Marcelo S. Reis

Comparative Genomics of Two Leptospira interrogans Serovars Reveals Novel Insights into Physiology and Pathogenesis

Leptospira species colonize a significant proportion of rodent populations worldwide and produce life-threatening infections in accidental hosts, including humans. Complete genome sequencing of Leptospira interrogans serovar Copenhageni and comparative analysis with the available Leptospira interrogans serovar Lai genome reveal that despite overall genetic similarity there are significant structural differences, including a large chromosomal inversion and extensive variation in the number and distribution of insertion sequence elements. Genome sequence analysis elucidates many of the novel aspects of leptospiral physiology relating to energy metabolism, oxygen tolerance, two-component signal transduction systems, and mechanisms of pathogenesis. A broad array of transcriptional regulation proteins and two new families of afimbrial adhesins which contribute to host tissue colonization in the early steps of infection were identified. Differences in genes involved in the biosynthesis of lipopolysaccharide O side chains between the Copenhageni and Lai serovars were identified, offering an important starting point for the elucidation of the organisms complex polysaccharide surface antigens. Differences in adhesins and in lipopolysaccharide might be associated with the adaptation of serovars Copenhageni and Lai to different animal hosts. Hundreds of genes encoding surface-exposed lipoproteins and transmembrane outer membrane proteins were identified as candidates for development of vaccines for the prevention of leptospirosis.

Proteomic and Glycoproteomic Profilings Reveal That Post-translational Modifications of Toxins Contribute to Venom Phenotype in Snakes

Snake venoms are biological weapon systems composed of secreted proteins and peptides that are used for immobilizing or killing prey. Although post-translational modifications are widely investigated because of their importance in many biological phenomena, we currently still have little understanding of how protein glycosylation impacts the variation and stability of venom proteomes. To address these issues, here we characterized the venom proteomes of seven Bothrops snakes using a shotgun proteomics strategy. Moreover, we compared the electrophoretic profiles of native and deglycosylated venoms and, in order to assess their subproteomes of glycoproteins, we identified the proteins with affinity for three lectins with different saccharide specificities and their putative glycosylation sites. As proteinases are abundant glycosylated toxins, we examined the effect of N-deglycosylation on their catalytic activities and show that the proteinases of the seven venoms were similarly affected by removal of N-glycans. Moreover, we prospected putative glycosylation sites of transcripts of a B. jararaca venom gland data set and detected toxin family related patterns of glycosylation. Based on our global analysis, we report that Bothrops venom proteomes and glycoproteomes contain a core of components that markedly define their composition, which is conserved upon evolution in parallel to other molecular markers that determine their phylogenetic classification.

A New Approach for Identification of Cancer-related Pathways using Protein Networks and Genomic Data.

Cancer cells have anomalous development and proliferation due to disturbances in their control systems. The study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. This fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profile and the signaling pathways. This article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verified the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. These differentially represented motifs are enriched with specific gene ontologies as well as with new putative cancer genes.

Solving Problems in Mathematical Morphology through Reductions to the U-Curve Problem

The U-curve problem is an optimization problem that consists in, given a finite set S, a Boolean lattice \((\mathcal{P}(S), \subseteq)\) and a chain \(\mathcal{L}\), minimize a function \(c:\mathcal{P}(S) \rightarrow \mathcal{L}\) that satisfies an extension of Matheron’s increasing-decreasing decomposition (i.e., a function that is decomposable in U-shaped curves). This problem may be used to model problems in the domain of Mathematical Morphology, for instance, morphological operator design and some types of combinatorial optimization problems. Recently, we introduced the U-Curve-Search (UCS) algorithm, which is a solver to the U-curve problem. In this paper, we recall the principles of the UCS algorithm, present a constrained version of Serra’s formulation of the Tailor problem, prove that this problem is a U-curve problem, apply the UCS algorithm to solve it and compare the performance of UCS with another optimization algorithm. Besides, we present applications of UCS in the context of W-operator design.