Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by in Silico Modeling and Virtual Screening Strategies to Combat Early Blight

High-throughput semi-automated emulsive liquid-liquid microextraction for detecting SDHI fungicides in water, juice, and alcoholic beverage samples via UHPLC-MS/MS

Herein, a High-Throughput Semi-automated Emulsive Liquid-Liquid Microextraction (HTSA-ELLME) method was developed to detect Succinate Dehydrogenase Inhibitor (SDHI) fungicides in food samples via UHPLC-MS/MS. The Oil-in-Water (O/W) emulsion comprising a hydrophobic extractant and water was dilutable with the aqueous sample solution. Upon injecting the primary emulsion into the sample solution, a secondary O/W emulsion was formed, allowing SDHI fungicides to be extracted. Subsequently, a NaCl-saturated solution was injected in the secondary O/W emulsion as a demulsifier to rapidly separate the extractant, eliminating the need for centrifugation. A 12-channel electronic micropipette was used to achieve a high-throughput semi-automation of the novel sample pretreatment. The linear range was 0.003-0.3 μg L-1 with R2 > 0.998. The limit of detection was 0.001 μg L-1. The HTSA-ELLME method successfully detected SDHI fungicides in water, juice, and alcoholic beverage samples, with recoveries and relative standard deviations of 82.6-106.9% and 0.8-5.8%, respectively. Unlike previously reported liquid-liquid microextraction approaches, the HTSA-ELLME method is the first to be both high-throughput and semi-automated and may aid in designing pesticide pretreatment processes in food samples.

Synergistic insights into pesticide persistence and microbial dynamics for bioremediation

Rampant use of fertilizers and pesticides for boosting agricultural crop productivity has proven detrimental impact on land, water, and air quality globally. Although fertilizers and pesticides ensure greater food security, their unscientific management negatively impacts soil fertility, structure of soil microbiome and ultimately human health and hygiene. Pesticides exert varying impacts on soil properties and microbial community functions, contingent on factors such as their chemical structure, mode of action, toxicity, and dose-response characteristics. The diversity of bacterial responses to different pesticides presents a valuable opportunity for pesticide remediation. In this context, OMICS technologies are currently under development, and notable advancements in gene editing, including CRISPR technologies, have facilitated bacterial engineering, opening promising avenues for reducing toxicity and enhancing biological remediation. This paper provides a holistic overview of pesticide dynamics, with a specific focus on organophosphate, organochlorine, and pyrethroids. It covers their occurrence, activity, and potential mitigation strategies, with an emphasis on the microbial degradation route. Subsequently, the pesticide degradation pathways, associated genes and regulatory mechanisms, associated OMICS approaches in soil microbes with a special emphasis on CRISPR/Cas9 are also being discussed. Here, we analyze key environmental factors that significantly impact pesticide degradation mechanisms and underscore the urgency of developing alternative strategies to diminish our reliance on synthetic chemicals.

Bioactive metabolites of licorice and thyme as potential inhibitors of Cox1 enzyme of phytopathogens of Capsicum annuum L.: In-silico approaches

Cytochrome c oxidase subunit 1 (Cox1), a key enzyme, has a crucial role in cellular respiration in eukaryotes and prokaryotes. Generally, respiratory inhibitors are considered one of the types of chemical pesticides. Thyme oil and licorice aqueous extract have been reported to have antifungal activities against fungal phytopathogens of Capsicum annuum L., i.e., Colletotrichum capsici, Fusarium oxysporum, and Pythium aphanidermatum. The present study focuses on identifying the key bioactive molecules of thyme and licorice botanicals inhibiting the activity of the Cox1 enzymes of the above mentioned phytopathogens, employing the in-silico approach. From a wide range of bioactive molecules screened, the molecular docking indicated trans-carveol, carvacrol, kaempferol 3-rhamnoside 7-xyloside, kaempferitrin, and astragalin 7-rhamnoside as the potential inhibitors for Cox1 of C. capsici, β-Caryophyllene, Caryophyllene acetate, hispaglabridin A, kaempferol 3-rhamnoside 7-xyloside and licorice glycoside A for Cox1 of F. oxysporum and (+)-Longifolen, Caryophyllene acetate, Hispaglabridin A, Neoliquiritin 2''-apioside and Licorice-saponin A3 for Cox1 of P. aphanidermatum. Most of the top-scoring bioactive molecules exhibited higher binding affinity with the targets than the chemical compound, i.e., carbendazim. Density functional theory (DFT) analysis confirmed the reactivity of the top-docked compounds. Molecular dynamic simulations confirmed the stability of docked complexes when evaluated through multiple descriptors. Additionally, MM/PBSA analysis supported the findings, indicating the spontaneous binding of the enzymes to the screened ligands. ADMET analysis revealed the safety of the selected bioactive compounds. The present findings could be useful in developing biopesticidal formulations as efficient and sustainable alternatives to chemical pesticides.Communicated by Ramaswamy H. Sarma.

Comprehensive transcriptome analysis of different potato cultivars provides insight into early blight disease caused by Alternaria solani

BACKGROUND

Early blight, caused by the necrotrophic fungal pathogen Alternaria solani, is an economically important disease affecting the tuber yield worldwide. The disease is mainly controlled by chemical plant protection agents. However, over-using these chemicals can lead to the evolution of resistant A. solani strains and is environmentally hazardous. Identifying genetic disease resistance factors is crucial for the sustainable management of early blight but little effort has been diverted in this direction. Therefore, we carried out transcriptome sequencing of the A. solani interaction with different potato cultivars with varying levels of early blight resistance to identify key host genes and pathways in a cultivar-specific manner.

RESULTS

In this study, we have captured transcriptomes from three different potato cultivars with varying susceptibility to A. solani,  namely Magnum Bonum, Désirée, and Kuras, at 18 and 36 h post-infection. We identified many differentially expressed genes (DEGs) between these cultivars, and the number of DEGs increased with susceptibility and infection time. There were 649 transcripts commonly expressed between the potato cultivars and time points, of which 627 and 22 were up- and down-regulated, respectively. Interestingly, overall the up-regulated DEGs were twice in number as compared to down-regulated ones in all the potato cultivars and time points, except Kuras at 36 h post-inoculation. In general, transcription factor families WRKY, ERF, bHLH, MYB, and C2H2 were highly enriched DEGs, of which a significant number were up-regulated. The majority of the key transcripts involved in the jasmonic acid and ethylene biosynthesis pathways were highly up-regulated. Many transcripts involved in the mevalonate (MVA) pathway, isoprenyl-PP, and terpene biosynthesis were also up-regulated across the potato cultivars and time points. Compared to Magnum Bonum and Désirée, multiple components of the photosynthesis machinery, starch biosynthesis and degradation pathway were down-regulated in the most susceptible potato cultivar, Kuras.

CONCLUSIONS

Transcriptome sequencing identified many differentially expressed genes and pathways, thereby contributing to the improved understanding of the interaction between the potato host and A. solani. The transcription factors identified are attractive targets for genetic modification to improve potato resistance against early blight. The results provide important insights into the molecular events at the early stages of disease development, help to shorten the knowledge gap, and support potato breeding programs for improved early blight disease resistance.

Discovery of succinate dehydrogenase candidate fungicides via lead optimization for effective resistance management of Fusarium oxysporum f. sp. capsici

Fusarium wilt of chili caused by the fungus Fusarium oxysporum f. sp. capsici (FCO) severely reduces the production of chili worldwide. There is growing evidence of resistance to commercial fungicides targeting succinate dehydrogenase (Sdh) of FCO soliciting the development of new Sdh inhibitors (SdhIs). In the current work, optimized docking and virtual screening were used to mine twelve SdhIs from the ZINC database, followed by in vitro antifungal evaluation on spore and radial mycelium development. Four new promising SdhIs exhibiting a mean mycelium inhibition rate greater than 85.6% (F = 155.8, P = 0.001, P < 0.05) were observed on ten strains of virulent and resistant FCO. Importantly, three of the discovered molecules exhibited potent spore germination inhibition (≥ 80%, P = 0.01, P < 0.05) compared to the commonly used fungicide penthiopyrad. A significant positive correlation (r* ≥ 0.67, P < 0.05) between the activities of the newly discovered SdhIs compared to penthiopyrad against all tested FCO strains indicated a broad-spectrum fungicidal activity. The current findings indicate that the four SdhI's discovered could judiciously replace certain commercial SdhIs that some FCO displays resistance to.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03157-8.

The research progress in and perspective of potential fungicides: Succinate dehydrogenase inhibitors

Succinate dehydrogenase inhibitors (SDHIs) have become one of the fastest growing classes of new fungicides since entering the market, and have attracted increasing attention as a result of their unique structure, high activity and broad fungicidal spectrum. The mechanism of SDHIs is to inhibit the activity of succinate dehydrogenase, thereby affecting mitochondrial respiration and ultimately killing pathogenic fungi. At present, they have become popular varieties researched and developed by major pesticide companies in the world. In the review, we focused on the mechanism, the history, the representative varieties, structure-activity relationship and resistance of SDHIs. Finally, the potential directions for the development of SDHIs were discussed. It is hoped that this review can strengthen the individuals' understanding of SDHIs and provide some inspiration for the development of new fungicides.

Transcriptome Analysis of Potato Infected with the Necrotrophic Pathogen Alternaria solani

Potato early blight is caused by the necrotrophic fungus Alternaria solani and can result in yield losses of up to 50% if left uncontrolled. At present, the disease is controlled by chemical fungicides, yet rapid development of fungicide resistance renders current control strategies unsustainable. On top of that, a lack of understanding of potato defences and the quantitative nature of resistance mechanisms against early blight hinders the development of more sustainable control methods. Necrotrophic pathogens, compared to biotrophs, pose an extra challenge to the plant, since common defence strategies to biotic stresses such as the hypersensitive response and programmed cell death are often beneficial for necrotrophs. With the aim of unravelling plant responses to both the early infection stages (i.e., before necrosis), such as appressorium formation and penetration, as well as to later responses to the onset of necrosis, we present here a transcriptome analysis of potato interactions with A. solani from 1 h after inoculation when the conidia have just commenced germination, to 48 h post inoculation when multiple cell necrosis has begun. Potato transcripts with putative functions related to biotic stress tolerance and defence against pathogens were upregulated, including a putative Nudix hydrolase that may play a role in defence against oxidative stress. A. solani transcripts encoding putative pathogenicity factors, such as cell wall degrading enzymes and metabolic processes that may be important for infection. We therefore identified the differential expression of several potato and A. solani transcripts that present a group of valuable candidates for further studies into their roles in immunity or disease development.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation

Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.

In Silico Modeling of Crimean Congo Hemorrhagic Fever Virus Glycoprotein-N and Screening of Anti Viral Hits by Virtual Screening

Crimean-Congo hemorrhagic fever (CCHF) is a widespread zoonotic viral disease, caused by a tick-born virus Crimean-Congo hemorrhagic fever virus (CCHFV). This disease is endemic in Middle East, Asia, Africa and South-Eastern Europe with the mortality rate of 5–30%. CCHFV genome is composed of three segments: large, medium and small segments. M segment encodes a polyprotein (glycoprotein) so called glycoprotein N (Gn) which is considered as a potential druggable target for the effective therapy of CCHF. The complete structure of Gn is still not characterized. The aim of the current study is to predict the complete three-dimensional (3D-) structure of CCHFV Gn protein via threading-based modeling and investigate the residues crucial for binding with CCHFV envelop. The developed model displayed excellent stereo-chemical and geometrical properties. Subsequently structure based virtual screening (SBVS) was applied to discover novel inhibitors of Gn protein. A library of > 1300 anti-virals was selected from PubChem database and directed to the predicted binding site of Gn. The SBVS results led to the identification of thirty-seven compounds that inhibit the protein in computational analysis. Those 37 hits were subject to pharmacokinetic profiling which demonstrated that 30/37 compound possess safer pharmacokinetic properties. Thus, by specifically targeting Gn, less toxic and more potent inhibitors of CCHFV were identified in silico.

Chitosan induces resistance to tuber rot in stored potato caused by Alternaria tenuissima

Alternaria tenuissima infects stored potatoes, and causes tuber rot, resulting in significant economic losses. As a naturally-occurring polysaccharide (poly-β-(1 → 4) N-acetyl-D-glucosamine), chitosan has been reported to be an eco-friendly alternative to synthetic fungicides for the control of postharvest diseases on agricultural commodities. In this study, application of 0.25-1.25 g/L chitosan significantly inhibited spore germination and mycelial growth of A. tenuissima in vitro, with the greatest inhibitory effect observed at the highest concentration. Cytological and biochemical analysis of A. tenuissima spores indicated that exposure to 1.25 g/L chitosan significantly damaged the plasma membrane and increased the level of lipid oxidation. Gene expression analysis in potato tuber revealed that an application of 1.25 g/L chitosan induced the expression of defense-related genes, including catalase, peroxidase, polyphenol oxidase, chitinase and β-1,3-glucanase, and the level of flavonoids and lignin. Chitosan effectively controlled tuber rot caused by A. tenuissima. Collectively, results of the current study indicate that the ability of chitosan to reduce Alternaria rot in stored potato tubers is due to its direct antifungal activity and its ability to induce defense responses in potato tuber tissues. Chitosan may have the potential as a substitute for synthetic fungicides to reduce postharvest losses in potato.

Evidence for the involvement of a GABAergic mechanism in the effectiveness of natural and synthetically modified incensole derivatives in neuropharmacological disorders: A computational and pharmacological approach

In the course of our continuing exploration for novel bioactive lead compounds (s) from the species Boswellia, we have recently reported incensole derivatives isolated from Boswellia papyrifera Hochst. Given the known antidepressant-like effects of incensole and incensole acetate, we herein present that the low dose intraperitoneal administration of incensole derivatives, namely, incensfuran and incensone, showed significant antidepressant-like effects in the forced swim test (FST) and tail suspension test (TST). Furthermore, these compounds were evaluated for their anxiolytic potential in the elevated plus maze (EPM) and light dark box (LDB) tests and anticonvulsant effects in pentylenetetrazole (PTZ)-induced seizure tests. In the EPM test, administration of these compounds led to dose-dependent increases in open arm entries and in the time spent in EPM open arms. Similar results were obtained in the LDB test, wherein compounds these caused significant increases in the number of transitions between lit and dark compartments and the time spent in the lit compartment. The anxiolytic-like effects in the EPM were not reversed by pretreatment with flumazenil, whereas PTZ and bicuculline (BIC) completely abolished the anxiolytic effects, showing the involvement of the non-benzodiazepine binding sites of GABA_A receptors. All four compounds induced significantly elevated brain GABA levels, indicating the involvement of a GABAergic mechanism. Additionally, molecular docking was conducted to elucidate the mode of action for the anxiolytic and anticonvulsant effects of these derivatives. Moreover, these compounds also possess drug-like properties and excellent ADMET profiles.

Gapless Genome Assembly of the Potato and Tomato Early Blight Pathogen Alternaria solani

The Alternaria genus consists of saprophytic fungi as well as plant-pathogenic species that have significant economic impact. To date, the genomes of multiple Alternaria species have been sequenced. These studies have yielded valuable data for molecular studies on Alternaria fungi. However, most of the current Alternaria genome assemblies are highly fragmented, thereby hampering the identification of genes that are involved in causing disease. Here, we report a gapless genome assembly of A. solani, the causal agent of early blight in tomato and potato. The genome assembly is a significant step toward a better understanding of pathogenicity of A. solani.