Essential gene identification and drug target prioritization in Leishmania species

Identifying inhibitors of Trypanosoma cruzi nucleoside diphosphate kinase 1 as potential repurposed drugs for Chagas' disease

Trypanosoma cruzi is the causative agent of Chagas' disease, an endemic and neglected disease. The treatment is limited to only two drugs, benznidazole (BZL) and nifurtimox (NFX), introduced more than fifty years ago and no new advances have been made since then. Nucleoside diphosphate kinases (NDPK) are key metabolic enzymes which have gained interest as drug targets of pathogen organisms. Taking advantage of the computer-assisted drug repurposing approaches, in the present work we initiate a search of potential T. cruzi nucleoside diphosphate kinase 1 (TcNDPK1) inhibitors over an ∼ 12,000 compound structures database to find drugs targeted to this enzyme with trypanocidal activity. Four medicines were selected and evaluated in vitro, ketorolac (KET, an anti-inflamatory), dutasteride (DUT, used to treat benign prostatic hyperplasia), nebivolol and telmisartan (NEB and TEL, used to treat high blood pressure). The four compounds were weak inhibitors and presented different trypanocidal effect on epimastigotes, trypomastigotes and intracellular stages. NEB and TEL were the most active drugs with increased effect on intracellular stages, (IC50 = 2.25 µM and 13.21 µM respectively), and selectivity indexes of 13.01 and 8.59 respectively, showing comparable effect to BZL, the first line drug for Chagas' disease treatment. In addition, both presented positive interactions when combined with BZL. Finally, transgenic epimastigotes with increased expression of TcNDPK1 were more resistant to TEL and NEB, suggesting that TcNDPK1 is at least one of the molecular targets. In view of the results, NEB and TEL could be repurposed medicines for Chagas' disease therapy.

Role of non-coding RNAs against salinity stress in Oryza species: Strategies and challenges in analyzing miRNAs, tRFs and circRNAs

Salinity is an imbalanced concentration of mineral salts in the soil or water that causes yield loss in salt-sensitive crops. Rice plant is vulnerable to soil salinity stress at seedling and reproductive stages. Different non-coding RNAs (ncRNAs) post-transcriptionally regulate different sets of genes during different developmental stages under varying salinity tolerance levels. While microRNAs (miRNAs) are well known small endogenous ncRNAs, tRNA-derived RNA fragments (tRFs) are an emerging class of small ncRNAs derived from tRNA genes with a demonstrated regulatory role, like miRNAs, in humans but unexplored in plants. Circular RNA (circRNA), another ncRNA produced by back-splicing events, acts as target mimics by preventing miRNAs from binding with their target mRNAs, thereby reducing the miRNA's action upon its target. Same may hold true between circRNAs and tRFs. Hence, the work done on these ncRNAs was reviewed and no reports were found for circRNAs and tRFs under salinity stress in rice, either at seedling or reproductive stages. Even the reports on miRNAs are restricted to seedling stage only, in spite of severe effects on rice crop production due to salt stress during reproductive stage. Moreover, this review sheds light on strategies to predict and analyze these ncRNAs in an effective manner.

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for Essentiality Prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs), operating on graph-structured data. Our model directly learns gene essentiality patterns from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with ROC AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.

Revisiting trypanosomatid nucleoside diphosphate kinases

BACKGROUND

An increasing amount of research has led to the positioning of nucleoside diphosphate kinases (NDPK/NDK) as key metabolic enzymes among all organisms. They contribute to the maintenance the intracellular di- and tri- phosphate nucleoside homeostasis, but they also are involved in widely diverse processes such as gene regulation, apoptosis, signal transduction and many other regulatory roles.

OBJETIVE

Examine in depth the NDPKs of trypanosomatid parasites responsible for devastating human diseases (e.g., Trypanosoma cruzi, Trypanosoma brucei and Leishmania spp.) which deserve special attention.

METHODS

The earliest and latest advances in the topic were explored, focusing on trypanosomatid NDPK features, multifunctionality and suitability as molecular drug targets.

FINDINGS

Trypanosomatid NDPKs appear to play functions different from their host counterparts. Evidences indicate that they would perform key roles in the parasite metabolism such as nucleotide homeostasis, drug resistance, DNA damage responses and gene regulation, as well as host-parasite interactions, infection, virulence and immune evasion, placing them as attractive pharmacological targets.

MAIN CONCLUSIONS

NDPKs are very interesting multifunctional enzymes. In the present review, the potential of trypanosomatid NDPKs was highlighted, raising awareness of their value not only with respect to parasite biology but also as molecular targets.

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.

Research on the Computational Prediction of Essential Genes

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

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.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

Insights on a putative aminoacyl-tRNA-protein transferase of Leishmania major

The N-end rule pathway leads to regulated proteolysis as an adaptive response to external stress and is ubiquitous from bacteria to mammals. In this study, we investigated a gene coding for a putative core enzyme of this post-translational regulatory pathway in Leishmania major, which may be crucial during cytodifferentiation and the environment adaptive responses of the parasite. Leucyl, phenylalanyl-tRNA protein transferase and arginyl-tRNA protein transferase are key components of this pathway in E. coli and eukaryotes, respectively. They catalyze the specific conjugation of leucine, phenylalanine or arginine to proteins containing exposed N-terminal amino acid residues, which are recognized by the machinery for the targeted proteolysis. Here, we characterized a conserved hypothetical protein coded by the LmjF.21.0725 gene in L. major. In silico analysis suggests that the LmjF.21.0725 protein is highly conserved among species of Leishmania and might belong to the Acyl CoA-N-acyltransferases (NAT) superfamily of proteins. Immunofluorescence cell imaging indicates that the cytosolic localization of the studied protein and the endogenous levels of the protein in promastigotes are barely detectable by western blotting assay. The knockout of the two alleles of LmjF.21.0725 by homologous recombination was only possible in the heterozygous transfectant expressing LmjF.21.0725 as a transgene from a plasmid. Moreover, the kinetics of loss of the plasmid in the absence of drug pressure suggests that maintenance of the gene is essential for promastigote survival. Here, evidence is provided that this putative aminoacyl tRNA-protein transferase is essential for parasite survival. The enzyme activity and corresponding post-translational regulatory pathway are yet to be investigated.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

Involvement of nucleoside diphosphate kinase b and elongation factor 2 in Leishmania braziliensis antimony resistance phenotype

Background

Nucleoside diphosphate kinase b (NDKb) is responsible for nucleoside triphosphates synthesis and it has key role in the purine metabolism in trypanosomatid protozoans. Elongation factor 2 (EF2) is an important factor for protein synthesis. Recently, our phosphoproteomic analysis demonstrated that NDKb and EF2 proteins were phosphorylated and dephosphorylated in antimony (Sb^III)-resistant L. braziliensis line compared to its Sb^III-susceptible pair, respectively.

Methods

In this study, the overexpression of NDKb and EF2 genes in L. braziliensis and L. infantum was performed to investigate the contribution of these proteins in the Sb^III-resistance phenotype. Furthermore, we examined the role of lamivudine on Sb^III susceptibility in clones that overexpress the NDKb gene, and the effect of EF2 kinase (EF2K) inhibitor on the growth of EF2-overexpressing parasites.

Results

Western blot analysis demonstrated that NDKb and EF2 proteins are more and less expressed, respectively, in Sb^III-resistant line of L. braziliensis than its wild-type (WTS) counterpart, corroborating our previous phosphoproteomic data. NDKb or EF2-overexpressing L. braziliensis lines were 1.6 to 2.1-fold more resistant to Sb^III than the untransfected WTS line. In contrast, no difference in Sb^III susceptibility was observed in L. infantum parasites overexpressing NDKb or EF2. Susceptibility assays showed that NDKb-overexpressing L. braziliensis lines presented elevated resistance to lamivudine, an antiviral agent, but it did not alter the leishmanicidal activity in association with Sb^III. EF2-overexpressing L. braziliensis clone was slightly more resistant to EF2K inhibitor than the WTS line. Surprisingly, this inhibitor increased the antileishmanial effect of Sb^III, suggesting that this association might be a valuable strategy for leishmaniasis chemotherapy.

Conclusion

Our findings represent the first study of NDKb and EF2 genes overexpression that demonstrates an increase of Sb^III resistance in L. braziliensis which can contribute to develop new strategies for leishmaniasis treatment.

Label-free offline versus online activity methods for nucleoside diphosphate kinase b using high performance liquid chromatography

Label-free methodologies for nucleoside diphosphate kinase fromLeishmaniaspp. (LmNDKb): anofflineLC-UV assay for solubleLmNDKb and anonlinetwo-dimensional LC-UV system based on immobilizedLmNDKb to help screenLmNDKb ligands and measure NDKb activity.