Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach. Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning the Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association. A pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis. We show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting.

Immune-mediated responses, like inflammation, are virtually ubiquitous events in most diseases such as infections and cancers. Although there are numerous ways of how scientific questions related to these processes can be addressed, metabolomics has been used rather sporadically to interrogate physiologic and pathologic mechanisms in these contexts. As a result, immunity-associated metabolism and metabolic conditions under infections or in tumor microenvironments remain largely unexplored. On the other hand, the activity of the immune cells during the clearance of infectious pathogens or cancer cells has significant metabolic consequences, leading to the production of cytokines or cytotoxic molecules like reactive oxygen species, which contribute to the elimination of invading pathogens. Metabolomics profiling provides significant insights into the mechanisms of the physiologic immune response and alterations thereof induced by therapeutic interventions. Therefore, we are broadly using metabolomics to address unknown questions about the processes and mechanisms underlying the immune responses under different scenarios. Altogether, with well-established strategies such as cytological phenotypic profiling, genomics, and proteomics, the use of metabolomics will contribute to a more comprehensive knowledge of how the immune and metabolic responses impact immune-mediated processes.

This work presents a comparison between the biosorption of Hg (II) by the raw almond shell and activated almond shell. Almond shell based activated carbon has been obtained by physicochemical activation. Batch biosorption results confirmed that activating condition has a strong influence on the final biosorption process. The biosorbent was characterized using scanning electron microscopy and Fourier transform infrared spectroscopy. To optimize the biosorption conditions pH, adsorbent dose, initial concentration, contact time, stirring speed, and temperature on Hg (II) removal were studied. The optimum conditions for maximum Hg (II) were achieved at 20 and 10 min for raw almond shell and activated almond shell, respectively. The equilibrium data were described well by Langmuir, Freundlich, Dubinin–Radushkevich isotherm models and applying a test of model fitness. The best fit of Langmuir and Freundlich models were found for experimental data, which reveal the homogenous surface of the raw almond shell and the heterogeneity of activated almond shell surface. The kinetic data had been divided into either pseudo first order or second order on the basis of the best fit obtained from calculations, confirmed by a test of kinetic validity. An industrial application was examined to improve high biosorption capacity of raw and activated almond shells toward Hg (II).

Concern for public health involves evaluation of population exposure to toxicants. To do this, robust and high-throughput approaches are required to perform analyses of a large number of samples collected from subjects enrolled in cohort studies. The targeted methods enable detection of only predetermined compounds but do not search for new or unknown markers. Global approaches such as metabolomics which aim to reveal metabolic changes due to environmental stress or diseases can detect even those metabolic biomarkers that are unknown. Direct introduction mass spectrometry (DIMS) characterized by a significant reduction in analysis time is more appropriate for large-scale high throughput analyses such as for phenotyping large cohorts. Additionally, its combination with high-resolution mass spectrometry (DI-HRMS) improves the efficiency of the DIMS approach. Nevertheless, DI-HRMS generates complex data containing several thousands of peaks. Therefore, the objective of my work was to develop a rapid, high-throughput workflow, including the development of chemometric tools, in order to highlight the metabolomic perturbations induced by exposure to toxicants. First, DIMS approach was performed on the urine of farmers professionally exposed to two pesticides using an Orbitrap instrument and a new chemometric tool called Independent Component - Discriminant Analysis was developed for supervised analysis of the DIMS data. The developed methodology was then applied to five types of exposure and two analytical approaches DIMS and LC/MS were examined in order to validate the DIMS approach as well as the developed chemometric data analysis tool [3]. Second, DIMS approach was applied to an instrument of higher performances the FT-ICR, to improve the quality of the DIMS data [4]. The procedure was applied to a large number of samples to test the robustness of the approach. All these works demonstrated the feasibility and effectiveness of our high-throughput metabolomic approach for metabolic phenotyping of large populations.

Karnal bunt (KB) is a major disease of wheat, caused by the hemibiotrophic fungus, Tilletia indica. It is regarded as an economically important disease as the pathogen is quarantined in more than 70 countries. Despite its quarantine significance, there is meager information on the molecular mechanisms of pathogenesis employed by this important fungus to cause disease. Moreover, all the methods used to manage the disease have proven futile. To develop an effective disease management strategy, it is essential to unravel the pathogenic mechanisms utilized by T. indica. In order to understand the molecular mechanisms involved in KB pathogenesis, hi-throughput Genomics, Proteomics and Metabolomics approaches were used. Comparative proteomic analysis of the proteins from T. indica isolates varying in their virulence behavior resulted in identification of putative pathogenicity factors (such as oxalic acid and melanin production by malate dehydrogenase, enolase, respectively), proteins that play crucial role in adhesion, invasion and colonization (Glyceraldehyde-3-phosphate dehydrogenase, Fructose-bisphosphate aldolase, Triose phosphate isomerase), penetration (Secretory lipase), degradation of cell wall and antifungal proteins (Glycoside hydrolase family GH45, Aspartate proteases), MAP kinase signaling (Ste7), protection against host-derived reactive oxygen species (Mannitol dehydrogenase, Thioredoxins). Complementation of proteomic analysis with hybrid genome sequence resulted in the identification of homologs of candidate pathogenicity/virulence factors in T. indica. Further, GC- MS-based metabolic profiling validated the role of oxalic acid as a potential virulence factor in T. indica. Unraveling such complexity of molecular pathogenesis would further help in devising novel and effective disease management strategies including the development of resistant plant genotypes through classical plant breeding or genetic engineering, novel biomarkers for pathogen detection and new targets for fungicide development.