Santiago J. Carmona

Genomic-scale prioritization of drug targets: the TDR Targets database

The increasing availability of genomic data for pathogens that cause tropical diseases has created new opportunities for drug discovery and development. However, if the potential of such data is to be fully exploited, the data must be effectively integrated and be easy to interrogate. Here, we discuss the development of the TDR Targets database (http://tdrtargets.org), which encompasses extensive genetic, biochemical and pharmacological data related to tropical disease pathogens, as well as computationally predicted druggability for potential targets and compound desirability information. By allowing the integration and weighting of this information, this database aims to facilitate the identification and prioritization of candidate drug targets for pathogens.

Identification of Attractive Drug Targets in Neglected-Disease Pathogens Using an In Silico Approach

Background The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni. Methodology/Principal Findings Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets. Conclusions/Significance Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research communitys current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

TDR Targets: a chemogenomics resource for neglected diseases

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context.

Towards high-throughput immunomics for infectious diseases: use of next-generation peptide microarrays for rapid discovery and mapping of antigenic determinants

Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens.

Diagnostic Peptide Discovery: Prioritization of Pathogen Diagnostic Markers Using Multiple Features

The availability of complete pathogen genomes has renewed interest in the development of diagnostics for infectious diseases. Synthetic peptide microarrays provide a rapid, high-throughput platform for immunological testing of potential B-cell epitopes. However, their current capacity prevent the experimental screening of complete “peptidomes”. Therefore, computational approaches for prediction and/or prioritization of diagnostically relevant peptides are required. In this work we describe a computational method to assess a defined set of molecular properties for each potential diagnostic target in a reference genome. Properties such as sub-cellular localization or expression level were evaluated for the whole protein. At a higher resolution (short peptides), we assessed a set of local properties, such as repetitive motifs, disorder (structured vs natively unstructured regions), trans-membrane spans, genetic polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, and sequence similarity against human proteins and other potential cross-reacting species (e.g. other pathogens endemic in overlapping geographical locations). A scoring function based on these different features was developed, and used to rank all peptides from a large eukaryotic pathogen proteome. We applied this method to the identification of candidate diagnostic peptides in the protozoan Trypanosoma cruzi, the causative agent of Chagas disease. We measured the performance of the method by analyzing the enrichment of validated antigens in the high-scoring top of the ranking. Based on this measure, our integrative method outperformed alternative prioritizations based on individual properties (such as B-cell epitope predictors alone). Using this method we ranked 10 million 12-mer overlapping peptides derived from the complete T. cruzi proteome. Experimental screening of 190 high-scoring peptides allowed the identification of 37 novel epitopes with diagnostic potential, while none of the low scoring peptides showed significant reactivity. Many of the metrics employed are dependent on standard bioinformatic tools and data, so the method can be easily extended to other pathogen genomes.

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA sequencing of lck:GFP cells in zebrafish and obtained the first transcriptome of specific immune cell types in a nonmammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T cells, a novel type of NK-like cells, and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune-cell-specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed the higher gene turnover of trans-membrane proteins in NK cells compared with T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Mapping Antigenic Motifs in the Trypomastigote Small Surface Antigen from Trypanosoma cruzi

ABSTRACT The trypomastigote small surface antigen (TSSA) is a mucin-like molecule from Trypanosoma cruzi, the etiological agent of Chagas disease, which displays amino acid polymorphisms in parasite isolates. TSSA expression is restricted to the surface of infective cell-derived trypomastigotes, where it functions as an adhesin and engages surface receptors on the host cell as a prerequisite for parasite internalization. Previous results have established TSSA-CL, the isoform encoded by the CL Brener clone, as an appealing candidate for use in serology-based diagnostics for Chagas disease. Here, we used a combination of peptide- and recombinant protein-based tools to map the antigenic structure of TSSA-CL at maximal resolution. Our results indicate the presence of different partially overlapping B-cell epitopes clustering in the central portion of TSSA-CL, which contains most of the polymorphisms found in parasite isolates. Based on these results, we assessed the serodiagnostic performance of a 21-amino-acid-long peptide that spans TSSA-CL major antigenic determinants, which was similar to the performance of the previously validated glutathione S-transferase (GST)-TSSA-CL fusion molecule. Furthermore, the tools developed for the antigenic characterization of the TSSA antigen were also used to explore other potential diagnostic applications of the anti-TSSA humoral response in Chagasic patients. Overall, our present results provide additional insights into the antigenic structure of TSSA-CL and support this molecule as an excellent target for molecular intervention in Chagas disease.

Genome-wide analysis of 3′-untranslated regions supports the existence of post-transcriptional regulons controlling gene expression in trypanosomes

In eukaryotic cells, a group of messenger ribonucleic acids (mRNAs) encoding functionally interrelated proteins together with the trans-acting factors that coordinately modulate their expression is termed a post-transcriptional regulon, due to their partial analogy to a prokaryotic polycistron. This mRNA clustering is organized by sequence-specific RNA-binding proteins (RBPs) that bind cis-regulatory elements in the noncoding regions of genes, and mediates the synchronized control of their fate. These recognition motifs are often characterized by conserved sequences and/or RNA structures, and it is likely that various classes of cis-elements remain undiscovered. Current evidence suggests that RNA regulons govern gene expression in trypanosomes, unicellular parasites which mainly use post-transcriptional mechanisms to control protein synthesis. In this study, we used motif discovery tools to test whether groups of functionally related trypanosomatid genes contain a common cis-regulatory element. We obtained conserved structured RNA motifs statistically enriched in the noncoding region of 38 out of 53 groups of metabolically related transcripts in comparison with a random control. These motifs have a hairpin loop structure, a preferred sense orientation and are located in close proximity to the open reading frames. We found that 15 out of these 38 groups represent unique motifs in which most 3′-UTR signature elements were group-specific. Two extensively studied Trypanosoma cruzi RBPs, TcUBP1 and TcRBP3 were found associated with a few candidate RNA regulons. Interestingly, 13 motifs showed a strong correlation with clusters of developmentally co-expressed genes and six RNA elements were enriched in gene clusters affected after hyperosmotic stress. Here we report a systematic genome-wide in silico screen to search for novel RNA-binding sites in transcripts, and describe an organized network of several coordinately regulated cohorts of mRNAs in T. cruzi. Moreover, we found that structured RNA elements are also conserved in other human pathogens. These results support a model of regulation of gene expression by multiple post-transcriptional regulons in trypanosomes.

TcSNP: a database of genetic variation in Trypanosoma cruzi

The TcSNP database (http://snps.tcruzi.org) integrates information on genetic variation (polymorphisms and mutations) for different stocks, strains and isolates of Trypanosoma cruzi, the causative agent of Chagas disease. The database incorporates sequences (genes from the T. cruzi reference genome, mRNAs, ESTs and genomic sequences); multiple sequence alignments obtained from these sequences; and single-nucleotide polymorphisms and small indels identified by scanning these multiple sequence alignments. Information in TcSNP can be readily interrogated to arrive at gene sets, or SNP sets of interest based on a number of attributes. Sequence similarity searches using BLAST are also supported. This first release of TcSNP contains nearly 170 000 high-confidence candidate SNPs, derived from the analysis of annotated coding sequences. As new sequence data become available, TcSNP will incorporate these data, mapping new candidate SNPs onto the reference genome sequences.

Characterization of Toxoplasma gondii subtelomeric-like regions: identification of a long-range compositional bias that is also associated with gene-poor regions

BackgroundChromosome ends are composed of telomeric repeats and subtelomeric regions, which are patchworks of genes interspersed with repeated elements. Although chromosome ends display similar arrangements in different species, their sequences are highly divergent. In addition, these regions display a particular nucleosomal composition and bind specific factors, therefore producing a special kind of heterochromatin. Using data from currently available draft genomes we have characterized these putative Telomeric Associated Sequences in Toxoplasma gondii.ResultsAn all-vs-all pairwise comparison of T. gondii assembled chromosomes revealed the presence of conserved regions of ∼ 30 Kb located near the ends of 9 of the 14 chromosomes of the genome of the ME49 strain. Sequence similarity among these regions is ∼ 70%, and they are also highly conserved in the GT1 and VEG strains. However, they are unique to Toxoplasma with no detectable similarity in other Apicomplexan parasites. The internal structure of these sequences consists of 3 repetitive regions separated by high-complexity sequences without annotated genes, except for a gene from the Toxoplasma Specific Family. ChIP-qPCR experiments showed that nucleosomes associated to these sequences are enriched in histone H4 monomethylated at K20 (H4K20me1), and the histone variant H2A.X, suggesting that they are silenced sequences (heterochromatin). A detailed characterization of the base composition of these sequences, led us to identify a strong long-range compositional bias, which was similar to that observed in other genomic silenced fragments such as those containing centromeric sequences, and was negatively correlated to gene density.ConclusionsWe identified and characterized a region present in most Toxoplasma assembled chromosomes. Based on their location, sequence features, and nucleosomal markers we propose that these might be part of subtelomeric regions of T. gondii. The identified regions display a unique trinucleotide compositional bias, which is shared (despite the lack of any detectable sequence similarity) with other silenced sequences, such as those making up the chromosome centromeres. We also identified other genomic regions with this compositional bias (but no detectable sequence similarity) that might be functionally similar.