Behavioural Evaluation of the Anticonvulsant Properties of the Traditional Medicine Anonna Senegalensis on ParabssDrosophila melanogaster Mutants

AbstractEpilepsy is the most common serious neurological disorder affecting 50 million people worldwide, 40 millions of which live in developing countries. Despite the introduction of a dozen of new Anti-Epileptic Drugs (AEDs), one third of the patients continue to have seizure regardless of receiving a AEDs treatment. This emphasize on the need to discover new drugs with different mechanisms of action. Traditional medicine (TM), pays a significant role in the treatment of epilepsy in many countries and offers an affordable and accessible alternative to AEDs. However, the lack of both empirical testing in animal models and clinical data places constrains to their clinical recommendation.In this study, we use Drosophila melanogaster as a model for epilepsy and tested the anti-seizure effect of leaf and stem bark aqueous extract form Annona senegalensis, a plant used as anti-convulsant by rural populations in Africa.Our results show, that at the concentrations tested, the leaf extract of A. senegalensis was more effective than the AEDs phenytoin and phenobarbital to control seizures. These promising results demonstrate that Drosophila is an excellent model for new drug discovery and that it could be used to do large scale screening of TMs for the treatment of epilepsy.

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Hypoglycemic and hypolipidemic activity of aqueous leaf extract of Passiflora suberosa L

PeerJ ◽

10.7717/peerj.4389 ◽

2018 ◽

Vol 6 ◽

pp. e4389 ◽

Cited By ~ 12

Author(s):

Hasani Prabodha Sudasinghe ◽

Dinithi C. Peiris

Keyword(s):

Blood Glucose ◽

Traditional Medicine ◽

Glucose Uptake ◽

Skeletal Muscles ◽

Leaf Extract ◽

Mechanisms Of Action ◽

Carbohydrate Content ◽

Phytochemical Screening ◽

Cholesterol Levels ◽

Aqueous Leaf Extract

Leaves of Passiflora suberosa L. (Family: Passifloraceae; common name: wild passion fruit, devil’s pumpkin) are used in Sri Lankan traditional medicine for treating diabetes. The present study investigated the in vivo ability of P. suberosa leaves to manage blood sugar status and associated cholesterol levels. Mechanisms of action and toxicity were also determined. Phytochemical screening of aqueous extracts of P. suberosa leaves and carbohydrate content of the leaves were determined according to previously published methods. In two group of male mice (n = 9), effects on fasting and random blood glucose levels (BGLs) of different acute doses (0, 25, 50, 100 and 200 mg/kg) of the aqueous leaf extract (ALE) were evaluated at 1, 3, and 5 h post-treatment. In another set of mice, the fasting BGL was evaluated following treatment of 0 or 50 mg/kg ALE (dose prescribed in traditional medicine) for 30 consecutive days. The lipid profile, some mechanism of ALE action (diaphragm glucose uptake, glycogen content in the liver and skeletal muscles) and its toxicity (behavioural observation, food and water intake, hepatoxicity) were also assessed following 30-day treatment. However, sucrose and glucose tolerance tests and intestinal glucose uptake were conducted to determine portion of mechanisms of action following single dose of 50 mg/kg ALE. Phytochemical screening revealed the presence of alkaloids, unsaturated sterols, triterpenes, saponins, flavonoids, tannins and proanthocyanidins. Carbohydrate content of the leaves was 12.97%. The maximum hypoglycemic effect was observed after 4 h of 50 and 100 mg/kg ALE administration. The extract decreased fasting BGL (18%) following an oral sucrose challenge and inhibited (79%) glucose absorption from the intestine. Correspondingly, the levels of glycogen in the liver (61%) and in the skeletal muscles (57%) were found be higher than that of the control group. The levels of total cholesterol (17%) and tri-glyceraldehyde levels (12%) found to be reduced in treated groups. Furthermore, no significant toxic effects were observed in treated groups. The present results suggest that the leaves of P. suberosa can be used to manage blood glucose and cholesterol levels. Isolation of active compounds are recommended for further analysis.

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Drosophila parabss Flies as a Screening Model for Traditional Medicine: Anticonvulsant Effects of Annona senegalensis

Frontiers in Neurology ◽

10.3389/fneur.2020.606919 ◽

2021 ◽

Vol 11 ◽

Author(s):

Samuel S. Dare ◽

Emiliano Merlo ◽

Jesus Rodriguez Curt ◽

Peter E. Ekanem ◽

Nan Hu ◽

...

Keyword(s):

Developing Countries ◽

Traditional Medicine ◽

Intractable Epilepsy ◽

Stem Bark ◽

Bark Extract ◽

Treatment Of Epilepsy ◽

Custard Apple ◽

Stem Bark Extract ◽

New Treatments ◽

And Control

Epilepsy is among the most common serious neurological disorders and affects around 50 million people worldwide, 80% of which live in developing countries. Despite the introduction of several new Anti-Epileptic Drugs (AEDs) in the last two decades, one third of treated patients have seizures refractory to pharmacotherapy. This highlights the need to develop new treatments with drugs targeting alternative seizure-induction mechanisms. Traditional medicine (TM) is used for the treatment of epilepsy in many developing countries and could constitute an affordable and accessible alternative to AEDs, but a lack of pre-clinical and clinical testing has so far prevented its wider acceptance worldwide. In this study we used Drosophila melanogaster paralyticbangsensitive(parabss) mutants as a model for epileptic seizure screening and tested, for the first time, the anti-seizure effect of a non-commercial AED. We evaluated the effect of the African custard-apple, Annona senegalensis, which is commonly used as a TM for the treatment of epilepsy in rural Africa, and compared it with the classical AED phenytoin. Our results showed that a stem bark extract from A. senegalensis was significantly more effective than a leaf extract and similar to phenytoin in the prevention and control of seizure-like behavior. These results support that Drosophila constitutes a robust animal model for the screening of TM with potential value for the treatment of intractable epilepsy.

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Training Aides to Screen Children for Speech and Language Problems

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.0704.236 ◽

1976 ◽

Vol 7 (4) ◽

pp. 236-241 ◽

Cited By ~ 5

Author(s):

Marisue Pickering ◽

William R. Dopheide

Keyword(s):

Rural Areas ◽

Large Scale ◽

School Personnel ◽

School Age Children ◽

Speech And Language ◽

Elementary School Age ◽

Evaluation Procedures ◽

Language Problems ◽

Competency Based ◽

Large Scale Screening

This report deals with an effort to begin the process of effectively identifying children in rural areas with speech and language problems using existing school personnel. A two-day competency-based workshop for the purpose of training aides to conduct a large-scale screening of speech and language problems in elementary-school-age children is described. Training strategies, implementation, and evaluation procedures are discussed.

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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190122151634 ◽

2019 ◽

Vol 19 (1) ◽

pp. 4-16 ◽

Cited By ~ 6

Author(s):

Qihui Wu ◽

Hanzhong Ke ◽

Dongli Li ◽

Qi Wang ◽

Jiansong Fang ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Large Scale ◽

Recent Progress ◽

High Specificity ◽

Learning Approaches ◽

Anticancer Peptides ◽

The Past ◽

Traditional Approaches ◽

Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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Antimicrobial Peptides and their Multiple Effects at Sub-Inhibitory Concentrations

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200427090912 ◽

2020 ◽

Vol 20 (14) ◽

pp. 1264-1273 ◽

Cited By ~ 1

Author(s):

Bruno Casciaro ◽

Floriana Cappiello ◽

Walter Verrusio ◽

Mauro Cacciafesta ◽

Maria Luisa Mangoni

Keyword(s):

Antimicrobial Peptides ◽

Inhibitory Concentration ◽

Multidrug Resistant ◽

Mechanisms Of Action ◽

New Drugs ◽

Lead Compounds ◽

Bacterial Pathogenicity ◽

Growth Inhibitory ◽

Resistant Strains ◽

Conventional Antibiotics

The frequent occurrence of multidrug-resistant strains to conventional antimicrobials has led to a clear decline in antibiotic therapies. Therefore, new molecules with different mechanisms of action are extremely necessary. Due to their unique properties, antimicrobial peptides (AMPs) represent a valid alternative to conventional antibiotics and many of them have been characterized for their activity and cytotoxicity. However, the effects that these peptides cause at concentrations below the minimum growth inhibitory concentration (MIC) have yet to be fully analyzed along with the underlying molecular mechanism. In this mini-review, the ability of AMPs to synergize with different antibiotic classes or different natural compounds is examined. Furthermore, data on microbial resistance induction are reported to highlight the importance of antibiotic resistance in the fight against infections. Finally, the effects that sub-MIC levels of AMPs can have on the bacterial pathogenicity are summarized while showing how signaling pathways can be valid therapeutic targets for the treatment of infectious diseases. All these aspects support the high potential of AMPs as lead compounds for the development of new drugs with antibacterial and immunomodulatory activities.

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A Drug Target Interaction Prediction Based on LINE-RF Learning

Current Bioinformatics ◽

10.2174/1574893615666191227092453 ◽

2020 ◽

Vol 15 (7) ◽

pp. 750-757

Author(s):

Jihong Wang ◽

Yue Shi ◽

Xiaodan Wang ◽

Huiyou Chang

Keyword(s):

Network Topology ◽

Drug Target ◽

Large Scale ◽

Representation Learning ◽

New Drugs ◽

Combination Method ◽

Learning Methods ◽

Network Representation ◽

On Line ◽

Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

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Analytical Validation and Clinical Application of Rapid Serological Tests for SARS-CoV-2 Suitable for Large-Scale Screening

Diagnostics ◽

10.3390/diagnostics11050869 ◽

2021 ◽

Vol 11 (5) ◽

pp. 869

Author(s):

Amedeo De Nicolò ◽

Valeria Avataneo ◽

Jessica Cusato ◽

Alice Palermiti ◽

Jacopo Mula ◽

...

Keyword(s):

Early Diagnosis ◽

Real Time Pcr ◽

Large Scale ◽

High Sensitivity ◽

Analytical Validation ◽

Pcr Assay ◽

Serological Tests ◽

Flow Test ◽

Lateral Flow Test ◽

Large Scale Screening

Recently, large-scale screening for COVID-19 has presented a major challenge, limiting timely countermeasures. Therefore, the application of suitable rapid serological tests could provide useful information, however, little evidence regarding their robustness is currently available. In this work, we evaluated and compared the analytical performance of a rapid lateral-flow test (LFA) and a fast semiquantitative fluorescent immunoassay (FIA) for anti-nucleocapsid (anti-NC) antibodies, with the reverse transcriptase real-time PCR assay as the reference. In 222 patients, LFA showed poor sensitivity (55.9%) within two weeks from PCR, while later testing was more reliable (sensitivity of 85.7% and specificity of 93.1%). Moreover, in a subset of 100 patients, FIA showed high sensitivity (89.1%) and specificity (94.1%) after two weeks from PCR. The coupled application for the screening of 183 patients showed satisfactory concordance (K = 0.858). In conclusion, rapid serological tests were largely not useful for early diagnosis, but they showed good performance in later stages of infection. These could be useful for back-tracing and/or to identify potentially immune subjects.

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BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

International Journal of Molecular Sciences ◽

10.3390/ijms22157773 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7773

Author(s):

Neann Mathai ◽

Conrad Stork ◽

Johannes Kirchmair

Keyword(s):

Large Scale ◽

Computational Approach ◽

Large Sets ◽

Compound Libraries ◽

Wide Range ◽

Protein Space ◽

High Chance ◽

Large Scale Screening ◽

Early Drug ◽

Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

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Large-scale screening of natural genetic resource in the hydrocarbon-producing microalga Botrycoccus braunii identified novel fast-growing strains

Scientific Reports ◽

10.1038/s41598-021-86760-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Koji Kawamura ◽

Suzune Nishikawa ◽

Kotaro Hirano ◽

Ardianor Ardianor ◽

Rudy Agung Nugroho ◽

...

Keyword(s):

Large Scale ◽

Biomass Productivity ◽

Botryococcus Braunii ◽

Algal Biofuel ◽

Fast Growing ◽

Wild Strains ◽

Glass Tubes ◽

The Tropics ◽

Large Scale Screening ◽

Hydrocarbon Productivity

AbstractAlgal biofuel research aims to make a renewable, carbon–neutral biofuel by using oil-producing microalgae. The freshwater microalga Botryococcus braunii has received much attention due to its ability to accumulate large amounts of petroleum-like hydrocarbons but suffers from slow growth. We performed a large-scale screening of fast-growing strains with 180 strains isolated from 22 ponds located in a wide geographic range from the tropics to cool-temperate. A fast-growing strain, Showa, which recorded the highest productivities of algal hydrocarbons to date, was used as a benchmark. The initial screening was performed by monitoring optical densities in glass tubes and identified 9 wild strains with faster or equivalent growth rates to Showa. The biomass-based assessments showed that biomass and hydrocarbon productivities of these strains were 12–37% and 11–88% higher than that of Showa, respectively. One strain, OIT-678 established a new record of the fastest growth rate in the race B strains with a doubling time of 1.2 days. The OIT-678 had 36% higher biomass productivity, 34% higher hydrocarbon productivity, and 20% higher biomass density than Showa at the same cultivation conditions, suggesting the potential of the new strain to break the record for the highest productivities of hydrocarbons.

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Identification of Six Loci in Which Mutations Partially Restore Peroxisome Biogenesis and/or Alleviate the Metabolic Defect of pex2 Mutants in Podospora

Genetics ◽

10.1093/genetics/161.3.1089 ◽

2002 ◽

Vol 161 (3) ◽

pp. 1089-1099

Author(s):

Gwenaël Ruprich-Robert ◽

Véronique Berteaux-Lecellier ◽

Denise Zickler ◽

Arlette Panvier-Adoutte ◽

Marguerite Picard

Keyword(s):

Sole Carbon Source ◽

Large Scale ◽

Podospora Anserina ◽

Developmental Defect ◽

Dominant Allele ◽

Peroxisome Biogenesis ◽

Metabolic Defects ◽

Metabolic Defect ◽

Mutant Strains ◽

Large Scale Screening

Abstract Peroxins (PEX) are proteins required for peroxisome biogenesis. Mutations in PEX genes cause lethal diseases in humans, metabolic defects in yeasts, and developmental disfunctions in plants and filamentous fungi. Here we describe the first large-scale screening for suppressors of a pex mutation. In Podospora anserina, pex2 mutants exhibit a metabolic defect [inability to grow on medium containing oleic acid (OA medium) as sole carbon source] and a developmental defect (inability to differentiate asci in homozygous crosses). Sixty-three mutations able to restore growth of pex2 mutants on OA medium have been analyzed. They fall in six loci (suo1 to suo6) and act as dominant, allele-nonspecific suppressors. Most suo mutations have pleiotropic effects in a pex2+ background: formation of unripe ascospores (all loci except suo5 and suo6), impaired growth on OA medium (all loci except suo4 and suo6), or sexual defects (suo4). Using immunofluorescence and GFP staining, we show that peroxisome biogenesis is partially restored along with a low level of ascus differentiation in pex2 mutant strains carrying either the suo5 or the suo6 mutations. The data are discussed with respect to β-oxidation of fatty acids, peroxisome biogenesis, and cell differentiation.

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