SANIST: a rapid mass spectrometric SACI/ESI data acquisition and elaboration platform for verifying potential candidate biomarkers

Albini, Adriana; Briga, Daniela; Conti, Matteo; Bruno, Antonino; Farioli, Daniela; Canali, Sara; Sogno, Ilaria; D’Ambrosio, Gioacchino; Consonni, P.; Noonan, Douglas M.

doi:10.1002/rcm.7270

Cited by 15 publications

(24 citation statements)

References 34 publications

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“…We then proceeded in calculating the comparison vector employed to build the progressively updated supervised database that will be used for sample classification on the basis of individual steroid enzymatic profiles. When similarity vectors are calculated following the SANIST algorithm ( 39 ), the cortisol and DHEAS exhibits a high weight on the calculation function and the other concentration parameters area weakly considered by the discrimination function. To avoid this problem, we “normalized” the weight of the cortisol and DHEAS by dividing their value by 10 and 100, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

et al. 2018

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BackgroundSerum 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.MethodsHere, 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 Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.ResultsA 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.ConclusionWe 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.

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Section: Resultsmentioning

confidence: 99%

“…The vector machine learning database is obtained by classifying each vector as a control, patient with PCa, or with benign prostate hyperplasia. Each subject was classified, using the SANIST algorithm ( 39 ), by similarity of its ratio vector versus the database by excluding the identity match.…”

Section: Methodsmentioning

confidence: 99%

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

et al. 2018

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“…The three compounds have been selected on the basis of their different polarity and consequently diverse retention times in the MRM mass chromatogram. The analytes are subjected to different matrix effects, depending on the analyte retention time to evaluate the influence on the SANIST database spectral match and analyte identification . In order to optimize the analyte detection efficiency, direct infusion experiments were performed on pure standards.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, a novel Surface Activated Chemical Ionization‐Electrospray‐NIST Bayesian model database search (SANIST) platform has been introduced and used for untargeted metabolomics studies . A basic step of SANIST approach is compound identification based on high mass accurate (m/z accuracy error < 5 ppm) approach.…”

Section: Introductionmentioning

confidence: 99%

SANIST: optimization of a technology for compound identification based on the European Union directive with applications in forensic, pharmaceutical and food analyses

Cristoni

Dusi

Brambilla³

et al. 2016

J. Mass Spectrom.

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Electrospray Ionization and collision induced dissociation tandem mass spectrometry are usually employed to obtain compound identification through a mass spectra match. Different algorithms have been developed for this purpose (for example the nist match algorithm). These approaches compare the tandem mass spectra of the unknown analyte with the tandem mass spectra spectra of known compounds inserted in a database. The compounds are usually identified on the basis of spectral match value associated with a probability of recognition. However, this approach is not usually applied to multiple reaction monitoring transition spectra achieved by means of triple quadrupole apparatus, mainly due to the lack of a transition spectra database. The Surface Activated Chemical Ionization-Electrospray-NIST Bayesian model database search (SANIST) platform has been recently developed for new potential metabolite biomarker discovery, to confirm their identity and to use them for clinical and diagnostic applications. Here, we present an improved version of the SANIST platform that extends its application to forensic, pharmaceutical, and food analysis studies, where the compound identification rules are strict. The European Union (EU) has set directives for compound identification (EU directive 2002/657/EC). We have applied the SANIST method to identification of 11-nor-9-carboxytetrahydro-cannabinol in urine samples (an example of a forensic application), circulating levels of the immunosuppressive drug tacrolimus in blood (an example of a pharmaceutical application) and glyphosate in fruit juice (an example of a food analysis application) that meet the EU directive requirements. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Herein, we introduce a new method to increase the prediction efficiency of intestinal dysbiosis detection, based on the antagonist and pathogen bacteria pharmacological activity. Basically, feces molecular profiles are analyzed by liquid chromatography/surface‐activated chemical‐ionization/electrospray ionization (LC/SASI‐MS) and tandem mass spectrometry (MS/MS) in randomized data‐dependent scan acquisition mode . The antagonist and pathogen pharmacological active metabolites are correlated to latent dysbiosis on the basis of literature data.…”

Section: Molecular Fingerprint Potentially Produced By Gut Microbiotamentioning

confidence: 99%

Predicting and preventing intestinal dysbiosis on the basis of pharmacological gut microbiota metabolism

Cristoni

Bernardi

Larini

et al. 2019

Rapid Comm Mass Spectrometry

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The possible relationship(s) between intestinal dysbiosis and various degenerative intestinal-based diseases, e.g. diabetes and obesity, is an ongoing focus of intense investigation. [1][2][3][4] As a consequence of the results of such research, the focus is now on therapeutic strategies for such diseases based on modification of the gut microbiota. 2,4 Fecal transplantation is one such strategy, which was suggested by Zhang and co-workers 2 as a treatment for diabetes mellitus and by Kang and co-workers 4 for treatment of obesity. Kang and co-workers also reported different strategies involving fecal gut microbiota transplantation to treat obesity. Other studies correlated other degenerative diseases to gut microbiota activities and suggested therapeutic strategies to alleviate those disease states. 1 This interesting prospect, i.e., fecal gut microbiota transplantation to combat intestine-related diseases, has led to the development of methods by different research groups to evaluate the gut microbiota status in human subjects. 5-8 These approaches can be classified as:a. Untargeted approaches that search for bacteria responsible for dysbiosis without any prior knowledge of which types of bacterium might be involved. 5,6 b. Targeted approaches that search for specific pathogens and the correlation of these to gut microbiota-related dysbiosis. 7,8 One example of an untargeted approach is that of Pinheiro de Oliveira and co-workers who developed a method that detects thousands of different species using DNA gene amplification. 5 For the aforementioned DNA approaches, it is difficult to determine a correlation between the presence of a bacterial species and its involvement in intestinal dysbiosis without knowledge of how its metabolism affects the intestinal environment. 5,6

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SANIST: a rapid mass spectrometric SACI/ESI data acquisition and elaboration platform for verifying potential candidate biomarkers

Cited by 15 publications

References 34 publications

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

SANIST: optimization of a technology for compound identification based on the European Union directive with applications in forensic, pharmaceutical and food analyses

Predicting and preventing intestinal dysbiosis on the basis of pharmacological gut microbiota metabolism

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