The indiscriminate use of chemical pesticides increasingly harms the health of living beings and the environment. Thus, biological control carried out by microorganisms has gained prominence, since it consists of an environmentally friendly alternative to the use of pesticides for controlling plant diseases. Herein, we evaluated the potential role of endophytic Trichoderma strains isolated from forest species of the Cerrado-Caatinga ecotone as biological control agents of crop pathogenic fungi. Nineteen Trichoderma strains were used to assess the antagonistic activity by in vitro bioassays against the plant pathogens Colletotrichum truncatum, Lasiodiplodia theobromae, Macrophomina phaseolina, and Sclerotium delphinii isolated from soybean, cacao, fava bean, and black pepper crops, respectively. All Trichoderma strains demonstrated inhibitory activity on pathogen mycelial growth, with maximum percent inhibition of 70% against C. truncatum, 78% against L. theobromae, 78% against M. phaseolina, and 69% against S. delphinii. Crude methanol extracts (0.5 to 2.0 mg mL^-1) of Trichoderma strains were able to inhibit the growth of C. truncatum, except Trichoderma sp. T3 (UFPIT06) and T. orientale (UFPIT09 and UFPIT17) at 0.5 mg mL^-1, indicating that the endophytes employ a biocontrol mechanism related to antibiosis, together with multiple mechanisms. Discriminant metabolites of Trichoderma extracts were unveiled by liquid chromatography-tandem mass spectrometry-based metabolomics combined with principal component analysis (PCA), which included antifungal metabolites and molecules with other bioactivities. These results highlight the biocontrol potential of Trichoderma strains isolated from the Cerrado-Caatinga ecotone against crop pathogenic fungi, providing support for ongoing research on disease control in agriculture.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

Progenesis QI (PQI) is a multiplatform bioinformatics tool that facilitates the identification workflow for metabolomics experiments. PQI uses fragmentation data provided by MassBank of North America (MoNA) libraries, among others, for metabolite annotation. However, PQI does not officially support MoNA libraries and other libraries based on structure-data files (.sdf). This paper describes the development and application of a software named MoNA to Progenesis QI Library Converter, allowing PQI and MoNA by correcting the fragmentation data of the library for Progenesis readability. We evaluated several public experimental datasets, including human plasma, plant extracts, cultured cells, bacteria, rat serum, and rat hippocampus. The results showed that it is mandatory to proceed with file conversion of each library to allow PQI to access fragmentation information from .msp (main spectra profile) files. This step is highly recommended to improve the identification level of the metabolites.