Unraveling genetic threads: Identifying novel therapeutic targets for allergic rhinitis through Mendelian randomization.

Background: Allergic rhinitis (AR) is a pervasive global health issue, and currently, there is a scarcity of targeted drug therapies available. This study aims to identify potential druggable target genes for AR using Mendelian randomization (MR) analysis. Methods: MR analysis was conducted to assess the causal effect of expression quantitative trait loci (eQTL) in the blood on AR. Data on AR were collected from 2 datasets: FinnGen(R9) (11,009 cases and 359,149 controls) and UK Biobank (25,486 cases and 87,097 controls). Colocalization analysis was utilized to assess the common causal genetic variations between the identified drug target genes and AR. We also employed available genome-wide association studies (GWAS) data to gauge the impact of druggable genes on AR biomarkers and other allergic diseases. Results: This study employs MR to analyze the relationship between 3410 druggable genes and AR. After Bonferroni correction, 10 genes were found to be significantly associated with AR risk (P < 0.05/3410). Colocalization analysis revealed a significant causal relationship between the expression variation of CFL1 and EFEMP2 genes and AR, sharing direct causal variants (colocalization probability PP.H3 + PP.H4 > 0.8), highlighting their importance as potential therapeutic targets for AR. The CFL1 gene showed a causal link with levels of thymic stromal lymphopoietin (TSLP), eosinophil count, and interleukin-13 (IL-13) (P = 0.016, 7.45E-16, 0.00091, respectively). EFEMP2 was also causally related to eosinophil count, IL-13, and interleukin-17 (IL-17) (P = 0.00012, 0.00091, 0.032, respectively). PheWAS analysis revealed significant associations of CFL1 with asthma, whereas EFEMP2 showed associations with both asthma and eczema. Protein-Protein Interaction (PPI) network analysis further unveiled the direct interactions of EFEMP2 and CFL1 with proteins related to immune regulation and inflammatory responses, with 77.64% of the network consisting of direct bindings, indicating their key roles in modulating AR-related immune and inflammatory responses. Notably, there was an 8.01% significant correlation between immune-related pathways and genes involved in inflammatory responses. Conclusion: These genes present notable associations with AR biomarkers and other autoimmune diseases, offering valuable targets for developing new AR therapies.

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for lung cancer.

BACKGROUND: Drug repurposing provides a cost-effective approach to address the need for lung cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR). METHODS: Summary-level data of gene expression quantitative trait loci (eQTLs) were sourced from the eQTLGen resource. We procured genetic associations with lung cancer and its subtypes from the TRICL, ILCCO studies (discovery) and the FinnGen study (replication). We implemented Summary-data-based Mendelian Randomization analysis to identify potential therapeutic targets for lung cancer. Colocalization analysis was further conducted to assess whether the identified signal pairs shared a causal genetic variant. FINDINGS: In the main analysis dataset, we identified 55 genes that demonstrate a causal relationship with lung cancer and its subtypes. However, in the replication cohort, only three genes were found to have such a causal association with lung cancer and its subtypes, and of these, HYKK (also known as AGPHD1) was consistently present in both the primary analysis dataset and the replication cohort. Following HEIDI tests and colocalization analyses, it was revealed that HYKK (AGPHD1) is associated with an increased risk of squamous cell carcinoma of the lung, with an odds ratio and confidence interval of OR = 1.28,95%CI = 1.24 to 1.33. INTERPRETATION: We have found that the HYKK (AGPHD1) gene is associated with an increased risk of squamous cell carcinoma of the lung, suggesting that this gene may represent a potential therapeutic target for both the prevention and treatment of lung squamous cell carcinoma.

Identifying therapeutic target genes for migraine by systematic druggable genome-wide Mendelian randomization.

BACKGROUND: Currently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine. METHODS: We obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs. RESULTS: We identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels. CONCLUSIONS: This study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs.

Hotspots of Somatic Genetic Variation in Pituitary Neuroendocrine Tumors.

The most common genetic drivers of pituitary neuroendocrine tumors (PitNETs) lie within mutational hotspots, which are genomic regions where variants tend to cluster. Some of these hotspot defects are unique to PitNETs, while others are associated with additional neoplasms. Hotspot variants in GNAS and USP8 are the most common genetic causes of acromegaly and Cushing's disease, respectively. Although it has been proposed that these genetic defects could define specific clinical phenotypes, results are highly variable among studies. In contrast, DICER1 hotspot variants are associated with a familial syndrome of cancer predisposition, and only exceptionally occur as somatic changes. A small number of non-USP8-driven corticotropinomas are due to somatic hotspot variants in USP48 or BRAF; the latter is a well-known mutational hotspot in cancer. Finally, somatic variants affecting a hotspot in SF3B1 have been associated with multiple cancers and, more recently, with prolactinomas. Since the associations of BRAF, USP48, and SF3B1 hotspot variants with PitNETs are very recent, their effects on clinical phenotypes are still unknown. Further research is required to fully define the role of these genetic defects as disease biomarkers and therapeutic targets.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Blockade of Activin Receptor IIB Protects Arthritis Pathogenesis by Non-Amplification of Activin A-ACVR2B-NOX4 Axis Pathway.

Although activin receptor IIB (ACVR2B) is emerging as a novel pathogenic receptor, its ligand and assembled components (or assembly) are totally unknown in the context of osteoarthritis (OA) pathogenesis. The present results suggest that upregulation of ACVR2B and its assembly could affect osteoarthritic cartilage destruction. It is shown that the ACVR2B ligand, activin A, regulates catabolic factor expression through ACVR2B in OA development. Activin A Tg mice (Col2a1-Inhba) exhibit enhanced cartilage destruction, whereas heterozygous activin A KO mice (Inhba+/- ) show protection from cartilage destruction. In silico analysis suggests that the Activin A-ACVR2B axis is involved in Nox4-dependent ROS production. Activin A Tg:Nox4 KO (Col2a1-Inhba:Nox4-/- ) mice show inhibition of experimental OA pathogenesis. NOX4 directly binds to the C-terminal binding site on ACVR2B-ACVR1B and amplifies the pathogenic signal for cartilage destruction through SMAD2/3 signaling. Together, the findings reveal that the ACVR2B assembly, which comprises Activin A, ACVR2B, ACVR1B, Nox4, and AP-1-induced HIF-2α, accelerates OA development. Furthermore, it is shown that shRNA-mediated ACVR2B knockdown or trapping ligands of ACVR2B abrogate OA development by competitively disrupting the ACVR2B-Activin A interaction. These results suggest that the ACVR2B assembly is required to amplify osteoarthritic cartilage destruction and could be a potential therapeutic target in efforts to treat OA.

Targeting peptide-mediated interactions in omics.

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.

Identifying actionable druggable targets for breast cancer: Mendelian randomization and population-based analyses.

BACKGROUND: Drug repurposing provides a cost-effective approach to address the need for breast cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR) and then validate the candidate drugs using population-based analyses. METHODS: We identified genetic instruments for 1406 actionable targets of approved non-oncological drugs based on gene expression, DNA methylation, and protein expression quantitative trait loci (eQTL, mQTL, and pQTL, respectively). Genome-wide association study (GWAS) summary statistics were obtained from the Breast Cancer Association Consortium (122,977 cases, 105,974 controls). We further conducted a nested case-control study using data retrieved from Swedish registers to validate the candidate drugs that were identified from MR analyses. FINDINGS: We identified six significant MR associations with gene expression levels (TUBB, MDM2, CSK, ULK3, MC1R and KCNN4) and two significant associations with gene methylation levels across 21 CpG islands (RPS23 and MAPT). Results from the nested case-control study showed that the use of raloxifene (targeting MAPT) was associated with 35% reduced breast cancer risk (odds ratio, OR, 0.65; 95% confidence interval, CI, 0.51-0.83). However, usage of estradiol, tolterodine, and nitrofurantoin (also targeting MAPT) was associated with increased breast cancer risk, with adjusted ORs and 95% CI of 1.10 (1.07-1.13), 1.16 (1.09-1.24), and 1.09 (1.05-1.13), respectively. The effect of raloxifene and nitrofurantoin lost significance in further validation analyses using active-comparator and new-user design. INTERPRETATION: This large-scale MR analysis, combined with population-based validation, identified eight druggable target genes for breast cancer and suggested that raloxifene is an effective chemoprevention against breast cancer. FUNDING: Swedish Research Council, Cancerfonden, Crafoordska Stiftelsen, Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, 111 Project and MAS cancer.

Investigating PSMA differential expression in canine uroepithelial carcinomas to aid disease-based stratification and guide therapeutic selection.

BACKGROUND: In male dogs, uroepithelial cancers include invasive urothelial carcinoma (iUC) and prostate carcinoma (PCA). The inability to distinguish iUC involving the prostate from PCA results in indiscriminate clinical management strategies that could be suboptimal as first-line chemotherapy for iUC (cisplatin) and PCA (docetaxel) differ in people. Prostate specific membrane antigen (PSMA) is a transmembrane protein, and its overexpression has been identified in human prostate carcinoma and neovasculature associated with solid tumor growth. This study investigates whether differential PSMA expression exists between presumptive canine iUC and PCA among cell lines and archived patient samples, which might allow for improved accuracy in disease-based stratification and optimal chemotherapy selection. Additionally, in vitro sensitivities of reported canine iUC and PCA cell lines to uroepithelial directed chemotherapeutic agents were characterized. RESULTS: Normalized PSMA gene and protein expressions were not significantly different between 5 iUC and 4 PCA cell lines. PSMA protein expression was uniformly observed in uroepithelial cancers regardless of anatomic origin from archived patient samples, further confirming that PSMA cannot differentiate iUC from PCA. In vitro sensitivity of cell lines to uroepithelial directed chemotherapeutics revealed that vinblastine exerted the broadest cytotoxic activity. CONCLUSIONS: Differential expression of PSMA was not identified between canine iUC and PCA cell lines or archived patient samples, and PSMA alone cannot be used for disease stratification. Nonetheless given its conserved overexpression, PSMA may be a targetable surface marker for both canine iUC and PCA. Lastly, in uroepithelial carcinomas, vinblastine might exert the broadest anticancer activity regardless of cellular origin.

P75 neurotrophin receptor as a therapeutic target for drug development to treat neurological diseases.

The interaction of neurotrophins with their receptors is involved in the pathogenesis and progression of various neurological diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, spinal cord injury and acute and chronic cerebral damage. The p75 neurotrophin receptor (p75NTR) plays a pivotal role in the development of neurological dysfunctions as a result of its high expression, abnormal processing and signalling. Therefore, p75NTR represents as a vital therapeutic target for the treatment of neurodegeneration, neuropsychiatric disorders and cerebrovascular insufficiency. This review summarizes the current research progress on the p75NTR signalling in neurological deficits. We also summarize the present therapeutic approaches by genetically and pharmacologically targeting p75NTR for the attenuation of pathological changes. Based on the evolving knowledge, the role of p75NTR in the regulation of tau hyperphosphorylation, Aß metabolism, the degeneration of motor neurons and dopaminergic neurons has been discussed. Its position as a biomarker to evaluate the severity of diseases and as a druggable target for drug development has also been elucidated. Several prototype small molecule compounds were introduced to be crucial in neuronal survival and functional recovery via targeting p75NTR. These small molecule compounds represent desirable agents in attenuating neurodegeneration and cell death as they abolish activation-induced neurotoxicity of neurotrophins via modulating p75NTR signalling. More comprehensive and in-depth investigations on p75NTR-based drug development are required to shed light on effective treatment of numerous neurological disorders.

Activation of a Bacterial Mechanosensitive Channel, MscL, Underlies the Membrane Permeabilization of Dual-Targeting Antibacterial Compounds.

Resistance to antibiotics is a serious and worsening threat to human health worldwide, and there is an urgent need to develop new antibiotics that can avert it. One possible solution is the development of compounds that possess multiple modes of action, requiring at least two mutations to acquire resistance. Compound SCH-79797 both avoids resistance and has two mechanisms of action: one inhibiting the folate pathway, and a second described as "membrane permeabilization"; however, the mechanism by which membranes from bacterial cells, but not the host, are disrupted has remained mysterious. The opening of the bacterial mechanosensitive channel of large conductance, MscL, which ordinarily serves the physiological role of osmotic emergency release valves gated by hypoosmotic shock, has been previously demonstrated to affect bacterial membrane permeabilization. MscL allows the rapid permeabilization of both ions and solutes through the opening of the largest known gated pore, which has a diameter of 30 Å. We found that SCH-79797 and IRS-16, a more potent derivative, directly bind to the MscL channel and produce membrane permeabilization as a result of its activation. These findings suggest that possessing or adding an MscL-activating component to an antibiotic compound could help to lower toxicity and evade antibiotic resistance.

The role of gut microbial ß-glucuronidase in drug disposition and development.

Gut microbial ß-glucuronidase (gmGUS) is involved in the disposition of many endogenous and exogenous compounds. Preclinical studies have shown that inhibiting gmGUS activity affects drug disposition, resulting in reduced toxicity in the gastrointestinal tract (GIT) and enhanced systemic efficacy. Additionally, manipulating gmGUS activity is expected to be effective in preventing/treating local or systemic diseases. Although results from animal studies are promising, challenges remain in developing drugs by targeting gmGUS. Here, we review the role of gmGUS in host health under physiological and pathological conditions, the impact of gmGUS on the disposition of phenolic compounds, models used to study gmGUS activity, and the perspectives and challenges in developing drugs by targeting gmGUS.

Multi-omics approaches identify SF3B3 and SIRT3 as candidate autophagic regulators and druggable targets in invasive breast carcinoma.

Autophagy is a critical cellular homeostatic mechanism, and its dysfunction is linked to invasive breast carcinoma (BRCA). Recently, several omics methods have been applied to explore autophagic regulators in BRCA; however, more reliable and robust approaches for identifying crucial regulators and druggable targets remain to be discovered. Thus, we report here the results of multi-omics approaches to identify potential autophagic regulators in BRCA, including gene expression (EXP), DNA methylation (MET) and copy number alterations (CNAs) from The Cancer Genome Atlas (TCGA). Newly identified candidate genes, such as SF3B3, TRAPPC10, SIRT3, MTERFD1, and FBXO5, were confirmed to be involved in the positive or negative regulation of autophagy in BRCA. SF3B3 was identified firstly as a negative autophagic regulator, and siRNA/shRNA-SF3B3 were shown to induce autophagy-associated cell death in in vitro and in vivo breast cancer models. Moreover, a novel small-molecule activator of SIRT3, 1-methylbenzylamino amiodarone, was discovered to induce autophagy in vitro and in vivo. Together, these results provide multi-omics approaches to identify some key candidate autophagic regulators, such as the negative regulator SF3B3 and positive regulator SIRT3 in BRCA, and highlight SF3B3 and SIRT3 as new druggable targets that could be used to fill the gap between autophagy and cancer drug development.

Structure-based identification of natural compound inhibitor against M. tuberculosis thioredoxin reductase: insight from molecular docking and dynamics simulation.

Antioxidant systems of M. tuberculosis (Mtb) play an important role in providing resistance in the hostile environment of mononuclear phagocytes. Thioredoxin system is a known antioxidant system that consists of three copies of thioredoxins (Trxs) and a single copy of thioredoxin reductase (TrxR). TrxR has been validated as an essential gene known to be involved in the reduction of peroxides, dinitrobenzenes and hydroperoxides, and is crucial in maintaining the survival of Mtb in macrophages. Recently, it has been demonstrated to be a druggable target. In this study, molecular docking was applied to screen more than 20,000 natural compounds from the Traditional Chinese Medicine database. Theoretical calculation of ΔGbinding by the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) methods indicated two top-hit compounds that bind with a high affinity to the allosteric site, consisting of a hinge region, of TrxR. Further, stability and binding analysis of both compounds were carried out with molecular dynamics simulation. An analysis of conformational variation by principal component analysis (PCA) and protein contact network (PCN) uncovered the conformational changes in the compound-bound forms of protein. The NADPH domain formed many new interactions with the FAD domain in the compound-bound form, signifying that the binding may render an effect on the protein structure and function. Our results suggest that these two compounds could potentially be used for structure-based lead inhibitors against TrxR. The inhibitor selected as lead compound will be used further as a scaffold to optimize as novel anti-tuberculosis therapeutic.Communicated by Ramaswamy H. Sarma.

ERα-related chromothripsis enhances concordant gene transcription on chromosome 17q11.1-q24.1 in luminal breast cancer.

BACKGROUND: Chromothripsis is an event of genomic instability leading to complex chromosomal alterations in cancer. Frequent long-range chromatin interactions between transcription factors (TFs) and targets may promote extensive translocations and copy-number alterations in proximal contact regions through inappropriate DNA stitching. Although studies have proposed models to explain the initiation of chromothripsis, few discussed how TFs influence this process for tumor progression. METHODS: This study focused on genomic alterations in amplification associated regions within chromosome 17. Inter-/intra-chromosomal rearrangements were analyzed using whole genome sequencing data of breast tumors in the Cancer Genome Atlas (TCGA) cohort. Common ERα binding sites were defined based on MCF-7, T47D, and MDA-MB-134 breast cancer cell lines using univariate K-means clustering methods. Nanopore sequencing technology was applied to validate frequent rearrangements detected between ATC loci on 17q23 and an ERα hub on 20q13. The efficacy of pharmacological inhibition of a potentially druggable target gene on 17q23 was evaluated using breast cancer cell lines and patient-derived circulating breast tumor cells. RESULTS: There are five adjoining regions from 17q11.1 to 17q24.1 being hotspots of chromothripsis. Inter-/intra-chromosomal rearrangements of these regions occurred more frequently in ERα-positive tumors than in ERα-negative tumors. In addition, the locations of the rearrangements were often mapped within or close to dense ERα binding sites localized on these five 17q regions or other chromosomes. This chromothriptic event was linked to concordant upregulation of 96 loci that predominantly regulate cell-cycle machineries in advanced luminal tumors. Genome-editing analysis confirmed that an ERα hub localized on 20q13 coordinately regulates a subset of these loci localized on 17q23 through long-range chromosome interactions. One of these loci, Tousled Like Kinase 2 (TLK2) known to participate in DNA damage checkpoint control, is an actionable target using phenothiazine antipsychotics (PTZs). The antiproliferative effect of PTZs was prominent in high TLK2-expressing cells, compared to low expressing cells. CONCLUSION: This study demonstrates a new approach for identifying tumorigenic drivers from genomic regions highly susceptible to ERα-related chromothripsis. We found a group of luminal breast tumors displaying 17q-related chromothripsis for which antipsychotics can be repurposed as treatment adjuncts.

The TATA-binding Protein DNA-binding domain of eukaryotic parasites is a potentially druggable target.

The TATA-binding protein (TBP) is a central transcription factor in eukaryotes that interacts with a large number of different transcription factors; thus, affecting these interactions will be lethal for any living being. In this work, we present the first structural and dynamic computational study of the surface properties of the TBP DNA-binding domain for a set of parasites involved in diseases of worldwide interest. The sequence and structural differences of these TBPs, as compared with human TBP, were proposed to select representative ensembles generated from molecular dynamics simulations and to evaluate their druggability by molecular ensemble-based docking of drug-like molecules. We found that potential druggable sites correspond to the NC2-binding site, N-terminal tail, H2 helix, and the interdomain region, with good selectivity for Plasmodium falciparum, Necator americanus, Entamoeba histolytica, Candida albicans, and Taenia solium TBPs. The best hit compounds share structural similarity among themselves and have predicted dissociation constants ranging from nM to µM. These can be proposed as initial scaffolds for experimental testing and further optimization. In light of the obtained results, we propose TBP as an attractive therapeutic target for treatment of parasitic diseases.

Metacaspases: Potential Drug Target Against Protozoan Parasites.

Among the numerous strategies/targets for controlling infectious diseases, parasites-derived proteases receive prime attention due to their essential contribution to parasite growth and development. Parasites produce a broad array of proteases, which are required for parasite entry/invasion, modification/degradation of host proteins for their nourishment, and activation of inflammation that ensures their survival to maintain infection. Presently, extensive research is focused on unique proteases termed as "metacaspases" (MCAs) in relation to their versatile functions in plants and non-metazoans. Such unique MCAs proteases could be considered as a potential drug target against parasites due to their absence in the human host. MCAs are cysteine proteases, having Cys-His catalytic dyad present in fungi, protozoa, and plants. Studies so far indicated that MCAs are broadly associated with apoptosis-like cell death, growth, and stress regulation in different protozoa. The present review comprises the important research outcomes from our group and published literature, showing the variable properties and function of MCAs for therapeutic purpose against infectious diseases.

Imaging metabotropic glutamate receptor system: Application of positron emission tomography technology in drug development.

The diversity seen in the mode of pharmacology and the numerous glutamate receptor subtypes have previously complicated drug development efforts. Nonetheless, recent clinical trials of drug candidates that accommodate the glutamatergic intricate pharmacology have yielded encouraging results. Target engagement is an important requirement for the advancement of central nervous system drug candidates into clinical trial. Positron emission tomography (PET) tracer technology has the unique ability to give the direct insight into the relationship between the level of receptor occupancy and the administered dose, thus establishing a direct link between the level of target exposure and the drug efficacy in human. This review is focused on the advancement of mGlu5 , mGlu 1 , and mGlu 2 receptor-related PET tracer technology: PET tracer development strategies, PET tracer selection in receptor occupancy studies, the scopes and limitations to use PET to measure receptor occupancy for the drug candidates with different pharmacology, and how to use the measurements of receptor occupancy as a translational biomarker for decision-making in an innovative drug development program.

Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. METHODS: We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein-peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein-peptide affinity and attempt to extend the content of generalized machine learning methods. RESULTS: Existing issues and future perspective on the statistical modeling and regression prediction of protein- peptide binding affinity are discussed. CONCLUSION: There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein-peptide affinity predictors.

Genomic analysis of liver cancer unveils novel driver genes and distinct prognostic features.

Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with a dismal prognosis. However, driver genes and prognostic markers in HCC remain to be identified. It is hoped that in-depth analysis of HCC genomes in relation to available clinicopathological information will give rise to novel molecular prognostic markers. Methods: We collected genomic data of 1,061 HCC patients from previous studies, and performed integrative analysis to identify significantly mutated genes and molecular prognosticators. We employed three MutSig algorithms (MutSigCV, MutSigCL and MutSigFN) to identify significantly mutated genes. The GISTIC2 algorithm was used to delineate focally amplified and deleted genomic regions. Nonnegative matrix factorization (NMF) was utilized to decipher mutational signatures. Kaplan-Meier survival and Cox regression analyses were used to associate gene mutation and copy number alteration with survival outcome. Logistic regression model was applied to test association between gene mutation and mutational signatures. Results: We discovered 11 novel driver genes, including RNF213, VAV3 and TNRC6B, with mutational prevalence ranging from 1% to 3%. Seven mutational signatures were also identified in HCC, some of which were associated with mutations of classical driver genes (e.g., TP53, TERT) as well as alcohol consumption. Focal amplifications of TERT and other druggable targets, including AURKA, were also revealed. Targeting AURKA by a small-molecule inhibitor potently induced apoptosis in HCC cells. We further demonstrated that HCC patients with TERT amplification displayed shortened overall survival independent of other clinicopathological parameters. In conclusion, our study identified novel cancer driver genes and prognostic markers in HCC, reiterating the translational importance of omics data in the precision medicine era.