Novel Photo- and Electrochemical Approaches to the Construction of Complex Molecular Architectures

La tesi di dottorato descrive l'utilizzo di tecniche innovative nell'ambito della sintesi organica. L'utilizzo di metodi fotochimici ed elettrochimici è stato ulteriormente valorizzato dalla combinazione con tecnologie di flusso e con l'utilizzo di CO2 come risorsa rinnovabile di atomi di carbonio.This PhD thesis describes the utilisation of innovative techniques in the field of organic synthesis. The exploitation of photochemical and electrochemical methods was further promoted by the combination with flow technology and with the use of CO2 as a renewable carbon atom resource

Meta-Statistics for Variable Selection: The R Package BioMark

Biomarker identification is an ever more important topic in the life sciences. With the advent of measurement methodologies based on microarrays and mass spectrometry, thousands of variables are routinely being measured on complex biological samples. Often, the question is what makes two groups of samples different. Classical hypothesis testing suffers from the multiple testing problem; however, correcting for this often leads to a lack of power. In addition, choosing α cutoff levels remains somewhat arbitrary. Also in a regression context, a model depending on few but relevant variables will be more accurate and precise, and easier to interpret biologically.We propose an R package, BioMark, implementing two meta-statistics for variable selection. The first, higher criticism, presents a data-dependent selection threshold for significance, instead of a cookbook value of α = 0.05. It is applicable in all cases where two groups are compared. The second, stability selection, is more general, and can also be applied in a regression context. This approach uses repeated subsampling of the data in order to assess the variability of the model coefficients and selects those that remain consistently important. It is shown using experimental spike-in data from the field of metabolomics that both approaches work well with real data. BioMark also contains functionality for simulating data with specific characteristics for algorithm development and testing

TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data

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Editorial: Spectroscopy for Crop and Product Phenotyping

Spectroscopy is a viable technique for exploring plant biochemistry in an efficient, accurate and typically non-destructive manner (Schie et al., 2018). By taking advantage of the properties of plant biomolecules and metabolites in different regions of the electromagnetic spectrum, advances have been made that allow for investigations into previously inaccessible aspects of biology in real-time (Akhgar et al., 2020). Other important advances involve disease diagnostics, early stress detection and plant product quality assessment (Gemperline et al., 2016). High-throughput technologies for nucleotide sequence analysis and detection of sequence variation have been increasingly used for plant genotyping and other fields of genetic testing. Polymerase chain reaction (PCR) is a simple and rapid method that can detect molecular genetic polymorphisms in basic and applied research applications. An important prospective use of PCR-based genotyping assays is to perform large-scale phenotyping analyses (Suyama and Matsuki, 2015), mutant screens, and comparative physiological analyses for Marker Assisted Breeding. This Research Topic highlights novel and innovative applications of all spectroscopic techniques that aim for characterization in plants in order to understand plant growth and productivity. Studies featuring impactful and innovative applications of well-established methodologies such as Raman spectroscopy, Near-infrared spectroscopy, Fluorescence resonance energy transfer, hyperspectral imaging, or a novel combination of spectrometric measurement techniques and novel spectrometric techniques were invited, resulting in 8 published articles. Infrared and Near-infrared spectroscopy (NIR) spectroscopy Infrared spectroscopy (Gillie et al., 2000) is becoming an increasingly popular and promising technology in agricultural and agri-food industries to measure intact, fresh, and unfrozen samples directly. The field of infrared spectroscopy is complex and comprehensive and could lead to specialized solutions for the agricultural sector in general, and for the viticultural industry in particular. Wyngaard et al., show that infrared spectroscopy is implemented for continuous monitoring of key metabolites in grapevine organs throughout the growing season. The observed spectral changes led to the classification of grapevine organs, providing individualized calibrations to compensate for the heterogeneity in grapevines, as well as developing more robust prediction models. Near-infrared spectroscopy is a non-destructive, fast, and low-cost method to measure the biochemicals for screening plant samples. Armstrong et al., investigate the feasibility of single kernel NIR spectroscopy for rapid determination of protein, oil, and weight in intact single sorghum seeds, highlighting the use of this non-destructive and quick method for screening these traits in sorghum breeding and industry applications. In the work of Ejaz et al., biochemical components of sorghum were predicted for enhancing grain sorting efficiency for food, feed, and fuel, using Fourier-transform NIR spectroscopy. Raman Spectroscopy-based Plant Pathology Diagnostics Raman spectroscopy (RS) is a label-free, non-invasive, non-destructive spectroscopic technique that is effective for studying the chemical structure of analyzed samples (Cialla-May et al., 2022). This technique has been widely used among biochemists, and has now found applications in agronomy, plant pathology and physiology for analysis of plant health status. Changes in plant biochemistry can be probed by Raman spectroscopy, allowing its use in confirmatory diagnosis of plant pathology. Dou et al., use RS to develop the diagnosis of Huanglongbing, a devastating disease caused by Candidatus Liberibacter spp. (Ca. L. asiaticus). By using a combination of HPLC and image studies of leaves, they created a ground truth concept demonstrating that a given signature in RS corresponds to increased p-coumaric acid and decreased lutein in infected grapefruit leaves. Since Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level, it represents a prospective and rapid technique for agronomy and plant pathology. Farber et al., show that RS can be used for highly accurate identification of stalk rot caused by Colletotrichum graminicola in maize at both early and late stages of disease progression, via spectroscopic analysis of both leaves and stalks. High-Resolution Microscopy and Spectrometry Approach The rhizosphere is a hotspot for microbial activity, organic carbon input, and carbon turnover in soils (Ilhardt et al., 2019). Several stand-alone and combinatorial methods have been developed to investigate the chemistry and the role of microbes in soil and the rhizosphere. Bandara et al., present a novel approach that allows simultaneous microbial identification and chemical analysis of the rhizosphere at a spatial resolution ranging from micro- to nanometers. This new method allows for comprehensive study of the spatio-temporal organization of nutrients and microbes in the rhizosphere at an unprecedented scale and provides a platform for a mechanistic understanding of complex patterns of interactions between roots, the microbiome and soil using a correlative microscopy approach. Lohse et al., present a novel workflow using laser desorption ionization combined with mass spectrometric imaging to directly analyze plant metabolites in a complex soil matrix. The target metabolites were detected with a spatial resolution of 25 μm in the root and surrounding soil, based on accurate masses using ultra-high mass resolution laser desorption ionization Fourier-transform ion cyclotron resonance mass spectrometry. Direct molecular imaging allows a non-targeted or targeted analysis of plant metabolites in undisturbed soil samples, paving the way to study the turnover of root-derived organic carbon in the rhizosphere with high chemical and spatial resolution. PCR genotyping Single-nucleotide polymorphisms (SNPs) represent the smallest type of genetic differences in DNA between biological samples (Campbell et al., 2015). SNP analysis has emerged as one of the most powerful tools employed over a wide range of research, from small-scale student-led investigations of specific SNPs to high-throughput microarray technologies to analyze thousands of SNPs simultaneously. Kalendar et al., propose a modification to improve the version of the existing Allele-specific PCR method that is similar to the Kompetitive allele specific PCR (KASP) technique for genotyping SNPs based on fluorescence resonance energy transfer (FRET). This new technique is based on the simultaneous presence of two components in the PCR: an allele-specific mixture (allele-specific and common primers), and a template-independent detector mixture that contains two to four universal probes and a single universal quencher oligonucleotide (Kalendar et al., 2022). The SNP site is positioned preferably at a penultimate base in each allele-specific primer, which increases the reaction specificity and allele discrimination. The proposed method was used for SNP genotyping in barley genes HvSAP16 and HvSAP8, and is suitable for bi-allelic uniplex, 3- or 4-allelic variants, or different SNPs in a multiplex format that can be used in a range of applications including medical, forensic, or any study involving SNP genotyping. Overall, the research collected on this Research Topic highlights innovative and promising applications of all spectroscopic techniques for characterizing plants to understand plant growth, productivity, and disease resistance, and for PCR-based genotyping to perform large-scale mutant screens, comparative analysis for Marker Assisted Breeding.Non peer reviewe

Application of a target-guided data processing approach in saturated peak correction of GC×GC analysis

5openInternationalItalian coauthor/editorDetector and column saturations are problematic in comprehensive two-dimensional gas chromatography (GC×GC) data analysis. This limits the application of GC×GC in metabolomics research. To address the problems caused by detector and column saturations, we propose a two-stage data processing strategy that will incorporate a targeted data processing and cleaning approach upstream of the “standard” untargeted analysis. By using the retention time and mass spectrometry (MS) data stored in a library, the annotation and quantification of the targeted saturated peaks have been significantly improved. After subtracting the nonperfected signals caused by saturation, peaks of coelutes can be annotated more accurately. Our research shows that the target-guided method has broad application prospects in the data analysis of GC×GC chromatograms of complex samples.openZhang, Penghan; Carlin, Silvia; Franceschi, Pietro; Mattivi, Fulvio; Vrhovsek, UrskaZhang, P.; Carlin, S.; Franceschi, P.; Mattivi, F.; Vrhovsek, U

MS imaging of small metabolites in fruits

MS Imaging of large molecules, e.g. proteins and lipids, have been reported with MALDI. On the other hand, few works have been done on mapping the small molecules by MALDI imaging. This is mainly due to the high chemical noise background interference in the low mass region caused by chemical matrices. DESI Imaging, however, could be complementary to MALDI in that sample can be analyzed directly without matrices. A large group of small metabolites are of considerable physiological and morphological importance in plants, e.g. flavonols involve in plant defense against environmental stresses and organic acids are one of important factors for fruit quality, but knowledge of their precise functions is limited due to insufficient characterization of their spatial responses. In this communication we will discuss methodological details about MS imaging of small metabolites in apple and grape in terms of sample preparation, imaging methods, and other experimental concerns by using MALDI [1] and DESI source coupled with a high resolution/accurate LTQ-Orbitrap mass spectrometer. Finally, we will describe the spatial distributions of flavonols and organic acids in apple and grape, respectively

Effect of dairy, season, and sampling position on physical properties of Trentingrana cheese: application of an LMM-ASCA model

7openInternationalItalian coauthor/editorTrentingrana hard cheese is a geographic specification of the PDO Grana Padano. It is produced according to an internal regulation by many cooperative dairy factories in the Trentino region (northern Italy), using a semi-artisanal process (the only allowed ingredients are milk, salt, and rennet). Within the PSR project TRENTINGRANA, colorimetric and textural measurements have been collected from 317 cheese wheels, which were sampled bi-monthly from all the consortium dairies (n = 15) within the timeframe of two years, to estimate the effect on physical properties related to the season of the year and the dairy factory implant. To estimate the effect of the dairy and the time of the year, considering the internal variability of each cheese wheel, a linear mixed-effect model combined with a simultaneous component analysis (LMM-ASCA) is proposed. Results show that all the factors have a significant effect on the colorimetric and textural properties of the cheese. There are five clusters of dairies producing cheese with similar properties, three different couples of months of the year when the cheese produced is significantly different from all the others, and the effect of the geometry of the cheese wheel is reported as well.openRicci, Michele; Gasperi, Flavia; Endrizzi, Isabella; Menghi, Leonardo; Cliceri, Danny; Franceschi, Pietro; Aprea, EugenioRicci, M.; Gasperi, F.; Endrizzi, I.; Menghi, L.; Cliceri, D.; Franceschi, P.; Aprea, E

IsotopicLabelling: an R package for the analysis of MS isotopic patterns of labelled analytes.

Abstract Motivation Labelling experiments in biology usually make use of isotopically enriched substrates, with the two most commonly employed isotopes for metabolism being 2H and 13C. At the end of the experiment some metabolites will have incorporated the labelling isotope, to a degree that depends on the metabolic turnover. In order to propose a meaningful biological interpretation, it is necessary to estimate the amount of labelling, and one possible route is to exploit the fact that MS isotopic patterns reflect the isotopic distributions. Results We developed the IsotopicLabelling R package, a tool able to extract and analyze isotopic patterns from liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-MS (GC-MS) data relative to labelling experiments. This package estimates the isotopic abundance of the employed stable isotope (either 2H or 13C) within a specified list of analytes. Availability and Implementation The IsotopicLabelling R package is freely available at https://github.com/RuggeroFerrazza/IsotopicLabelling. Supplementary information Supplementary data are available at Bioinformatics online

Metabolomic characterization of commercial, old, and red-fleshed apple varieties

In this study, a metabolomic investigation was presented to correlate single polyphenolic compounds in apple pulp with quality characteristics such as antioxidant activity and content of phenolic compounds and anthocyanins in apple skin. Since the concentration of these compounds is influenced by environmental factors, the twenty-two apple cultivars originate from the same site. The polyphenolic compounds were analyzed by ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QqQ-MS/MS). The antioxidant activity, phenolic content, and anthocyanins were evaluated on the sunny and the shady sides of apple skin by spectrometric assays. In old apple varieties, the measured parameters were higher than in the commercial and red-fleshed varieties. By contrast, the profile of flavan-3-ols and anthocyanins was variable amongst commercial and red-fleshed varieties. The partial least square (PLS) method was applied to investigate the association between the skin proprieties and the metabolic profile of the pulp. The highest coefficients of determination in prediction (Q2) were obtained for compounds quantified in old cultivars. These results provided information to define the old apple varieties as a reliable group based on the pathway of the antioxidant compounds and anthocyanins content. Our results show the possibility to find cultivars with promising health features based on their content of polyphenols suitable for commercialization or breedin