Molecular omics最新文献

Glycosyl-inositol-phospho-ceramides (GIPCs) or glycosylphosphatidylinositol-anchored fungal polysaccharides are major lipids in plant and fungal plasma membranes and play an important role in stress adaption. However, their analysis remains challenging due to the multiple steps involved in their extraction and purification prior to mass spectrometry analysis. To address this challenge, we report here a novel simplified method to identify GIPCs from Aspergillus fumigatus using the new Bruker MBT lipid Xtract assay. A. fumigatus reference strains and clinical isolates were cultured, harvested, heat-inactivated and suspended in double-distilled water. A fraction of this fungal preparation was then dried in a microtube, mixed with an MBT lipid Xtract matrix (Bruker Daltonik, Germany) and loaded onto a MALDI target plate. Analysis was performed using a Bruker MALDI Biotyper Sirius system in the linear negative ion mode. Mass spectra were scanned from m/z 700 to m/z 2 000. MALDI-TOF MS analysis of cultured fungi showed a clear signature of GIPCs in Aspergillus fumigatus reference strains and clinical isolates. Here, we have demonstrated that routine MALDI-TOF in the linear negative ion mode combined with the MBT lipid Xtract is able to detect Aspergillus fumigatus GIPCs.

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Pancreatic cancer (PC) is a highly malignant cancer characterized by poor prognosis, high heterogeneity, and intricate heterocellular systems. Selecting an appropriate experimental model for studying its progression and treatment is crucial. Patient-derived models provide a more accurate representation of tumor heterogeneity and complexity compared to cell line-derived models. This review initially presents relevant patient-derived models, including patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived explants (PDEs), which are essential for studying cell communication and pancreatic cancer progression. We have emphasized the utilization of these models in comprehending intricate intercellular communication, drug responsiveness, mechanisms underlying tumor growth, expediting drug discovery, and enabling personalized medical approaches. Additionally, we have comprehensively summarized single-cell analyses of these models to enhance comprehension of intercellular communication among tumor cells, drug response mechanisms, and individual patient sensitivities.

Maren Runchang pill (MRRCP) is a Chinese patent medicine used to treat constipation in clinics. It has multi-component and multi-target characteristics, and there is an urgent need to screen markers to ensure its quality. The aim of this study was to screen quality markers of MRRCP based on a “differential compounds-bioactivity” strategy using machine learning and network pharmacology to ensure the effectiveness and stability of MRRCP. In this study, UPLC-Q-TOF-MS/MS was used to identify chemical compounds in MRRCP and machine learning algorithms were applied to screen differential compounds. The quality markers were further screened by network pharmacology. Meanwhile, molecular docking was used to verify the screening results of machine learning and network pharmacology. A total of 28 constituents in MRRCP were identified, and four differential compounds were screened by machine learning algorithms. Subsequently, a total of two quality markers (rutin and rubiadin) in MRRCP. Additionally, the molecular docking results showed that quality markers could spontaneously bind to core targets. This study provides a reference for improving the quality evaluation method of MRRCP to ensure its quality. More importantly, it provided a new approach to screen quality markers in Chinese patent medicines.

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host–pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen–citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen–host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Nanoflow liquid chromatography-mass spectrometry is key to enabling in-depth proteome profiling of trace samples, including single cells, but these separations can lack robustness due to the use of narrow-bore columns that are susceptible to clogging. In the case of single-cell proteomics, offline cleanup steps are generally omitted to avoid losses to additional surfaces, and online solid-phase extraction/trap columns frequently provide the only opportunity to remove salts and insoluble debris before the sample is introduced to the analytical column. Trap columns are traditionally short, packed columns used to load and concentrate analytes at flow rates greater than those employed in analytical columns, and since these first encounter the uncleaned sample mixture, trap columns are also susceptible to clogging. We hypothesized that clogging could be avoided by using large-bore porous layer open tubular trap columns (PLOTrap). The low back pressure ensured that the PLOTraps could also serve as the sample loop, thus allowing sample cleanup and injection with a single 6-port valve. We found that PLOTraps could effectively remove debris to avoid column clogging. We also evaluated multiple stationary phases and PLOTrap diameters to optimize performance in terms of peak widths and sample loading capacities. Optimized PLOTraps were compared to conventional packed trap columns operated in forward and backflush modes, and were found to have similar chromatographic performance of backflushed traps while providing improved debris removal for robust analysis of trace samples.

Plants should be probably thought of as the most formidable chemical laboratory that can be exploited for the production of an incredible number of molecules with remarkable structural and chemical diversity that cannot be matched by any synthetic libraries of small molecules. The bryophytes chemistry has been neglected for too long, but in the last ten years, this scenery is changing, with several studies being made using extracts from bryophytes, aimed at the characterization of interesting metabolites, with their metabolome screened. The main objective of this study was to analyze the metabolome of Brittonodoxa subpinnata, a native Brazilian moss species, which occurs in the two Brazilian hotspots. GC-MS and LC-MS² were performed. All extracts were analyzed using the molecular networking approach. The four extracts of B. subpinnata (polar, non-polar, soluble, and insoluble) resulted in 928 features detected within the established parameters. 189 (20.4%) compounds were annotated, with sugars, fatty acids, flavonoids, and biflavonoids as the major constituents. Sucrose was the sugar with the highest quantity; palmitic acid the major fatty acid but with great presence of very long-chain fatty acids rarely found in higher plants, glycosylated flavonoids were the major flavonoids, and biflavonoids majorly composed by units of flavones and flavanones, exclusively found in the cell wall. Despite the high percentage, this work leaves a significant gap for future works using other structure elucidation techniques, such as NMR.

Clubroot is a destructive root disease of canola (Brassica napus L.) caused by Plasmodiophora brassicae Woronin. Despite extensive research into the molecular responses of B. napus to P. brassicae, there is limited information on proteome- and metabolome-level changes in response to the pathogen, especially during the initial stages of infection. In this study, we have investigated the proteome- and metabolome- level changes in the roots of clubroot-resistant (CR) and -susceptible (CS) doubled-haploid (DH) B. napus lines, in response to P. brassicae pathotype 3H at 1-, 4-, and 7-days post-inoculation (DPI). Root proteomes were analyzed using nanoflow liquid chromatography coupled with tandem mass spectrometry (nano LC-MS/MS). Comparisons of pathogen-inoculated and uninoculated root proteomes revealed 2515 and 1556 differentially abundant proteins at one or more time points (1-, 4-, and 7-DPI) in the CR and CS genotypes, respectively. Several proteins related to primary metabolites (e.g., amino acids, fatty acids, and lipids), secondary metabolites (e.g., glucosinolates), and cell wall reinforcement-related proteins [e.g., laccase, peroxidases, and plant invertase/pectin methylesterase inhibitors (PInv/PMEI)] were identified. Eleven nucleotides and nucleoside-related metabolites, and eight fatty acids and sphingolipid-related metabolites were identified in the metabolomics study. To our knowledge, this is the first report of root proteome-level changes and associated alterations in metabolites during the early stages of P. brassicae infection in B. napus.

The present study deals with the metabolomic status of Ulva cells undergoing phase transition (vegetative, determination and differentiation) when exposed to different abiotic conditions. The objective was to study whether metabolite changes occurring during the phase transition reveal any commonality among differential abiotic conditions. The phase transition was followed through microscopic observations and ¹H NMR characterization at 0 h, 24 h, and 48 h after the incubation of the thallus under abiotic conditions, such as different salinities (20–35 psu), temperatures (20–35 °C), photoperiods (18 : 6, 12 : 12, and 6 : 18 D/N), light intensities (220, 350, and 500 μmol photons m⁻² s⁻¹), nitrate (0.05–0.2 g L⁻¹) and phosphate (0.05–0.2 g L⁻¹) concentrations. Microscopic analysis revealed the role of all abiotic conditions except variable salinity and phosphate concentration in phase transition. NMR analysis revealed that glucose increased in the determination phase [7.58 to 9.62 normalized intensity (AU)] and differentiation phase (5.85 to 6.41 AU) from 20 °C to 25 °C temperature. Coniferyl aldehyde increased in vegetative (5.79 to 6.83 AU) and differentiation (6.66 to 7.40 AU) phases from 20 °C to 30 °C temperature. The highest average (22.97) was found in photoperiod (average range = 0–122.91) and the highest SD (24.73) in salinity (SD range = 1.86–57.04) in region 9 (creatinine and cysteine) of the differentiation phase. A total of 30 metabolites were identified under the categories of sugars, amino acids, and aromatic compounds. The present study will aid in understanding the mechanisms underlying cell differentiation during reproduction. The result may serve as an important reference point for future studies, besides helping in controlling seedling preparation for commercial farming as well as the management of rapid green tide formation.