Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents

Identifying potential drug targets in the kinomes of two monogenean species

Protein kinases are enzymes involved in essential biological processes such as signal transduction, transcription, metabolism, and the cell cycle. Human kinases are targets for several drugs approved by the US Food and Drug Administration. Therefore, the identification and classification of kinases in other organisms, including pathogenic parasites, is an interesting subject of study. Monogeneans are platyhelminths, mainly ectoparasites, capable of causing health problems in farmed fish. Although some genomes and transcriptomes are available for monogenean species, their full repertoire of kinases is unknown. The aim of this study was to identify and classify the putative kinases in the transcriptomes of two monogeneans, Rhabdosynochus viridisi and Scutogyrus longicornis, and then to predict potential monogenean drug targets (MDTs) and selective inhibitor drugs using computational approaches. Monogenean kinases having orthologs in the lethal phenotype of C. elegans but not in fish or humans were considered MDTs. A total of 160 and 193 kinases were identified in R. viridisi and S. longicornis, respectively. Of these, 22 kinases, belonging mainly to the major groups CAMK, AGC, and TK, were classified as MDTs, five of which were evaluated further. Molecular docking analysis indicated that dihydroergotamine, ergotamine, and lomitapide have the highest affinity for the kinases BRSK and MEKK1. These well-known drugs could be evaluated in future studies for potential repurposing as anti-monogenean agents. The present study contributes valuable data for the development of new antiparasitic candidates for finfish aquaculture.

Evaluation of bromocriptine and plumbagin against the monogenean Rhabdosynochus viridisi: Computational drug repositioning and in vitro approaches

Monogeneans are parasitic platyhelminths that can harm the health of farmed fish. Few treatments are available against monogeneans, and the incentive to develop new antiparasitic agents is similar or even lower than the incentive for neglected parasitic diseases in humans. Considering that searching for and developing new antimonogenean compounds may require enormous investments of time, money, and animal sacrifice, the use of a computer-guided drug repositioning approach is a reasonable alternative. Under this context, this study aimed to evaluate the effectiveness of plumbagin and bromocriptine against adults and eggs of the monogenean Rhabdosynochus viridisi (Diplectanidae). Plumbagin is a phytochemical compound that has recently emerged as a potent antimonogenean; however, further investigation is required to determine its effects on different monogenean species. Bromocriptine was selected through a computational approach that included molecular docking analyses of 77 receptors of monogeneans (putative drug targets) and 77 ligands (putative inhibitors). In vitro experiments showed that bromocriptine does not exhibit mortality at concentrations of 0.1, 1, and 10 mg/L whereas plumbagin at 2 and 10 mg/L caused 100% monogenean mortality after 3 h and 30 min, respectively. The most effective concentration of plumbagin (10 mg/L) did not completely inhibit egg hatching. These findings underscore plumbagin as a highly effective agent against adult monogeneans and highlight the need for research to evaluate its effect(s) on fish. Although computational drug repositioning is useful for selecting candidates for experimental testing, it does not guarantee success due to the complexity of biological interactions, as observed here with bromocriptine. Therefore, it is crucial to examine the various compounds proposed by this method.

Current drug strategies for the treatment and control of schistosomiasis

INTRODUCTION

Schistosomiasis, one of the current Neglected Tropical Diseases (NTDs) affects over 230 million people globally, with nearly 700 million at risk in more than 74 countries. Praziquantel (PZQ) has served as the primary treatment for the past four decades; however, its effectiveness is limited as it solely eliminates adult worms. In regions where infections are frequent, PZQ exhibits only temporary efficacy and has restricted potential to disrupt the prolonged transmission of the disease.

AREAS COVERED

A comprehensive exploration using the PubMed database was conducted to review current pharmacotherapy approaches for schistosomiasis. This review also encompasses recent research findings related to potential novel therapeutics and the repurposing of existing drugs.

EXPERT OPINION

Current schistosoma treatment strategies, primarily relying on PZQ, face challenges like temporary effectiveness and limited impact on disease transmission. Drug repurposing, due to economic constraints, is decisive for NTDs. Despite PZQ's efficacy, its failure to prevent reinfection highlights the need for complementary strategies, especially in regions with persistent environmental foci. Integrating therapies against diverse schistosome stages boosts efficacy and impedes resistance. Uncovering novel agents is essential to address resistance concerns in tackling this neglected tropical disease. Integrated strategies present a comprehensive approach to navigate the complex challenges.

Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors

Rheumatoid arthritis (RA) remains one of the most prevalent autoimmune diseases worldwide. Janus kinase 3 (JAK3) is an essential enzyme for treating autoimmune diseases, including RA. Molecular modeling techniques play a crucial role in the search for new drugs by reducing time delays. In this study, the 3D-QSAR approach is employed to predict new JAK3 inhibitors. Two robust models, both field-based with R2 = 0.93, R = 0.96, and Q2 = 87, and atom-based with R2 = 0.94, R = 0.97, and Q2 = 86, yielded good results by identifying groups that may readily direct their interaction. A reliable pharmacophore model, DHRRR1, was provided in this work to enable the clear characterization of chemical features, leading to the design of 13 inhibitors with their pIC50 values. The DHRRR1 model yielded a validation result with a ROC value of 0.87. Five promising inhibitors were selected for further study based on an ADMET analysis of their pharmacokinetic properties and covalent docking (CovDock). Compared to the FDA-approved drug tofacitinib, the pharmaceutical features, binding affinity and stability of the inhibitors were analyzed through CovDock, 300 ns molecular dynamics simulations, free energy binding calculations and ADMET predictions. The results show that the inhibitors have strong binding affinity, stability and favorable pharmaceutical properties. The newly predicted molecules, as JAK3 inhibitors for the treatment of RA, are promising candidates for use as drugs.

Breaking bad: necroptosis in the pathogenesis of gastrointestinal diseases

A delicate balance between programmed cell death and proliferation of intestinal epithelial cells (IEC) exists in the gut to maintain homeostasis. Homeostatic cell death programs such as anoikis and apoptosis ensure the replacement of dead epithelia without overt immune activation. In infectious and chronic inflammatory diseases of the gut, this balance is invariably disturbed by increased levels of pathologic cell death. Pathological forms of cell death such as necroptosis trigger immune activation barrier dysfunction, and perpetuation of inflammation. A leaky and inflamed gut can thus become a cause of persistent low-grade inflammation and cell death in other organs of the gastrointestinal (GI) tract, such as the liver and the pancreas. In this review, we focus on the advances in the molecular and cellular understanding of programmed necrosis (necroptosis) in tissues of the GI tract. In this review, we will first introduce the reader to the basic molecular aspects of the necroptosis machinery and discuss the pathways leading to necroptosis in the GI system. We then highlight the clinical significance of the preclinical findings and finally evaluate the different therapeutic approaches that attempt to target necroptosis against various GI diseases. Finally, we review the recent advances in understanding the biological functions of the molecules involved in necroptosis and the potential side effects that may occur due to their systemic inhibition. This review is intended to introduce the reader to the core concepts of pathological necroptotic cell death, the signaling pathways involved, its immuno-pathological implications, and its relevance to GI diseases. Further advances in our ability to control the extent of pathological necroptosis will provide better therapeutic opportunities against currently intractable GI and other diseases.

Drug-disease association prediction with literature based multi-feature fusion

Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

De novo transcriptome assembly and identification of G-Protein-Coupled-Receptors (GPCRs) in two species of monogenean parasites of fish

Genomic resources for Platyhelminthes of the class Monogenea are scarce, despite the diversity of these parasites, some species of which are highly pathogenic to their fish hosts. This work aimed to generate de novo-assembled transcriptomes of two monogenean species, Scutogyrus longicornis (Dactylogyridae) and Rhabdosynochus viridisi (Diplectanidae), providing a protocol for cDNA library preparation with low input samples used in single cell transcriptomics. This allowed us to work with sub-microgram amounts of total RNA with success. These transcriptomes consist of 25,696 and 47,187 putative proteins, respectively, which were further annotated according to the Swiss-Prot, Pfam, GO, KEGG, and COG databases. The completeness values of these transcriptomes evaluated with BUSCO against Metazoa databases were 54.1% and 73%, respectively, which is in the range of other monogenean species. Among the annotations, a large number of terms related to G-protein-coupled receptors (GPCRs) were found. We identified 109 GPCR-like sequences in R. viridisi, and 102 in S. longicornis, including family members specific for Platyhelminthes. Rhodopsin was the largest family according to GRAFS classification. Two putative melatonin receptors found in S. longicornis represent the first record of this group of proteins in parasitic Platyhelminthes. Forty GPCRs of R. viridisi and 32 of S. longicornis that were absent in Vertebrata might be potential drug targets. The present study provides the first publicly available transcriptomes for monogeneans of the subclass Monopisthocotylea, which can serve as useful genomic datasets for functional genomic research of this important group of parasites.

wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms

Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating relevant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of phenotypes, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds promise for enabling collaboration among investigators with diverse interests.

Drug Repurposing and De Novo Drug Discovery of Protein Kinase Inhibitors as New Drugs against Schistosomiasis

Schistosomiasis is a neglected tropical disease affecting more than 200 million people worldwide. Chemotherapy relies on one single drug, praziquantel, which is safe but ineffective at killing larval stages of this parasite. Furthermore, concerns have been expressed about the rise in resistance against this drug. In the absence of an antischistosomal vaccine, it is, therefore, necessary to develop new drugs against the different species of schistosomes. Protein kinases are important molecules involved in key cellular processes such as signaling, growth, and differentiation. The kinome of schistosomes has been studied and the suitability of schistosomal protein kinases as targets demonstrated by RNA interference studies. Although protein kinase inhibitors are mostly used in cancer therapy, e.g., for the treatment of chronic myeloid leukemia or melanoma, they are now being increasingly explored for the treatment of non-oncological conditions, including schistosomiasis. Here, we discuss the various approaches including screening of natural and synthetic compounds, de novo drug development, and drug repurposing in the context of the search for protein kinase inhibitors against schistosomiasis. We discuss the status quo of the development of kinase inhibitors against schistosomal serine/threonine kinases such as polo-like kinases (PLKs) and mitogen-activated protein kinases (MAP kinases), as well as protein tyrosine kinases (PTKs).

Schistosoma mansoni α-N-acetylgalactosaminidase (SmNAGAL) regulates coordinated parasite movement and egg production

α-galactosidase (α-GAL) and α-N-acetylgalactosaminidase (α-NAGAL) are two glycosyl hydrolases responsible for maintaining cellular homeostasis by regulating glycan substrates on proteins and lipids. Mutations in the human genes encoding either enzyme lead to neurological and neuromuscular impairments seen in both Fabry- and Schindler/Kanzaki- diseases. Here, we investigate whether the parasitic blood fluke Schistosoma mansoni, responsible for the neglected tropical disease schistosomiasis, also contains functionally important α-GAL and α-NAGAL proteins. As infection, parasite maturation and host interactions are all governed by carefully-regulated glycosylation processes, inhibiting S. mansoni's α-GAL and α-NAGAL activities could lead to the development of novel chemotherapeutics. Sequence and phylogenetic analyses of putative α-GAL/α-NAGAL protein types showed Smp_089290 to be the only S. mansoni protein to contain the functional amino acid residues necessary for α-GAL/α-NAGAL substrate cleavage. Both α-GAL and α-NAGAL enzymatic activities were higher in females compared to males (p<0.05; α-NAGAL > α-GAL), which was consistent with smp_089290's female biased expression. Spatial localisation of smp_089290 revealed accumulation in parenchymal cells, neuronal cells, and the vitellaria and mature vitellocytes of the adult schistosome. siRNA-mediated knockdown (>90%) of smp_089290 in adult worms significantly inhibited α-NAGAL activity when compared to control worms (siLuc treated males, p<0.01; siLuc treated females, p<0.05). No significant reductions in α-GAL activities were observed in the same extracts. Despite this, decreases in α-NAGAL activities correlated with a significant inhibition in adult worm motility as well as in egg production. Programmed CRISPR/Cas9 editing of smp_089290 in adult worms confirmed the egg reduction phenotype. Based on these results, Smp_089290 was determined to act predominantly as an α-NAGAL (hereafter termed SmNAGAL) in schistosome parasites where it participates in coordinating movement and oviposition processes. Further characterisation of SmNAGAL and other functionally important glycosyl hydrolases may lead to the development of a novel anthelmintic class of compounds.

Protein Kinases: Potential Drug Targets Against Schistosoma japonicum

Schistosoma japonicum (S. japonicum) infection can induce serious organ damage and cause schistosomiasis japonica which is mainly prevalent in Asia and currently one of the most seriously neglected tropical diseases. Treatment of schistosomiasis largely depends on the drug praziquantel (PZQ). However, PZQ exhibits low killing efficacy on juvenile worms and the potential emergence of its drug resistance is a continual concern. Protein kinases (PKs) are enzymes that catalyze the phosphorylation of proteins and can participate in many signaling pathways in vivo. Recent studies confirmed the essential roles of PKs in the growth and development of S. japonicum, as well as in schistosome-host interactions, and researches have screened drug targets about PKs from S. japonicum (SjPKs), which provide new opportunities of developing new treatments on schistosomiasis. The aim of this review is to present the current progress on SjPKs from classification, different functions and their potential to become drug targets compared with other schistosomes. The efficiency of related protein kinase inhibitors on schistosomes is highlighted. Finally, the current challenges and problems in the study of SjPKs are proposed, which can provide future guidance for developing anti-schistosomiasis drugs and vaccines.

Comparative analysis of Rosetta stone events in Klebsiella pneumoniae and Streptococcus pneumoniae for drug target identification

Background

Drug target identification is a fast-growing field of research in many human diseases. Many strategies have been devised in the post-genomic era to identify new drug targets for infectious diseases. Analysis of protein sequences from different organisms often reveals cases of exon/ORF shuffling in a genome. This results in the fusion of proteins/domains, either in the same genome or that of some other organism, and is termed Rosetta stone sequences. They help link disparate proteins together describing local and global relationships among proteomes. The functional role of proteins is determined mainly by domain-domain interactions and leading to the corresponding signaling mechanism. Putative proteins can be identified as drug targets by re-annotating their functional role through domain-based strategies.

Results

This study has utilized a bioinformatics approach to identify the putative proteins that are ideal drug targets for pneumonia infection by re-annotating the proteins through position-specific iterations. The putative proteome of two pneumonia-causing pathogens was analyzed to identify protein domain abundance and versatility among them. Common domains found in both pathogens were identified, and putative proteins containing these domains were re-annotated. Among many druggable protein targets, the re-annotation of EJJ83173 (which contains the GFO_IDH_MocA domain) showed that its probable function is glucose-fructose oxidoreduction. This protein was found to have sufficient interactor proteins and homolog in both pathogens but no homolog in the host (human), indicating it as an ideal drug target. 3D modeling of the protein showed promising model parameters. The model was utilized for virtual screening which revealed several ligands with inhibitory activity. These ligands included molecules documented in traditional Chinese medicine and currently marketed drugs.

Conclusions

This novel strategy of drug target identification through domain-based putative protein re-annotation presents a prospect to validate the proposed drug target to confer its utility as a typical protein targeting both pneumonia-causing species studied herewith.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

A novel heterocyclic compound inhibits human breast tumor cells via ROS mediated apoptosis

The new heterocyclic compound 2-((6-chloro-2-methylpyrimidin-4-yl)amino)-N-(2-chloro-6-methylphenyl) thiazole-5-carboxamide (1), designed using 2-chloro-N-(2-chloro-6-methylphenyl)thiazole-5-carboxamide (2) as start material, was successfully obtained via multistep synthesis route and finally characterized by IR (infrared radiation), 1H NMR (nuclear magnetic resonance), and single crystal X-ray crystallography. The inhibitory effect of compound 1 on human breast tumor cell line BS524 was further explored. The MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) and IC50 (half maximal inhibitory concentration) values suggested that compound 1 has significant anti-proliferation activity on BS524 cells and with low side effect. Then, serial experiments, such as the Annexin V-FITC/PI assay, TUNEL staining and autophagy detection revealed that compound 1 could inhibit cell proliferation via induce cells apoptosis, and the apoptosis is induced by (reactive oxygen species) ROS generation in BS524 cells.

Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases

Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.

Computational Chemogenomics Drug Repositioning Strategy Enables the Discovery of Epirubicin as a New Repurposed Hit for Plasmodium falciparum and P. vivax

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity.

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity. Among the seven drugs identified as promising antimalarial candidates, the anthracycline epirubicin was selected for further experimental validation. Epirubicin was shown to be potent in vitro against sensitive and multidrug-resistant P. falciparum strains and P. vivax field isolates in the nanomolar range, as well as being effective against an in vivo murine model of Plasmodium yoelii. Transmission-blocking activity was observed for epirubicin in vitro and in vivo. Finally, using yeast-based haploinsufficiency chemical genomic profiling, we aimed to get insights into the mechanism of action of epirubicin. Beyond the target predicted in silico (a DNA gyrase in the apicoplast), functional assays suggested a GlcNac-1-P-transferase (GPT) enzyme as a potential target. Docking calculations predicted the binding mode of epirubicin with DNA gyrase and GPT proteins. Epirubicin is originally an antitumoral agent and presents associated toxicity. However, its antiplasmodial activity against not only P. falciparum but also P. vivax in different stages of the parasite life cycle supports the use of this drug as a scaffold for hit-to-lead optimization in malaria drug discovery.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.

Identification of Schistosoma japonicum GSK3β interacting partners by yeast two-hybrid screening and its role in parasite survival

Schistosoma is the causative agent of schistosomiasis, a common infectious disease distributed worldwide. Our previous phosphoproteomic analysis suggested that glycogen synthase kinase 3 (GSK3), a conserved protein kinase in eukaryotes, is likely involved in protein phosphorylation of Schistosoma japonicum. Here, we aimed to identify the interacting partners of S. japonicum GSK3β (SjGSK3β) and to evaluate its role in parasite survival. Toward these ends, we determined the transcription levels of SjGSK3β at different developmental stages and identified its interacting partners of SjGSK3β by screening a yeast two-hybrid S. japonicum cDNA library. We further used RNA interference (RNAi) to inhibit the expression of SjGSK3β in adult worms in vitro and examined the resultant changes in transcription of its putative interacting proteins and in worm viability compared with those of control worms. Reverse transcription-quantitative polymerase chain analysis indicated that SjGSK3β is expressed throughout the life cycle of S. japonicum, with higher expression levels detected in the eggs and relatively higher expression level found in male worms than in female worms. By screening the yeast two-hybrid library, eight proteins were identified as potentially interacting with SjGSK3β including cell division cycle 37 homolog (Cdc37), 14-3-3 protein, tegument antigen (I(H)A), V-ATPase proteolipid subunit, myosin alkali light chain 1, and three proteins without recognized functional domains. In addition, SjGSK3β RNAi reduced the SjGSK3β gene transcript level, leading to a significant decrease in kinase activity, cell viability, and worm survival. Collectively, these findings suggested that SjGSK3β may interact with its partner proteins to influence worm survival by regulating kinase activity.

Reference gene analysis and its use for kinase expression profiling in Fasciola hepatica

The liver fluke Fasciola hepatica causes fasciolosis, a foodborne zoonosis affecting humans and livestock worldwide. A reliable quantification of gene expression in all parasite life stages relevant for targeting by anthelmintics in the mammalian host is fundamental. The aim of this study was to define a set of stably expressed reference genes for qRT-PCR in Fasciola studies. We determined the expression stabilities of eight candidate reference genes by the algorithms NormFinder, geNorm, BestKeeper, and comparative ΔCT method. The most stably expressed reference genes for the comparison of intra-mammalian life stages were glutamyl-prolyl-tRNA synthetase (Fheprs) and tubulin-specific chaperone D (Fhtbcd). The two best reference genes for analysis of in vitro-cultured juveniles were Fhtbcd and proteasome subunit beta type-7 (Fhpsmb7). These genes should replace the housekeeping gene gapdh which is used in most Fasciola studies to date, but in fact was differentially expressed in our analysis. Based on the new reference genes, we quantified expression of five kinases (Abl1, Abl2, PKC, Akt1, Plk1) discussed as targets in other parasitic flatworms. Distinct expression patterns throughout development were revealed and point to interesting biological functions. We like to motivate using this set of validated reference genes for future F. hepatica research, such as studies on drug targets or parasite development.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.