Identification Of Targets Research Articles

A time course analysis through diapause reveals dynamic temporal patterns of microRNAs associated with endocrine regulation in the butterfly Pieris napi.

Organisms inhabiting highly seasonal environments must cope with a wide range of environmentally induced challenges. Many seasonal challenges require extensive physiological modification to survive. In winter, to survive extreme cold and limited resources, insects commonly enter diapause, which is an endogenously derived dormant state associated with minimized cellular processes and low energetic expenditure. Due to the high degree of complexity involved in diapause, substantial cellular regulation is required, of which our understanding primarily derives from the transcriptome via messenger RNA expression dynamics. Here we aim to advance our understanding of diapause by investigating microRNA (miRNA) expression in diapausing and direct developing pupae of the butterfly Pieris napi. We identified coordinated patterns of miRNA expression throughout diapause in both head and abdomen tissues of pupae, and via miRNA target identification, found several expression patterns to be enriched for relevant diapause-related physiological processes. We also identified two candidate miRNAs, miR-14-5p and miR-2a-3p, that are likely involved in diapause progression through their activity in the ecdysone pathway, a critical regulator of diapause termination. miR-14-5p targets phantom, a gene in the ecdysone synthesis pathway, and is upregulated early in diapause. miR-2a-3p has been found to be expressed in response to ecdysone, and is upregulated during diapause termination. Together, the expression patterns of these two miRNAs match our current understanding of the timing of hormonal regulation of diapause in P. napi and provide interesting candidates to further explore the mechanistic role of microRNAs in diapause regulation.

Revolutionizing Neurological Disorder Treatment: Integrating Innovations in Pharmaceutical Interventions and Advanced Therapeutic Technologies.

Neurological disorders impose a significant burden on individuals, leading to disabilities and a reduced quality of life. However, recent years have witnessed remarkable advancements in pharmaceutical interventions aimed at treating these disorders. This review article aims to provide an overview of the latest innovations and breakthroughs in neurological disorder treatment, with a specific focus on key therapeutic areas such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, and stroke. This review explores emerging trends in drug development, including the identification of novel therapeutic targets, the development of innovative drug delivery systems, and the application of personalized medicine approaches. Furthermore, it highlights the integration of advanced therapeutic technologies such as gene therapy, optogenetics, and neurostimulation techniques. These technologies hold promise for precise modulation of neural circuits, restoration of neuronal function, and even disease modification. While these advancements offer hopeful prospects for more effective and tailored treatments, challenges such as the need for improved diagnostic tools, identification of new targets for intervention, and optimization of drug delivery methods remain. By addressing these challenges and continuing to invest in research and collaboration, we can revolutionize the treatment of neurological disorders and significantly enhance the lives of those affected by these conditions.

Identification of Novel Drug Targets for Alzheimer’s Disease by Integrating Genetics and Proteomes from Brain and Blood (P7-9.012)

Identification of Novel Drug Targets for Alzheimer’s Disease by Integrating Genetics and Proteomes from Brain and Blood (P7-9.012)

Single-cell RNA sequencing and ATAC sequencing identify novel biomarkers for bicuspid aortic valve-associated thoracic aortic aneurysm.

Bicuspid aortic valve (BAV) is the most prevalent congenital cardiovascular defect and known to cause thoracic aortic aneurysms (TAAs). To improve our understanding of BAV pathogenesis, we characterized the cellular composition of BAV tissues and identified molecular changes in each cell population. Tissue samples from two patients with BAV and two heart transplant donors were analyzed using single-cell RNA sequencing, assay for transposase-accessible chromatin using sequencing, and weighted gene coexpression network analysis for differential gene analysis. TAA-related changes were evaluated by comparing the proportion of each cell type and gene expression profiles between TAA and control tissues. Further, by combining our single-cell RNA sequencing data with publicly available data from genome-wide association studies, we determined critical genes for BAV. We found 20 cell subpopulations in TAA tissues, including multiple subtypes of smooth muscle cells, fibroblasts, macrophages, and T lymphocytes. This result suggested that these cells play multiple functional roles in BAV development. Several differentially expressed genes, including CD9, FHL1y, HSP90AA1, GAS6, PALLD, and ACTA2, were identified. We believe that this comprehensive assessment of the cellular composition of TAA tissues and the insights into altered gene expression patterns can facilitate identification of novel diagnostic biomarkers and therapeutic targets for BAV-associated TAA.

Co-Expression of Niemann-Pick Type C1-Like1 (NPC1L1) with ACE2 Receptor Synergistically Enhances SARS-CoV-2 Entry and Fusion.

The entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into human embryonic kidney (HEK293T) cells has been shown to be a cholesterol-rich, lipid raft-dependent process. In this study, we investigated if the presence of a cholesterol uptake receptor Niemann-pick type c1-like1 (NPC1L1) impacts SARS-CoV-2 cell entry. Initially, we utilized reporter-based pseudovirus cell entry assays and a spike (S) glycoprotein-mediated cell-to-cell fusion assay. Using Chinese hamster ovary (CHO-K1) cells, which lack endogenous receptors for SARS-CoV-2 entry, our data showed that the co-expression of NPC1L1 together with the ACE2 receptor synergistically increased SARS-CoV-2 pseudovirus entry even more than the cells expressing ACE-2 receptor alone. Similar results were also found with the HEK293T cells endogenously expressing the ACE2 receptor. Co-cultures of effector cells expressing S glycoprotein together with target cells co-expressing ACE-2 receptor with NPC1L1 significantly promoted quantitative cell-to-cell fusion, including syncytia formation. Finally, we substantiated that an elevated expression of NPC1L1 enhanced entry, whereas the depletion of NPC1L1 resulted in a diminished SARS-CoV-2 entry in HEK293T-ACE2 cells using authentic SARS-CoV-2 virus in contrast to their respective control cells. Collectively, these findings underscore the pivotal role of NPC1L1 in facilitating the cellular entry of SARS-CoV-2. Importance: Niemann-Pick type C1-like1 (NPC1L1) is an endosomal membrane protein that regulates intracellular cholesterol trafficking. This protein has been demonstrated to play a crucial role in the life cycle of several clinically important viruses. Although SARS-CoV-2 exploits cholesterol-rich lipid rafts as part of its viral entry process, the role of NPC1L1 in SARS-CoV-2 entry remains unclear. Our research represents the first-ever demonstration of NPC1L1's involvement in facilitating SARS-CoV-2 entry. The observed role of NPC1L1 in human kidney cells is not only highly intriguing but also quite relevant. This relevance stems from the fact that NPC1L1 exhibits high expression levels in several organs, including the kidneys, and the fact that kidney damages are reported during severe cases of SARS-CoV-2. These findings may help us understand the new functions and mechanisms of NPC1L1 and could contribute to the identification of new antiviral targets.

Identification of a molecular network regulated by multiple ASD high risk genes.

Genetic sequencing has identified high-confidence ASD risk genes with loss-of-function mutations. How the haploinsufficiency of distinct ASD risk genes causes ASD remains to be elucidated. In this study, we examined the role of four top-ranking ASD risk genes, ADNP, KDM6B, CHD2, and MED13, in gene expression regulation. ChIP-seq analysis reveals that gene targets with the binding of these ASD risk genes at promoters are enriched in RNA processing and DNA repair. Many of these targets are found in ASD gene database (SFARI), and are involved in transcription regulation and chromatin remodeling. Common gene targets of these ASD risk genes include a network of high confidence ASD genes associated with gene expression regulation, such as CTNNB1 and SMARCA4. We further directly examined the transcriptional impact of the deficiency of these ASD risk genes. Our mRNA profiling with qPCR assays in cells with the knockdown of Adnp, Kdm6b, Chd2 or Med13 has revealed an intricate pattern of their cross-regulation, as well as their influence on the expression of other ASD genes. In addition, some synaptic genes, such as Snap25 and Nrxn1, are strongly regulated by deficiency of the four ASD risk genes, which could be through the direct binding at promoters or indirectly through the targets like Ctnnb1 or Smarca4. The identification of convergent and divergent gene targets that are regulated by multiple ASD risk genes will help to understand the molecular mechanisms underlying common and unique phenotypes associated with haploinsufficiency of ASD-associated genes.

Recognising pedestrian behaviour using a multi-channel spatiotemporal fusion network

To protect public safety, video abnormal behaviour detection is essential. The low accuracy of identifying abnormal behaviour in pedestrians is the focus of this study. To address this, an abnormal target identification method based on multi-feature fusion of trajectory skeleton is proposed. The process begins with defining the type of abnormal behaviour in accordance with the environmental requirements. Next, pedestrian identification is carried out in the designated area, the pedestrian is tracked to determine its movement trajectory, and the image coordinates of the corresponding relevant nodes are calculated by analysing the human posture. Lastly, the track and skeleton features are integrated to classify the normal and abnormal behaviours, and the identification of abnormal target behaviours is accomplished. Test results on the behavioural analysis database indicate that this algorithm's accuracy is 87.08%, which is higher than the single-feature identification method's detection efficiency of abnormal behaviours.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases

BackgroundDespite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD.MethodsBased on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets.ResultsCompared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15–1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure.ConclusionsThis study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control – An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow.We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

Application of an NMR/Crystallography Fragment Screening Platform for the Assessment and Rapid Discovery of New HIV-CA Binding Fragments.

Identification and assessment of novel targets is essential to combat drug resistance in the treatment of HIV/AIDS. HIV Capsid (HIV-CA), the protein playing a major role in both the early and late stages of the viral life cycle, has emerged as an important target. We have applied an NMR fragment screening platform and identified molecules that bind to the N-terminal domain (NTD) of HIV-CA at a site close to the interface with the C-terminal domain (CTD). Using X-ray crystallography, we have been able to obtain crystal structures to identify the binding mode of these compounds. This allowed for rapid progression of the initial, weak binding, fragment starting points to compounds 37 and 38, which have 19F-pKi values of 5.3 and 5.4 respectively.

Identification of potential therapeutic targets for breast cancer using Mendelian randomization analysis and drug target prediction.

Breast cancer stands as the foremost cause of cancer-related mortality among women, presenting a substantial economic impact on society. The limitations in current therapeutic options, coupled with poor patient tolerance, underscore the urgent need for novel treatments. Our study embarked on a genomic association exploration of breast cancer, leveraging whole-genome sequencing data from the Finngen database, complemented by expression quantitative trait loci (eQTL) insights from the eQTLGen and GTEx Consortiums. An initial investigation was conducted through summary-based Mendelian randomization (MR) to pinpoint primary eQTLs. Analysis of blood specimens revealed 103 eQTLs significantly correlated with breast cancer. Focusing our efforts, we identified 19 candidates with potential therapeutic significance. Further scrutiny via two-sample MR pinpointed UROD, LMO4, HORMAD1, and ZSWIM5 as promising targets for breast cancer therapy. Our research sheds light on new avenues for the treatment of breast cancer, highlighting the potential of genomic association studies in uncovering viable therapeutic targets.

Abstract LB432: Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme

Abstract Glioblastoma Multiforme (GBM) is one of the most aggressive and lethal brain tumor types with a five-year survival rate of 6.9%. In 2023, more than 14,490 Americans were expected to receive a diagnosis. Although great progress has been achieved in protein target identification and treatment over the last few years, current treatment options are limited, and tumor recurrence occurs at a high rate due to treatment resistance. Aptamers, short single-stranded oligonucleotide ligands with high-affinity binding to molecular targets, are well suited tools for exploring novel targets in highly heterogeneous tumors with complex etiologies like GBM, where clinically relevant biomarkers are limited. In this study, aptamer-based cancer-specific drug target identification was performed by Adaptive Dynamic Artificial Polyligand Targeting (ADAPT) Biotargeting System. In short, a GBM-specific library was enriched through a Systematic Evolution of Ligands by EXponential enrichment (SELEX) process using micro dissected Formalin-Fixed Paraffin-Embedded (FFPE) tissues from GBM patients, along with whole body normal tissue for counterselection. Microdissection of tissue allows the partitioning of different types of cells to focus on specific cells of interest. The enriched libraries have been evaluated on clinical GBM and normal brain control samples through aptamer mediated tissue staining, resulting in preferential binding of enriched libraries compared to controls in the vast majority of GBM patients with little binding to normal brain samples. The final library was subsequently applied in an in-house developed library-based affinity pull-down assay using lysates from FFPE tissue of the same group of GBM patients and normal brain samples, followed by unbiased Mass Spectrometry for protein target identification. Combining the results from this proteomics analysis and data from the internal Whole Transcriptome Sequencing (WTS) database, we were able to identify several targets that are highly expressed in GBM and other cancer lineages. Antibody staining provided further validation of the expression levels and the cellular localization. The targets identified in this study include not only potential novel therapeutic targets, but also well-studied biomarkers and targets with available drugs already in clinical trials, demonstrating the platform has great potential for identifying high affinity binding and selectivity compared to traditional proteomics and other methods in drug target discovery. Citation Format: Xixi Wei, Myunggi An, Radhika Santhanam, Barbara Richardson, Lucas Vu, Justin Saul, Anna Walton, Michael Gee, Daniel Martin, Amanda Hubbard, Alexis Rodin, Matthew Rosenow, Tassilo Hornung. Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB432.

Abstract LB430: Identification of therapeutic target in breast cancer using G-quadruplex binding ligands

Abstract G-quadruplex (G4) motifs are noncanonical nucleic acid structures formed from staked guanine rich tetrads. G4 motifs have been shown to play significant effects on gene expression in all domains of life and are predominantly enriched in the regulatory regions such as promoters and enhancers of the human genome. G4-ligand mediated stabilization or destabilization of G4 structures has been discussed as a potential therapeutic strategy against tumor cells. However, the structure-specificity of G4-binding ligands remains a considerable challenge in determining the target-specificity. Therefore, it is necessary to understand the global effects of such G4-binding ligands at the transcriptome level to identify novel target(s) based on molecular structures and binding modes of both G4 motifs and ligands. In this study, we reported a top-down approach to extrapolate transcriptome-wide effects of two eminent G4-binding ligands Quarfloxin and Pyridostatin in human breast adenocarcinoma (MCF7) cell lines by RNA-seq analysis. Quadparser analysis was performed to analyze putative G4 forming sequences in protein coding breast cancer genes promoters. RNA-seq data was combined with Quadparser analysis to reveal that over 85% of the differentially expressed genes in both Pyridostatin and Quarfloxin contained G4 forming sequences in promoter region. Gene ontology analysis showed that both ligands affected key genes involved in extracellular matrix (ECM), architecture and cellular functions. A notable tumor suppressor deubiquitinase (DUBs) Cylindromatosis (CYLD) gene expression was particularly increased with Pyridostatin compared to Quarfloxin. In vitro assays also confirmed an increase in CYLD expression by qRT-PCR and Western blot. The detailed molecular mechanism of G4-mediated increase in CYLD expression is under study. Collectively, the current results present a new approach to identify G4-ligands binding target in breast cancer cells and provide insights into designing G4-ligands as an anti-cancer therapeutics. Citation Format: Maria Razzaq, Subramaniyam Ravichandran, Nazia Parveen, Kyeong Kyu Kim. Identification of therapeutic target in breast cancer using G-quadruplex binding ligands [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB430.

Abstract LB054: A novel chemical proteomics approach toward the identification of kinase targets to sensitize tumour cells to the standard of care drug paclitaxel

Abstract Introduction: Chemotherapy, through various signaling pathways changes the kinase activation profiles in tumour cells and it has been shown that blockade of specific kinases led to sensitization of tumour cells to chemotherapeutic drugs. We surmised that, following chemotherapeutic drug treatment, determination of the kinase binding profile of a specific inhibitor using its corresponding chemoprobe may lead to the identification of kinases to be targeted for potentiating the action of the chemotherapeutic drug. The approach we chose to study was to design a chemoprobe carrying a kinase inhibitor warhead to capture and read the kinase binding profiles before and after the chemotherapeutic drug treatment. Our proof-of-concept was made with AB19, a chemoprobe designed with the scaffold of crizotinib, a MET receptor tyrosine kinase inhibitor. The ability of AB19 to bind to MET was validated using KATO-II gastric cancer cells in which MET is amplified. Material and Methods: Gastric cancer cell line KATO-II, prostate cancer cells DU145, PC3 and 22RV1 and normal prostate cells RWPE-1 were used to determine IC50 values for the different drugs alone and in combination with paclitaxel. Cells were lysed 2 h and 24 h after drug treatment. The chemoprobes (control probe or AB19) were subsequently added and kinases captured using magnetic pull down. Proteomics analyses were performed by mass spectrometry. Results: The results showed that AB19 pulled down MET (its primary target) and many other kinases, including LCK, Src, EPHA2, and FAK1 that play an important role in cancer progression. Importantly, the kinase pull-down profiles by the chemoprobe were similar to that reported for free crizotinib, thereby validating the ability of the chemoprobe to simulate the binding of the free drug. In vitro studies with AB19 revealed the following: (a) the kinase pull-down profiles varied with cell lines, with the presence or absence of some important kinases and (b) under conditions where the cells were pretreated with paclitaxel, the kinase profiles between treated and non-treated cells were similar. However, disappearance and appearance of key targets were observed. Based upon the detection of an increase in aurora kinase A (AURKA) and mammalian target of rapamycin (mTOR) spectral counts, we designed 2-drug combinations of specific inhibitors of the latter targets with paclitaxel under equieffective and sequential administrations. Of all the combinations studied, Alisertib (AURKA inhibitor) + paclitaxel was the most synergistic. Conclusion: Taken together, the results suggest that AURKA is a unique target for sensitizing cells to paclitaxel and this novel chemical proteomics approach can be developed as a novel strategy to identify kinase inhibitors capable of significantly sensitizing cancer cells to standard of care drugs. Citation Format: Alaa Omar Baryyan, Ana Belen Fraga Timiraos, Kurt Dejgaard, Bertrand J. Jean-Claude. A novel chemical proteomics approach toward the identification of kinase targets to sensitize tumour cells to the standard of care drug paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB054.

Abstract LB005: Identification of novel targets for MYC-driven hepatocellular carcinoma via transomics

Abstract Hepatocellular carcinoma (HCC) is an aggressive tumor, and treatment options for patients with unresectable HCC are limited to targeted therapies and immunotherapy. The activation and dysregulation of MYC have been implicated in multiple cancers, including HCC, where approximately 30% of human HCC samples show MYC gene amplification. However, it is challenging to target MYC directly. Therefore, we applied a novel approach to circumvent targeting MYC directly by leveraging Pepper’s proprietary transomic analysis platform, COMPASS, to identify novel targets that are predicted to mimic the effect of turning MYC off. Specifically, COMPASS unlocks functional drivers of disease to identify novel drug targets. To study the role of MYC in tumor biology, we utilize an HCC cell line with tunable MYC expression. We generate genomic, transcriptomic, proteomic, and phosphoproteomic data from samples with MYC-on and MYC-off and compare via COMPASS to identify novel targets that mimic turning MYC off. The MYC-conditional HCC cell line (EC4) allows the regulation of MYC expression via the tetracycline regulatory (Tet-Off) system. Four omic datasets were collected from each sample: genomics (next-generation sequencing, NGS), transcriptomics (NGS), proteomics (mass spectrometry), and phosphoproteomics (mass spectrometry). We employed the COMPASS target prioritization algorithm to identify and rank novel targets that mimic turning MYC off. Targets were then filtered to select those with an available pharmacological tool compound (PTC). The PTCs were used to evaluate the targets in the Huh-7 xenograft model of HCC. All targets were kinases or related to kinase activity and pathways. All PTCs inhibited tumor growth from 18.1-78.3%. One multi-tyrosine kinase inhibitor resulted in maximum tumor growth inhibition, resulting in stasis of tumor growth and increased survival compared with control vehicle-treated mice. We have previously shown how the COMPASS transomics analysis approach can identify novel drug targets for drug resistant EGFRm NSCLC. The data presented here on MYC-driven HCC further validates this approach for novel target identification. Ongoing work will investigate novel targets using gene silencing, as PTCs were unavailable for many of these novel high-ranked targets identified in this study. Further validation studies are ongoing against other difficult-to-treat MYC-driven cancers to identify novel clinical targets and drug candidates to circumvent the MYC pathway. Citation Format: Simon P. Fricker, Christopher J. Nicholson, Samuel J. Roth, Arudhir Singh, Caitlin Brown, Jon Hu, Petronela Buiga, Vishnu P. Kanakaveti, Anja Deutzmann, Dean Felsher, Samantha D. Strasser. Identification of novel targets for MYC-driven hepatocellular carcinoma via transomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB005.

Identification of Cellular Protein Targets of a Half-Sandwich Iridium(III) Complex Reveals Its Dual Mechanism of Action via Both Electrophilic and Oxidative Stresses.

Identification of intracellular targets of anticancer drug candidates provides key information on their mechanism of action. Exploiting the ability of the anticancer (C∧N)-chelated half-sandwich iridium(III) complexes to covalently bind proteins, click chemistry with a bioorthogonal azido probe was used to localize a phenyloxazoline-chelated iridium complex within cells and profile its interactome at the proteome-wide scale. Proteins involved in protein folding and actin cytoskeleton regulation were identified as high-affinity targets. Upon iridium complex treatment, the folding activity of Heat Shock Protein HSP90 was inhibited in vitro and major cytoskeleton disorganization was observed. A wide array of imaging and biochemical methods validated selected targets and provided a multiscale overview of the effects of this complex on live human cells. We demonstrate that it behaves as a dual agent, inducing both electrophilic and oxidative stresses in cells that account for its cytotoxicity. The proposed methodological workflow can open innovative avenues in metallodrug discovery.

Spectroscopic and computational studies of the biomolecular complex of chemotherapy drug, lomustine and a nucleotide base adenine

Cancer is still the leading cause of death in both developing and economically developed countries. There is still a need to create new drugs with higher efficacy despite substantial research on the biological pathways of cancer, target identification, and development of therapeutic interventions. Lomustine is an anticancer medication, while Adenine is a purine derivative with great potential for use as a cancer therapy. The present study is carried out to understand the geometrical and quantum chemical parameters of the Adenine + Lomustine as a potential anti-cancer agent by means of the density functional theory method and vibrational spectroscopic techniques. Natural bond orbital analysis, powder X-ray diffraction and quantum theory of atom in molecule analysis are used for investigating the intermolecular hydrogen bond formed in the biomolecular complex. The oral bioavailability and pharmacological activity of the Adenine + Lomustine complex is estimated by drug likeness and bioactivity score prediction. Docking of the biomolecular complex is also carried out against the chosen receptors.

A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

Identification of prospective aging drug targets via Mendelian randomization analysis.

Aging represents a multifaceted process culminating in the deterioration of biological functions. Despite the introduction of numerous anti-aging strategies, their therapeutic outcomes have often been less than optimal. Consequently, discovering new targets to mitigate aging effects is of critical importance. We applied Mendelian randomization (MR) to identify potential pharmacological targets against aging, drawing upon summary statistics from both the Decode and FinnGen cohorts, with further validation in an additional cohort. To address potential reverse causality, bidirectional MR analysis with Steiger filtering was utilized. Additionally, Bayesian co-localization and phenotype scanning were implemented to investigate previous associations between genetic variants and traits. Summary-data-based Mendelian randomization (SMR) analysis was conducted to assess the impact of genetic variants on aging via their effects on protein expression. Additionally, mediation analysis was orchestrated to uncover potential intermediaries in these associations. Finally, we probed the systemic implications of drug-target protein expression across diverse indications by MR-PheWas analysis. Utilizing a Bonferroni-corrected threshold, our MR examination identified 10 protein-aging associations. Within this cohort of proteins, MST1, LCT, GMPR2, PSMB4, ECM1, EFEMP1, and ISLR2 appear to exacerbate aging risks, while MAX, B3GNT8, and USP8 may exert protective influences. None of these proteins displayed reverse causality except EFEMP1. Bayesian co-localization inferred shared variants between aging and proteins such as B3GNT8 (rs11670143), ECM1 (rs61819393), and others listed. Mediator analysis pinpointed 1,5-anhydroglucitol as a partial intermediary in the influence LCT exhibits on telomere length. Circulating proteins play a pivotal role in influencing the aging process, making them promising candidates for therapeutic intervention. The implications of these proteins in aging warrant further investigation in future clinical research.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.