Autophagy as a Mechanism for Adaptive Prediction-Mediated Emergence of Drug Resistance.
Ontology highlight
ABSTRACT:
SUBMITTER: Nivedita N
PROVIDER: S-EPMC8461305 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundOsteosarcoma is the most common primary bone tumor in children and adolescents. Unfortunately, osteosarcoma treatments often fail due to the development of chemoresistance, of which the underlying molecular mechanisms still remain unclear. In this study, we demonstrated that HSP90AA1 gene is responsible for drug resistance in osteosarcoma through an autophagy-related mechanism.MethodsshRNAs were transfected into osteosarcoma cells for knockdown of HSP90AA1 gene. Stable HSP90AA1 overexpressing osteosarcoma cell lines were obtained by lentivirus infection. mRNA and protein expressions of HSP90AA1 in osteosarcoma cells were tested by quantitative real-time PCR and western blot, respectively. Autophagy of osteosarcoma cells was detected by western blot of LC3, transmission electron microscopy and fluorescence microscope. mRFP-GFP-LC3 lentiviral transfection was also performed to detect autophagic flux. NOD/SCID mices were inoculated with MG-63 tumor cells transfected with HSP90AA1 specific shRNA. TUNEL and LC3 staining were performed to detect apoptosis and autophagy of resected tumor tissues.ResultsDoxorubicin, cisplatin, and methotrexate, which are commonly used in chemotherapy, each induced HSP90AA1 upregulation in human osteosarcoma cells. Suppression of HSP90AA1 restored the sensitivity of osteosarcoma cells to chemotherapy both in vivo and in vitro. Mechanism study indicated that autophagy is responsible for the chemoresistance in osteosarcoma cells. HSP90AA1 increased drug resistance by inducing autophagy and inhibiting apoptosis. Suppression of HSP90AA1 diminished autophagic protection in response to chemotherapy in osteosarcoma cells. Moreover, HSP90AA1 promotes autophagy through PI3K/Akt/mTOR pathway and inhibits apoptosis through JNK/P38 pathway.ConclusionWe showed that chemotherapy agents can induce HSP90AA1 expression in osteosarcoma cells. And HSP90AA1, acting as an important regulator of autophagy, is a critical factor in the development of osteosarcoma chemoresistance both in vitro and in vivo. HSP90AA1 provides a novel therapeutic target for improving osteosarcoma treatment.
| S-EPMC6114771 | biostudies-literature
Project description:Cancer cells have a characteristic metabolism, mostly caused by alterations in signal transduction networks rather than mutations in metabolic enzymes. For metabolic drugs to be cancer-selective, signaling alterations need to be identified that confer a druggable vulnerability. Here, we demonstrate that many tumor cells with an acquired cancer drug resistance exhibit increased sensitivity to mechanistically distinct inhibitors of cancer metabolism. We demonstrate that this metabolic vulnerability is driven by mTORC1, which promotes resistance to chemotherapy and targeted cancer drugs, but simultaneously suppresses autophagy. We show that autophagy is essential for tumor cells to cope with therapeutic perturbation of metabolism and that mTORC1-mediated suppression of autophagy is required and sufficient for generating a metabolic vulnerability leading to energy crisis and apoptosis. Our study links mTOR-induced cancer drug resistance to autophagy defects as a cause of a metabolic liability and opens a therapeutic window for the treatment of otherwise therapy-refractory tumor patients.
| S-EPMC7499183 | biostudies-literature
Project description:Autophagy plays an important role in multiple myeloma (MM) for homeostasis, survival and drug resistance, but which genes participate in this process is unclear. We identified several cytoskeleton genes upregulated in MM patients by gene expression profiling (GEP) datasets; in particular, patients with high profilin 1 (PFN1) expression had poor prognosis in MM. In vitro, overexpressed PFN1 promotes proliferation and bortezomib (BTZ) resistance in MM cells. Further study indicated overexpression of PFN1 significantly promoted the process of autophagy and induced BTZ resistance in MM. Otherwise, knockdown of PFN1 blocked autophagy and sensitized MM to BTZ. Co-immunoprecipitation in MM cells indicated that PFN1 could bind Beclin1 complex and promote the initiation of autophagy. Inhibition of autophagy by blocking the formation of Beclin1 complex could reverse the phenotype of BTZ resistance in MM. Our findings suggested that PFN1 could promote autophagy through taking part in Beclin1 complex and contribute to BTZ resistance, which may become a novel molecular target in the therapy of MM.
| S-EPMC6125445 | biostudies-literature
Project description:Antiviral drug resistance is a matter of great clinical importance that, historically, has been investigated mostly from a virological perspective. Although the proximate mechanisms of resistance can be readily uncovered using these methods, larger evolutionary trends often remain elusive. Recent interest by population geneticists in studies of antiviral resistance has spurred new metrics for evaluating mutation and recombination rates, demographic histories of transmission and compartmentalization, and selective forces incurred during viral adaptation to antiviral drug treatment. We present up-to-date summaries on antiviral resistance for a range of drugs and viral types, and review recent advances for studying their evolutionary histories. We conclude that information imparted by demographic and selective histories, as revealed through population genomic inference, is integral to assessing the evolution of antiviral resistance as it pertains to human health.
| S-EPMC5499642 | biostudies-literature
Project description:The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.
| S-EPMC6363771 | biostudies-literature
Project description:Although targeted therapy is yielding promising results in the treatment of specific cancers, drug resistance poses a problem. We develop a mathematical framework that can be used to study the principles underlying the emergence and prevention of resistance in cancers treated with targeted small-molecule drugs. We consider a stochastic dynamical system based on measurable parameters, such as the turnover rate of tumor cells and the rate at which resistant mutants are generated. We find that resistance arises mainly before the start of treatment and, for cancers with high turnover rates, combination therapy is less likely to yield an advantage over single-drug therapy. We apply the mathematical framework to chronic myeloid leukemia. Early-stage chronic myeloid leukemia was the first case to be treated successfully with a targeted drug, imatinib (Novartis, Basel). This drug specifically inhibits the BCR-ABL oncogene, which is required for progression. Although drug resistance prevents successful treatment at later stages of the disease, our calculations suggest that, within the model assumptions, a combination of three targeted drugs with different specificities might overcome the problem of resistance.
| S-EPMC1172248 | biostudies-literature
Project description:Adaptive resistance to targeted therapy such as BRAF inhibitors represents in melanoma a major drawback to this otherwise powerful treatment. Some of the underlying molecular mechanisms have recently been described: hyperactivation of the BRAF-MAPK pathway, of the AKT pathway, of the TGFβ/EGFR/PDGFRB pathway, or the low MITF/AXL ratio. Nevertheless, the phenomenon of early resistance is still not clearly understood. In this report, we show that knockdown of neural crest-associated gene ID3 increases the melanoma sensitivity to vemurafenib short-term treatment. In addition, we observe an ID3-mediated regulation of cell migration and of the expression of resistance-associated genes such as SOX10 and MITF. In sum, these data suggest ID3 as a new key actor of melanoma adaptive resistance to vemurafenib and as a potential drug target. Molecular mechanisms that are responsible for the development of human skin epithelial cells are not completely understood so far. As a consequence, the efficiency to establish a pure skin epithelial cell population from human induced pluripotent stem cells (hiPSC) remains poor. Using an approach including RNA interference and high-throughput imaging of early epithelial cells, we could identify candidate kinases which are involved in skin epithelial differentiation. Among them, we found HIPK4 to be an important inhibitor of this process. Indeed, its silencing increased the amount of generated skin epithelial precursors, increased the amount of generated keratinocytes and improved growth and differentiation of organotypic cultures, allowing for the formation of a denser basal layer and stratification with the expression of several keratins. Our data bring substantial input in the regulation of human skin epithelial differentiation and for improving differentiation protocols from pluripotent stem cells.
2018-02-16 | GSE104849 | GEO
Project description:One contributor to the high mortality of osteosarcoma is its reduced sensitivity to chemotherapy, but the mechanism involved is unclear. Improving the sensitivity of osteosarcoma to chemotherapy is urgently needed to improve patient survival. We found that chemotherapy triggered apoptosis of human osteosarcoma cells in vitro and in vivo; this was accompanied by increased Sestrin2 expression. Importantly, autophagy was also enhanced with increased Sestrin2 expression. Based on this observation, we explored the potential role of Sestrin2 in autophagy of osteosarcoma. We found that Sestrin2 inhibited osteosarcoma cell apoptosis by promoting autophagy via inhibition of endoplasmic reticulum stress, and this process is closely related to the PERK-eIF2α-CHOP pathway. In addition, our study showed that low Sestrin2 expression can effectively reduce autophagy of human osteosarcoma cells after chemotherapy, increase p-mTOR expression, decrease Bcl-2 expression, promote osteosarcoma cell apoptosis, and slow down tumour progression in NU/NU mice. Sestrin2 activates autophagy by inhibiting mTOR via the PERK-eIF2α-CHOP pathway and inhibits apoptosis via Bcl-2. Therefore, our results explain one underlying mechanism of increasing the sensitivity of osteosarcoma to chemotherapy and suggest that Sestrin2 is a promising gene target.
| S-EPMC8502982 | biostudies-literature
Project description:Background/aimsDiffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.MethodsUtilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.ResultsDLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8+ T cell, CD4+ memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.ConclusionsThis study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.
| S-EPMC10866451 | biostudies-literature
Project description:High antibiotic resistance frequencies have become a major public health issue. The decrease in new antibiotics' production, combined with increasing frequencies of multi-drug resistant (MDR) bacteria, cause substantial limitations in treatment options for some bacterial infections. To diminish overall resistance, and especially the occurrence of bacteria that are resistant to all antibiotics, certain drugs are deliberately scarcely used--mainly when other options are exhausted. We use a mathematical model to explore the efficiency of such antibiotic restrictions. We assume two commonly used drugs and one restricted drug. The model is examined for the mixing strategy of antibiotic prescription, in which one of the drugs is randomly assigned to each incoming patient. Data obtained from Rabin medical center, Israel, is used to estimate realistic single and double antibiotic resistance frequencies in incoming patients. We find that broad usage of the hitherto restricted drug can reduce the number of incorrectly treated patients, and reduce the spread of bacteria resistant to both common antibiotics. Such double resistant infections are often eventually treated with the restricted drug, and therefore are prone to become resistant to all three antibiotics. Thus, counterintuitively, a broader usage of a formerly restricted drug can sometimes lead to a decrease in the emergence of bacteria resistant to all drugs. We recommend re-examining restriction of specific drugs, when multiple resistance to the relevant alternative drugs already exists.
| S-EPMC4481510 | biostudies-literature