System network analysis of Rosmarinus officinalis transcriptome and metabolome-Key genes in biosynthesis of secondary metabolites

PepGAT, a chitinase-derived peptide, alters the proteomic profile of colorectal cancer cells and perturbs pathways involved in cancer survival

Colorectal cancer (CRC) affects the population worldwide, occupying the first place in terms of death and incidence. Synthetic peptides (SPs) emerged as alternative molecules due to their activity and low toxicity. Proteomic analysis of PepGAT-treated HCT-116 cells revealed a decreased abundance of proteins involved in ROS metabolism and energetic metabolisms, cell cycle, DNA repair, migration, invasion, cancer aggressiveness, and proteins involved in resistance to 5-FU. PepGAT induced earlier ROS and apoptosis in HCT-116 cells, cell cycle arrest, and inhibited HCT-116 migration. PepGAT enhances the action of 5-FU against HCT-116 cells by dropping down 6-fold the 5-FU toward HCT-116 and reduces its toxicity for non-cancerous cells. These findings strongly suggest the multiple mechanisms of action displayed by PepGAT against CRC cells and its potential to either be studied alone or in combination with 5-FU to develop new studies against CRC and might develop new drugs against it.

Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction

BACKGROUND/OBJECTIVES

This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. This discipline, which involves the comprehensive analysis of metabolites in a biological system, provides valuable insights into complex biological processes and disease states. As metabolomics assumes an increasingly prominent role in the diagnosis of human diseases and in precision medicine, there is a pressing need for more robust artificial intelligence tools that can offer enhanced reliability and accuracy in medical applications. The proposed DW-EML model addresses this by integrating multiple classifiers within a double-weighted voting scheme, which assigns weights based on the cross-validation accuracy and classification confidence, ensuring a more reliable prediction framework.

METHODS

The model was applied to publicly available datasets derived from studies on critical illness in children, chronic typhoid carriage, and early detection of ovarian cancer.

RESULTS

The results demonstrate that the DW-EML approach outperformed methods traditionally used in metabolomics, such as the Partial Least Squares Discriminant Analysis in terms of accuracy and predictive power.

CONCLUSIONS

The DW-EML model is a promising tool for metabolomic data analysis, offering enhanced robustness and reliability for diagnostic and prognostic applications and potentially contributing to the advancement of personalized and precision medicine.

Exploring New Structures of Kinase Inhibitors and Multitarget Strategies in Alzheimer's Disease Treatment

Alzheimer's disease (AD) treatments currently available have ineffective results. Previously employed Acetylcholine esterase inhibitors and memantine, an NMDA receptor antagonist, target a single target structure that plays a complex role in the multifactorial progression of disease. Memantine moderates the toxic effects of excessive glutamate activity by blocking NMDA receptors, which decreases neurotoxicity in AD, while acetylcholine esterase inhibitors function by blocking cholinergic receptors (muscarinic and nicotinic), preventing the breakdown of acetylcholine, thereby enhancing cholinergic transmission, thus improving cognitive functions in mild to moderate stages of AD. Every drug class targets a distinct facet of the intricate pathophysiology of AD, indicating the diverse strategy required to counteract the advancement of this neurodegenerative disorder. Thus, patients are currently not getting much benefit from current drugs. A closer look at the course of AD revealed several potential target structures for future drug discovery. AD drug development strategies focus on developing new target structures in addition to well-established ones for combination treatment regimens, ideally with a single drug that can target two different target structures. Because of their roles in AD progression pathways like pathologic tau protein phosphorylations as well as amyloid β toxicity, protein kinases have been identified as potential targets. This review will give a quick rundown of the first inhibitors of single protein kinases, such as glycogen synthase kinase (gsk3) β, along with cyclin-dependent kinase 5. We will also look into novel inhibitors that target recently identified protein kinases in Alzheimer's disease, such as dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A). Additionally, multitargeting inhibitors, which target multiple protein kinases as well as those thought to be involved in other processes related to AD will be discussed. This kind of multitargeting offers prospective hope for improved patient outcomes down the road since it is the most effective way to impede multifactorial disease development.

Unveiling the insecticidal efficiency of Viola ignobilis against Macrosiphum rosae and Agonoscena pistaciae: From chemical composition to cytotoxicity analysis

Currently, there is a growing preference for eco-friendly bioinsecticides over chemical insecticides due to their safety. Plant extracts have emerged as a promising solution for this purpose. Therefore, this study aimed to evaluate the insecticidal effectiveness of Viola ignobilis extract against two key pests of rose aphid (Macrosiphum rosae) and pistachio psylla (Agonoscena pistaciae). Significant compounds were identified using GC-MS and MALDI-TOF MS. Three bioassay methods were employed to assess the extract's insecticidal potential, and its cytotoxicity was tested on HEK293 cells. Results revealed that the highest insecticidal efficacy occurred at a concentration of 20 mg/mL after a 72 h exposure. The contact bioassay method displayed greater efficiency against M. rosae than A. pistaciae, while the oral bioassay demonstrated the highest efficiency against A. pistaciae. The extract also acted as a feeding deterrent, with indices of 77.47 ± 7.98 % and 87.98 ± 3.84 % for A. pistaciae and M. rosae, respectively. Furthermore, the insecticidal potency of the extract was assessed, resulting in LC50 values of 3.58 mg/mL and 6.77 mg/mL for the contact bioassay, and 0.87 mg/mL and 0.61 mg/mL for the oral bioassay against M. rosae and A. pistaciae, respectively. Importantly, the extract showed no detrimental cytotoxic effects on the HEK293 cell line within the tested concentration range, indicating its potential safety as a bioinsecticide. Overall, these findings highlight the potential of V. ignobilis extract as a promising candidate for further development in pest control.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Transcriptome and Metabolome Reveal Accumulation of Key Metabolites with Medicinal Properties of Phylloporia pulla

Phylloporia pulla, a macrofungal species in the Hymenochaetales, Basidiomycota, is known to enhance the nutritional and bioactive properties of rice through co-fermentation; however, its own secondary metabolites are not well understood. In this study, an integrative analysis of transcriptome and metabolome data revealed that the accumulation of steroids, steroid derivatives, and triterpenoids in P. pulla peaks during the mid-growth stage, while the genes associated with these metabolites show higher expression levels from the early to mid-growth stages. Weighted gene co-expression network analysis identified several modules containing candidate genes involved in the synthesis of steroids, steroid derivatives, and triterpenoids. Specifically, six key hub genes were identified, along with their connectivity to other related genes, as potential catalysts in converting the precursor lanosterol to celastrol. This study enhances our understanding of the secondary metabolites of P. pulla and is essential for the selective utilization of these bioactive compounds.

Revealing the aromatic sonata through terpenoid profiling and gene expression analysis of aromatic and non-aromatic coconut varieties

Aromatic coconut represents an exceptional variety of coconut known for its distinct and delightful flavor and aroma, both of which are highly cherished by consumers. Despite its popularity, there has been a lack of systematic research on aroma components and the associated synthetic genes. In this report, we developed the metabolite profiles of terpenoids by targeted metabolomics and obtained the expression profile of genes related to terpenoid biosynthesis by RNA-seq during different coconut fruit developmental stages. Totally, we separated 26 different terpenoids in aromatic coconut pulp, among which, geranyl acetate and (-)-isosyngene emerged as the most abundant. The integrated analysis of metabolism and RNA-seq data showed that HMGS2, HMGS3, IPI/IDI1, HMGR1, HMGR3, and CMK2 as potentially key genes involved in the synthesis of terpenoids in aromatic coconut. To validate these findings, qRT-PCR was conducted on terpenoid-related genes. These findings lay a foundation for understanding aroma formation and the molecular mechanism of terpenoids in coconut fruit.

Development of a high-speed and ultrasensitive UV/Vis-CM for detecting total triterpenes in traditional Chinese medicine and its application

This study proposes a novel colorimetric method based on the ultraviolet/visible spectrophotometry-colorimetric method (UV/Vis-CM) for detecting and quantifying total triterpenoids in traditional Chinese medicine. By incorporating the colourants 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid, triterpenoid compounds colour development became more sensitive, and the detection accuracy was significantly improved. 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid were incorporated in a 1:3 vol ratio at room temperature to react with the total triterpenes for 25 min, incorporated to an ice bath for 5 min, and then detected at the optimal absorption wavelength. The accuracy and reliability of this method were verified by comparison with high-performance liquid chromatography and four other colorimetric methods. Additionally, this approach has the advantages of not requiring heating during operation, high sensitivity, short usage time, low solvent usage, and low equipment costs. This study not only offers a reliable method for detecting total triterpenes in traditional Chinese medicine but also offers a rapid detection tool for on-site testing and large-scale screening, laying a foundation for the modernization of traditional Chinese medicine research, quality control, and drug development.

Analysis of Primary and Secondary Metabolites, Physical Properties, Antioxidant and Antidiabetic Activities, and Chemical Composition of Rosmarinus officinalis Essential Oils under Differential Water Stress Conditions

This study investigated the effects of varying water stress levels on Rosmarinus officinalis essential oils (EO). Three samples (S1, S2, and S3) were cultivated under different stress levels (40, 60, and 80%). Increased water stress led to changes in primary and secondary metabolites, EO contents, and physical properties. Antioxidant activity varied, with S2 exhibiting the highest IC50 value. In terms of antidiabetic activity, S2 showed robust α-amylase inhibition, while S3 displayed a commendable influence. For α-galactosidase inhibition, S3 had a moderate effect, and S2 stood out with increased efficacy. Gas chromatography-mass spectrometry analysis revealed stress-induced changes in major compounds. The study enhances the understanding of plant responses to water stress, with potential applications in antioxidant therapy and diabetes management. The findings emphasize the importance of sustainable water management for optimizing the EO quality in its various uses.

Essential oils: a systematic review on revolutionizing health, nutrition, and omics for optimal well-being

Purpose

Essential oils from various plants have diverse therapeutic properties and are researched extensively. They have applications in medicine, aromatherapy, microbiology, agriculture, livestock, and the food industry, benefiting the population.

Methods

This systematic review followed the PRISMA verification protocol. The study focused on the anti-inflammatory effects, nutraceutical properties, antioxidant and antibacterial activity of essential oils in lemon, orange, cumin, cinnamon, coriander, rosemary, thyme, and parsley. We also looked at their presence in the diet, their effect, their mechanism of action on health, and the most important active compounds. The search was conducted in the PubMed database for the last 12 years of publications, including in vitro, in vivo, and online cell model tests.

Results

Essential oils have been shown to have multiple health benefits, primarily due to their antimicrobial and anti-inflammatory effects. The mechanism of action of cinnamon oil alters bacterial membranes, modifies lipid profiles, and inhibits cell division, giving a potential benefit in protection against colitis. On the other hand, a significant improvement was observed in the diastolic pressure of patients with metabolic syndrome when supplementing them with cumin essential oil. The antimicrobial properties of coriander essential oil, especially its application in seafood like tilapia, demonstrate efficacy in improving health and resistance to bacterial infections. Cumin essential oil treats inflammation. Parsley essential oil is an antioxidant. Orange peel oil is antibacterial, antifungal, antiparasitic, and pro-oxidative. Lemon essential oil affects mouse intestinal microbiota. Thyme essential oil protects the colon against damage and DNA methylation. Carnosic acid in rosemary oil can reduce prostate cancer cell viability by modifying the endoplasmic reticulum function.

Conclusion and discussion

Essential oils have many therapeutic and antiparasitic properties. They are beneficial to human health in many ways. However, to understand their potential benefits, more research is needed regarding essential oils such as coriander, parsley, rosemary, cumin, and thyme. These research gaps are relevant since they restrict understanding of the possible benefits of these crucial oils for health-related contexts.