto Tecnológico Vale, Brazil
  • Markus Gastauer, Instituto Tecnológico Vale, Brazil
  • José Augusto Bittencourt, Instituto Tecnológico Vale, Brazil
  • Rafael Valadares, Instituto Tecnológico Vale, Brazil
  • Ronnie Alves, Instituto Tecnológico Vale, Brazil

Abstract: Show

The altitude ferruginous savannas (known as Canga) of the Amazon are unique environments inhabited by a distinctive set of plant species, typically used for environmental assessment and monitoring. We postulate that microbial communities will be typical for each Canga environment and, therefore, suitable for environmental monitoring. We obtained composite samples from the four different phytophisiognomies, from two mountain ranges, and altitude lakes. The metagenomes were sequence by the shotgun approach and were analyzed using a modified MGP-EBI pipeline followed by multidimensional clustering. The different phytophisiognomies contained unique microbial communities. Functional analysis revealed an increase in complexity, being the forest soil the most functionally diverse. We also observed nitrogen-fixing taxa. The altitude lakes had similar taxonomic and functional components that were modulated by the local chemistry. Flow-cytometry sorting coupled to selective culture produced mini-metagenomes. The DNA was shotgun sequenced and assembled for the generation of larger contigs or genomes to aid in the taxonomical and functional annotations. Metaproteomic assays also indicated unique functionalities of the sampled sites. The results will contribute to the establishment of new tools for environmental monitoring, and will be essential baseline data for mined land rehabilitation work.

Friday, November 9th
10:45 AM-11:00 AM
Knowledge networks reveal novel drug candidates and genetic mechanisms for Psychiatric Diseases
  • Thomaz Luscher Dias, Universidade Federal de Minas Gerais, Brazil
  • Viviane Schuch, University of São Paulo, Brazil
  • Glória Regina Franco, Universidade Federal de Minas Gerais, Brazil
  • Helder Imoto Nakaya, University of São Paulo, Brazil

Abstract: Show

Knowledge networks (KN) are useful tools to gain novel insight from known, yet previously unconnected scientific data. Using KNs obtained with the artificial intelligence text-mining supercomputer IBM Watson for Drug Discovery we investigated genes and drugs related to 9 of the most studied Psychiatric Diseases (PDs - 1.2 million scientific articles published to date): Alzheimer’s disease (AD), Schizophrenia (SCZ), Autism (ASD), Depression (MDD), Anxiety (AX), Parkinson’s disease (PKD), Huntington disease (HD), Bipolar disorder (BD) and Dementia (DM). We obtained a KN with 1588 genes connected to at least one PD and 2750 drugs connected to these genes. Five clusters, grouping clinically similar PDs and their shared genes, were detected: AD/DM with genes enriched for microRNAs in cancer and apoptosis, HD/PD - apoptosis and pancreatic cancer, MDD/AX - anterograde transynaptic signaling and hypertension, BD/SCZ - glutamatergic transmission and axon guidance and ASD - post-synaptic organization and inflammatory bowel disease (IBD). Of the 1588 genes, 121 had been previously shown to be co-expressed in PDs. We then found that 388 of the initial 2750 drugs were specific to these co-expressed genes and were also not directly connected to any PD in the KN, providing potential candidates for targeted drug re-purposing or re-positioning. Our results yield new knowledge on the molecular bases of PDs - the genetic association between ASD and IBD being one that caught our attention - and unravels unexpected opportunities for therapeutic approaches in Psychiatry.

11:00 AM-11:15 AM
Defining non-coding genetic variants contributing to Autism Spectrum Disorder severity using Elastic-Net regression
  • Leandro Roser, Elson S. Floyd College of Medicine, Washington State University, United States
  • Taylor Wintler, Elson S. Floyd College of Medicine, Washington State University, United States
  • Lauren Swineford, Elson S. Floyd College of Medicine, Washington State University, United States
  • Lucía Peixoto, Elson S. Floyd College of Medicine, Washington State University, United States

Abstract: Show

Autism Spectrum Disorder (ASD) is an incredibly heterogeneous neurodevelopmental disorder with strong genetic component. ASD severity is currently defined by the amount of support needed by the subjects, a measure not easily quantifiable. Understanding and quantifying ASD severity are key for diagnosis, treatment planning, and prognostic predictions. The contribution of non-coding variation to ASD severity remains mostly unexplored. The goal of this study was to detect non-coding variants related to ASD severity in individuals of the Simons Simplex Collection. Using unsupervised methods with 21 behavioral tests in 2857 individuals, we were able to define groups of low and high ASD severity. We then used whole-genome-sequencing data for 278 of those individuals to evaluate the contribution of non-coding variation to severity. We focused on a subset learning-regulated non-coding regions, significantly associated with known ASD risk genes. The binary classification for severity obtained from the phenotypic analysis was used for selection of SNPs and CNVs via Elastic-Net. The parameters were tuned by 10-fold cross-validation. The final set of parameters showed an area under the ROC curve of 0.733 ± 0.02. Using these parameters a total of 191 variants were selected. The variants mapped to 198 genes. The gene list includes several important components of the synapse, as mGluR5, whose dysregulation has been associated with ASD. Overall, our preliminary studies show that ASD severity can be quantified using a combination of phenotypic traits, and that genetic variants located in learning-regulated regions are useful for prediction.

11:15 AM-11:30 AM
Automatic Reconstruction of Rule-Based Gene Regulatory Network Models and its Calibration
  • Rodrigo Santibáñez, NetBioLab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Chile
  • Daniel Garrido, Laboratorio de Microbiología de Sistemas, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Chile
  • Alberto J.M. Martin, NetBioLab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Chile

Abstract: Show

Background. Gene Regulatory Networks (GRNs) are central to the behavior of any cell. As the advent of single-cell omics technologies, variability has been recognized as an essential property of GRNs, and its impact is well documented. Examples include filtering extrinsic noise in cell responses or harnessing stochastic gene expression to ensure survival to environmental changes. However, appropriate modeling methods such as Rule-Based Models (RBMs) exhibit narrow adoption, and there is a handful of tools relying on expert usage. Here, we developed Atlas to automatically model GRNs and Pleione to calibrate parameters with the purpose of expanding tools and fostering the adoption of RBMs by the community.

Methods. Atlas writes RBMs complying with the Kappa BioBrick Framework and the PySB syntax while Pleione encodes a Genetic Algorithm. Atlas was used to reconstruct the Core Model composed of ten genes essentials for gene transcription in Escherichia coli. After that, we used transcriptomic data to calibrate the model. Finally, we expanded the model to include an E. coli GRN involving gene regulation of glycolysis, ATP synthesis, and Kreb’s cycle enzymes.

Results. Before calibration, the model reproduced qualitatively available free RNAP (mean around 18%). Moreover, the calibrated models reproduced experimental data quantitatively (25-26% free RNAP, 30% experimental) and statistically (U-test).

Conclusion. The reconstructed and calibrated models can reproduce experimental data quantitative and statistically. Besides, the developed methods could be applied to any arbitrary GRN including synthetic GRNs. Ongoing work involves extending Atlas to consider metabolism and propose a whole-cell model of E. coli.

11:30 AM-11:45 AM
Defining a Core Genome for the Herpesvirales and Exploring their Evolutionary Relationship with the Caudovirales
  • Juan Sebastián Andrade Martínez, Universidad de los Andes, Colombia
  • Alejandro Reyes, Universidad de los Andes, Colombia

Abstract: Show

The order Herpesvirales encompasses a wide variety of important and widely distributed human pathogens. During the last decades, similarities in the viral cycle and the structure of some of their proteins with those of the tailed phages have brought speculation regarding the existence of an evolutionary relationship between these clades. To evaluate such hypothesis, we used over 500 Herpesvirales and 2000 Caudovirales complete genomes to search for the presence or absence of clusters of orthologous protein domains, and construct a dendrogram based on their compositional similarities. The results obtained allowed to propose a Core Genome for the Herpesvirales, composed of 4 proteins, including the ATPase subunit of the DNA-packaging terminase, the only protein with previously verified conservation. Furthermore, phylogenetic analyses made through those proteins for the Caudovirales and Herpesvirales strongly suggest an evolutionary relationship among them. Accordingly, a phylogenetic tree constructed with sequences derived from the clusters associated to these peptides grouped the strains employed in the established families and subfamilies. Overall, this work provides important results supporting the hypothesis that the two orders are evolutionarily related and contributes to the understanding of the evolutionary history of the Herpesvirales.

11:45 AM-12:00 PM
Active Site Flexibility as a Hallmark for Efficient PET Degradation by I. sakaiensis PETase
  • Pablo Galaz-Davison, Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Chile
  • Tobias Fecker, Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Chile
  • Felipe Engelberger, Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Chile
  • Yoshie Narui, Department of Chemistry and Biochemistry, The Ohio State University, United States
  • Marcos Sotomayor, Department of Chemistry and Biochemistry, The Ohio State University, United States
  • Loreto P. Parra, Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Chile
  • Cesar A Ramirez-Sarmiento, Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Chile

Abstract: Show

Polyethylene terephthalate (PET) is one of the most-consumed synthetic polymers, with annual productions of 50 million tons. Unfortunately, PET accumulates as waste and is resistant to biodegradation. Recently, fungal and bacterial thermophilic hydrolases were found to hydrolyse PET with optimal activities at high temperatures. Strikingly, an enzyme from Ideonella sakaiensis, termed PETase, was described to efficiently degrade PET at room temperature, but the molecular basis of its activity was not understood. We determined the crystal structure of PETase at 2.02 Å resolution and employed it in MD simulations, showing that the active site of PETase has higher flexibility at room temperature than its thermophilic counterparts. This flexibility is controlled by a novel disulfide bond in its active site, with its removal destabilising its catalytic triad and reducing its hydrolase activity. Docking of a model substrate predicts that PET binds to PETase in a unique conformation facilitated by several residue substitutions within its active site when compared to other enzymes. These predictions are in excellent agreement with recent mutagenesis and PET film degradation analyses. Finally, we rationalised the catalytic activity of PETase at room temperature through molecular dynamics simulations of enzyme-ligand complexes for PETase and other thermophilic PET-degrading enzymes at several temperatures, showing that both the binding pose and residue substitutions within PETase favor proximity between the catalytic residues and the substrate at room temperature, suggesting a more favorable hydrolytic reaction. These results are valuable for rationally increasing the efficiency of PETase and similar enzymes toward plastic degradation. FUNDING: INACH RG_47-16

12:15 PM-12:30 PM
Insights into the genome and variability of Phytophthora palmivora, the causal agent of the bud rot disease in oil palm
  • Juanita Gil, Cenipalma; Universidad de Los Andes, Colombia
  • Hernán Mauricio Romero, Cenipalma; Universidad Nacional de Colombia, Colombia
  • Mariana Herrera, Cenipalma, Colombia

Abstract: Show

Phytophthora species are the most commonly studied oomycetes due to their economic importance, because they are able to affect several different crops, leading to annual losses of billions of dollars. Phytophthora palmivora is an important species of the genus due to the different hosts that it attacks and the diverse diseases that it causes, since it is able to infect many tissues on the same host, making it a relevant species to study plant-pathogen interactions and complex disease cycles. In oil palm (Elaeis guineensis), P. palmivora causes the bud rot disease, one of the major constraints limiting the oil production in Colombia, the main palm oil producer in Latinamerica and the fourth worlwide. We sequenced 12 different isolates of the pathogen collected from different palm oil producing regions in Colombia and assembled and annotated the reference genome for P. palmivora. This study aimed to characterize the genome of P. palmivora from oil palm and the study of the diversity of the pathogen in a group of isolates in Colombia taking advantage of next-generation sequencing technologies and cutting-edge bioinformatics tools. We expect that the identification of effector proteins in P. palmivora as well as an understanding of the variability of the pathogen in Colombia will provide broader knowledge about the host-pathogen interaction and contribute to the development of effective strategies for disease control.

Deciphering the lifestyle of the plant pathogen Pseudomonas syringae under various nutrient-limiting conditions via genome-scale flux and proteomic analysis

Abstract: Show

Background: Prokaryotic plant pathogens cause important economical losses worldwide. This is due to the elevated mutation rate as well as high metabolic versatility that they display. Pseudomonas syrinage pv. DC3000 is a model pathogen microorganism able to infect a wide variety of plants such as tomato, kiwi, and Arabidopsis thaliana. In the quest to understand the metabolic response and molecular rearrangement of P. syringae under several nutrients limiting conditions at a system level, specially the one under iron deprivation, systems biology offers various approaches to integrate high-throughput data with the resulting phenotypes into a mathematical and more rational fashion.

Results: A genome-scale metabolic reconstruction model of P. syringae was developed, which encompasses more than 1400 enzymatic reactions. This model was further validated with experimental data from batch cultures in minimal medium and glucose a carbon substrate. In addition, chemostat cultures were carried out using P. syringae under carbon-, iron-, and trace element-limiting cultures, and the proteomic response captured by using a shotgun proteomic approach. We were capable of integrating this molecular level into the created genome-scale model, which predicted the observed phenotypes in the tested conditions.

Conclusion: This is the first study showing the metabolic rearrangements of P. syringae under well-controlled conditions integrating proteomic and genome-scale flux data. This allowed us to better predict the resulting phenotypes, providing the basis for the systematic understanding of bacterial pathogenesis.

This study is financed by FONDECYT Regular (Nº1170259).

12:30 PM-12:50 PM
Fighting antimicrobial resistance with computational and experimental biophysics

Abstract: Show

Molecular Biophysics and Bioinformatics Group, Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile

Resistance and reduced sensitivity to antibiotics are global problems spanning both Gram-positive and Gram-negative bacteria (GNB). The most distinctive characteristic of GNB is the presence of a second bilayer called the Outer Membrane (OM), which outer leaflet is composed mostly by lipopolysaccharides (LPS) that are the first barrier against antibiotics. As the LPS intermolecular interactions define the LPS supramolecular structure, we characterized them through a combination of experimental and computational calorimetry methods, to understand the phase structure adopted at different temperatures of LPS vesicles extracted from bacterial strains that present specific LPS chemotypes and differential sensibility to particular antimicrobials. This combined theoretical-experimental approach allows us to determine how the components of the LPS core oligosaccharide contribute to the LPS characteristics at the studied temperature, linked to the antibiotic permeation through the OM and how they contribute to the AMPs bacterial sensitivity under this conditions, which could be useful to develop new therapeutic strategies to solve the antimicrobial resistance problem.

2:30 PM-2:45 PM
The COST Action CHARME: a European initiative for the harmonisation of standards for life-science research
  • Domenica D'Elia, Institute for Biomedical Technologies -CNR, Italy

Abstract: Show

Open Science (OS) describes the ongoing transitions in the way research is performed, i.e. researchers collaborate, knowledge is shared, and science is organised. OS is driven by digital technologies and by the growth of data, globalisation, enlargement of the scientific community and the need to address societal challenges. It has now widely been recognised that making research results more accessible contributes to more efficient science and innovation in the public and private sectors.
To unlock that potential, data needs to be made computer readable and interoperable. Therefore, they must be generated, formatted and stored according to Standard Operating Procedures and Data Management Plans included as an integral part of any projects.
Although several initiatives are actives in different life science research fields, they are still too much fragmented. A significant barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles is the lack of awareness and insufficient education of scientists and other stakeholders in the process of producing, handling, publishing and transferring knowledge to biotech applications.
CHARME: “Harmonising standardisation strategies to increase the efficiency and competitiveness of European life-science research”, is an Action funded through COST (European Cooperation in Science and Technology). CHARME aims to identify needs and gaps in standards, teaming up with other initiatives and organisations, and proposing new strategies for successful assimilation of standards into the daily work-flow of researchers. This presentation underlines the scenario in which CHARME moves and actions needed to increase the efficiency and competitiveness of life-science research.

2:45 PM-3:15 PM
MassComp, a Lossless Compressor for Mass Spectrometry Data
  • Ruochen Yang, University of Southern California, United States
  • Xi Chen, University of Illinois at Urbana-Champaign, United States
  • Idoia Ochoa, University of Illinois at Urbana-Champaign, United States

Abstract: Show

Background: Mass Spectrometry (MS) is a widely used technique in biology research, and has become key in proteomics and metabolomics analyses. As a result, the amount of MS data has significantly increased in recent years. For example, the MS repository MassIVE contains more than 123TB of data. Somehow surprisingly, these data are stored uncompressed, hence incurring a significant storage cost. Efficient representation of these data is therefore paramount to lessen the burden of storage and facilitate its dissemination.

Results: We present \emph{MassComp}, a lossless compressor optimized for the numerical (m/z)-intensity pairs that account for most of the MS data. We tested MassComp on several MS data and show that it delivers on average a 46\% reduction on the size of the numerical data, and up to 89\%. These results correspond to an average improvement of more than 27\% when compared to the general compressor \emph{gzip} and of 40\% when compared to the state-of-the-art numerical compressor \emph{FPC}. When tested on entire files retrieved from the MassIVE repository, MassComp achieves on average a 59\% size reduction. MassComp is written in C++ and freely available at \url{https://github.com/iochoa/MassComp}.

Conclusions: The compression performance of MassComp demonstrates its potential to significantly reduce the footprint of MS data, and shows the benefits of designing specialized compression algorithms tailored to MS data. MassComp is an addition to the family of omics compression algorithms designed to lessen the storage burden and facilitate the exchange and dissemination of omics data.

Keywords: Mass Spectrometry, lossless compression, storage.