scholarly journals Phylum-Spanning Neuropeptide GPCR Identification and Prioritization: Shaping Drug Target Discovery Pipelines for Nematode Parasite Control

2021 ◽  
Vol 12 ◽  
Author(s):  
Louise E. Atkinson ◽  
Ciaran J. McCoy ◽  
Bethany A. Crooks ◽  
Fiona M. McKay ◽  
Paul McVeigh ◽  
...  
Keyword(s):  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Parasitic Nematode ◽  
Anthelmintic Resistance ◽  
Life Stage ◽  
Nematode Parasite ◽  
Parasitic Nematodes ◽  
Parasite Control ◽  
Specific Expression

Nematode parasites undermine human health and global food security. The frontline anthelmintic portfolio used to treat parasitic nematodes is threatened by the escalation of anthelmintic resistance, resulting in a demand for new drug targets for parasite control. Nematode neuropeptide signalling pathways represent an attractive source of novel drug targets which currently remain unexploited. The complexity of the nematode neuropeptidergic system challenges the discovery of new targets for parasite control, however recent advances in parasite ‘omics’ offers an opportunity for the in silico identification and prioritization of targets to seed anthelmintic discovery pipelines. In this study we employed Hidden Markov Model-based searches to identify ~1059 Caenorhabditis elegans neuropeptide G-protein coupled receptor (Ce-NP-GPCR) encoding gene homologs in the predicted protein datasets of 10 key parasitic nematodes that span several phylogenetic clades and lifestyles. We show that, whilst parasitic nematodes possess a reduced complement of Ce-NP-GPCRs, several receptors are broadly conserved across nematode species. To prioritize the most appealing parasitic nematode NP-GPCR anthelmintic targets, we developed a novel in silico nematode parasite drug target prioritization pipeline that incorporates pan-phylum NP-GPCR conservation, C. elegans-derived reverse genetics phenotype, and parasite life-stage specific expression datasets. Several NP-GPCRs emerge as the most attractive anthelmintic targets for broad spectrum nematode parasite control. Our analyses have also identified the most appropriate targets for species- and life stage- directed chemotherapies; in this context we have identified several NP-GPCRs with macrofilaricidal potential. These data focus functional validation efforts towards the most appealing NP-GPCR targets and, in addition, the prioritization strategy employed here provides a blueprint for parasitic nematode target selection beyond NP-GPCRs.

scholarly journals In silico analyses of neuropeptide-like protein (NLP) profiles in parasitic nematodes

2021 ◽  
Author(s):  
Fiona M McKay ◽  
Ciaran J McCoy ◽  
Nikki J Marks ◽  
Aaron G Maule ◽  
Louise E Atkinson ◽  
...  
Keyword(s):  
Drug Targets ◽  
Anthelmintic Resistance ◽  
Physiological Activity ◽  
Nematode Species ◽  
Nematode Parasite ◽  
Parasitic Nematodes ◽  
Global Food Security ◽  
Nematode Parasites ◽  
Parasite Infections ◽  
Novel Drug

Nematode parasite infections cause disease in humans and animals and threaten global food security by reducing productivity in livestock and crop farming. The escalation of anthelmintic resistance in economically important nematode parasites underscores the need for the identification of novel drug targets in these worms. Nematode neuropeptide signalling is an attractive system for potential chemotherapeutic exploitation, with neuropeptide G-protein coupled receptors (NP-GPCRs) representing the leading target candidates therein. In order to successfully validate NP-GPCRs as targets for parasite control it is necessary to characterise their function and importance to nematode biology. This can be aided through identifying receptor activating ligand(s) in a process known as receptor deorphanisation. Such efforts first require the identification of all neuropeptide ligands within parasites. Here we comb the genomes of nine therapeutically relevant pathogenic nematode species to comprehensively characterise the nematode parasite neuropeptide-like protein (NLP) complements, and details the discovery of several previously unreported, yet conserved, neuropeptides and neuropeptide-encoding genes. We identify the neuropeptides that are most highly conserved in all parasites examined, and characterise their physiological activity on the reproductive musculature of the parasite, Ascaris suum. These data suggest conserved neuropeptide functions in both free living and parasitic nematodes, and support the potential for exploitation of the neuropeptide signalling system as an anthelmintic target.

scholarly journals Genomic landscape of drug response reveals novel mediators of anthelmintic resistance

2021 ◽  
Author(s):  
Stephen R Doyle ◽  
Roz Laing ◽  
David Bartley ◽  
Alison Morrison ◽  
Nancy Holroyd ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Genetic Basis ◽  
Parasitic Nematode ◽  
Anthelmintic Resistance ◽  
Forward Genetics ◽  
Parasitic Nematodes ◽  
Genomic Landscape ◽  
Genetic Cross ◽  
Novel Alleles ◽  
Host Parasite

Understanding the genetic basis of anthelmintic drug resistance in parasitic nematodes is key to tracking and combatting their spread. Here, we use a genetic cross in a natural host-parasite system to simultaneously map resistance loci for the three major classes of anthelmintics. This approach identifies novel alleles for resistance to benzimidazoles and levamisole and implicates the transcription factor, cky-1, in ivermectin resistance. This gene is within a locus under selection in ivermectin resistant populations worldwide; functional validation using knockout and gene expression experiments supports a role for cky-1 overexpression in ivermectin resistance. Our work demonstrates the feasibility of high-resolution forward genetics in a parasitic nematode, and identifies variants for the development of molecular diagnostics to combat drug resistance in the field.

scholarly journals Complementary Approaches to Understand Anthelmintic Resistance Using Free-Living and Parasitic Nematodes

2020 ◽  
Author(s):  
Janneke Wit ◽  
Clayton Dilks ◽  
Erik Andersen
Keyword(s):  
Haemonchus Contortus ◽  
Parasitic Nematode ◽  
Anthelmintic Resistance ◽  
Nematode Species ◽  
The Other ◽  
Parasitic Nematodes ◽  
Mechanisms Of Resistance ◽  
Free Living ◽  
Sustained Efficacy ◽  
Modes Of Action

Anthelmintic drugs are the major line of defense against parasitic nematode infections, but the arsenal is limited and resistance threatens sustained efficacy of the available drugs. Discoveries of the modes of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss how our understanding of anthelmintic resistance and modes of action came from the interplay of results from each of these species. We argue that this “cycle of discovery”, where results from one species inform the design of experiments in the other, can use the complementary strengths of both to understand anthelmintic modes of action and mechanisms of resistance.

Faecal dry matter and nematode infection in parasite-resistant Merino sheep in Australia

2009 ◽  
Vol 2009 ◽  
pp. 29-29
Author(s):  
A R Williams ◽  
L J E Karlsson ◽  
D G Palmer ◽  
P E Vercoe ◽  
I H Williams ◽  
...  
Keyword(s):  
Dry Matter ◽  
Parasitic Nematode ◽  
Parasitic Nematodes ◽  
Parasite Control ◽  
Merino Sheep ◽  
Infective Larvae ◽  
The United Kingdom ◽  
Teladorsagia Circumcincta ◽  
Nematode Larvae ◽  
Southern Half

Scouring (diarrhoea) is a major concern for sheep producers as the accumulation of faecal material (dags) around the breech pre-disposes sheep to flystrike. Scouring occurs when the consistency of faeces is fluid with a low percentage of dry matter. In temperate areas such as the southern half of Australia, New Zealand and the United Kingdom, scouring is associated with ingestion of parasitic nematode larvae, mainly Teladorsagia circumcincta and Trichostrongylus colubriformis (Larsen et al., 1994). Breeding sheep to be resistant to these nematodes is a sustainable parasite-control strategy due to reduced reliance on chemical treatment. However, in adult sheep, scouring appears equally prevalent in resistant animals and, in some environments, is even more severe than in susceptible sheep (Karlsson et al., 2004). In this experiment, we investigated how faecal dry matter (FDM) in sheep from a flock bred for resistance to parasitic nematodes changed when challenged with infective larvae. We expected that FDM would be lower in challenged sheep compared to unchallenged controls, and FDM would also be lower in sheep with high dag scores compared to sheep with low dag scores.

Complementary Approaches to Understand Anthelmintic Resistance Using Free-Living and Parasitic Nematodes

2020 ◽  
Author(s):  
Janneke Wit ◽  
Clayton Dilks ◽  
Erik Andersen
Keyword(s):  
Haemonchus Contortus ◽  
Parasitic Nematode ◽  
Anthelmintic Resistance ◽  
Animal Health ◽  
Nematode Species ◽  
Parasitic Nematodes ◽  
Mechanisms Of Resistance ◽  
Free Living ◽  
Powerful Approach ◽  
Anthelmintic Drugs

Parasitic nematode infections impact human and animal health globally, especially in the developing world. Anthelmintic drugs are the major line of defense against these infections, but the arsenal is limited. Additionally, anthelmintic resistance is widespread in veterinary parasites and an emerging threat in human parasites. Discoveries of the mode of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss recent progress understanding anthelmintic resistance using these two species and how that progress relates to laboratory and field-based studies of veterinary helminths. We present a powerful approach enabled by the strengths of both nematode species to understand mechanisms of resistance and modes of action of anthelmintic drugs.

scholarly journals QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest

2021 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Interaction Network ◽  
Network Data ◽  
Data Set ◽  
Target Interaction

Network data is composed of nodes and edges. Successful application of machine learning/deep<br>learning algorithms on network data to make node classification and link prediction have been shown<br>in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on<br>biological network data to generate predictions of scientific usefulness. In the presented work,<br>compound-drug target interaction network data set from bindingDB has been used to train deep<br>learning neural network and a multi class classification has been implemented to classify PubChem<br>compound queried by the user into class labels of PBD IDs. This way target interaction prediction for<br>PubChem compounds is carried out using deep learning. The user is required to input the PubChem<br>Compound ID (CID) of the compound the user wishes to gain information about its predicted<br>biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for<br>the input CID. Further the tool also optimizes the compound of interest of the user toward drug<br>likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In<br>Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand<br>interaction profiles. The program is hosted, supported and maintained at the following GitHub<br><div>repository</div><div><br></div><div>https://github.com/bengeof/Compound2DeNovoDrugPropMax</div><div><br></div>Anticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use<br>the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep<br>learning models into a quantum layer and introduce quantum layers into classical models to produce a<br>quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the<br><div>same is provided below</div><div><br></div>https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax<br>

scholarly journals Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Judith Gracia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Biology ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Qsar Model ◽  
Learning Tools ◽  
Active Compounds ◽  
Qsar Modelling ◽  
Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

Synthesis of New 3-Arylaminophthalides and 3-Indolyl-phthalides using Ammonium Chloride, Evaluation of their Anti-Mycobacterial Potential and Docking Study

2020 ◽  
Vol 23 (8) ◽  
pp. 723-739 ◽  
Author(s):  
Avinash Patil ◽  
Harleen Duggal ◽  
Kamini T. Bagul ◽  
Sonali Kamble ◽  
Pradeep Lokhande ◽  
...  
Keyword(s):  
Ammonium Chloride ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Antioxidant Activities ◽  
Target Identification ◽  
Docking Study ◽  
Structural Level ◽  
Current Series ◽  
Microtiter Assay

Objective: The study aims at the derivatization of “Phthalides” and synthesizes 3- arylaminophthalides & 3-indolyl-phthalides compounds, and evaluates their anti-tubercular and antioxidant activities. The study has also intended to employ the in silico methods for the identification of possible drug targets in Mycobacterium and evaluate the binding affinities of synthesized compounds. Methods: This report briefly explains the synthesis of phthalide derivatives using ammonium chloride. The synthesized compounds were characterized using spectral analysis. Resazurin Microtiter Assay (REMA) plate method was used to demonstrate the anti-mycobacterial activity of the synthesized compounds. An in-silico pharmacophore probing approach was used for target identification in Mycobacterium. The structural level interaction between the identified putative drug target and synthesized phthalides was studied using Lamarckian genetic algorithm-based software. Results and Discussion: In the present study, we report an effective, environmentally benign scheme for the synthesis of phthalide derivatives. Compounds 5c and 5d from the current series appear to possess good anti-mycobacterial activity. dCTP: deaminasedUTPase was identified as a putative drug target in Mycobacterium. The docking results clearly showed the interactive involvement of conserved residues of dCTP with the synthesized phthalide compounds. Conclusion: On the eve of evolving anti-TB drug resistance, the data on anti-tubercular and allied activities of the compounds in the present study demonstrates the enormous significance of these newly synthesized derivatives as possible candidate leads in the development of novel anti-tubercular agents. The docking results from the current report provide a structural rationale for the promising anti-tubercular activity demonstrated by 3-arylaminophthalides and 3-indolyl-phthalides compounds.

scholarly journals Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Rafal Madaj ◽  
Akhil Sanker ◽  
Pavan Preetham Valluri ◽  
Judith Gracia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Computational Biology ◽  
In Silico ◽  
Drug Target ◽  
Drug Targets ◽  
Qsar Model ◽  
Learning Tools ◽  
Active Compounds ◽  
Qsar Modelling ◽  
Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

scholarly journals Two novel loci underlie natural differences in Caenorhabditis elegans macrocyclic lactone responses

2021 ◽  
Author(s):  
Kathryn S. Evans ◽  
Janneke Wit ◽  
Lewis Stevens ◽  
Steffen R. Hahnel ◽  
Briana Rodriguez ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Haemonchus Contortus ◽  
Macrocyclic Lactone ◽  
Parasitic Nematode ◽  
Anthelmintic Resistance ◽  
Recombinant Inbred Lines ◽  
Parasitic Nematodes ◽  
C Elegans ◽  
The Future ◽  
Genomic Regions

AbstractParasitic nematodes cause a massive worldwide burden on human health along with a loss of livestock and agriculture productivity. Anthelmintics have been widely successful in treating parasitic nematodes. However, resistance is increasing, and little is known about the molecular and genetic causes of resistance. The free-living roundworm Caenorhabditis elegans provides a tractable model to identify genes that underlie resistance. Unlike parasitic nematodes, C. elegans is easy to maintain in the laboratory, has a complete and well annotated genome, and has many genetic tools. Using a combination of wild isolates and a panel of recombinant inbred lines constructed from crosses of two genetically and phenotypically divergent strains, we identified three genomic regions on chromosome V that underlie natural differences in response to the macrocyclic lactone (ML) abamectin. One locus was identified previously and encodes an alpha subunit of a glutamate-gated chloride channel (glc-1). Here, we validate and narrow two novel loci using near-isogenic lines. Additionally, we generate a list of prioritized candidate genes identified in C. elegans and in the parasite Haemonchus contortus by comparison of ML resistance loci. These genes could represent previously unidentified resistance genes shared across nematode species and should be evaluated in the future. Our work highlights the advantages of using C. elegans as a model to better understand ML resistance in parasitic nematodes.Author SummaryParasitic nematodes infect plants, animals, and humans, causing major health and economic burdens worldwide. Parasitic nematode infections are generally treated efficiently with a class of drugs named anthelmintics. However, resistance to many of these anthelmintic drugs, including macrocyclic lactones (MLs), is rampant and increasing. Therefore, it is essential that we understand how these drugs act against parasitic nematodes and, conversely, how nematodes gain resistance in order to better treat these infections in the future. Here, we used the non-parasitic nematode Caenorhabditis elegans as a model organism to study ML resistance. We leveraged natural genetic variation between strains of C. elegans with differential responses to abamectin to identify three genomic regions on chromosome V, each containing one or more genes that contribute to ML resistance. Two of these loci have not been previously discovered and likely represent novel resistance mechanisms. We also compared the genes in these two novel loci to the genes found within genomic regions linked to ML resistance in the parasite Haemonchus contortus and found several cases of overlap between the two species. Overall, this study highlights the advantages of using C. elegans to understand anthelmintic resistance in parasitic nematodes.

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