Curr Opin Struct Biol. 2024 Jul 10;88:102881. doi: 10.1016/j.sbi.2024.102881. Online ahead of print.

ABSTRACT

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

PMID:38991238 | DOI:10.1016/j.sbi.2024.102881

JAMA Ophthalmol. 2024 Jul 11. doi: 10.1001/jamaophthalmol.2024.2516. Online ahead of print.

NO ABSTRACT

PMID:38990521 | DOI:10.1001/jamaophthalmol.2024.2516

AIMS Neurosci. 2024 Jun 26;11(2):203-211. doi: 10.3934/Neuroscience.2024013. eCollection 2024.

ABSTRACT

Obsessive-compulsive disorder (OCD) is a chronic psychiatric disease in which patients suffer from obsessions compelling them to engage in specific rituals as a temporary measure to alleviate stress. In this study, deep learning-based methods were used to build three models which predict the likelihood of a molecule interacting with three biological targets relevant to OCD, SERT, D2, and NMDA. Then, an ensemble model based on those models was created which underwent external validation on a large drug database using random sampling. Finally, case studies of molecules exhibiting high scores underwent bibliographic validation showcasing that good performance in the ensemble model can indicate connection with OCD pathophysiology, suggesting that it can be used to screen molecule databases for drug-repurposing purposes.

PMID:38988885 | PMC:PMC11230860 | DOI:10.3934/Neuroscience.2024013

Heliyon. 2024 Jun 13;10(12):e32998. doi: 10.1016/j.heliyon.2024.e32998. eCollection 2024 Jun 30.

ABSTRACT

The development of novel drugs from basic science to clinical practice requires several years, much effort, and cost. Drug repurposing can promote the utilization of clinical drugs in cancer therapy. Recent studies have shown the potential effects of lomitapide on treating malignancies, which is currently used for the treatment of familial hypercholesterolemia. We systematically review possible functions and mechanisms of lomitapide as an anti-tumor compound, regarding the aspects of apoptosis, autophagy, and metabolism of tumor cells, to support repurposing lomitapide for the clinical treatment of tumors.

PMID:38988566 | PMC:PMC11234027 | DOI:10.1016/j.heliyon.2024.e32998

Exp Hematol Oncol. 2024 Jul 10;13(1):66. doi: 10.1186/s40164-024-00535-1.

ABSTRACT

CAR T cells are widely applied for relapsed hematological cancer patients. With six approved cell therapies, for Multiple Myeloma and other B-cell malignancies, new insights emerge. Profound evidence shows that patients who fail CAR T-cell therapy have, aside from antigen escape, a more glycolytic and weakened metabolism in their CAR T cells, accompanied by a short lifespan. Recent advances show that CAR T cells can be metabolically engineered towards oxidative phosphorylation, which increases their longevity via epigenetic and phenotypical changes. In this review we elucidate various strategies to rewire their metabolism, including the design of the CAR construct, co-stimulus choice, genetic modifications of metabolic genes, and pharmacological interventions. We discuss their potential to enhance CAR T-cell functioning and persistence through memory imprinting, thereby improving outcomes. Furthermore, we link the pharmacological treatments with their anti-cancer properties in hematological malignancies to ultimately suggest novel combination strategies.

PMID:38987856 | DOI:10.1186/s40164-024-00535-1

Ann Hematol. 2024 Jul 11. doi: 10.1007/s00277-024-05881-y. Online ahead of print.

NO ABSTRACT

PMID:38987404 | DOI:10.1007/s00277-024-05881-y

Clin Transl Med. 2024 Jul;14(7):e1688. doi: 10.1002/ctm2.1688.

NO ABSTRACT

PMID:38984773 | DOI:10.1002/ctm2.1688

Curr Cancer Drug Targets. 2024 Jul 9. doi: 10.2174/0115680096303479240614061136. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Globally, Gastric Cancer (GC) ranks as the fifth leading cause of cancer-related deaths. GC is a multifaceted malignancy with diverse etiologies; however, understanding the shared molecular mechanisms can aid in discovering novel targeted therapies for GC. This study has employed a drug repositioning approach to explore new drug candidates for treating GC.

METHODS: The human GC cell lines AGS, MKN-45, and KATO-III were treated with different concentrations of dopamine, cabergoline, thioridazine, and entacapone to determine effective doses and IC50 values. In vitro, cytotoxic activity on cancer cell lines was screened based on dose/time using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) was used to measure the mRNA expression of B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax), and Proliferating Cell Nuclear Antigen (PCNA) in each group. The percentage of apoptotic cells was evaluated using Annexin V/PI staining.

RESULTS: Dopamine, cabergoline, thioridazine, and entacapone elicited cytotoxic effects on AGS and KATO-III cells in a dose-dependent manner and elevated the percentage of Annexin V-positive cells, suggesting the occurrence of apoptosis. The expression of Bcl-2 and PCNA was significantly decreased, whereas the expression of Bax was considerably increased in the AGS and KATO-III cells compared to that in the blank group (p < 0.05); however, no similar effect was observed in MKN-45 cells.

CONCLUSION: Through in vitro experiments, this study provides evidence that the antipsychotic drugs cabergoline, dopamine, thioridazine, and entacapone can inhibit gastric cancer growth in AGS and KATO-III cells. These findings suggest that these drugs could be repurposed as novel therapeutic agents for the treatment of gastric cancer.

PMID:38984576 | DOI:10.2174/0115680096303479240614061136

Chin Clin Oncol. 2024 Jun;13(3):32. doi: 10.21037/cco-23-151.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC.

METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC.

RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors.

CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

PMID:38984486 | DOI:10.21037/cco-23-151

Pharmacol Res Perspect. 2024 Aug;12(4):e1244. doi: 10.1002/prp2.1244.

ABSTRACT

We hypothesize that a "Faustian bargain"-the trading of increased SARS-CoV2 viral infection with a concurrent potential for prevention of life-threatening lower lung infection explains the previous and future morbidity and mortality from COVID-19. Further, this trade-off is made feasible by fundamental principles of thermodynamics and receptor affinity.

PMID:38982716 | DOI:10.1002/prp2.1244

Med Oncol. 2024 Jul 9;41(8):198. doi: 10.1007/s12032-024-02419-0.

ABSTRACT

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In the present study, 150 FDA-approved drugs have been screened using the concept of drug repurposing against VEGFR-2 by employing the molecular docking, molecular dynamics, grouping data with Machine Learning algorithms, and density functional theory (DFT) approaches. The identified compounds such as Pazopanib, Atogepant, Drosperinone, Revefenacin and Zanubrutinib shown the binding energy - 7.0 to - 9.5 kcal/mol against VEGF receptor in the molecular docking studies and have been observed as stable in the molecular dynamic simulations performed for the period of 500 ns. The MM/GBSA analysis shows that the value ranging from - 44.816 to - 82.582 kcal/mol. Harnessing the machine learning approaches revealed that clustering with K = 10 exhibits the relevance through high binding energy and satisfactory logP values, setting them apart from compounds in distinct clusters. Therefore, the identified compounds are found to be potential to inhibit the VEGFR-2 and the present study will be a benchmark to validate the compounds experimentally.

PMID:38981988 | DOI:10.1007/s12032-024-02419-0

J Microbiol. 2024 Jul 9. doi: 10.1007/s12275-024-00154-9. Online ahead of print.

ABSTRACT

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

PMID:38980578 | DOI:10.1007/s12275-024-00154-9

Brief Bioinform. 2024 May 23;25(4):bbae328. doi: 10.1093/bib/bbae328.

ABSTRACT

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

PMID:38980370 | DOI:10.1093/bib/bbae328

J Med Microbiol. 2024 Jul;73(7). doi: 10.1099/jmm.0.001850.

ABSTRACT

Introduction. The development of new antifungal drugs has become a global priority, given the increasing cases of fungal diseases together with the rising resistance to available antifungal drugs. In this scenario, drug repositioning has emerged as an alternative for such development, with advantages such as reduced research time and costs.Gap statement. Propafenone is an antiarrhythmic drug whose antifungal activity is poorly described, being a good candidate for further study.Aim. This study aims to evaluate propafenone activity against different species of Candida spp. to evaluate its combination with standard antifungals, as well as its possible action mechanism.Methodology. To this end, we carried out tests against strains of Candida albicans, Candida auris, Candida parapsilosis, Candida tropicalis, Candida glabrata and Candida krusei based on the evaluation of the MIC, minimum fungicidal concentration and tolerance level, along with checkerboard and flow cytometry tests with clinical strains and cell structure analysis by scanning electron microscopy (SEM).Results. The results showed that propafenone has a 50% MIC ranging from 32 to 256 µg ml-1, with fungicidal activity and positive interactions with itraconazole in 83.3% of the strains evaluated. The effects of the treatments observed by SEM were extensive damage to the cell structure, while flow cytometry revealed the apoptotic potential of propafenone against Candida spp.Conclusion. Taken together, these results indicate that propafenone has the potential for repositioning as an antifungal drug.

PMID:38979984 | DOI:10.1099/jmm.0.001850

OMICS. 2024 Jul 9. doi: 10.1089/omi.2024.0121. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

PMID:38979602 | DOI:10.1089/omi.2024.0121

Front Immunol. 2024 Jun 24;15:1390821. doi: 10.3389/fimmu.2024.1390821. eCollection 2024.

ABSTRACT

INTRODUCTION: IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear.

METHODS: We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning.

RESULTS: Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS.

CONCLUSION: Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

PMID:38979419 | PMC:PMC11229174 | DOI:10.3389/fimmu.2024.1390821

bioRxiv [Preprint]. 2024 Jun 28:2024.06.24.600340. doi: 10.1101/2024.06.24.600340.

ABSTRACT

The landscape of sex differences in Colorectal Cancer (CRC) has not been well characterized with respect to the mechanisms of action for oncogenes such as KRAS. However, our recent study showed that tumors from male patients with KRAS mutations have decreased iron-dependent cell death called ferroptosis. Building on these findings, we further examined ferroptosis in CRC, considering both sex of the patient and KRAS mutations, using public databases and our in-house CRC tumor cohort. Through subsampling inference and variable importance analysis (VIMP), we identified significant differences in gene expression between KRAS mutant and wild type tumors from male patients. These genes suppress (e.g., SLC7A11 ) or drive (e.g., SLC1A5 ) ferroptosis, and these findings were further validated with Gaussian mixed models. Furthermore, we explored the prognostic value of ferroptosis regulating genes and discovered sex- and KRAS-specific differences at both the transcriptional and metabolic levels by random survival forest with backward elimination algorithm (RSF-BE). Of note, genes and metabolites involved in arginine synthesis and glutathione metabolism were uniquely associated with prognosis in tumors from males with KRAS mutations. Additionally, drug repurposing is becoming popular due to the high costs, attrition rates, and slow pace of new drug development, offering a way to treat common and rare diseases more efficiently. Furthermore, increasing evidence has shown that ferroptosis inhibition or induction can improve drug sensitivity or overcome chemotherapy drug resistance. Therefore, we investigated the correlation between gene expression, metabolite levels, and drug sensitivity across all CRC primary tumor cell lines using data from the Genomics of Drug Sensitivity in Cancer (GDSC) resource. We observed that ferroptosis suppressor genes such as DHODH , GCH1 , and AIFM2 in KRAS mutant CRC cell lines were resistant to cisplatin and paclitaxel, underscoring why these drugs are not effective for these patients. The comprehensive map generated here provides valuable biological insights for future investigations, and the findings are supported by rigorous analysis of large-scale publicly available data and our in-house cohort. The study also emphasizes the potential application of VIMP, Gaussian mixed models, and RSF-BE models in the multi-omics research community. In conclusion, this comprehensive approach opens doors for leveraging precision molecular feature analysis and drug repurposing possibilities in KRAS mutant CRC.

PMID:38979294 | PMC:PMC11230177 | DOI:10.1101/2024.06.24.600340

Front Cell Neurosci. 2024 Jun 24;18:1403734. doi: 10.3389/fncel.2024.1403734. eCollection 2024.

ABSTRACT

Mitochondrial diseases are a group of severe pathologies that cause complex neurodegenerative disorders for which, in most cases, no therapy or treatment is available. These organelles are critical regulators of both neurogenesis and homeostasis of the neurological system. Consequently, mitochondrial damage or dysfunction can occur as a cause or consequence of neurodevelopmental or neurodegenerative diseases. As genetic knowledge of neurodevelopmental disorders advances, associations have been identified between genes that encode mitochondrial proteins and neurological symptoms, such as neuropathy, encephalomyopathy, ataxia, seizures, and developmental delays, among others. Understanding how mitochondrial dysfunction can alter these processes is essential in researching rare diseases. Three-dimensional (3D) cell cultures, which self-assemble to form specialized structures composed of different cell types, represent an accessible manner to model organogenesis and neurodevelopmental disorders. In particular, brain organoids are revolutionizing the study of mitochondrial-based neurological diseases since they are organ-specific and model-generated from a patient's cell, thereby overcoming some of the limitations of traditional animal and cell models. In this review, we have collected which neurological structures and functions recapitulate in the different types of reported brain organoids, focusing on those generated as models of mitochondrial diseases. In addition to advancements in the generation of brain organoids, techniques, and approaches for studying neuronal structures and physiology, drug screening and drug repositioning studies performed in brain organoids with mitochondrial damage and neurodevelopmental disorders have also been reviewed. This scope review will summarize the evidence on limitations in studying the function and dynamics of mitochondria in brain organoids.

PMID:38978706 | PMC:PMC11228165 | DOI:10.3389/fncel.2024.1403734

Nat Commun. 2024 Jul 8;15(1):5703. doi: 10.1038/s41467-024-50024-6.

ABSTRACT

Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug's therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson's disease.

PMID:38977662 | DOI:10.1038/s41467-024-50024-6

J Mol Graph Model. 2024 Jul 3;131:108817. doi: 10.1016/j.jmgm.2024.108817. Online ahead of print.

ABSTRACT

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

PMID:38976944 | DOI:10.1016/j.jmgm.2024.108817