PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee

Screening Anti-Rheumatoid Arthritis Synovitis Effective Ingredients of Total Flavonoid From Artemisia argyi Folium Based on Spectrum-Effect Relationship

INTRODUCTION

Flavonoids are the main nonvolatile component responsible for the anti-rheumatoid arthritis (RA) synovitis activities of Artemisia argyi Folium. However, the effective ingredient remains unidentified. Spectrum-effect relationships analysis was a reliable and efficient strategy for herbal effective ingredient discovery.

OBJECTIVES

This study aimed to screen the effective ingredients within the total flavonoid from Artemisia argyi Folium (TFAA) that exhibit anti-RA synovitis activities based on spectrum-effect relationship.

METHODS

TFAA was obtained through ethanol extraction and subsequent purification with D101 resin from 15 distinct batches of Artemisia argyi Folium. The fingerprint of TFAA was established using HPLC, and its efficacy against RA synovitis was evaluated by determining the inhibition rate of nitric oxide (NO) on MH7A synovioblast induced by TNF-α. Common peaks were identified using HPLC-MS/MS and authentic standards. The spectrum-effect relationships between the fingerprints and efficacy were analyzed by gray relational analysis (GRA), canonical correlation analysis (CCA), and partial least squares regression analysis (PLSR) to pinpoint the peaks responsible for the anti-RA synovitis activity, and the results were further verified by in vitro anti-RA synovitis experiments and molecular docking studies.

RESULTS

The fingerprint revealed 14 common peaks, and 12 compounds were identified, including four caffeoylquinic acids and eight flavonoids. Among them, five flavonoids-X10 (hispidulin), X11 (jaceosidin), X12 (centaureidin), X13 (eupatilin), and X14 (casticin)-were highly relevant to anti-RA synovitis activity. Verification experiments confirmed their inhibitory effect on NO production and cytokine secretion in MH7A cells, showing anti-RA synovitis potential, which was consistent with the established spectrum effect relationship. The underlying mechanism might be related to the inhibition of iNOS.

CONCLUSION

Hispidulin, jaceosidin, centaureidin, eupatilin, and casticin were the key effective ingredient of TFAA responsible for its anti-RA synovitis effects. These compounds can serve as quality control markers for Artemisia argyi Folium and as lead compounds for anti-RA synovitis treatment.

β-Cyclocitral: Emerging Bioactive Compound in Plants

β-cyclocitral (βCC), a main apocarotenoid of β-carotene, increases plants’ resistance against stresses. It has recently appeared as a novel bioactive composite in a variety of organisms from plants to animals. In plants, βCC marked as stress signals that accrue under adverse ecological conditions. βCC regulates nuclear gene expression through several signaling pathways, leading to stress tolerance. In this review, an attempt has been made to summarize the recent findings of the potential role of βCC. We emphasize the βCC biosynthesis, signaling, and involvement in the regulation of abiotic stresses. From this review, it is clear that discussing compound has great potential against abiotic stress tolerance and be used as photosynthetic rate enhancer. In conclusion, this review establishes a significant reference base for future research.

A double-population chaotic self-adaptive evolutionary dynamics model for the prediction of supercritical carbon dioxide solubility in polymers

Solubility of gas in polymers is an important physico-chemical property of foam materials and widely used in the preparation and modification of new materials. Under the conditions of high temperature and high pressure, the dissolution process is a nonlinear, non-equilibrium and dynamic process, so it is difficult to establish an accurate solubility calculation model. Inspired by particle dynamics and evolutionary algorithm, this paper proposes a hybrid model based on chaotic self-adaptive particle dynamics evolutionary algorithm (CSA-PD-EA), which can use the iterative process of particles in evolutionary algorithms at the dynamic level to simulate the mutual diffusion process of molecules during dissolution. The predicted solubility of supercritical CO₂ in poly(d,l-lactide-co-glycolide), poly(l-lactide) and poly(vinyl acetate) indicated that the comprehensive prediction performance of the CSA-PD-EA model was high. The calculation error and correlation coefficient were, respectively, 0.3842 and 0.9187. The CSA-PD-EA model showed prominent advantages in accuracy, efficiency and correlation over other computational models, and its calculation time was 4.144-15.012% of that of other dynamic models. The CSA-PD-EA model has wide application prospects in the computation of physical and chemical properties and can provide the basis for the theoretical calculation of multi-scale complex systems in chemistry, materials, biology and physics.

On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities

For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC₅₀ predictions can screen molecules that have desirable activity before assessment by docking studies.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

Background:

Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites.

Methods:

In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria.

Results:

The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme.

Conclusion:

Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Apocarotenoids Involved in Plant Development and Stress Response

Carotenoids are isoprenoid pigments synthesized by all photosynthetic organisms and many heterotrophic microorganisms. They are equipped with a conjugated double-bond system that builds the basis for their role in harvesting light energy and in protecting the cell from photo-oxidation. In addition, the carotenoids polyene makes them susceptible to oxidative cleavage, yielding carbonyl products called apocarotenoids. This oxidation can be catalyzed by carotenoid cleavage dioxygenases or triggered nonenzymatically by reactive oxygen species. The group of plant apocarotenoids includes important phytohormones, such as abscisic acid and strigolactones, and signaling molecules, such as β-cyclocitral. Abscisic acid is a key regulator of plant's response to abiotic stress and is involved in different developmental processes, such as seed dormancy. Strigolactone is a main regulator of plant architecture and an important signaling molecule in the plant-rhizosphere communication. β-Cyclocitral, a volatile derived from β-carotene oxidation, mediates the response of cells to singlet oxygen stress. Besides these well-known examples, recent research unraveled novel apocarotenoid growth regulators and suggests the presence of yet unidentified ones. In this review, we describe the biosynthesis and biological functions of established regulatory apocarotenoids and touch on the recently identified anchorene and zaxinone, with emphasis on their role in plant growth, development, and stress response.

Highly discriminant rate of Dianhong black tea grades based on fluorescent probes combined with chemometric methods

We established a novel Dianhong black tea grades discriminant analytic technique based on a fluorescence image along with carbon quantum dots (CDs) as fluorescent probes. Different grades of Dianhong black tea contain different various amounts of tea polyphenols. Tea polyphenols can quench the fluorescent intensity of CDs, resulting in different fluorescent peaks; Dianhong black tea grades can then be discriminated through the use of principal component analysis and Bayesian analysis. Compared with the additional data processing required in other methods, the advantage of our method is that the fluorescence curve can be used directly, and it achieves satisfactory results. We firstly used CDs combined with chemometrics to identify eight grades of Dianhong black tea, and we also provide a new method that improves the identification rate using nanotechnology to avoid performing complex data processing.

β-Cyclocitral is a conserved root growth regulator

Natural compounds capable of increasing root depth and branching are desirable tools for enhancing stress tolerance in crops. We devised a sensitized screen to identify natural metabolites capable of regulating root traits in Arabidopsis β-Cyclocitral, an endogenous root compound, was found to promote cell divisions in root meristems and stimulate lateral root branching. β-Cyclocitral rescued meristematic cell divisions in ccd1ccd4 biosynthesis mutants, and β-cyclocitral-driven root growth was found to be independent of auxin, brassinosteroid, and reactive oxygen species signaling pathways. β-Cyclocitral had a conserved effect on root growth in tomato and rice and generated significantly more compact crown root systems in rice. Moreover, β-cyclocitral treatment enhanced plant vigor in rice plants exposed to salt-contaminated soil. These results indicate that β-cyclocitral is a broadly effective root growth promoter in both monocots and eudicots and could be a valuable tool to enhance crop vigor under environmental stress.

Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling

To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.