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.

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.

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.

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.

Drug target discovery by magnetic nanoparticles coupled mass spectrometry / 药物分析学报

Drug target discovery is the basis of drug screening.It elucidates the cause of disease and the mechanism of drug action,which is the essential of drug innovation.Target discovery performed in biological sys-tems is complicated as proteins are in low abundance and endogenous compounds may interfere with drug binding.Therefore,methods to track drug-target interactions in biological matrices are urgently required.In this work,a Fe3O4 nanoparticle-based approach was developed for drug-target screening in biofluids.A known ligand-protein complex was selected as a principle-to-proof example to validate the feasibility.After incubation in cell lysates,ligand-modified Fe3O4 nanoparticles bound to the target protein and formed complexes that were separated from the lysates by a magnet for further analysis.The large surface-to-volume ratio of the nanoparticles provides more active sites for the modification of chemical drugs.It enhances the opportunity for ligand-protein interactions,which is beneficial for capturing target proteins,especially for those with low abundance.Additionally,a one-step magnetic separation simplifies the pre-processing of ligand-protein complexes,so it effectively reduces the endogenous interference.Therefore,the present nanoparticle-based approach has the potential to be used for drug target screening in biological systems.

Application of text mining in drug target discovery / 中华医学图书情报杂志

Co-citations of foreign and domestic highly cited papers on drug target discovery were analyzed by clustering analysis using BICOMB2.01 and gCLUTO.Semantic analysis of the titles and abstracts in these highly cited papers and their important source literature showed that general trend, theoretical foundation, main methods and principal resources are the major hotspots of text mining in drug target discovery.