Università Cattolica del Sacro Cuore Showcase

Supplementary Table S1. Excel file containing the following sheets: a) plasma metabolites annotated by UHPLC-HRMS analysis; b) RSD (%) of the different annotated metabolites in pooled QC samples; c) VIP discriminant compounds included in the significant metabolomic pathways and resulting from the pairwise comparison "MDP vs MTX"; d) VIP discriminant compounds included in the significant metabolomic pathways and resulting from the pairwise comparison "MDP vs CTR"; e) VIP discriminant compounds included in the significant metabolomic pathways and resulting from the pairwise comparison "MTX vs CTR"; f) Exclusive VIP discriminant compounds for the different pairwise comparisons and resulting from the Venn diagram.

Heavy metal (HM) contamination in agricultural soils significantly threatens soil health and plant productivity. This study investigates cadmium (Cd) and zinc (Zn) stress impact on tomato plants (Solanum lycopersicum) while exploring the mitigation potential of microbial biostimulants (MBs)—arbuscular mycorrhizal fungi (AMF) and Pseudomonas fluorescens So_08 (PGPR)— employing multi-omics approaches. Specifically, the investigation delves deeply into soil-plant communication mechanisms mediated by root exudates and rhizosphere microbial communities. Root exudate profiling revealed distinct metabolic changes under HM stress, which compromised soil-plant interactions. Under Cd stress, key classes of metabolites, including phenylpropanoids, lipids, and isoprenoids, show reduced secretion. These metabolites play crucial roles in antioxidative defense, suggesting a shift in resource allocation mechanisms. Moreover, Cd negatively impacted rhizosphere fungal populations. Conversely, Zn stress prompted an increased exudation of lipids, including sphingolipids and sterols, reflecting an adaptive strategy to preserve membrane integrity and functionality. This stress also influenced rhizobacterial community structures. The MB application mitigated HM-induced stress by enhancing specialized metabolite syntheses, including cinnamic acids, terpenoids, and flavonoids, which promoted crop resilience. MBs also reshaped microbial diversity, fostering beneficial species like Portibacter spp., Alkalitalea saponilacus under Cd stress, and stimulating rhizobacteria like Aggregatilinea spp. under Zn stress. Multi-omics data integration combined with network analysis highlighted key features associated with improved nutrient availability and reduced HM toxicity under MB treatments, including metabolites and microbial taxa linked to sulfur cycling, nitrogen metabolism, and iron reduction pathways. These findings demonstrate that MBs can modulate plant metabolic responses and restore rhizosphere microbial communities under Cd and Zn stress, with PGPR showing broader metabolomic recovery effects and AMF influencing specific metabolite pathways. This study provides new insights into plant-microbe interactions in HM-contaminated environments, supporting the potential application of biostimulants for sustainable soil remediation and plant health improvement.

Supplemental Table S1. Excel file containing the following sheets: a) rumen metabolites annotated by UHPLC-HRMS analysis; b) milk metabolites annotated by UHPLC-HRMS analysis; c) Correlation Network resulting from data fusion of rumen and milk metabolites; d) Log2FC values of the VIP compounds discriminating the rumen metabolomic profile for the pairwise comparison 3-NOP vs CTR groups; e) Log2FC values of the VIP compounds discriminating the milk metabolomic profile for the pairwise comparison 3-NOP vs CTR group.

Supplementary material of manuscript - A Pseudomonas Plant Growth Promoting Rhizobacterium and Arbuscular Mycorrhiza differentially modulate the growth, photosynthetic performance, nutrients allocation, and stress response mechanisms triggered by a mild Zinc and Cadmium stress in tomato

RNA-seq raw count matrix from Orthotopic human immune system reconstituted (HIR) patient-derived xenograft (PDX) PDAC mouse models. metadata file match sample IDs and treatment conditions

Supplementary table 1. Diet ingredient formulation considering the different nutritional strategy-based clusters (Gallo et al., 2022).

dataset of the manuscript "A new metabolomics insight for origin and processing authentication of thyme by comprehensive UHPLC-HRMS fingerprinting and chemometrics" by Araceli Rivera-Pérez, Pascual García-Pérez, Roberto Romero-González, Antonia Garrido Frenich, and Luigi Lucini The data are produced by LC-HRMS foodomics and include annotated features, Ms and MS/MS spectra, as well as individual abundance across samples and treatments.

Supplementary material 3: metabolomics dataset

Dataset of phenolic compounds and sterols annotated in different extra-virgin olive oils gained from different origins, altitudes, and cultivars. This dataset was used to investigate the use of phenolics and sterols screening coupled to multivariate statistics and artificial neural networks, for authenticity and traceability purposes. TableS1: a detailed list of samples used in the study; TableS2: Matrix used for Artificial Neural Network (ANN) model. Columns represent the different Taggiasca and non-Taggiasca oils, while rows correspond to the chemical compounds used as variables; TableS3: Entire list of phenolic and sterol compounds obtained through UHPLC-ESI-QTOF mass spectrometry in olive oils from different altitudes, grouped in classes and subclasses together with annotations and composite mass spectrum; TableS4: Discriminant phenolic and sterol compounds of olive oils from different altitudes, identified by VIP (variable importance in projection) method; TableS5: Entire list of phenolic and sterol compounds obtained through UHPLC-ESI-QTOF mass spectrometry of olive oils from Imperia and Savona, defined as “Authentic” and olive oils from other Ligurian cultivars, defined as “Others”, grouped in classes and subclasses together with annotations and composite mass spectrum; TableS6: Discriminant phenolic and sterol compounds of olive oils from Imperia and Savona, defined as “Authentic” and olive oils from other Ligurian cultivars, defined as “Others”, identified by VIP (variable importance in projection) method; TableS7: Entire list of phenolic and sterol compounds obtained through UHPLC-ESI-QTOF mass spectrometry of counterfeits olive oils and olive oils Taggiasca, grouped in classes and subclasses together with annotations and composite mass spectrum; TableS8: Discriminant phenolic and sterol compounds of counterfeits olive oils and Taggiasca olive oils, identified by VIP (variable importance in projection) method; TableS9: Entire list of phenolic and sterol compounds obtained through UHPLC-ESI-QTOF mass spectrometry of Taggiasca olive oils and different olive oil varieties and origins, grouped in classes and subclasses together with annotations and composite mass spectrum; TablesS10: Discriminant phenolic and sterol compounds of Taggiasca olive oils and different olive oil varieties and origins, identified by VIP (variable importance in projection) method; TableS11:List of compounds used for Artificial Neural Network (ANN) model together with their transvariation probability index.

DATA from "Psychopathological burden and coping strategies among Italian healthcare workers facing the COVID-19 emergency: data from the COMET collaborative network "