Identification Of Novel Drug Targets Research Articles

Construction of Parkinson's disease marker-based weighted protein-protein interaction network for prioritization of co-expressed genes

Background: Parkinson's disease (PD) is a complex neurodegenerative movement disorder that primarily results due to the loss of dopaminergic neurons in the substantia nigra region. Studying gene expression in the substantia nigra region would potentially unravel disease-relevant protein-protein interactions. MethodsIn this study we have used network science approach to prioritize candidate genes by focussing on differentially expressed genes (DEGs) that interact with established PD associated-genes (PDAG). Prioritizing genes that interact with already established PDAG would reduce the probability of spurious protein-protein associations. The dataset GSE54282 with Parkinson's disease affected substantia nigra samples was extracted from Gene Expression Omnibus (GEO) database. Protein-Protein Interaction Network (PPIN) was constructed by retrieving all possible interactions between DEGs from high-throughput experiments and literature data using Bisogenet. This complex PPIN was decomposed to construct a subnetwork of Parkinson's Disease-Protein Interaction Map (PD-PIM) by including PDAG and following well-established concepts of network biology such as degree and betweenness centrality. We then implemented a “two-way analysis” where we selected genes belonging to PDPIM subnetwork with their primary interacting partners and highly coexpressed genes on the basis of Pearson score. ResultsA complex PPIN comprised of 5340 nodes (genes) and 39,199 edges (interactions) was obtained. A list of 205 genes (123 PDAGs, 69 hub genes and 13 bottleneck genes) with their respective first level interacting partners were extracted from PPIN interactome to build a PD-specific subnetwork, PD-PIM. This subnetwork PD-PIM comprised of 5078 nodes and 38,357 edges. We then employed a “two-way” gene prioritization method that delineated 267 genes of which 16 genes were found to intersect in the two networks of the “two-way analysis”. Of the 16 genes, we narrowed down to 7 novel candidate genes (VCAM1, BACH1, CALM3, EGR1, IKBKE, MYC and YWHAG) displaying significant changes in their network interactions between control and disease samples. Interestingly, these genes were associated with neuroinflammation signaling pathway, MAPK signaling apoptosis pathway, movement disorders and development of neurons that are linked with development of PD. ConclusionWe propose that VCAM1, BACH1, CALM3, EGR1, IKBKE, MYC and YWHAG genes might play important roles in PD pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative and network based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for Parkinson's disease.

Enterobacteriaceae protein virulence prediction based on hybrid composition features using support vector machines

Abstract The drastic increase in the amount of completed bacterial genome sequences has opened the possibility for the identification of novel and significant virulence factors that are associated with pathogenesis. In recent days, the emergence of many drug-resistant bacteria has raised an alarming situation for the identification of novel drug targets. Thus in this study, we emphasize an efficient prediction approach for predicting protein virulence sequences of Enterobacteriaceae. In this approach, we adopted support vector machine module for predicting the sequence by using amino acid composition (AAC) and dipeptide (i + 1) composition (DPC), higher order dipeptide (i+2) (HODC) and cascade methods (combination of AAC, DPC, and HODC). The overall accuracy of 82.22% was observed for the predictions from cascade methods when compared to AAC (78.12%), DPC (76.23%) and HODC (77.14%). At present this web-based prediction server ( http://evirdb.info/ ) can significantly predict the protein virulence of five most significant human pathogens such as Salmonella spp., Escherichia spp., Shigella spp., Klebsiella spp. and Proteus spp., of Enterobacteriaceae. Also serve is designed for the selection of virulence factor based on the pathogenesis-related Gene Ontology terms.

Identification of Calpain-Activated Protein Functions.

As opposed to proteasome-mediated proteolysis that leads to protein degradation, calpain proteases carry out limited proteolytic cleavages of their substrates. The cleavage of some substrates can produce active fragments that perform functions that are different from those performed by the full-length proteins. Therefore, cleavage by calpains can operate as a posttranslational modification and increase the functional diversity of target proteins. Nevertheless, activation of protein function by calpain cleavage is still an understudied area in molecular biology. Identifying and functionally characterizing by products generated by calpain cleavage could lead to the discovery of biomarkers and the identification of novel drug targets for the treatment of human diseases. This chapter contains a workflow designed to experimentally characterize novel calpain substrates, including identification of potential calpain targets via Western blotting, characterization of calpain cleavage sites, and the study of cellular functions played by such cleaved products. We will employ MYC as an example for these experiments.

Boolean Modeling in Quantitative Systems Pharmacology: Challenges and Opportunities

Drug research and development has a high attrition rate, with many promising drugs failing for efficacy or safety in the clinic. Increased use of detailed modeling approaches like quantitative systems pharmacology (QSP) may help in reducing overall failure rate, by helping the industry in decisions to fail early and cheaply, or to focus on patients and drug combinations that are more likely to respond or synergize, respectively. QSP offers computational methods to simulate how well different therapies may work in a patient, and therefore to better predict drug performance and reduce the cost in the development of new drug therapies. However, the development of detailed models requires a significant amount of biological data, and models often require knowledge of specific mechanisms. Coarse-grained, network-based models, such as Boolean and logic models, provide a tool for simulating complex systems without knowledge of specific mechanisms. These tools can be used to make early predictions about a biological system and can facilitate the development of more complex models. We offer a literature review of how Boolean modeling techniques are used in the identification of novel drug targets, as well as how they fall into the pipeline of developing in-depth ordinary differential equation models.

Preclinical Validation Studies Support Causal Machine Learning Based Identification of Novel Drug Targets for High-Risk Multiple Myeloma

IntroductionRegardless of significant advances in the therapy of multiple myeloma (MM) there is still a lack of effective treatment options for patients with high-risk disease. In this context, we recently developed a network of high-risk disease based on more than 30 000 genomic and clinical variables from 645 patients of the CoMMpass dataset (Gruber et al., ASH 2016). Validation of these findings has been performed in the IFM/DFCI 2009 trial dataset (Furchtgott et al., ASH 2017). This comprehensive computational approach revealed a network of 17 genes driving high-risk (defined as progression or myeloma-related death within 18 months). Here, we performed preclinical validation of potential novel drug targets to confirm the utility of in silico guided target discovery in high-risk MM.MethodsTTK (CFI402257, BAY-1217389), PLK4 (CFI400945, Centrinone), MELK (OTSSP167) and CDK1 (CPG71514) inhibitors were studied in a panel of human MM cell lines (n=11) for their activity in cell viability, cell growth, cell cycle, apoptosis, colony formation, drug combination and co-culture experiments. PKMYT1, TTK and PLK4 were targeted with doxycycline-inducible shRNAs. Analysis of gene expression (GEP) data (GSE24080) was used to link candidate genes to certain MM subgroups.ResultsThe network of 17 genes driving high-risk disease contained eight kinases that serve as attractive drug targets (AURKA, NEK2, CDK1, BUB1B, MELK, TTK, PKMYT1, and PLK4), all of them involved in cell cycle regulation. Accordingly, expression levels of all kinases (except PKMYT1) were enriched in the GEP-defined proliferation associated subgroup of MM and thus linked to poor outcome. To study the interconnectedness of the individual network genes we first investigated the impact of previously reported CDK1 and MELK inhibitors on other network members. This demonstrated rapid loss of CDK1, NEK2, MELK, PKMYT1 and FOXM1 protein levels.We then selected TTK and PLK4 as putative novel MM targets with available inhibitors undergoing clinical testing in solid tumors. Protein and mRNA expression of both genes was confirmed in all MM cell lines. Two selective compounds per gene were used for preclinical studies. All four inhibitors significantly reduced MM cell viability and single dose IC70 treatment impaired cell growth up to 10 days (60-98% reduction, P<0.01). This growth inhibitory effect was confirmed with inducible shRNAs. Mechanistically, growth impairment was linked to G2M cell cycle arrest followed by the accumulation of polyploid cells (15-90% of cells 72h post treatment) which is in line with the role of both genes in chromosome segregation. The formation of aberrant mitoses led to the induction of apoptosis 3-5 days post treatment (≤20% viable cells at day 5 post treatment with 3/4 inhibitors) and was accompanied by the presence of active caspase 3 and cleaved PARP. Importantly, the activity of these drugs persisted in the presence of BMSCs and showed potent activity in colony formation assays (DMSO: 168±15 and 131±57, BAY-1217389: 39±29 and 41±3, CFI-400945: 19±26 and 30±6 colonies in KMS12BM and OPM2 cells at day 14, P<0.05). Drug combination studies pointed to favorable activity in combination with dexamethasone and lenalidomide. Furthermore, confirmatory TTK knockdown with two independent shRNAs sensitized MM cells to dexamethasone.Finally, PKMYT1 was chosen as putative target based on its role as major driver of high-risk in our model. We transduced MM cells with three doxycycline-inducible PKMYT1-targeting shRNAs and observed an impressive impact on myeloma cell growth upon doxycycline induction compared to non-targeting control shRNA (up to 85% reduction at day 10, P<0.01). Furthermore, PKMYT1 knockdown led to the induction of apoptosis in all MM cell lines tested. Based on these encouraging results we currently perform in-depth in silico and in vitro analyses of the underlying PKMYT1 signaling network. Detailed results of these sub-studies will be presented at the meeting.ConclusionsOur results confirm the utility of computational based modelling of high-risk disease. This strategy not only revealed a genetic network closely associated to adverse prognosis, but also enabled the identification of so far unnoticed drug targets. Importantly, inhibitors of TTK and PLK4 are already in clinical testing and thus enable rapid clinical translation of our findings to MM patients in need of alternative therapeutic options. DisclosuresGruber:GNS Healthcare: Employment. Furchtgott:GNS Healthcare: Employment. Raut:GNS Healthcare: Employment. Wuest:GNS Healthcare: Employment. Runge:GNS Healthcare: Employment. Khalil:GNS Healthcare: Employment. Munshi:OncoPep: Other: Board of director. Hayete:GNS Healthcare: Employment. Ludwig:Amgen: Research Funding, Speakers Bureau; BMS: Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Cilag-Janssen: Speakers Bureau; Celgene: Speakers Bureau.

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

AimAnaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed.Materials & MethodsWe re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein–protein interaction networks, weighted gene co-expression network analysis and genes’ cancer/testis expression pattern. We also further characterized these genes using data from the Cancer Genome Atlas (TCGA) project and gene ontology annotation.ResultsCell cycle-related pathways were significantly enriched in upregulated genes in ATC. We identified TRIP13, DLGAP5, HJURP, CDKN3, NEK2, KIF15, TTK, KIF2C, AURKA and TPX2 as cell cycle-related key genes with cancer/testis expression pattern. We further uncovered that most of these putative key genes were critical components during chromosome segregation.ConclusionWe predicted several key genes harboring potential therapeutic value in ATC. Cell cycle-related processes, especially chromosome segregation, may be the key to tumorigenesis and treatment of ATC.

A Systematic In Silico Analysis of the Legionellaceae Family for Identification of Novel Drug Target Candidates.

The family Legionellaceae consists of Gram-negative bacteria that are widely distributed in aquatic environments around the world. This family consists of a single genus, Legionella, that is recognized as an important cause of community-acquired pneumonia and hospital-acquired pneumonia. Legionella consists of intracellular pathogens, thus cellular pharmacokinetic and pharmacodynamic properties of an antibiotic against these bacteria as well as uptake and subcellular distribution into macrophages should be considered for a successful outcome of disease. Treatment strategies for Legionella infection require a combination of multiple antibiotics. Hence, because of the possible development of resistance to the drugs during therapy, a new alternative targeted therapy is yielding promising results. In this study, a comprehensive in silico target identification pipeline was performed on members of the family Legionellaceae to identify the best targets. Using a homology-based computational pipeline method, new drug targets were identified. Of 4,358 analyzed proteins, 18 proteins, including proteins involved in metabolism (amino acid, energy, and lipid metabolisms), cellular transport, cell division, and cell motility, were selected as the final putative drug targets. These proteins play an important role in the survival and propagation of Legionella infection. In conclusion, homology-based methods could improve the identification of novel drug targets and the drug discovery process, which can potentially be effective for the prevention and treatment of Legionella infections.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments.

BackgroundBovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.Materials and methodsWe have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.ResultsWe have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials – oxytetracycline.ConclusionThis approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.

Validation and discovery of mechanisms that promote vitiligo pathogenesis using single-cell RNA-sequencing of cells isolated from skin interstitial fluid

Abstract Vitiligo is an autoimmune disease in which CD8+ T cells destroy melanocytes, leading to patchy, depigmented lesions of the skin. To investigate possible immune processes in the skin that could explain the patchy appearance of vitiligo inflammation, we performed single-cell RNA-sequencing on cells isolated from the skin using a modified suction blistering technique. We compared the transcriptomes of cells isolated from lesional and non-lesional skin of six vitiligo subjects with active, spreading lesions as well as normal skin from six healthy subjects. Using differential gene analysis to cluster more than 18,000 cell transcriptomes in an unbiased fashion, we were able to identify the cellular components of the skin: melanocytes, keratinocytes, and Langerhans cells, as well as subgroups of immune cells. Consistent with previous findings by our group and others, this approach revealed an IFNγ signature in vitiligo lesions. This method identified 87 ligand-receptor pairs that may promote vitiligo pathogenesis, including CXCR6 and CXCL16 that were just recently implicated in vitiligo. The data set also revealed that Langerhans cells actively express many genes which have known risk alleles for vitiligo, highlighting a potential pivotal role in the development of vitiligo. Overall, single-cell RNA-sequencing of the skin infiltrate in vitiligo lesions is providing robust data that confirms many previous findings, but adds additional layers of nuance missed by conventional studies of bulk material. This level of resolution will yield powerful insight into the identification of novel drug targets in previously unknown signaling pathways in vitiligo and other diseases of the skin.

Identification of novel drug targets for diamond-blackfan anemia based on RPS19 gene mutation using protein-protein interaction network

BackgroundDiamond-Blackfan anemia (DBA) is a congenital erythroid aplasia that usually presents in infancy. In order to explore the molecular mechanisms of wild and mutated samples from DBA patients were exposed to bioinformatics investigation. Biological network of differentially expressed genes was constructed. This study aimed to identify novel therapeutic signatures in DBA and uncovered their mechanisms. The gene expression dataset of GSE14335 was used, which consists of 6 normal and 4 diseased cases. The gene ontology (GO), as well as Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and then protein–protein interaction (PPI) network of the identified differentially expressed genes (DEGs) was constructed by Cytoscape software.ResultsA total of 607 DEGs were identified in DBA, including 433 upregulated genes and 174 downregulated genes. GO analysis results showed that upregulated DEGs were significantly enriched in biological processes, negative regulation of transcription from RNA polymerase II promoter, chemotaxis, inflammatory response, immune response, positive regulation of cell proliferation, negative regulation of cell proliferation, response to mechanical stimulus, positive regulation of cell migration, response to lipopolysaccharide, and defence response. KEGG pathway analysis revealed the TNF signalling pathway, Osteoclast differentiation, Chemokine signalling pathway, Cytokine -cytokine receptor interaction, Rheumatoid arthritis, Biosynthesis of amino acids, Biosynthesis of antibiotics and Glycine, serine and threonine metabolism. The top 10 hub genes, AKT1, IL6, NFKB1, STAT3, STAT1, RAC1, EGR1, IL8, RELA, RAC3, mTOR and CCR2 were identified from the PPI network and sub-networks.ConclusionThe present study flagged that the identified DEGs and hub genes enrich our understanding of the molecular mechanisms underlying the development of DBA, and might shine some lights on identifying molecular targets and diagnostic biomarkers for DBA.

Characterization of Plasmodium Atg3-Atg8 Interaction Inhibitors Identifies Novel Alternative Mechanisms of Action in Toxoplasma gondii.

Protozoan parasites, including the apicomplexan pathogens Plasmodium falciparum (which causes malaria) and Toxoplasma gondii (which causes toxoplasmosis), infect millions of people worldwide and represent major human disease burdens. Despite their prevalence, therapeutic strategies to treat infections caused by these parasites remain limited and are threatened by the emergence of drug resistance, highlighting the need for the identification of novel drug targets. Recently, homologues of the core autophagy proteins, including Atg8 and Atg3, were identified in many protozoan parasites. Importantly, components of the Atg8 conjugation system that facilitate the lipidation of Atg8 are required for both canonical and parasite-specific functions and are essential for parasite viability. Structural characterization of the P. falciparum Atg3-Atg8 (PfAtg3-Atg8) interaction has led to the identification of compounds that block this interaction. Additionally, many of these compounds inhibit P. falciparum growth in vitro, demonstrating the viability of this pathway as a drug target. Given the essential role of the Atg8 lipidation pathway in Toxoplasma, we sought to determine whether three PfAtg3-Atg8 interaction inhibitors identified in the Medicines for Malaria Venture Malaria Box exerted a similar inhibitory effect in Toxoplasma While all three inhibitors blocked Toxoplasma replication in vitro at submicromolar concentrations, they did not inhibit T. gondii Atg8 (TgAtg8) lipidation. Rather, high concentrations of two of these compounds induced TgAtg8 lipidation and fragmentation of the parasite mitochondrion, similar to the effects seen following starvation and monensin-induced autophagy. Additionally, we report that one of the PfAtg3-Atg8 interaction inhibitors induces Toxoplasma egress and provide evidence that this is mediated by an increase in intracellular calcium in response to drug treatment.

Evolutionary and genetic features of drug targets.

In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of "more investments, fewer drugs" in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community.

Advances on genome-wide association study in diagnosis and treatment of osteoporosis

Osteoporosis is a complex disease caused by genetic and environmental factors, which the genetic architecture is not completely understood. Genome-wide association study (GWAS) have reformed the identification of genetic determinants of complex diseases including osteoporosis. In the past decade, more than 30 GWASes which were related to bone mass density identified nearly 100 independent loci. The bivariate GWAS approach is an effective approach to detect pleiotropic genes for two osteoporosis traits. However, genetic analyses must be combined with functional studies to understand the underlying mechanisms. The insight of bone metabolism by GWAS is of huge potential for the identification of novel drug targets for osteoporosis. (Chin J Lab Med, 2017, 40: 848-851) Key words: Osteoporosis; Genome-wide association study; Bone mineral density; Diagnosis

Sequence-based information-theoretic features for gene essentiality prediction

BackgroundIdentification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.ResultsWe developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).ConclusionsThe proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.

Inter-individual variation in genes governing human hippocampal progenitor differentiation in vitro is associated with hippocampal volume in adulthood

Hippocampal volumes are smaller in psychiatric disorder patients and lower levels of hippocampal neurogenesis are the hypothesized cause. Understanding which molecular processes regulate hippocampal progenitor differentiation might aid in the identification of novel drug targets that can promote larger hippocampal volumes. Here we use a unique human cell line to assay genome-wide expression changes when hippocampal progenitor cells differentiate. RNA was extracted from proliferating cells versus differentiated neural cells and applied to Illumina Human HT-12 v4 Expression BeadChips. Linear regressions were used to determine the effect of differentiation on probe expression and we assessed enrichment for gene ontology (GO) terms. Genetic pathway analysis (MAGMA) was used to evaluate the relationship between hippocampal progenitor cell differentiation and adult hippocampal volume, using results from the imaging genomics consortium, ENIGMA. Downregulated transcripts were enriched for mitotic processes and upregulated transcripts were enriched for cell differentiation. Upregulated (differentiation) transcripts specifically, were also predictive of adult hippocampal volume; with Early growth response protein 2 identified as a hub transcription factor within the top GO term, and a potential drug target. Our results suggest that genes governing differentiation, rather than mitosis, have an impact on adult hippocampal volume and that these genes represent important drug targets.

Quinazolinone Derivatives as a Potential Class of Compounds in Malaria Drug Discovery

Malaria causes over a million deaths each year (2 percent of the global total of deaths), with hundreds of millions of clinical episodes per annum. The greatest challenge to malaria control and eradication is the emergence of malaria parasites that are resistant to antimalarial drugs. The development of resistance to conventionally used anti-malarial drugs, such as chloroquine (CQ) and Sulfadoxine-Pyrimethamine (SP) has been documented. To counter this WHO recommended that artemisinin-based combination therapy (ACT) should be used for treating uncomplicated Plasmodium falciparum malaria to ensure efficacy and reduce the emergence of drug-resistant parasites. Currently available antimalarial drugs are ineffective and their number is declining because of the widespread resistance. Thus, the new antimalarial agent is in urgent demand; however, the development of new antimalarial drug presents challenges due to resistance, toxicity, minimal efficacy of those on the pipeline and high cost of drug research. Identification of novel drug targets and design of new chemical compounds acting on new targets is important to control the emergence of resistance to existing drugs. In this regard, a natural product derived synthetic analogs of febrifugine containing quinazolinone scaffold can be considered best. Therefore, quinazolinones are potential compounds in seeking for novel drugs that act against the malarial pathogen. Hence, in this review compounds containing quinazolinone structure and possessing antimalarial activities are covered.

A Review of Anti-Angiogenic Targets for Monoclonal Antibody Cancer Therapy.

Tumor angiogenesis is a key event that governs tumor progression and metastasis. It is controlled by the complicated and coordinated actions of pro-angiogenic factors and their receptors that become upregulated during tumorigenesis. Over the past several decades, vascular endothelial growth factor (VEGF) signaling has been identified as a central axis in tumor angiogenesis. The remarkable advent of recombinant antibody technology has led to the development of bevacizumab, a humanized antibody that targets VEGF and is a leading clinical therapy to suppress tumor angiogenesis. However, despite the clinical efficacy of bevacizumab, its significant side effects and drug resistance have raised concerns necessitating the identification of novel drug targets and development of novel therapeutics to combat tumor angiogenesis. This review will highlight the role and relevance of VEGF and other potential therapeutic targets and their receptors in angiogenesis. Simultaneously, we will also cover the current status of monoclonal antibodies being developed to target these candidates for cancer therapy.

A brain proteomic investigation of rapamycin effects in the Tsc1+/\u2212 mouse model

BackgroundTuberous sclerosis complex (TSC) is a rare monogenic disorder characterized by benign tumors in multiple organs as well as a high prevalence of epilepsy, intellectual disability and autism. TSC is caused by inactivating mutations in the TSC1 or TSC2 genes. Heterozygocity induces hyperactivation of mTOR which can be inhibited by mTOR inhibitors, such as rapamycin, which have proven efficacy in the treatment of TSC-associated symptoms. The aim of the present study was (1) to identify molecular changes associated with social and cognitive deficits in the brain tissue of Tsc1+/− mice and (2) to investigate the molecular effects of rapamycin treatment, which has been shown to ameliorate genotype-related behavioural deficits.MethodsMolecular alterations in the frontal cortex and hippocampus of Tsc1+/− and control mice, with or without rapamycin treatment, were investigated. A quantitative mass spectrometry-based shotgun proteomic approach (LC-MSE) was employed as an unbiased method to detect changes in protein levels. Changes identified in the initial profiling stage were validated using selected reaction monitoring (SRM). Protein Set Enrichment Analysis was employed to identify dysregulated pathways.ResultsLC-MSE analysis of Tsc1+/− mice and controls (n = 30) identified 51 proteins changed in frontal cortex and 108 in the hippocampus. Bioinformatic analysis combined with targeted proteomic validation revealed several dysregulated molecular pathways. Using targeted assays, proteomic alterations in the hippocampus validated the pathways “myelination”, “dendrite,” and “oxidative stress”, an upregulation of ribosomal proteins and the mTOR kinase. LC-MSE analysis was also employed on Tsc1+/− and wildtype mice (n = 34) treated with rapamycin or vehicle. Rapamycin treatment exerted a stronger proteomic effect in Tsc1+/− mice with significant changes (mainly decreased expression) in 231 and 106 proteins, respectively. The cellular pathways “oxidative stress” and “apoptosis” were found to be affected in Tsc1+/− mice and the cellular compartments “myelin sheet” and “neurofilaments” were affected by rapamycin treatment. Thirty-three proteins which were altered in Tsc1+/− mice were normalized following rapamycin treatment, amongst them oxidative stress related proteins, myelin-specific and ribosomal proteins.ConclusionsMolecular changes in the Tsc1+/− mouse brain were more prominent in the hippocampus compared to the frontal cortex. Pathways linked to myelination and oxidative stress response were prominently affected and, at least in part, normalized following rapamycin treatment. The results could aid in the identification of novel drug targets for the treatment of cognitive, social and psychiatric symptoms in autism spectrum disorders. Similar pathways have also been implicated in other psychiatric and neurodegenerative disorders and could imply similar disease processes. Thus, the potential efficacy of mTOR inhibitors warrants further investigation not only for autism spectrum disorders but also for other neuropsychiatric and neurodegenerative diseases.

An in silico strategy for identification of novel drug targets against Plasmodium falciparum.

The apicomplexan parasite Plasmodium falciparum is responsible for global malaria burden. With the reported resistance to artemisinin chemotherapy, there is an urgent need to maintain early phase drug discovery and identify novel drug targets for successful eradication of the pathogen from the host. In our previous work on comparative genomics study for identification of putative essential genes and therapeutic candidates in P. falciparum, we predicted 11 proteins as anti-malarial drug targets from PlasmoDB database. In this paper, we made an attempt for identification of novel drug targets in P. falciparum genome using a sequence of computational methods from Malaria Parasite Metabolic Pathway database. The study reported the identification of 71 proteins as potential drug targets for anti-malarial interventions. Furthermore, homology modeling and molecular dynamic simulation study of one of the potential drug targets, aminodeoxychorismate lyase, was carried to predict the 3D structure of the protein. Structure and ligand-based drug designing reported MMV019742 from Pathogen Box and TCAMS-141515 from GSK-TCAMS library as potential hits. The reliability of the binding mode of the inhibitors is confirmed by GROMACS for a simulation time of 20ns in water environment. This will be helpful for experimental validation of the small-molecule inhibitor.

Long non-coding RNAs in leukemia: biology and clinical impact.

Over the last years, long non-coding RNAs (lncRNAs) have emerged as putative regulators of malignant hematopoietic development. Here, we review recent literature on the involvement of lncRNAs in leukemia, including their role in driving or sustaining disease and their potential impact on diagnosis, classification, and prognosis. Leukemogenesis is a complex process resulting from the accumulation of multiple genetic alterations. Over the last years, advances in high-throughput sequencing and transcriptome profiling have enabled the identification of lncRNAs involved in leukemia development. lncRNAs are able to distinguish different subtypes of human leukemia and several reports have identified specific patterns of lncRNA expression associated with clinical patient characteristics. Although functional studies on the actual role of these lncRNAs during transformation remain scarce, emerging evidence suggests that complex interactions between coding and non-coding transcript are truly involved in leukemia development. Introduction of lncRNAs as an additional layer of complexity in human leukemia might provide new molecular genetic insights in the biology of this disease and could create unique opportunities for the identification of novel drug targets and diagnostic or prognostic biomarkers.