Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Discovery of drug targets based on traditional Chinese medicine microspheres (TCM-MPs) fishing strategy combined with bio-layer interferometry (BLI) technology.

Target discovery of natural products is a key step in the development of new drugs, and it is also a difficult speed-limiting step. In this study, a traditional Chinese medicine microspheres (TCM-MPs) target fishing strategy was developed to discover the key drug targets from complex system. The microspheres are composed of Fe3O4 magnetic nanolayer, oleic acid modified layer, the photoaffinity group (4- [3-(Trifluoromethyl)-3H-diazirin-3-yl] benzoic acid, TAD) layer and active small molecule layer from inside to outside. TAD produces highly reactive carbene under ultraviolet light, which can realize the self-assembly and fixation of drug active small molecules with non-selective properties. Here, taking Shenqi Jiangtang Granules (SJG) as an example, the constructed TCM-MPs was used to fish the related proteins of human glomerular mesangial cells (HMCs) lysate. 28 differential proteins were screened. According to the target analysis based on bioinformatics, GNAS was selected as the key target, which participated in insulin secretion and cAMP signaling pathway. To further verify the interaction effect of GNAS and small molecules, a reverse fishing technique was established based on bio-layer interferometry (BLI) coupled with UHPLC-Q/TOF-MS/MS. The results displayed that 26 small molecules may potentially interact with GNAS, and 7 of them were found to have strong binding activity. In vitro experiments for HMCs have shown that 7 active compounds can significantly activate the cAMP pathway by binding to GNAS. The developed TCM-MPs target fishing strategy combined with BLI reverse fishing technology to screen out key proteins that directly interact with active ingredients from complex target protein systems is significant for the discovery of drug targets for complex systems of TCM.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

A Drug Discovery Approach to a Reveal Novel Antioxidant Natural Source: The Case of Chestnut Burr Biomass.

Currently, many environmental and energy-related problems are threatening the future of our planet. In October 2022, the Worldmeter recorded the world population as 7.9 billion people, estimating that there will be an increase of 2 billion by 2057. The rapid growth of the population and the continuous increase in needs are causing worrying conditions, such as pollution, climate change, global warming, waste disposal, and natural resource reduction. Looking for novel and innovative methods to overcome these global troubles is a must for our common welfare. The circular bioeconomy represents a promising strategy to alleviate the current conditions using biomass-like natural wastes to replace commercial products that have a negative effect on our ecological footprint. Applying the circular bioeconomy concept, we propose an integrated in silico and in vitro approach to identify antioxidant bioactive compounds extracted from chestnut burrs (an agroforest waste) and their potential biological targets. Our study provides a novel and robust strategy developed within the circular bioeconomy concept aimed at target and drug discovery for a wide range of diseases. Our study could open new frontiers in the circular bioeconomy related to target and drug discovery, offering new ideas for sustainable scientific research aimed at identifying novel therapeutical strategies.

Applications of transcriptomics in ischemia reperfusion research in lung transplantation.

Ischemia-reperfusion (IR) injury contributes to primary graft dysfunction, a major cause of early mortality after lung transplantation. Transcriptomics uses high-throughput techniques to profile the RNA transcripts within a sample and provides a unique view of the mechanisms underlying various biological processes. This review aims to highlight the applications of transcriptomics in lung IR injury studies, which have thus far revealed inflammatory responses to be the major event activated by IR, identified potential biomarkers and therapeutic targets, and investigated the mechanisms of therapeutic interventions. Ex vivo lung perfusion, together with advanced bioinformatic and transcriptomic techniques, including single-cell RNA-sequencing, microRNA profiling, and multi-omics, continue to expand the capabilities of transcriptomics. In the future, the construction of biospecimen banks and the promotion of international collaborations among clinicians and researchers have the potential to advance our understanding of IR injury and improve the management of lung transplant recipients.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.

BACKGROUND: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. METHODS: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. RESULTS: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. CONCLUSIONS: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Mitigating pathogenesis for target discovery and disease subtyping.

Treatments ideally mitigate pathogenesis, or the detrimental effects of the root causes of disease. However, existing definitions of treatment effect fail to account for pathogenic mechanism. We therefore introduce the Treated Root causal Effects (TRE) metric which measures the ability of a treatment to modify root causal effects. We leverage TREs to automatically identify treatment targets and cluster patients who respond similarly to treatment. The proposed algorithm learns a partially linear causal model to extract the root causal effects of each variable and then estimates TREs for target discovery and downstream subtyping. We maintain interpretability even without assuming an invertible structural equation model. Experiments across a range of datasets corroborate the generality of the proposed approach.

The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets?

Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.

Comprehensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer: A systematic review.

BACKGROUND: Colorectal cancer (CRC) is the third most frequent and the second most fatal cancer. The search for more effective drugs to treat this disease is ongoing. A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies. Ubiquitin-specific peptidases (USPs), the largest group of the deubiquitinase protein family, have long been implicated in various cancers. There have been numerous studies on the role of USPs in CRC; however, a comprehensive view of this role is lacking. AIM: To provide a systematic review of the studies investigating the roles and functions of USPs in CRC. METHODS: We systematically queried the MEDLINE (via PubMed), Scopus, and Web of Science databases. RESULTS: Our study highlights the pivotal role of various USPs in several processes implicated in CRC: Regulation of the cell cycle, apoptosis, cancer stemness, epithelial-mesenchymal transition, metastasis, DNA repair, and drug resistance. The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC. The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms. CONCLUSION: Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.

Pan-cancer ion transport signature reveals functional regulators of glioblastoma aggression.

Ion channels, transporters, and other ion-flux controlling proteins, collectively comprising the "ion permeome", are common drug targets, however, their roles in cancer remain understudied. Our integrative pan-cancer transcriptome analysis shows that genes encoding the ion permeome are significantly more often highly expressed in specific subsets of cancer samples, compared to pan-transcriptome expectations. To enable target selection, we identified 410 survival-associated IP genes in 33 cancer types using a machine-learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma, the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived glioblastoma cells induces transcriptome-wide changes involving neuron projection and proliferation pathways, impairs cell viability and tumor sphere formation in vitro, perturbs tunneling nanotube dynamics, and extends the survival of glioblastoma-bearing mice. Thus, aberrant activation of genes encoding ion transport proteins appears as a pan-cancer feature defining tumor heterogeneity, which can be exploited for mechanistic insights and therapy development.

A Multimodal Omics Framework to Empower Target Discovery for Cardiovascular Regeneration.

Ischaemic heart disease is a global healthcare challenge with high morbidity and mortality. Early revascularisation in acute myocardial infarction has improved survival; however, limited regenerative capacity and microvascular dysfunction often lead to impaired function and the development of heart failure. New mechanistic insights are required to identify robust targets for the development of novel strategies to promote regeneration. Single-cell RNA sequencing (scRNA-seq) has enabled profiling and analysis of the transcriptomes of individual cells at high resolution. Applications of scRNA-seq have generated single-cell atlases for multiple species, revealed distinct cellular compositions for different regions of the heart, and defined multiple mechanisms involved in myocardial injury-induced regeneration. In this review, we summarise findings from studies of healthy and injured hearts in multiple species and spanning different developmental stages. Based on this transformative technology, we propose a multi-species, multi-omics, meta-analysis framework to drive the discovery of new targets to promote cardiovascular regeneration.

Current advances in comprehensive omics data mining for oncology and cancer research.

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles.

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP-treated cell lines. The model achieves mean cross-validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best-performing selectively cytotoxic NPs. As proof-of-concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

Applying Artificial Intelligence to Identify Common Targets for Treatment of Asthma, Eczema, and Food Allergy.

INTRODUCTION: Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients. METHODS: We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease. RESULTS: Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies. CONCLUSION: Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.

The evolution and application progress of non-modified drug target discovery CETSA technology / 药学学报

Understanding the research methods for drug protein targets is crucial for the development of new drugs, clinical applications of drugs, drug mechanisms, and the pathogenesis of diseases. Cellular thermal shift assay (CETSA), a target research method without modification, has been widely used since its development. Now, there are various CETSA-based technology combinations, such as mass spectrometry-based cellular thermal shift assay (MS-CETSA), isothermal dose response-cellular thermal shift assay (ITDR-CETSA), amplified luminescent proximity homogeneous assay-cellular thermal shift assay (Alpha-CETSA), etc., which combine their respective advantages and further expand the application scope of CETSA. These technologies are suitable for the entire drug development chain, from drug screening to monitoring the target binding and off-target toxicity of drugs in patients. Based on the author's research experience, this paper reviews the principles of CETSA and related binding technologies, their application in target discovery, and the progress of data processing and analysis in recent years, aiming to provide reference and reference for the further application of CETSA.

Mapping the protein-protein interactome in the tumor immune microenvironment.

The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein-protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein-protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.

CRISPR/Cas9 system: recent applications in immuno-oncology and cancer immunotherapy.

Clustered regulatory interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) is essentially an adaptive immunity weapon in prokaryotes against foreign DNA. This system inspires the development of genome-editing technology in eukaryotes. In biomedicine research, CRISPR has offered a powerful platform to establish tumor-bearing models and screen potential targets in the immuno-oncology field, broadening our insights into cancer genomics. In translational medicine, the versatile CRISPR/Cas9 system exhibits immense potential to break the current limitations of cancer immunotherapy, thereby expanding the feasibility of adoptive cell therapy (ACT) in treating solid tumors. Herein, we first explain the principles of CRISPR/Cas9 genome editing technology and introduce CRISPR as a tool in tumor modeling. We next focus on the CRISPR screening for target discovery that reveals tumorigenesis, immune evasion, and drug resistance mechanisms. Moreover, we discuss the recent breakthroughs of genetically modified ACT using CRISPR/Cas9. Finally, we present potential challenges and perspectives in basic research and clinical translation of CRISPR/Cas9. This review provides a comprehensive overview of CRISPR/Cas9 applications that advance our insights into tumor-immune interaction and lay the foundation to optimize cancer immunotherapy.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Target discovery of bioactive natural products with native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB): Applications, mechanisms and outlooks.

Natural products (NPs) represent a treasure trove for drug discovery and development due to their chemical structural diversity and a broad spectrum of biological activities. Uncovering the biological targets and understanding their molecular mechanism of actions are crucial steps in the development of clinical therapeutics. However, the structural complexity of NPs and intricate nature of biological system present formidable challenges in target identification of NPs. Although significant advances have been made in the development of new chemical tools, these methods often require high levels of synthetic skills for preparing chemical probes. This can be costly and time-consuming relaying on operationally complicated procedures and instruments. In recent efforts, we and others have successfully developed an operationally simple and practical chemical tool known as native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB) for NP target identification. In this approach, a native compound readily reacts with commercial CNBr-activated Sepharose 4B beads with a process that is easily performed in any biology laboratory. Based on NCCB, our group has identified the direct targets of more than 60 NPs. In this review, we will elucidate the application scopes, including flavonoids, quinones, terpenoids and others, characteristics, chemical mechanisms, procedures, advantages, disadvantages, and future directions of NCCB in specific target discovery.