BACKGROUND: The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders.
OBJECTIVE: Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia.
METHODS: Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform.
RESULTS: The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened.
CONCLUSIONS: Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.

UNASSIGNED: Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities.
UNASSIGNED: We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides.
UNASSIGNED: Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival.
UNASSIGNED: PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

Carfilzomib (CFZ) is the second-generation proteasome inhibitor that is approved by Food and Drug Administration (FDA) of USA for the treatment of relapsed and refractory multiple myeloma. Although the preclinical and clinical efficacy of CFZ is obvious, the mechanism by which CFZ leads to cell death has not been fully elucidated. Since CFZ primarily functions as a proteasome inhibitor, profiling CFZ-induced changes in protein turnover at the systematic level is sufficient and necessary. In this study, we characterize the effects of CFZ on the stability of 15,000 human proteins using Protein Turnover Assay (ProTA). CFZ affects fundamental cellular glycolysis, nitric oxide production and proteasome subunit homeostasis in multiple myeloma cells. In addition, LY294002 or KU-0063794 has synergistic effects with CFZ in multiple myeloma treatment. A profound understanding of how cells respond to chemotherapeutic agents provides insights into the basic mechanism of drug function and the rationale for CFZ combination therapy.

Extracellular vesicles (EV) and the microRNAs that they contain are increasingly recognised as a rich source of informative biomarkers, reflecting pathological processes and fundamental biological pathways and responses. Their presence in biofluids makes them particularly attractive for biomarker identification. However, a frequent caveat in relation to clinical studies is low abundance of EV RNA content. In this study, we used NanoString nCounter technology to assess the microRNA profiles of n = 64 EV low concentration RNA samples (180-49125 pg), isolated from serum and cell culture media using precipitation reagent or sequential ultracentrifugation. Data was subjected to robust quality control parameters based on three levels of limit of detection stringency, and differential microRNA expression analysis was performed between biological subgroups. We report that RNA concentrations > 100 times lower than the current NanoString recommendations can be successfully profiled using nCounter microRNA assays, demonstrating acceptable output ranges for imaging parameters, binding density, positive/negative controls, ligation controls and normalisation quality control. Furthermore, despite low levels of input RNA, high-level differential expression analysis between biological subgroups identified microRNAs of biological relevance. Our results demonstrate that NanoString nCounter technology offers a sensitive approach for the detection and profiling of low abundance EV-derived microRNA, and may provide a solution for research studies that focus on limited sample material.

Thanks to the recent development of innovative instruments and software with high accuracy and resolution, 3D modelling provides useful insights in several sectors (from industrial metrology to cultural heritage). Moreover, the 3D reconstruction of objects of artistic interest is becoming mandatory, not only because of the risks to which works of art are increasingly exposed (e.g., wars and climatic disasters) but also because of the leading role that the virtual fruition of art is taking. In this work, we compared the performance of four 3D instruments based on different working principles and techniques (laser micro-profilometry, structured-light topography and the phase-shifting method) by measuring four samples of different sizes, dimensions and surface characteristics. We aimed to assess the capabilities and limitations of these instruments to verify their accuracy and the technical specifications given in the suppliers\' data sheets. To this end, we calculated the point densities and extracted several profiles from the models to evaluate both their lateral (XY) and axial (Z) resolution. A comparison between the nominal resolution values and those calculated on samples representative of cultural artefacts was used to predict the performance of the instruments in real case studies. Overall, the purpose of this comparison is to provide a quantitative assessment of the performance of the instruments that allows for their correct application to works of art according to their specific characteristics.

Profiling social media users is an analytical approach to generate an extensive blueprint of user\'s personal characteristics, which can be useful for a diverse range of applications, such as targeted marketing and personalized recommendations. Although social user profiling has gained substantial attention in recent years, effectively constructing a collaborative model that could describe long and short-term profiles is still challenging. In this paper, we will discuss the profiling problem from two perspectives; how to mathematically model and track user\'s behavior over short and long periods and how to enhance the classification of user\'s activities. Using mathematical equations, our model can define periods in which the user\'s interests abruptly changed. A dataset consisting of 30,000 tweets was built and manually annotated into 10 topic categories. Bi-LSTM and GRU models are applied to classify the user\'s activities representing his interests, which then are utilized to create and model the dynamic profile. In addition, the effect of word embedding techniques and pre-trained classification models on the accuracy of the classification process is explored in this research.

Amplicon sequencing stands as a cornerstone in microbiome profiling, yet concerns persist regarding its resolution and accuracy. The enhancement of reference databases and annotations marks a new era for 16S rRNA-based profiling. Capitalizing on this potential, we introduce PM-profiler, a novel tool for profiling amplicon short reads. PM-profiler is implemented by C++-based advanced algorithms, such as pre-allocated hash for reference construction, hybrid and dynamic short-read matching, big-data-guided dual-mode hierarchical taxonomy annotation strategy, and full-procedure parallel computing. This tool delivers species-level resolution and ultrafast speed for large-scale microbiomes, surpassing alignment-based approaches and the Naïve-Bayesian model. Furthermore, recognizing the global uneven distribution of microbes, we delineate optimal annotation strategies for each sampling habitat based on microbial patterns over 270,000 microbiomes. Integrated with the established workflow of Parallel-Meta Suite and the latest curated reference databases, this endeavor offers a swift and dependable solution for high-precision microbiome surveys.IMPORTANCEOur study introduces PM-profiler, a new tool that deciphers the complexity of microbial communities. With advanced algorithms, flexible annotation strategies, and well-organized big-data, PM-profiler provides a faster and more accurate way to study on microbiomes, paving the way for discoveries that could improve our understanding of microbiomes and their impact on the world.

Comensal Bacteroidota (Bacteroidota) and Enterobacteriacea are often linked to gut inflammation. However, the causes for variability of pro-inflammatory surface antigens that affect gut commensal/opportunistic dualism in Bacteroidota remain unclear. By using the classical lipopolysaccharide/O-antigen \'rfb operon\' in Enterobacteriaceae as a surface antigen model (5-rfb-gene-cluster rfbABCDX), and a recent rfbA-typing strategy for strain classification, we characterized the integrity and conservancy of the entire rfb operon in Bacteroidota. Through exploratory analysis of complete genomes and metagenomes, we discovered that most Bacteroidota have the rfb operon fragmented into nonrandom patterns of gene-singlets and doublets/triplets, termed \'rfb-gene-clusters\', or rfb-\'minioperons\' if predicted as transcriptional. To reflect global operon integrity, contiguity, duplication, and fragmentation principles, we propose a six-category (infra/supra-numerary) cataloging system and a Global Operon Profiling System for bacteria. Mechanistically, genomic sequence analyses revealed that operon fragmentation is driven by intra-operon insertions of predominantly Bacteroides-DNA (thetaiotaomicron/fragilis) and likely natural selection in gut-wall specific micro-niches or micropathologies. Bacteroides-insertions, also detected in other antigenic operons (fimbriae), but not in operons deemed essential (ribosomal), could explain why Bacteroidota have fewer KEGG-pathways despite large genomes. DNA insertions, overrepresenting DNA-exchange-avid (Bacteroides) species, impact our interpretation of functional metagenomics data by inflating by inflating gene-based pathway inference and by overestimating \'extra-species\' abundance. Of disease relevance, Bacteroidota species isolated from cavitating/cavernous fistulous tract (CavFT) microlesions in Crohn\'s Disease have supra-numerary fragmented operons, stimulate TNF-alpha from macrophages with low potency, and do not induce hyperacute peritonitis in mice compared to CavFT Enterobacteriaceae. The impact of \'foreign-DNA\' insertions on pro-inflammatory operons, metagenomics, and commensalism/opportunism requires further studies to elucidate their potential for novel diagnostics and therapeutics, and to elucidate the role of co-existing pathobionts in Crohn\'s disease microlesions.

Due to its non-invasive nature and easy accessibility, urine serves as a convenient bi-ological fluid for research purposes. Furthermore, urine samples are uncomplicated to preserve and relatively inexpensive. MicroRNA (miRNAs), small molecules that regulate gene expression post-transcriptionally, play vital roles in numerous cellular processes, including apoptosis, cell differentiation, development, and proliferation. Their dysregulated expression in urine has been proposed as a potential bi-omarker for various human diseases, including bladder cancer. To draw reliable conclusions about the roles of urinary miRNAs in human diseases, it is essential to have dependable and reproducible methods for miRNA extraction and profiling. In this review, we address the technical challenges associated with studying urinary miRNAs and provide an update on the current technologies used for urinary miRNA isolation, quality control assessment, and miRNA profiling, highlighting both their advantages and limitations.

Seaweed extracts have enormous potential as bio-stimulants and demonstrated increased growth and yield in different crops. The presence of physiologically active component stimulate plant stress signaling pathways, enhances growth and productivity, as well as serve as plant defense agents. The seaweed extracts can reduce the use of chemicals that harm the environment for disease management. In the present study, the Sargassum tenerrimum extract treatment was applied, alone and in combination with Sclerotium rolfsii, to Arachis hypogea, to study the differential metabolite expression. The majority of metabolites showed maximum accumulation with Sargassum extract-treated plants compared to fungus-treated plants. The different classes of metabolite compounds like sugars, carboxylic acids, polyols, showed integrated peaks in different treatments of plants. The sugars were higher in Sargassum extract and Sargassum extract + fungus treatments compared to control and fungus treatment, respectively. Interestingly, Sargassum extract + fungus treatment showed maximum accumulation of carboxylic acids. Pathway enrichment analysis showed regulation of different metabolites, highest impact with galactose metabolism pathway, identifying sucrose, myo-inositol, glycerol and fructose. The differential metabolite profiling and pathway analysis of groundnut in response to Sargassum extract and S. rolfsii help in understanding the groundnut- S. rolfsii interactions and the potential role of the Sargassum extract towards these interactions.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12298-024-01418-9.