Florencio M. Ubeira

Proteomics, networks and connectivity indices

Describing the connectivity of chemical and/or biological systems using networks is a straight gate for the introduction of mathematical tools in proteomics. Networks, in some cases even very large ones, are simple objects that are composed at least by nodes and edges. The nodes represent the parts of the system and the edges geometric and/or functional relationships between parts. In proteomics, amino acids, proteins, electrophoresis spots, polypeptidic fragments, or more complex objects can play the role of nodes. All of these networks can be numerically described using the so‐called Connectivity Indices (CIs). The transformation of graphs (a picture) into CIs (numbers) facilitates the manipulation of information and the search for structure‐function relationships in Proteomics. In this work, we review and comment on the challenges and new trends in the definition and applications of CIs in Proteomics. Emphasis is placed on 1‐D‐CIs for DNA and protein sequences, 2‐D‐CIs for RNA secondary structures, 3‐D‐topographic indices (TPGIs) for protein function annotation without alignment, 2‐D‐CIs and 3‐D‐TPGIs for the study of drug‐protein or drug‐RNA quantitative structure‐binding relationships, and pseudo 3‐D‐CIs for protein surface molecular recognition. We also focus on CIs to describe Protein Interaction Networks or RNA co‐expression networks. 2‐D‐CIs for patient blood proteome 2‐DE maps or mass spectra are also covered.

Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for Input-Coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds

Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.

AN ULTRASENSITIVE CAPTURE ELISA FOR DETECTION OF FASCIOLA HEPATICA COPROANTIGENS IN SHEEP AND CATTLE USING A NEW MONOCLONAL ANTIBODY (MM3)

A capture enzyme-linked immunosorbent assay (ELISA) using a new monoclonal antibody (mAb MM3) is reported for the detection of Fasciola hepatica excretory–secretory antigens (ESAs) in feces of infected hosts. The mAb MM3 was produced by immunization of mice with a 7- to 40-kDa purified and O-deglycosylated fraction of F. hepatica ESAs, which has previously been shown to be specific for the parasite. The specificity and sensitivity of the MM3 capture ELISA were assessed using feces from sheep and cattle. Sheep feces were obtained from a fluke-free herd (with most animals harboring other nematodes and cestodes), from lambs experimentally infected with 5–40 F. hepatica metacercariae and in some cases treated with triclabendazole at 14 wk postinfection (PI), and from uninfected control lambs. Cattle feces were collected at the slaughterhouse from adult cows naturally infected with known numbers of flukes (from 1 to 154) or free of F. hepatica infection (though in most cases harboring other helminths). The MM3 capture ELISA assay had detection limits of 0.3 (sheep) and 0.6 (cattle) ng of F. hepatica ESA per milliliter of fecal supernatant. The assay detected 100% of sheep with 1 fluke, 100% of cattle with 2 flukes, and 2 of 7 cattle with 1 fluke. The false-negative animals (5/7) were probably not detected because the F. hepatica individuals in these animals were immature (5–11 mm in length). As expected, coproantigen concentration correlated positively (r = 0.889; P < 0.001) with parasite burden and negatively (r = 0.712; P < 0.01) with the time after infection at which coproantigen was first detected. Nevertheless, even in animals with low fluke burdens (1–36 parasites), the first detection of F. hepatica–specific coproantigens by the MM3 capture ELISA preceded the first detection in egg count by 1–5 wk. In all sheep that were experimentally infected and then untreated, coproantigen remained detectable until at least 18 wk PI, whereas in sheep that were experimentally infected and then flukicide treated, coproantigen became undetectable from 1 to 3 wk after treatment. None of the fecal samples from sheep or cattle negative for fascioliasis but naturally infected with other parasites including Dicroelium dendriticum showed reactivity in the MM3 capture ELISA. These results indicate that this assay is a reliable and ultrasensitive method for detecting subnanogram amounts of F. hepatica antigens in feces from sheep and cattle, facilitating early diagnosis.

Modulation of rat macrophage function by the Mangifera indica L. extracts Vimang and mangiferin.

Vimang is an aqueous extract of Mangiferia indica L., traditionally used in Cuba as an anti-inflammatory, analgesic and antioxidant. In the present study, we investigated the effects of Vimang and of mangiferin (a C-glucosylxanthone present in the extract) on rat macrophage functions including phagocytic activity and the respiratory burst. Both Vimang and mangiferin showed inhibitory effects on macrophage activity: (a) intraperitoneal doses of only 50-250 mg/kg markedly reduced the number of macrophages in peritoneal exudate following intraperitoneal injection of thioglycollate 5 days previously (though there was no significant effect on the proportion of macrophages in the peritoneal-exudate cell population); (b) in vitro concentrations of 0.1-100 microg/ml reduced the phagocytosis of yeasts cells by resident peritoneal and thioglycollate-elicited macrophages; (c) in vitro concentrations of 1-50 microg/ml reduced nitric oxide (NO) production by thioglycollate-elicited macrophages stimulated in vitro with lipopolysaccharide (LPS) and IFNgamma; and (d) in vitro concentrations of 1-50 microg/ml reduced the extracellular production of reactive oxygen species (ROS) by resident and thioglycollate-elicited macrophages stimulated in vitro with phorbol myristate acetate (PMA). These results suggest that components of Vimang, including the polyphenol mangiferin, have depressor effects on the phagocytic and ROS production activities of rat macrophages and, thus, that they may be of value in the treatment of diseases of immunopathological origin characterized by the hyperactivation of phagocytic cells such as certain autoimmune disorders.

Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach.

The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic Indices (sto-TPGIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining of molecular and macromolecular chemical structures within large databases. In the work described here, we review and comment on the several applications of MARCH-INSIDE to the Medicinal Chemistry of Antimicrobial agents as well as their molecular targets. First we revised the use of classic sto-TIs to predict antiparasite compounds for the treatment of Fascioliasis. Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict new antibacterial, antiviral and anti-coccidial compounds. After that, we review multi-target sto-TIs (mt-sto-TIs), which unifying QSAR models predicting antifungal, antibacterial, or anti-parasite drugs with multiple targets (microbial species). We also discussed the uses of mt-sto-TIs to assemble drug-drug similarity Complex Networks of antimicrobial compounds based on molecular structure. Last, we review the use of MARCH-INSIDE to generate macromolecular TIs and TPGIs for proteins or RNA targets for antimicrobial drugs.

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug–drug complex networks

One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug-drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.

Antigenic cross-reactivity in mice between third-stage larvae of Anisakis simplex and other nematodes

Abstract We used ELISA and immunoblotting to investigate antigenic cross-reactivity in mice between third-stage larvae of Anisakis simplex and five other nematodes: the ascaridoids Ascaris suum, Toxocara canis and Hysterothylacium aduncum, and the nonascaridoids Trichinella spiralis and Trichuris muris. Two sera were raised against each species (including A. simplex, but excluding A. suum), by infection or by immunization with somatic antigens. Serum against A. suum was raised by immunization only. The reactivities of each serum with A. simplex somatic antigens (SA), excretion-secretion antigens (ES), pseudocoelomic fluid antigens (PF) and cuticular antigens (CA) were investigated. The results of ELISA indicated high antigenic cross-reactivity between A. simplex and the remaining ascaridoid nematodes, confirming that there is extensive antigenic similarity within this group of nematode parasites. Immunoblotting again confirmed the high degree of cross-reactivity between the SA of A. simplex and SAs of the other ascaridoids, although several A. simplex SA components in the 11–18 kDA range were only recognized by sera from mice infected with A. simplex. In addition, two A. simplex PF components of 22 and 27 kDA, were recognized only by sera from mice infected with, or immunized with the SA of, A. simplex. Finally, the anti-phosphorylcholine monoclonal antibody BH8 recognized only a small number of A. simplex antigens, indicating that phosphorylcholine epitopes are not significant contributors to the observed cross-reactivity with the other nematodes.

Philasterides dicentrarchi (Ciliophora: Scuticociliatida) expresses surface immobilization antigens that probably induce protective immune responses in turbot.

Philasterides dicentrarchi is a histophagous ciliate causing systemic scuticociliatosis in cultured turbot. This study demonstrates that turbot which survive this disease have serum antibodies that recognize ciliary antigens of this ciliate in ELISA and immobilize/agglutinate the ciliate in vitro. Mouse sera raised against ciliary antigens and integral membrane proteins are likewise capable of immobilizing/agglutinating the ciliates, indicating that P. dicentrarchi, like other ciliates, expresses surface immobilization antigens. Furthermore, the antigen agglutinating reaction induces the parasite to shed its surface antigens rapidly, replacing them with others with different specific serology. This antigen shedding and variation response is similar to that detected in other protozoan parasites. Immunization of turbot with ciliate lysate plus adjuvant or with formalin-fixed ciliates induced synthesis of agglutinating antibodies and conferred a degree of protection against challenge infection, suggesting that the response to surface antigens may play an important role in defence against this pathogen, SDS-PAGE and immunoblotting studies indicated the existence of a predominant polypeptide of about 38 kDa in the ciliary antigen and membrane protein fractions, and this may be the principal surface antigen of P. dicentrarchi.

MM3-ELISA evaluation of coproantigen release and serum antibody production in sheep experimentally infected with Fasciola hepatica and F. gigantica.

During an experimental infection of sheep with Fasciola hepatica or F. gigantica, MM3-SERO and MM3-COPRO ELISA tests were applied to compare the kinetics of antibody production and coproantigen release between the 2nd and 32nd week post-infection (wpi). The Kato-Katz technique was used to measure the kinetics of egg shedding by both Fasciola species (eggs per gram of feces, epg). The kinetics of IgG antibodies for all sheep infected with F. hepatica and F. gigantica followed a similar pattern. Optical density (OD) increased rapidly between the 4th until the 12th wpi, when the highest values were reached and then decreased slowly until the 32nd wpi. Coproantigen levels increased above the cut-off value between 6 and 9 wpi in the F. hepatica group, and between 9 and 11wpi in the F. gigantica group. The comparison between coproantigen levels and epg indicated that F. hepatica-infected sheep had detectable amounts of coproantigens 4-7 weeks before patency (egg shedding), while F. gigantica-infected sheep had detectable amounts of coproantigens 3-6 weeks before patency. When comparing the kinetics of coproantigen release vs the kinetics of epg, a similar pattern emerged, but with a two-week time-lag in epg, for both F. hepatica and F. gigantica infections. The amount of coproantigen release by each adult was not burden dependent for F. hepatica infection (burden of 33-66 adults), while it was for F. gigantica infection (burden of 17-69 adults). The results demonstrate the usefulness of the MM3-SERO and MM3-COPRO ELISAs as tools for the diagnosis of early as well as long-term fascioliasis infections, and suggest that they can potentially be applied to human fascioliasis even in countries where F. hepatica and F. gigantica co-exist. These tests can be employed not only in the diagnosis, but also in studies on epidemiology as well as pathogenesis and treatment in animals and humans since they allow post-treatment infection monitoring.

MIND-BEST: Web Server for Drugs and Target Discovery; Design, Synthesis, and Assay of MAO-B Inhibitors and Theoretical−Experimental Study of G3PDH Protein from Trichomonas gallinae

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a public Web server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemented the model into the Web portal Bio-AIMS as an online server entitled MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (MIND-BEST), located at http://miaja.tic.udc.es/Bio-AIMS/MIND-BEST.php . This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally, we illustrated two practical uses of this server with two different experiments. In experiment 1, we report for the first time a MIND-BEST prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of eight rasagiline derivatives, promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, 3D structure modeling with LOMETS, and MIND-BEST prediction for different peptides as new protein of the found in the proteome of the bird parasite Trichomonas gallinae, which is promising for antiparasite drug targets discovery.