Life Sciences
STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer
Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is therefore essential for both disease treatment and novel drug discovery. However it is costly and time-consuming to verify these relationships through traditional wet lab approaches.
We proposed an efficient computational model STNMDA that integrated a Structure-Aware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbe-drug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally the DNN classifier calculated the probability of associations between microbes and drugs.
Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations.
Hence STNMDA showed promise as a valuable tool for future prediction of microbe-drug associations.
Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration
Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction many of these methods primarily concentrate on the amino acid sequence of proteins. Yet the interactions between drug compounds and targets occur within distinct segments within the protein structures whereas the primary sequence primarily captures global protein features. Consequently it falls short of fully elucidating the intricate relationship between drugs and their respective targets.
This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins.
In our research both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drug-target interactions more effectively.
We have introduced a novel method for predicting DTA. In comparison to DeepDTA our approach demonstrates significant enhancements achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset.
In conclusion our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.
A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins
Computational molecular docking plays an important role in determining the precise receptor-ligand conformation which becomes a powerful tool for drug discovery. In the past 30 years most computational docking methods have treated the receptor structure as a rigid body although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pocket residues exhibit different degrees of flexibility semi-flexible docking methods balancing rigid docking and flexible docking have demonstrated success in predicting highly accurate conformations with a relatively low computational cost.
In our study the number of flexible pocket residues was assessed by quantitative analysis and a novel adaptive residue flexibility prediction method named A-RFP was proposed to improve the docking performance. Based on the homologous information a joint strategy is used to predict the pocket residue flexibility by combining RMSD the distance between the residue sidechain and the ligand and the sidechain orientation. For each receptor-ligand pair A-RFP provides a docking conformation with the optimal affinity.
By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10 we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However a certain number of counterexamples still exist. To validate the effectiveness of A-RFP the experimental assessment was tested in a small-scale virtual screening on 5 proteins which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover we studied three receptors with FDA-approved drugs which further proved A-RFP can play a suitable role in ligand discovery.
Our analysis confirms that the screening performance of the various numbers of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus we presented A-RFP an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs A-RFP provides the optimal docking result.
Genotype and Phenotype Association Analysis Based on Multi-omics Statistical Data
When using clinical data for multi-omics analysis there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy the requirements of data management by various institutions and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships using statistical data in the absence of clinical data.
We proposed a novel approach to exploit easily accessible statistics in public databases. This approach introduces phenotypic associations that are not included in the clinical data and uses these data to build a three-layer heterogeneous network. To simplify the analysis we decomposed the three-layer network into double two-layer networks to predict the weights of the inter-layer associations. By adding a hyperparameter β the weights of the two layers of the network were merged and then k-fold cross-validation was used to evaluate the accuracy of this method. In calculating the weights of the two-layer networks the RWR with fixed restart probability was combined with PBMDA and CIPHER to generate the PCRWR with biased weights and improved accuracy.
The area under the receiver operating characteristic curve was increased by approximately 7% in the case of the RWR with initial weights.
Multi-omics statistical data were used to establish genotype and phenotype correlation networks for analysis which was similar to the effect of clinical multi-omics analysis.
Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism
The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G) being one of the most important epigenetic modifications plays an important role in gene expression processing metabolism and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed cost-effectiveness and robustness.
In this study we aim to develop a computational model to help predict the exact location of m7G sites in humans.
Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently.
Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model named Sia-m7G adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition multiple 10-fold cross-validation tests were conducted to evaluate our predictor.
Sia-m7G achieved the highest sensitivity specificity and accuracy on 10-fold cross-validation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences.
Sia-m7G has significant advantages over other classifiers and predictors which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.
Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review
Artificial Intelligence is a field within computer science that endeavors to replicate the intricate structures and operational mechanisms inherent in the human brain. Machine learning is a subfield of artificial intelligence that focuses on developing models by analyzing training data. Deep learning is a distinct subfield within artificial intelligence characterized by using models that depict geometric transformations across multiple layers. The deep learning has shown significant promise in various domains including health and life sciences. In recent times deep learning has demonstrated successful applications in drug discovery. In this self-review we present recent methods developed with the aid of deep learning. The objective is to give a brief overview of the present cutting-edge advancements in drug discovery from our group. We have systematically discussed experimental evidence and proof of concept examples for the deep learning-based models developed such as DeepBindBC DeepPep and DeepBindRG. These developments not only shed light on the existing challenges but also emphasize the achievements and prospects for future drug discovery and development progress.
Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer
Bladder cancer is a prevalent malignancy globally characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore there is a need to develop a stratification model specific to bladder cancer.
This study aims to establish a prognostic prediction model for bladder cancer with the primary goal of accurately predicting prognosis and treatment outcomes.
We collected datasets from 10 bladder cancer datasets sourced from the Gene Expression Omnibus (GEO) the Cancer Genome Atlas (TCGA) databases and IMvigor210 dataset. The machine learning based on feature selection algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score all the patients were classified into two different risk score groups.
The two groups of bladder cancer patients exhibited significant differences in prognosis biological functions and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility.
The risk score constructed in this study can be utilized to predict the prognosis response to drug treatment and immunotherapy of bladder cancer patients providing assistance for personalized clinical treatment of bladder cancer.
CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning
With the increasing development of biotechnology many cancer solutions have been proposed nowadays. In recent years Neo-peptides-based methods have made significant contributions with an essential prerequisite of bindings between peptides and HLA molecules. However the binding is hard to predict and the accuracy is expected to improve further.
Therefore we propose the Crossed Feature Correction Network (CFCN) with deep learning method which can automatically extract and adaptively learn the discriminative features in HLA-peptide binding in order to make more accurate predictions on HLA-peptide binding tasks. With the fancy structure of encoding and feature extracting process for peptides as well as the feature fusion process between fine-grained and coarse-grained level it shows many advantages on given tasks.
The experiment illustrates that CFCN achieves better performances overall compared with other fancy models in many aspects.
In addition we also consider to use multi-view learning methods for the feature fusion process in order to find out further relations among binding features. Eventually we encapsulate our model as a useful tool for further research on binding tasks.
Anticancer Drug Development, Pharmaceutical Progress
Anticancer drug development is becoming complex and demanding because human cancer leads to 12% of global human mortality. Chemical and pharmacological breakthroughs play leading roles in updating drug evaluation and development for different types of tumors.
Chemical and pharmacological breakthroughs manifest in different facets. A large proportion of financial and workload increases in drug discovery must be paid off. In front of complexity difficulties and financial increase of drug development evaluative promotion must go miniature-wise and single-cell-wise. Multi-omics knowledge and technology are greatly expanded and understood in depth. This type of technical trend is suitable for current experimental exploration and clinical occasions. Technical and pharmacologic advances are especially emphasized to address this trend.
Presently the anticancer pharmaceutical study is multi-faceted and risk-taking. To keep up this momentum multi-disciplinary drug evaluation clinical selection and combination principles should be discovered. Past and futuristic chemical and pharmacological interactions and breakthroughs are discussed.
In summary the landscape of pharmaceutical investigation should be integrated with chemical and pharmacological knowledge in every facet of drug development and clinical personalization.
Endoscopic Ultrasound Assessment of Solid Pseudopapillary Neoplasm of the Pancreas: Case Series in Latin American Population
Solid pseudopapillary tumors of the pancreas (SPN) are uncommon lesions. Endoscopic ultrasound is considered the standard examination because of its capability to acquire a suitable core tissue sample. This study details the experience of eleven cases within a Latin American population diagnosed with SPNs through the endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) approach.
Retrospective reviews of records from a 6-year period from January 2018 to December 2023 were performed at Clínica Reina Sofía in Bogotá Colombia. We included eleven patients with surgically proven solid pseudopapillary tumors who had undergone preoperative EUS−FNB. The clinical history EUS findings biopsies and immunohistochemical profile were reviewed.
In this study of eleven SPN patients (median age: 31.9 years 90.1% female all Hispanic) abdominal pain (63.6%) was the predominant symptom. The indication for EUS was predominantly a pancreas-dependent mass (90.1%). Tumors were located more frequently on the pancreatic neck with regular morphology well-defined borders and predominantly hypoechoic and heterogeneous appearances on EUS. The average tumor diameter was 4.3 cm [range 1.2- 10 cm]. Most tumors were solid (63.6%) and elastography revealed a mixed pattern. EUS-Doppler indicated hypovascularity in all cases. Vascular compression occurred in three patients. No lymph nodes were observed. There were no complications related to the procedure. The histopathological analysis using EUS-FNB yielded consistent results with post-surgical biopsies.
In the context of diagnostic evaluation for SPNs EUS-FNB emerges as a pivotal procedure. In this descriptive study EUS-FNB showed a remarkable preoperative diagnostic yield of 100% compared to post-surgical histopathology for solid pseudopapillary tumors.
Exercise Alters FBF1-Regulated Novel-miRNA-1135 Associated with Hydrolethalus Syndrome 1 in Rheumatoid Arthritis: A Preliminary Study
Hydrolethalus Syndrome 1 (HYDS1) is a rare disorder that occurs commonly in Finnish infants but originates from the mother. This autosomal recessive syndrome is associated with the FBF1 which is usually expressed in the centriole. The FBF1 is an inheritable arthritis disease phenotype that includes rheumatoid arthritis. Several studies have investigated males with FBF1 mutation carriers also related to arthritis diseases including those under rheumatoid arthritis conditions which revealed the possibility of conferring the gene mutation to the next generation of offspring. Nonetheless there are some complications of FBF1 mutation with target miRNAs that can be affected by exercise.
The objective of this study was to evaluate the different exercises that can be utilized to suppress the FBF1 mutation targeted by Novel-rno-miRNAs-1135 as a biomarker and assess the effectiveness of exercise in mitigating the FBF1 mutation.
Four exercise interventional groups were divided into exercise and non-exercise groups. One hundred microliter pristane-induced arthritis (PIA) was injected at the dorsal region of the tails of rodents and introduced to the two PIA interventional groups. On day forty-five all animals were euthanized and total RNA was extracted from the blood samples of rodents while polymerase chain reaction (PCR) was amplified by using 5-7 primers. Computerization was used for miRNA regulation and analysis of target gene candidates.
The novel-rno-miRNA-1135 was downregulated to FBF1 in exercise groups. The exercise was found to have no significant impact in terms of change in novel-rno-miRNA-1135 regulation of FBF1 expression.
Exercise has no impact on novel-rno-miRNA-1135 targeted for FBF1 in autosomal recessive disease.
The Landscape of microRNAs in Bone Tumor: A Comprehensive Review in Recent Studies
Cancer the second greatest cause of mortality worldwide frequently causes bone metastases in patients with advanced-stage carcinomas such as prostate breast and lung cancer. The existence of these metastases contributes to the occurrence of skeletal-related events (SREs) which are defined by excessive pain pathological fractures hypercalcemia and spinal cord compression. These injurious incidents leave uncomfortably in each of the cancer patient’s life quality. Primary bone cancers including osteosarcoma (OS) chondrosarcoma (CS) and Ewing's sarcoma (ES) have unclear origins. MicroRNA (miRNA) expression patterns have been changed in primary bone cancers such as OS CS and ES indicating a role in tumor development invasion metastasis and treatment response. These miRNAs are persistent in circulation and exhibit distinct patterns in many forms of bone tumors making them potential biomarkers for early detection and treatment of such diseases. Given their crucial regulatory functions in various biological processes and conditions including cancer this study aims to look at miRNAs' activities and possible contributions to bone malignancies focusing on OS CS and ES. In conclusion miRNAs are valuable tools for diagnosing monitoring and predicting OS CS and ES outcomes. Further research is required to fully comprehend the intricate involvement of miRNAs in these bone cancers and to develop effective miRNA-based treatments.
Investigating the Association between LncRNA NR2F2-AS1, miR-320b, and BMI1 in Gastric Cancer: Insights into Expression Profiles as Potential Biomarkers for Disease Management
This study aims to investigate the potential role of lncRNA NR2F2-AS1 in the development of gastric cancer by affecting the levels of miR-320b and BMI1.
Gastric cancer is a high-mortality malignancy and understanding the underlying molecular mechanisms is crucial. Non-coding RNAs play an important role in gene expression and their dysregulation can lead to tumor initiation and progression.
This study aims to determine the pathological role of LncRNA NR2F2-AS1 in gastric cancer progression and its association with the clinicopathological characteristics of patients.
Bioinformatics databases were used to predict the expression levels and interactions between the studied factors to achieve this objective. The expression pattern of NR2F2-AS1/miR-320b/BMI1 in 40 pairs of tumor and adjacent normal tissues was examined using RT-PCR IHC and western blot. The correlation ROC curve and survival analyses were also conducted for the aforementioned factors.
The results showed an increase of more than 2-fold for BMI-1 and lncRNA NR2F2-AS1 in lower stages and the elevation continued with the increasing stage of the disease. This correlated with significant downregulation of miR-320b and PTEN indicating their association with gastric cancer progression and decreased patient survival. LncRNA NR2F2-AS1 acts as an oncogene by influencing the level of miR-320b altering the amount of BMI1. A reduction in the amount of miR-320b against lncRNA NR2F2-AS1 and BMI1 directly correlates with a reduced overall survival rate of patients especially if this disproportion is more than 3.0. ROC curve analysis indicated that alteration in the lncRNA NR2F2-AS1 level showed more than 98.0% sensitivity and specificity to differentiate the lower from higher stages of GC and predict the early onset of metastasis.
In conclusion these results suggest that NR2F2-AS1/miR-320b/BMI1 has the potential to be a prognostic as well as diagnostic biomarker for gastric cancer.
The Effect of MiR320a on Lung Cancer
Lung cancer has a high mortality rate among cancers in both women and men. Currently lung cáncer diagnosis is made with clinical examination low-dose CT scan and molecular-based methods and its treatment options include chemotherapy surgery radiotherapy or immunotherapy. However the life expectancy of lung cancer is not very high and still it is usually diagnosed very lately which leads to poorer prognosis. MicroRNAs [miRNAs] are small noncoding RNAs that regulate many diverse activities in the cell that can affect tumorigenesis by regulating many cell functions related to cancer such as cell cycle metastasis angiogenesis metabolism and apoptosis. Also it can have a potential diagnostic therapeutic and prognostic value for lung cancer. MiR320a is a promising microRNA that may help us in the diagnosis treatment and prognosis of lung cancer but some aspects of its clinical application are still vague especially its effect on heavy smokers delivery mechanism toxicity and lack of reliable critical value. In this paper we examined its comprehensive molecular interactions that lead to its tumor suppressor effect and we reviewed its clinical application until now.
Evaluation of Fluctuations of BRAF Gene Expression and its Polymorphism at rs1267623 in Colorectal Cancer
Molecular markers in Colorectal Cancer (CRC) are needed for more accurate classification and personalized treatment. In this way we investigated the effects of the BRAF gene on clinical outcomes of its expression fluctuations and its polymorphism at rs1267623 in CRC.
In this study 36.36 percent of patients with CRC were women and 63.63 percent were men. After the pathology department confirmed the tumor of the samples the stage and grade of the tumor were determined according to the TNM system. Real-time PCR was used to check the expression of the BRAF gene in tumor and non-tumor tissues and its polymorphism in rs1267623 was also checked using the Tetra-ARMs PCR technique.
The expression of BRAF in tumor tissues was significantly higher than in non-tumoral tissues (P = 0.001) indicating an upregulation of BRAF gene expression in tumoral tissues. The user's text is empty. Furthermore there was a significant correlation between BRAF expression and tumor stage (P = 0.001) as well as tumor grade (P = 0.003). However no significant link was found between lymph node metastasis and distant metastasis of BRAF gene expression (P = 0.3). Additionally no mutation was detected in the investigation of rs1267623 polymorphism.
The BRAF gene was upregulated in tumoral tissues. Remarkably no mutation was found in the rs1267623 polymorphism. As a result this gene can be used as a biomarker in the diagnosis and treatment of CRC.
Glioblastoma Multiforme miRNA based Comprehensive Study to Validate Phytochemicals for Effective Treatment against Deadly Tumour through In Silico Evaluation
Glioblastoma Multiforme (GBM) is a prevalent and deadly type of primary astrocytoma constituting over 60% of adult brain tumors and has a poor prognosis with a high relapse rate within 7 months of diagnosis. Despite surgical radiotherapy and chemotherapy treatments GBM remains challenging due to resistance. MicroRNA (miRNAs) control gene expression at transcriptional and post-transcriptional levels by targeting their messenger RNA (mRNA) and also contribute to the development of various neoplasms including GBM.
The present study focuses on exploring the miRNAs-based pathogenesis of GBM and evaluating most potential plant-based therapeutic agents with in silico analysis. Gene chips were retrieved from the Gene Expression Omnibus (GEO) database followed by the Robust- RankAggereg algorithm to determine the Differentially Expressed miRNAs (DEMs). The predicted targets were intersected with the GBM-associated genes and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the overlapping genes was performed. At the same time five phytochemicals were selected for the Connectivity map (CMap) and the most efficient ones were those that had undergone molecular docking analysis to obtain the potential therapeutic agents.
The hsa-miR-10b hsa-miR-21 and hsa-miR-15b were obtained and eight genes were found to be associated with glioma pathways; VSIG4 PROCR PLAT and ITGB2 were upregulated while CAMK2B PDE1A GABRA1 and KCNJ6 were downregulated. The drugs Resveratrol and Quercetin were identified as the most prominent drugs.
These miRNAs-based drugs can be used as a curative agent for the treatment of GBM. However in vivo experimental data and clinical trials are necessary to provide an alternative to conventional GBM cancer chemotherapy.
MicroRNA Biomarkers on Day of Injury Among Patients with Post Concussive Symptoms at 28-Days: A Prospective Cohort Study
After mild traumatic brain injury (mTBI) some patients experience symptoms that persist for weeks to months. Recovery from mTBI is primarily assessed using self-reported symptom questionnaires. Blood biomarkers including microRNA species have shown promise to assist diagnosis of mTBI however little is known about how blood microRNA measures might predict symptom recovery.
The aim of this study was to investigate the variances in plasma microRNAs on the day of injury between individuals with mTBI who report post-concussive symptoms at the 28-day mark and those who do not.
Patients who presented to an adult tertiary referral hospital emergency department on the day of the injury and were diagnosed with isolated mTBI (n=35) were followed up for 28 days. Venous blood samples were collected and symptom severity was assessed using the Rivermead Post-Concussion Symptom Questionnaire (RPQ) on the day of injury and at 28 days. Patients who reported ongoing symptoms of total RPQ score ≥10 or at least one symptom severity ≥2 were compared to those with lesser symptom severity or symptom resolution.
There were 9 (25.7%; 95%CI: 12.5-43.3) patients who reported persistent symptoms. Day of injury plasma miR-223-3p levels were significantly higher in individuals with ongoing symptoms compared to those without however no such differences were observed for miRs 142-3p 423-3p 32-5p 144-3p and let-7f-5p.
Acute plasma miR-223-3p levels appear to detect patients who later have persistent symptoms after mTBI. The results demonstrate the potential utility for such biomarkers to assist in decisions towards early referral for therapy after mTBI.
Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model
Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However tumor features' complexity and imaging data variability still pose challenges.
This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy recall precision and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6% precision of 99.4% recall of 99.7% F1 score of 98.6% and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets.
Similarly the INBreast dataset achieved an accuracy of 93.8% a precision of 94.2% a recall of 94.5% and an F1-score of 93.4%.
The proposed model is a significant advancement in breast cancer diagnosis with high accuracy and potential as an automated grading.
Use of Essential Oils for the Treatment of Fusarium oxysporum f. sp. Albedinis: Chemical Profile, In Vitro Antifungal Activity, and In Silico Investigation by Molecular Docking Study
Fusarium oxysporum f. sp. Albedinis a telluric fungal pathogen commonly found in soils is the causal agent of fungal vascular wilt of date palms in Moroccan oases. The infection by the pathogen leads to the death of the date palm after six months to two years which causes enormous economic and environmental damage.
The framework of this paper is to determine the chemical composition of six essential oils using GC-MS and their antifungal activity on the mycelial growth of Fusarium oxysporum f. sp. Albedinis as well as the molecular docking study to evaluate the inhibitory potential of fungal trypsin.
The essential oils were extracted from different parts of the plants (whole plant flowers and leaves) by steam distillation and were identified using gas chromatography-mass spectrometry (GC/MS). The antifungal assay of the extracted essential oils and their main components was assessed using the direct contact method with the fungus at different concentrations; the obtained results were evaluated by calculating the minimum inhibitory concentration (MIC) of each essential oil followed by an in-silico study of the major identified compounds for better understanding of the inhibitory potential against fungal trypsin activity.
The identification of the different bioactive compounds using GC-MS revealed that Rosmarinus officinalis Eo was characterized by eucalyptol 46.26% camphor 10.03% and β-pinene 6.63%; while Lavandula officinalis Eo was endowed by the presence of linalool 14.93% camphor 14.11% and linalyl acetate 11.17%. Furthermore Artemisia herba alba was rich in 135-cycloheptatriene 16-dimethyl- 36.44% camphor 22.50% and α-thujone 7.21%. While Eucalyptus globulus was rich in eucalyptol 74.32% β-Cymene 11.41% α-Pinene 6.96%. Finally Mentha pepirita and Mentha pulegium were both characterized by the presence of D-limonene 20.15% trans-carveol 19.59% D-Carvone 14.96% and pulegone (42.40%) 3-cyclopentene-1-ethanol 224-trimethyl- (11.28%) 134-trimethyl-3-cyclohexenyl-1-carboxaldehyde (9.68%) respectively. Regarding the in vitro all Eos from different plants exhibited pronounced antifungal effect. The MIC values recorded for E. globulus were MIC= 1.75 mg/L M. pulegium and L. officinalis (MIC= 1.80 mg/L) and M. piperita (MIC= 1.90 mg/L). The strongest inhibition potential was associated with R. officinalis EO (MIC= 1.15 mg/L) and A. herba alba EO (MIC= 1.60 mg/L). As for the computational study performed camphor one of the bioactive compounds showed its ability to act against trypsin which could be considered a potential candidate against Fusarium oxysporum f. sp. Albedinis.
The studied essential oils from different medicinal and aromatic plants showed significant antifungal activity probably due to the Camphor which could have an inhibitory effect on the Fusarium oxysporum f. sp. Albedinis trypsin. Further research should be conducted in vivo for a better understanding of the mechanism of action of these essential oils.