ProtozoaDB: dynamic visualization and exploration of protozoan genomes

Integrative Approaches to Understand the Mastery in Manipulation of Host Cytokine Networks by Protozoan Parasites with Emphasis on Plasmodium and Leishmania Species

Diseases by protozoan pathogens pose a significant public health concern, particularly in tropical and subtropical countries, where these are responsible for significant morbidity and mortality. Protozoan pathogens tend to establish chronic infections underscoring their competence at subversion of host immune processes, an important component of disease pathogenesis and of their virulence. Modulation of cytokine and chemokine levels, their crosstalks and downstream signaling pathways, and thereby influencing recruitment and activation of immune cells is crucial to immune evasion and subversion. Many protozoans are now known to secrete effector molecules that actively modulate host immune transcriptome and bring about alterations in host epigenome to alter cytokine levels and signaling. The complexity of multi-dimensional events during interaction of hosts and protozoan parasites ranges from microscopic molecular levels to macroscopic ecological and epidemiological levels that includes disrupting metabolic pathways, cell cycle (Toxoplasma and Theileria sp.), respiratory burst, and antigen presentation (Leishmania spp.) to manipulation of signaling hubs. This requires an integrative systems biology approach to combine the knowledge from all these levels to identify the complex mechanisms of protozoan evolution via immune escape during host-parasite coevolution. Considering the diversity of protozoan parasites, in this review, we have focused on Leishmania and Plasmodium infections. Along with the biological understanding, we further elucidate the current efforts in generating, integrating, and modeling of multi-dimensional data to explain the modulation of cytokine networks by these two protozoan parasites to achieve their persistence in host via immune escape during host-parasite coevolution.

ProtozoaDB 2.0: A Trypanosoma Brucei Case Study

Over the last decade new species of Protozoa have been sequenced and deposited in GenBank. Analyzing large amounts of genomic data, especially using Next Generation Sequencing (NGS), is not a trivial task, considering that researchers used to deal or focus their studies on few genes or gene families or even small genomes. To facilitate the information extraction process from genomic data, we developed a database system called ProtozoaDB that included five genomes of Protozoa in its first version. In the present study, we present a new version of ProtozoaDB called ProtozoaDB 2.0, now with the genomes of 22 pathogenic Protozoa. The system has been fully remodeled to allow for new tools and a more expanded view of data, and now includes a number of analyses such as: (i) similarities with other databases (model organisms, the Conserved Domains Database, and the Protein Data Bank); (ii) visualization of KEGG metabolic pathways; (iii) the protein structure from PDB; (iv) homology inferences; (v) the search for related publications in PubMed; (vi) superfamily classification; and (vii) phenotype inferences based on comparisons with model organisms. ProtozoaDB 2.0 supports RESTful Web Services to make data access easier. Those services were written in Ruby language using Ruby on Rails (RoR). This new version also allows a more detailed analysis of the object of study, as well as expanding the number of genomes and proteomes available to the scientific community. In our case study, a group of prenyltransferase proteinsalready described in the literature was found to be a good drug target for Trypanosomatids.

Improved orthologous databases to ease protozoan targets inference

Background

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools.

Methods

Our analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome.

Results

The protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB”, with 16,938 and 27,701 orthologous groups, respectively.

Such new orthologous databases were used for a regular OrthoSearch run. By confronting “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB” databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %).

Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach.

We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams.

Conclusions

The orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1090-0) contains supplementary material, which is available to authorized users.

The Comparative Genomics and Phylogenomics of Leishmania amazonensis Parasite

Leishmaniasis is an infectious disease caused by Leishmania species. Leishmania amazonensis is a New World Leishmania species belonging to the Mexicana complex, which is able to cause all types of leishmaniasis infections. The L. amazonensis reference strain MHOM/BR/1973/M2269 was sequenced identifying 8,802 codifying sequences (CDS), most of them of hypothetical function. Comparative analysis using six Leishmania species showed a core set of 7,016 orthologs. L. amazonensis and Leishmania mexicana share the largest number of distinct orthologs, while Leishmania braziliensis presented the largest number of inparalogs. Additionally, phylogenomic analysis confirmed the taxonomic position for L. amazonensis within the “Mexicana complex”, reinforcing understanding of the split of New and Old World Leishmania. Potential non-homologous isofunctional enzymes (NISE) were identified between L. amazonensis and Homo sapiens that could provide new drug targets for development.

An orthology-based analysis of pathogenic protozoa impacting global health: an improved comparative genomics approach with prokaryotes and model eukaryote orthologs

A key focus in 21(st) century integrative biology and drug discovery for neglected tropical and other diseases has been the use of BLAST-based computational methods for identification of orthologous groups in pathogenic organisms to discern orthologs, with a view to evaluate similarities and differences among species, and thus allow the transfer of annotation from known/curated proteins to new/non-annotated ones. We used here a profile-based sensitive methodology to identify distant homologs, coupled to the NCBI's COG (Unicellular orthologs) and KOG (Eukaryote orthologs), permitting us to perform comparative genomics analyses on five protozoan genomes. OrthoSearch was used in five protozoan proteomes showing that 3901 and 7473 orthologs can be identified by comparison with COG and KOG proteomes, respectively. The core protozoa proteome inferred was 418 Protozoa-COG orthologous groups and 704 Protozoa-KOG orthologous groups: (i) 31.58% (132/418) belongs to the category J (translation, ribosomal structure, and biogenesis), and 9.81% (41/418) to the category O (post-translational modification, protein turnover, chaperones) using COG; (ii) 21.45% (151/704) belongs to the categories J, and 13.92% (98/704) to the O using KOG. The phylogenomic analysis showed four well-supported clades for Eukarya, discriminating Multicellular [(i) human, fly, plant and worm] and Unicellular [(ii) yeast, (iii) fungi, and (iv) protozoa] species. These encouraging results attest to the usefulness of the profile-based methodology for comparative genomics to accelerate semi-automatic re-annotation, especially of the protozoan proteomes. This approach may also lend itself for applications in global health, for example, in the case of novel drug target discovery against pathogenic organisms previously considered difficult to research with traditional drug discovery tools.

Vaccine development against Trypanosoma cruzi and Leishmania species in the post-genomic era

Trypanosoma cruzi and the genus Leishmania are protozoan parasites causing diseases of major public health importance, and the recent completion of the sequencing of their genomes has opened new opportunities to further our understanding of the mechanisms required for protection and the development of vaccines. For example, trans-sialidases, one of the largest protein families from T. cruzi, contain dominant CD8+ T cell epitopes, and their use as preventive or therapeutic vaccines in different animal models has provided encouraging results. A much wider range of antigens and vaccine formulations have been tested against Leishmania, and new correlates for protection are being defined, such as the induction of multifunctional Th1 effector cells capable of producing a complex set of cytokines. Also, while a large number of these vaccine candidates have been rather successful in mouse models, their usefulness in more relevant animal models is still poor, in spite of significant immunogenicity. Novel proteomics and genomics approaches are being used for antigen discovery and the identification of new vaccine candidates, some of which have shown promise for the control of infection. These studies cast little doubt that T. cruzi and Leishmania genomes represent major resources for understanding key aspects of the mechanisms of immune protection against these parasites, and the increasing use of these tools will greatly impact vaccine development.