Turkish journal of biology = Turk biyoloji dergisi最新文献

Background/aim: Small extracellular vesicles (SEVs) are known to have an impact on the physiological conditions of target cells, are a critical component of cell-to-cell communication, and have been implicated in a variety of diseases. Although it has been proposed that edible plant-derived nanoparticles have an effect on communication with mammalian cells, the influence of these nanoparticles on cancer cell development has yet to be explored.

Materials and methods: In order to characterize small extracellular vesicles obtained from garlic, specific SEV surface markers, antibodies, and size detections were identified using scanning electron microscopy and nanoparticle tracking analysis. Human hepatoma (Hep3B), human neuroblastoma (SH-SY5Y), human pancreatic adenocarcinoma (Panc-1a), human glioblastoma (U87), prostate cancer (PC-3), and human umbilical vein endothelial (HUVEC) cell lines were treated with garlic SEVs to examine their anticancer properties.

Results: Annexin V FITC/PI staining for apoptosis, mRNA, and protein expression levels via RT-PCR and ELISA indicated that garlic SEVs triggered apoptosis by activating the intrinsic pathway. Our findings support the idea that SEVs produced from garlic may trigger apoptotic cell death in cancer cells while having no effect on healthy cells.

Conclusion: It was discovered that plant SEVs had anti-cancer effects by activating caspase-mediated apoptosis.

Background: Nanocarrier-based systems have cultivated significant improvements in prostate cancer therapy. However, the efforts are still limited in clinical applicability, and more research is required for the development of effective strategies. Here, we describe a novel nanoliposomal system for targeted apoptotic gene delivery to prostate cancer.

Methods: Poly (2-ethyl-2-oxazoline) (PEtOx) dioleoyl phosphatidylethanolamine (DOPE) nanoliposomes were conjugated with the prostate-specific membrane antigen (PSMA)-targeting peptide GRFLTGGTGRLLRIS (P563) and loaded with BikDDA, a mutant form of the proapoptotic Bik. We selected 22Rv1 cells with moderate upregulation of PSMA to test the in vitro uptake, cell death, and in vivo anticancer activity of our formulation, P563-PEtOx-DOPE-BikDDA.

Results: BikDDA was upregulated in 22Rv1 cells, inducing cell death, and CD-1 nude mice xenografts administered with the formulation showed significant tumor regression.

Conclusion: We suggest that P563-PEtOx-DOPE-BikDDA nanoliposomes can serve as prominent gene carriers against prostate cancer.

Background/aim: Ischemic heart diseases continue to be a significant global cardiovascular problem in today's world. Myocardial reperfusion (R) is provided with an effective and rapid treatment; however, it can lead to fatal results, as well as ischemia (I). This study aims to use proteomic analysis to assess proteins and pathways in H9C2 cardiomyoblast cells exposed to hypoxic conditions, followed by reoxygenation, representing I/R injury for both short and long terms, reflecting acute and chronic hypoxia, respectively. Utilizing advanced techniques, our goal is to identify and characterize key proteins undergoing alterations during these critical phases.

Materials and methods: H9C2 cardiomyoblasts, a commonly used cell line for simulating in vivo I/R damage, were exposed to normoxia and hypoxia (0.4% O₂) in six experimental groups: normoxia (3h), acute hypoxia (3h), acute hypoxia (3h) + reoxygenation (3h), normoxia (21h), chronic hypoxia (21h), and chronic hypoxia (21h) + reoxygenation (3h). Analyses were conducted using Nano LC/MSMS from tryptic digest of the whole cell lysates. Proteins were quantified using the label-free quantification (LFQ) algorithm in Proteome Discoverer 2.4.

Results: Proteomic analysis resulted in identification of 2383 protein groups. Proteins that differentially expressed in the various groups were identified (p < 0.05 among mean values for groups). Short-term hypoxia induces mitochondrial damage, energy demand, and cytoskeletal modifications. Chronic hypoxia triggers metabolic shifts, stress-response proteins, and extracellular matrix alterations. Data are available via ProteomeXchange with identifier PXD047994.

Conclusion: Our research provides in-depth insights into how H9C2 cardiomyoblasts respond to both short-term and prolonged oxygen deprivation. Understanding hypoxia-related pathophysiology provides avenues for therapeutic intervention in hypoxia-related disorders.

Background/aim: Type 2 diabetes mellitus (T2DM) is the most common type of diabetes and occurs due to insufficient insulin secretion or inability to use existing insulin and the effects of environmental factors. Although there are many studies on the pathophysiology of T2DM, the mechanisms contributing to the pathogenesis of insulin resistance and pancreatic beta-cell dysfunction have not been completely elucidated. Some adipokines secreted from adipose tissue, which are the primary regulators of insulin resistance, affect immune and inflammatory functions. Altered adipokine profiles have been observed in obesity and T2DM, leading to severe metabolic risks and changes in insulin sensitivity.

Materials and methods: This study used quantitative PCR and ELISA techniques to analyze samples from individuals without diabetes (control group) and with T2DM (macrovascular and microvascular complications and without complications) for at least 10 years.

Results: The mRNA expression and protein levels of NAMPT, IL-6, and vaspin genes were determined. While there was no significant difference in NAMPT, IL-6, and vaspin mRNA expression levels between diabetic groups, there was a significant decrease between the patient and control groups (p < 0.001). For serum protein levels, NAMPT protein levels decreased significantly in the uncomplicated group, while IL-6 and vaspin protein levels increased significantly in both microvascular and macrovascular complication groups (p < 0.001).

Conclusion: The correlations between gene expressions, clinical parameters, and protein levels are crucial to understanding the implications of the findings.

Drug repurposing is the strategy of drug utilization for a treatment option other than the intended indications. This strategy has witnessed increased adoption over the past decades, especially within cancer nanomedicine. Cancer nanomedicine has been facilitated through nanoparticle-based (NP-based) delivery systems which can combat nonsmall-cell lung cancer (NSCLC) via recent advances in nanotechnology and apply its benefits to existing drugs. The repurposing of drugs, coupled with NP-based drug delivery systems, presents a promising avenue for achieving effective therapeutic solutions with accelerated outcomes. This review aims to present an overview of NSCLC treatments, with a specific focus on drug repurposing. It seeks to elucidate the latest advances in clinical studies and the utilization of NP-based drug delivery systems tailored for NSCLC treatment. First, the molecular mechanisms of Food and Drug Administration (FDA)-approved drugs for NSCLC, including ROS1 tyrosine kinase inhibitors (TKI) like repotrectinib, approved in November 2023, are detailed. Further, in vitro studies employing a combination strategy of drug repurposing and NP-based drug delivery systems as a treatment approach against NSCLC are listed. It includes the latest study on nanoparticle-based drug delivery systems loaded with repurposed drugs.

Background and aim: Cancer cell's innate chemotherapeutic resistance continues to be an obstacle in molecular oncology. This theory is firmly tied to the cancer cells' integral DNA repair mechanisms continuously neutralizing the effects of chemotherapy. Amidst these mechanisms, the nuclear excision repair pathway is crucial in renovating DNA lesions prompted by agents like Cisplatin. The ERCC1/XPF complex stands center-stage as a structure-specific endonuclease in this repair pathway. Targeting the ERCC1/XPF dimerization brings forth a strategy to augment chemotherapy by eschewing the resistance mechanism integral to cancer cells. This study tracks and identifies small anticancer molecules, with ERCC1/XPF inhibiting potential, within extensive small-molecule compound libraries.

Materials and methods: A novel hybrid virtual screening algorithm, conjoining ligand- and target-based approaches, was developed. All-atom molecular dynamics (MD) simulations were then run on the obtained hit molecules to reveal their structural and dynamic contributions within the binding site. MD simulations were followed by MM/GBSA calculations to qualify the change in binding free energies of the protein/ligand complexes throughout MD simulations.

Results: Conducted analyses highlight new potential inhibitors AN-487/40936989 from the SPECS SC library, K219-1359, and K786-1161 from the ChemDiv Representative Set library as showing better predicted activity than previously discovered ERCC1/XPF inhibitor, CHEMBL3617209.

Conclusion: The algorithm implemented in this study expands our comprehension of chemotherapeutic resistance and how to overcome it through identifying ERCC1/XPF inhibitors with the aim of enhancing chemotherapeutic impact giving hope for ameliorated cancer treatment outcomes.

Background/aim: Studies highlighted the bidirectional crosstalk between the HER family members in breast cancer as resistance mechanism to anti-HER agents. Cross-signaling between HER2/EGFR and ER/IGF1R could play role in the development of resistance to therapeutics hence stimulating cell growth. To overcome this resistance, combined therapies targeting both pathways simultaneously have been proposed as an effective strategy. The involvement of miRNAs in resistance of targeted therapies like trastuzumab was demonstrated in recent studies. Hence the regulation of miRNAs in resistance state could reverse the cell behaviour to drugs. Previously we found that overexpression of miR-770-5p downregulated AKT and ERK expression through HER2 signaling and potentiated the effect of trastuzumab. In this study we examined the impact of miR-770-5p on trastuzumab resistance.

Materials and methods: Cells were treated with tamoxifen or trastuzumab to examine their role in bidirectional crosstalk. The molecule mechanism of miR-770-5p on HER2/EGFR/IGF1R bidirectional crosstalk was explored by western blot. The expression of miR-770-5p in trastuzumab resistant cells was examined by q-PCR. To investigate the effect of miR-770-5p on cancer cell proliferation in trastuzumab resistance state, resistant cells were analyzed by iCELLigence real-time cell analyzer.

Results: miR-770-5p expression was significantly downregulated in trastuzumab-resistant BT-474 and SK-BR-3 cells. Overexpression of miR-770-5p sensitized the resistant cells to trastuzumab, as evidenced by reduced cell proliferation and increased cell viability. Additionally, in resistant cells, increased expression and activation of EGFR and IGF1R were observed. However, miR-770-5p overexpression resulted in decreased phosphorylation of AKT and ERK, indicating its suppressive role in EGFR/HER2 signaling. Furthermore, miR-770-5p downregulated the expression of IGF1R and mTOR, suggesting its involvement in regulating the escape signaling mediated by IGF1R in resistance.

Conclusion: In conclusion, our findings demonstrate the critical role of miR-770-5p in regulating bidirectional crosstalk and overcoming trastuzumab resistance in breast cancer cells. These results highlight the potential of miR-770-5p as a therapeutic target to improve the efficacy of targeted therapies and address resistance mechanisms in breast cancer.

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.

Background/aim: Single-cell transcriptomics (scRNA-Seq) explores cellular diversity at the gene expression level. Due to the inherent sparsity and noise in scRNA-Seq data and the uncertainty on the types of sequenced cells, effective clustering and cell type annotation are essential. The graph-based clustering of scRNA-Seq data is a simple yet powerful approach that presents data as a "shared nearest neighbour" graph and clusters the cells using graph clustering algorithms. These algorithms are dependent on several user-defined parameters.Here we present SUMA, a lightweight tool that uses a random forest model to predict the optimum number of neighbours to obtain the optimum clustering results. Moreover, we integrated our method with other commonly used methods in an RShiny application. SUMA can be used in a local environment (https://github.com/hkarakurt8742/SUMA) or as a browser tool (https://hkarakurt.shinyapps.io/suma/).

Materials and methods: Publicly available scRNA-Seq datasets and 3 different graph-based clustering algorithms were used to develop SUMA, and a large range for number of neighbours and variant genes was taken into consideration. The quality of clustering was assessed using the adjusted Rand index (ARI) and true labels of each dataset. The data were split into training and test datasets, and the model was built and optimised using Scikit-learn (Python) and randomForest (R) libraries.

Results: The accuracy of our machine learning model was 0.96, while the AUC of the ROC curve was 0.98. The model indicated that the number of cells in scRNA-Seq data is the most important feature when deciding the number of neighbours.

Conclusion: We developed and evaluated the SUMA model and implemented the method in the SUMAShiny app, which integrates SUMA with different clustering methods and enables nonbioinformatician users to cluster and visualise their scRNA data easily. The SUMAShiny app is available both for desktop and browser use.

Background/aim: Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms.

Materials and methods: We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona.

Results: One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots.

Conclusion: Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.