Drug Targets Research Articles

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations.

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval. In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.

Causality of genetically determined blood metabolites on inflammatory bowel disease: a two-sample Mendelian randomization study

Inflammatory bowel disease (IBD) is a chronic and recurrent inflammatory disease of the gastrointestinal tract, including two subtypes: Crohn’s disease (CD) and ulcerative colitis (UC). Metabolic disorders are important factors in the development of IBD. However, the evidence for the causal relationship between blood metabolites and IBD remains limited. A two-sample MR analysis was applied to evaluate relationships between 486 blood metabolites and IBD. The inverse variance weighted method was chosen as the primary MR analysis method. False discovery rate correction was used to control for false positives in multiple testing. Following complementary and sensitivity analyses were conducted using methods such as weight median, MR-egger, weighted mode, simple mode, Cochran Q test, and MR-PRESSO. Moreover, we performed replication, meta-analysis, Steiger test, and linkage disequilibrium score regression to enhance the robustness of the results. Additionally, we performed metabolic pathway analysis to identify potential metabolic pathways. As a result, we identified four significant causal associations between four blood metabolites and two IBD subtypes. Specifically, one metabolite was identified as being associated with the development of CD (mannose: odds ratio (OR) = 0.19, 95% confidence interval (CI) 0.08–0.43, P = 8.54 × 10–5). Three metabolites were identified as being associated with the development of UC (arachidonate (20:4n6): OR = 0.18, 95% CI 0.11–0.30, P = 2.09 × 10–11; 1, 5-anhydroglucitol: OR = 2.21, 95% CI 1.47–3.34, P = 1.50 × 10–4; 2-stearoylglycerophosphocholine: OR = 2.66, 95% CI 1.53–4.63, P = 5.30 × 10–4). The findings of our study suggested that the identified metabolites and metabolic pathways can be considered as useful circulating metabolic biomarkers for the screening and prevention of IBD in clinical practice, as well as candidate molecules for future mechanism exploration and drug target selection.

Design and Efficient Synthesis of New 4-Amino Substituted 2-(4-bromobenzyl)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidines of Anticancer Interest and their In Silico Study

Thienopyrimidines are one of the emerging classes among fused pyrimidines owing to their broad spectrum of pharmacological properties, including antimicrobial, anti-inflammatory, antimalarial, anticancer, etc. The anticancer activity of these compounds is mechanistically proven via the inhibition of validated drug targets such as EGFR, VEGFR-2, PI3K, and c-kit. In this research article, we designed and attempted synthesis of new 4-amino substituted 2-(4-bromobenzyl)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidines to explore their anticancer potential further. These heterocycles were designed based on pharmacophoric features of the core heterocycle, varying its C-4 substitution with a variety of amines and considering cancer protein-ligand interactions aiming to get potent lead molecules. The target compound-protein interaction complexes were analyzed, and lead compounds were identified based on their better binding affinity in molecular docking studies.

Interim Phase II Results Using Panitumumab-IRDye800CW During Transoral Robotic Surgery in Patients with Oropharyngeal Cancer.

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has continually increased during the past several decades. Using transoral robotic surgery (TORS) significantly improves functional outcomes relative to open surgery for OPSCC. However, TORS limits tactile feedback, which is often the most important element of cancer surgery. Fluorescence guided surgery (FGS) strategies to aid surgeon assessment of malignancy for resection are in various phases of clinical research but have the greatest potential impact for improving patient care when the surgeon has limited tactile feedback, such as during TORS. Here, we assessed the feasibility of intraoperative fluorescence imaging using panitumumab-IRDye800CW (PAN800) during TORS in OPSCC patients. 12 consecutive patients with OPSCC were enrolled as part of a non-randomized, prospective, phase II FGS clinical trial using PAN800. TORS was performed with an integrated robot camera for surgeon assessment of fluorescence. Intraoperative and ex vivo fluorescence signals in tumors and normal tissue were quantified and correlated with histopathology. Intraoperative robot fluorescence views delineated OPSCC from normal tissue throughout the TORS procedure (10.7 mean tumor-to-background ratio), including in tumors with low expression of the molecular target. Tumor-specific fluorescence was consistent with surgeon-defined tumor borders requiring resection. Intraoperative robot fluorescence imaging revealed an OPSCC fragment initially overlooked during TORS based on brightfield views, further substantiating the clinical benefit of this FGS approach. Results from this OPSCC patient cohort support further clinical assessment of FGS during TORS to aid resection of solid tumors.

Building a translational cancer dependency map for The Cancer Genome Atlas.

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

The Long Noncoding RNA PANDAR as a Prognostic and Diagnostic Biomarker in Panceratic Cancer ‏‏.

Pancreatic cancer (PC) is a lethal complication in the world, affecting around half a million individuals each year. The treatment of PC is relatively difficult due to the difficulty in making an early diagnosis. Most PC patients are confronted with locally metastatic or advanced diseases in the asymptomatic phase, and about 80% have late diagnosis with metastasis. Recently, long noncoding RNAs (lncRNAs) have drawn attention as a novel biological regulation layer. They take part in the regulation of mRNA and can be used as a prognostic factor or drug target. Based on their functions as regulators of PC initiation and progress, the lncRNAs can be categorized as tumor suppressors or oncogenic. They can be considered as a target for finding new biomarkers for prognosis, diagnosis, monitoring, and treating drug response in PC. Therefore, the present study summarizes the lncRNAs role in PC and the probable strategies to deal with their expression and controlling tumorigenesis and detection of the prognosis of PC.

Integrated analyses of single-cell transcriptome and Mendelian randomization reveal the protective role of FCRL3 in multiple sclerosis

BackgroundMultiple sclerosis (MS) represents a multifaceted autoimmune ailment, prompting the development and widespread utilization of numerous therapeutic interventions. However, extant medications for MS have proven inadequate in mitigating relapses and halting disease progression. Innovative drug targets for preventing multiple sclerosis are still required. The objective of this study is to discover novel therapeutic targets for MS by integrating single-cell transcriptomics and Mendelian randomization analysis.MethodsThe study integrated MS genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data for analysis and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and MS. Sequential analyses involving colocalization and Phenome-wide association studies (PheWAS) were conducted to validate the causal role of candidate genes.ResultsFollowing stringent quality control preprocessing of scRNA-seq data, 1,123 expression changes across seven peripheral cell types were identified. Among the seven most prevalent cell types, 97 genes exhibiting at least one eQTL were discerned. Examination of MR associations between 28 proteins with available index pQTL signals and the risk of MS outcomes was conducted. Co-localization analyses and PheWAS indicated that FCRL3 may exert influence on MS.ConclusionThe integration of scRNA-seq and MR analysis facilitated the identification of potential therapeutic targets for MS. Notably, FCRL3, implicated in immune function, emerged as a significant drug target in the deCODE databases. This research underscores the importance of FCRL3 in MS therapy and advocates for further investigation and clinical trials targeting FCRL3.

Correlation of FDG PET/CT, tumor markers and Ki-67 index with EGFR mutation or positive ALK expression in patients with non-small cell lung cancer.

Epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) are the two most common druggable targets in non-small cell lung cancer (NSCLC). To investigate whether the EGFR mutation and ALK rearrangement could be predicted by the combination of FDG avidity, tumor markers and Ki-67 Index. A total of 168 newly diagnosed NSCLC patients who had undergone 18F-FDG PET/CT for staging were enrolled. PET/CT parameters of primary tumors including maximum standardized uptake value (pSUVmax), metabolic tumor volume (pMTV) and total lesion glycolysis (pTLG) were measured. Five serous tumor markers for lung cancer were recorded. Ki-67 labeling index was counted by immunohistochemical staining. EGFR mutation and ALK status were detected by ARMS-PCR and RT-PCR, respectively. Univariate and multivariate analyses were applied to identify the predictors of EGFR mutation and ALK positivity. EGFR mutation rate was 38.1% (64/168), which were found more frequently in female, ≤60 years old, non-smokers and adenocarcinoma patients, and were not related to lymph node involvements, distant metastases, stage and serum tumor markers. Low pSUVmax, pMTV, pTLG and Ki-67 were significantly associated with EGFR mutation. Logistic regression demonstrated that pSUVmax <6.75 and gender (female) were the independent factors affecting EGFR mutation, and the combination of them had a certain predictive value with the area under the curve of 0.784. ALK positive rate was 6.0% (10/168), all of them were adenocarcinoma patients, which were more common in non-smokers, low serum cytokeratin-19 fragment antigen (CYFRA21-1) and low Ki-67, and were not related to FDG activity. No independent factor for ALK positivity was found on Logistic regression. Low pSUVmax, rather than tumor markers or Ki-67, was correlated with EGFR mutation independently, which could be integrated with gender (female) to improve the identification for EGFR mutation in NSCLC patients.

Current trends and future perspectives in hadron therapy: radiobiology

Abstract Purpose The purpose of this article was to highlight current and future trends in radiobiology in an effort to move hadron therapy forward through the application of new knowledge in DNA damage and subsequent response to heavy ion radiotherapy, immune oncology and the interconnection between. Methods The subject matter begins with a description of the role of radiation in eliciting either an immunogenic or tolerogenic response to radiation exposure. The role of fragmented DNA in an immunogenic response is described, followed by the definitive role that DNA damage and subsequent repair, or not, of complex DNA damage after hadron exposure plays in the survival response of hadron irradiated cells. Results The process by which ionizing radiation elicits an immunogenic rather than tolerogenic response is becoming clearer. The timing of fractionated radiotherapy when combined with an immune checkpoint inhibitor is not clear and may be tumor site specific. Furthermore, whether hadron therapy is more effective at generating a durable immunogenic response is unknown. Conclusions Cytosolic DNA plays a significant role in eliciting an innate immune response with the likelihood that hadron therapy would generate complex DNA damage that because it is less likely to be repaired, is more likely to become cytosolic DNA, and more likely to activate an immunogenic response. Lastly, DNA repair pathway choice appears to be a credible bio-indicator for hadron therapy selection as well identify druggable targets to enhance hadron therapy.

Integrating plasma proteomics with genome-wide association data to identify novel drug targets for inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn’s disease (CD) and ulcerative colitis (UC). Although genome-wide association studies (GWASs) have identified many relevant genetic risk loci, the impact of these loci on protein abundance and their potential utility as clinical therapeutic targets remain uncertain. Therefore, this study aimed to investigate the pathogenesis of IBD and identify effective therapeutic targets through a comprehensive and integrated analysis. We systematically integrated GWAS data related to IBD, UC and CD (N = 25,305) by the study of de Lange KM with the human blood proteome (N = 7213) by the Atherosclerosis Risk in Communities (ARIC) study. Proteome-wide association study (PWAS), mendelian randomisation (MR) and Bayesian colocalisation analysis were used to identify proteins contributing to the risk of IBD. Integrative analysis revealed that genetic variations in IBD, UC and CD affected the abundance of five (ERAP2, RIPK2, TALDO1, CADM2 and RHOC), three (VSIR, HGFAC and CADM2) and two (MST1 and FLRT3) cis-regulated plasma proteins, respectively (P < 0.05). Among the proteins identified via Bayesian colocalisation analysis, CADM2 was found to be an important common protein between IBD and UC. A drug and five druggable target genes were identified from DGIdb after Bayesian colocalisation analysis. Our study's findings from genetic and proteomic approaches have identified compelling proteins that may serve as important leads for future functional studies and potential drug targets for IBD (UC and CD).

MGPPI: multiscale graph neural networks for explainable protein–protein interaction prediction

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

Targeting apoptotic pathways for cancer therapy.

Apoptosis is a form of programmed cell death that is mediated by intrinsic and extrinsic pathways. Dysregulation of and resistance to cell death are hallmarks of cancer. For over three decades, the development of therapies to promote treatment of cancer by inducing various cell death modalities, including apoptosis, has been a main goal of clinical oncology. Apoptosis pathways also interact with other signaling mechanisms, such as the p53 signaling pathway and the integrated stress response (ISR) pathway. In addition to agents directly targeting the intrinsic and extrinsic pathway components, anticancer drugs that target the p53 and ISR signaling pathways are actively being developed. In this Review, we discuss selected and promising anticancer therapies in various stages of development, including drug targets, mechanisms, and resistance to related treatments, focusing especially on B cell lymphoma 2 (BCL-2) inhibitors, TRAIL analogues, DR5 antibodies, and strategies that target p53, mutant p53, and the ISR.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves’ disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Drug–Target Interaction Prediction Based on an Interactive Inference Network

Drug–target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug–target interactions (DTIs). In this study, a novel drug–target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug–target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug–target interactions. Additionally, we predicted and validated 22 Alzheimer’s disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

The Immunological Profile of Adipose Mesenchymal Stromal/Stem Cells after Cell Expansion and Inflammatory Priming

Background: AT-MSCs display great immunoregulatory features, making them potential candidates for cell-based therapy. This study aimed to evaluate the “RBC lysis buffer” isolation protocol and immunological profiling of the so-obtained AT-MSCs. Methods: We established an immune-comparative screening of AT-MSCs throughout in vitro cell expansion (PM, P1, P2, P3, P4) and inflammatory priming regarding the expression of 28 cell-surface markers, 6 cytokines/chemokines, and 10 TLR patterns. Findings: AT-MSCs were highly expandable and sensitive to microenvironment challenges, hereby showing plasticity in distinct expression profiles. Both cell expansion and inflammation differentially modulated the expression profile of CD34, HLA-DR, CD40, CD62L, CD200 and CD155, CD252, CD54, CD58, CD106, CD274 and CD112. Inflammation resulted in a significant increase in the expression of the cytokines IL-6, IL-8, IL-1β, IL-1Ra, CCL5, and TNFα. Depending on the culture conditions, the expression of the TLR pattern was distinctively altered with TLR1–4, TLR7, and TLR10 being increased, whereas TLR6 was downregulated. Protein network and functional enrichment analysis showed that several trophic and immune responses are likely linked to these immunological changes. Conclusions: AT-MSCs may sense and actively respond to tissue challenges by modulating distinct and specific pathways to create an appropriate immuno-reparative environment. These mechanisms need to be further characterized to identify and assess a molecular target that can enhance or impede the therapeutic ability of AT-MSCs, which therefore will help improve the quality, safety, and efficacy of the therapeutic strategy.

Soft drug inhibitors for the epigenetic targets lysine-specific demethylase 1 and histone deacetylases.

Epigenetic modulators such as lysine-specific demethylase 1 (LSD1) and histone deacetylases (HDACs) are drug targets for cancer, neuropsychiatric disease, or inflammation, but inhibitors of these enzymes exhibit considerable side effects. For a potential local treatment with reduced systemic toxicity, we present here soft drug candidates as new LSD1 and HDAC inhibitors. A soft drug is a compound that is degraded in vivo to less active metabolites after having achieved its therapeutic function. This has been successfully applied for corticosteroids in the clinic, but soft drugs targeting epigenetic enzymes are scarce, with the HDAC inhibitor remetinostat being the only example. We have developed new methyl ester-containing inhibitors targeting LSD1 or HDACs and compared the biological activities of these to their respective carboxylic acid cleavage products. In vitro activity assays, cellular experiments, and a stability assay identified potent HDAC and LSD1 soft drug candidates that are superior to their corresponding carboxylic acids in cellular models.

Prevalence and correlates of common mental disorders among participants of the Uganda Genome Resource: Opportunities for psychiatric genetics research.

Genetics research has potential to alleviate the burden of mental disorders in low- and middle-income-countries through identification of new mechanistic pathways which can lead to efficacious drugs or new drug targets. However, there is currently limited genetics data from Africa. The Uganda Genome Resource provides opportunity for psychiatric genetics research among underrepresented people from Africa. We aimed at determining the prevalence and correlates of major depressive disorder (MDD), suicidality, post-traumatic stress disorder (PTSD), alcohol abuse, generalised anxiety disorder (GAD) and probable attention-deficit hyperactivity disorder (ADHD) among participants of the Uganda Genome Resource. Standardised tools assessed for each mental disorder. Prevalence of each disorder was calculated with 95% confidence intervals. Multivariate logistic regression models evaluated the association between each mental disorder and associated demographic and clinical factors. Among 985 participants, prevalence of the disorders were: current MDD 19.3%, life-time MDD 23.3%, suicidality 10.6%, PTSD 3.1%, alcohol abuse 5.7%, GAD 12.9% and probable ADHD 9.2%. This is the first study to determine the prevalence of probable ADHD among adult Ugandans from a general population. We found significant association between sex and alcohol abuse (adjusted odds ratio [AOR] = 0.26 [0.14,0.45], p < 0.001) and GAD (AOR = 1.78 [1.09,2.49], p = 0.019) respectively. We also found significant association between body mass index and suicidality (AOR = 0.85 [0.73,0.99], p = 0.041), alcohol abuse (AOR = 0.86 [0.78,0.94], p = 0.003) and GAD (AOR = 0.93 [0.87,0.98], p = 0.008) respectively. We also found a significant association between high blood pressure and life-time MDD (AOR = 2.87 [1.08,7.66], p = 0.035) and probable ADHD (AOR = 1.99 [1.00,3.97], p = 0.050) respectively. We also found a statistically significant association between tobacco smoking and alcohol abuse (AOR = 3.2 [1.56,6.67], p = 0.002). We also found ever been married to be a risk factor for probable ADHD (AOR = 2.12 [0.88,5.14], p = 0.049). The Uganda Genome Resource presents opportunity for psychiatric genetics research among underrepresented people from Africa.

Qinghao-Biejia Herb Pair attenuates SLE atherosclerosis by regulating macrophage polarization via ABCA1/G1-mediated cholesterol efflux

Ethnopharmacological relevanceQinghao-Biejia herb pair (QB) is the core herb pair of “Jieduquyuziyin prescription" and is one of the commonly used herb pairs for the clinical treatment of systemic lupus erythematosus (SLE). Previous studies have shown that QB reduces the expression of inflammatory cytokines like IL-6 and TNF-α in the serum and kidney of MRL/lpr mice. Additionally, it inhibits the expression of TLR4 and MyD88 in the kidney and aorta and reduces the deposition of renal complement C3 and aortic plaque after treatment. These findings suggest that QB has a preventive and therapeutic effect on lupus rats. Aim of the studyThis study sought to investigate the mechanisms underlying the anti-SLE combined with atherosclerosis activity of the Qinghao-Biejia herb pair. Materials and methodsDrug targets for QB were identified using the HERB database, while targets associated with SLE and atherosclerosis were retrieved from the GeneCards database. The intersection of these drug and disease targets was then analyzed using a protein-protein interaction (PPI) network with GO and KEGG pathway enrichment analysis. In vivo, apolipoprotein E-deficient (ApoE−/−) mice were induced to develop SLE-AS by intraperitoneal injection of pristane and continued feeding of a high-fat diet. The changes in relevant indexes were observed after 12 weeks of gavage treatment with hydroxychloroquine, QB, Q (Qinghao alone), and B (Biejia alone). Bone marrow-derived macrophages from ApoE−/− mice and Raw 264.7 macrophages were used to explore the mechanisms of QB treatment. ResultsThe levels of inflammatory cytokines in serum and pathological liver changes in mice were improved to varying degrees in the treatment groups. Additionally, there was a reduction in aortic atheromatous plaque formation and some improvement in cholesterol efflux. Furthermore, QB suppressed the expression of inflammatory cytokines in M1 macrophages, suggesting a role in regulating macrophage polarization. ConclusionQB demonstrates clear efficacy for treating SLE-AS, and its therapeutic mechanism may involve the regulation of macrophage phenotypes by promoting cholesterol efflux.

Non-genetic heterogeneity and immune subtyping in breast cancer: Implications for immunotherapy and targeted therapeutics

Breast cancer (BC) is a complex and multifactorial disease, driven by genetic alterations that promote tumor growth and progression. However, recent research has highlighted the importance of non-genetic factors in shaping cancer evolution and influencing therapeutic outcomes. Non-genetic heterogeneity refers to diverse subpopulations of cancer cells within breast tumors, exhibiting distinct phenotypic and functional properties. These subpopulations can arise through various mechanisms, including clonal evolution, genetic changes, epigenetic changes, and reversible phenotypic transitions. Although genetic and epigenetic changes are important points of the pathology of breast cancer yet, the immune system also plays a crucial role in its progression. In clinical management, histologic and molecular classification of BC are used. Immunological subtyping of BC has gained attention in recent years as compared to traditional techniques. Intratumoral heterogeneity revealed by immunological microenvironment (IME) has opened novel opportunities for immunotherapy research. This systematic review is focused on non-genetic variability to identify and interlink immunological subgroups in breast cancer. This review provides a deep understanding of adaptive methods adopted by tumor cells to withstand changes in the tumor microenvironment and selective pressure imposed by medications. These adaptive methods include alterations in drug targets, immune system evasion, activation of survival pathways, and alterations in metabolism. Understanding non-genetic heterogeneity is essential for the development of targeted therapies.

Prioritizing drug targets in systemic lupus erythematosus from a genetic perspective: a druggable genome-wide Mendelian randomization study.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with an unsatisfactory state of treatment. We aim to explore novel targets for SLE from a genetic standpoint. Cis-expression quantitative trait loci (eQTLs) for whole blood from 31,684 samples provided by the eQTLGen Consortium as well as two large SLE cohorts were utilized for screening and validating genes causally associated with SLE. Colocalization analysis was employed to further investigate whether changes in the expression of risk genes, as indicated by GWAS signals, influence the occurrence and development of SLE. Targets identified for drug development were evaluated for potential side effects using a phenome-wide association study (PheWAS). Based on the multiple databases, we explored the interactions between drugs and genes for drug prediction and the assessment of current medications. The analysis comprised 5427 druggable genes in total. The two-sample Mendelian randomization (MR) in the discovery phase identified 20 genes causally associated with SLE and validated 8 genes in the replication phase. Colocalization analysis ultimately identified five genes (BLK, HIST1H3H, HSPA1A, IL12A, NEU1) with PPH4 > 0.8. PheWAS further indicated that drugs acting on BLK and IL12A are less likely to have potential side effects, while HSPA1A and NEU1 were associated with other traits. Four genes (BLK, HSPA1A, IL12A, NEU1) have been targeted for drug development in autoimmune diseases and other conditions. .This study identified five genes as therapeutic targets for SLE. Repurposing and developing drugs targeting these genes is anticipated to improve the existing treatment state for SLE. Key Points • We identified five gene targets of priority for the treatment of SLE, with BLK and IL12A indicating fewer side effects. • Among the existing drugs that target these candidate genes, Ustekinumab, Ebdarokimab, and Briakinumab (targeting the IL12 gene) and CD24FC (targeting HSPA1A) may potentially be repurposed for the treatment of SLE.