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1.
Article in English | MEDLINE | ID: mdl-37779364

ABSTRACT

OBJECTIVE: Sporadic and familial amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease that results in loss of motor neurons and, in some patients, associates with frontotemporal dementia (FTD). Apart from the accumulation of proteinaceous deposits, emerging literature indicates that aberrant mitochondrial bioenergetics may contribute to the onset and progression of ALS/FTD. Here we sought to investigate the pathophysiological signatures of mitochondrial dysfunction associated with ALS/FTD. METHODS: By means of label-free mass spectrometry (MS) and mRNA sequencing (mRNA-seq), we report pre-symptomatic changes in the cortices of TDP-43 and FUS mutant mouse models. Using tissues from transgenic mouse models of mitochondrial diseases as a reference, we performed comparative analyses and extracted unique and common mitochondrial signatures that revealed neuroprotective compensatory mechanisms in response to early damage. RESULTS: In this regard, upregulation of both Acyl-CoA Synthetase Long-Chain Family Member 3 (ACSL3) and mitochondrial tyrosyl-tRNA synthetase 2 (YARS2) were the most representative change in pre-symptomatic ALS/FTD tissues, suggesting that fatty acid beta-oxidation and mitochondrial protein translation are mechanisms of adaptation in response to ALS/FTD pathology. CONCLUSIONS: Together, our unbiased integrative analyses unveil novel molecular components that may influence mitochondrial homeostasis in the earliest phase of ALS.


Subject(s)
Amyotrophic Lateral Sclerosis , Frontotemporal Dementia , Mitochondrial Diseases , Neurodegenerative Diseases , Pick Disease of the Brain , Mice , Animals , Humans , Frontotemporal Dementia/metabolism , Amyotrophic Lateral Sclerosis/pathology , Proteomics , Mice, Transgenic , Gene Expression Profiling , RNA, Messenger
2.
Breast Cancer Res ; 21(1): 43, 2019 03 21.
Article in English | MEDLINE | ID: mdl-30898150

ABSTRACT

BACKGROUND: The oncogenic receptor tyrosine kinase (RTK) ERBB2 is known to dimerize with other EGFR family members, particularly ERBB3, through which it potently activates PI3K signalling. Antibody-mediated inhibition of this ERBB2/ERBB3/PI3K axis has been a cornerstone of treatment for ERBB2-amplified breast cancer patients for two decades. However, the lack of response and the rapid onset of relapse in many patients now question the assumption that the ERBB2/ERBB3 heterodimer is the sole relevant effector target of these therapies. METHODS: Through a systematic protein-protein interaction screen, we have identified and validated alternative RTKs that interact with ERBB2. Using quantitative readouts of signalling pathway activation and cell proliferation, we have examined their influence upon the mechanism of trastuzumab- and pertuzumab-mediated inhibition of cell growth in ERBB2-amplified breast cancer cell lines and a patient-derived xenograft model. RESULTS: We now demonstrate that inactivation of ERBB3/PI3K by these therapeutic antibodies is insufficient to inhibit the growth of ERBB2-amplified breast cancer cells. Instead, we show extensive promiscuity between ERBB2 and an array of RTKs from outside of the EGFR family. Paradoxically, pertuzumab also acts as an artificial ligand to promote ERBB2 activation and ERK signalling, through allosteric activation by a subset of these non-canonical RTKs. However, this unexpected activation mechanism also increases the sensitivity of the receptor network to the ERBB2 kinase inhibitor lapatinib, which in combination with pertuzumab, displays a synergistic effect in single-agent resistant cell lines and PDX models. CONCLUSIONS: The interaction of ERBB2 with a number of non-canonical RTKs activates a compensatory signalling response following treatment with pertuzumab, although a counter-intuitive combination of ERBB2 antibody therapy and a kinase inhibitor can overcome this innate therapeutic resistance.


Subject(s)
Breast Neoplasms/metabolism , Drug Resistance, Neoplasm , Protein Kinase Inhibitors/pharmacology , Protein Multimerization , Receptor, ErbB-2/chemistry , Receptor, ErbB-2/metabolism , Animals , Antibodies, Monoclonal, Humanized/pharmacology , Breast Neoplasms/drug therapy , Breast Neoplasms/pathology , Cell Line, Tumor , Disease Models, Animal , Dose-Response Relationship, Drug , Female , Fluorescent Antibody Technique , Humans , Immunohistochemistry , Mice , Phosphorylation , Receptor, ErbB-2/antagonists & inhibitors , Signal Transduction/drug effects , Trastuzumab/pharmacology , Xenograft Model Antitumor Assays
4.
Sci Rep ; 8(1): 10614, 2018 Jul 13.
Article in English | MEDLINE | ID: mdl-30006631

ABSTRACT

Osteosarcoma (OS) is an aggressive sarcoma, where novel treatment approaches are required. Genomic studies suggest that a subset of OS, including OS tumour cell lines (TCLs), exhibit genomic loss of heterozygosity (LOH) patterns reminiscent of BRCA1 or BRCA2 mutant tumours. This raises the possibility that PARP inhibitors (PARPi), used to treat BRCA1/2 mutant cancers, could be used to target OS. Using high-throughput drug sensitivity screening we generated chemosensitivity profiles for 79 small molecule inhibitors, including three clinical PARPi. Drug screening was performed in 88 tumour cell lines, including 18 OS TCLs. This identified known sensitivity effects in OS TCLs, such as sensitivity to FGFR inhibitors. When compared to BRCA1/2 mutant TCLs, OS TCLs, with the exception of LM7, were PARPi resistant, including those with previously determined BRCAness LoH profiles. Post-screen validation experiments confirmed PARPi sensitivity in LM7 cells as well as a defect in the ability to form nuclear RAD51 foci in response to DNA damage. LM7 provides one OS model for the study of PARPi sensitivity through a potential defect in RAD51-mediated DNA repair. The drug sensitivity dataset we generated in 88 TCLs could also serve as a resource for the study of drug sensitivity effects in OS.


Subject(s)
BRCA1 Protein/genetics , BRCA2 Protein/genetics , Drug Resistance, Neoplasm/genetics , Osteosarcoma/drug therapy , Poly(ADP-ribose) Polymerase Inhibitors/therapeutic use , CRISPR-Cas Systems/genetics , Cell Line, Tumor , Cell Nucleus/drug effects , Cell Nucleus/genetics , Cell Nucleus/metabolism , DNA Damage/drug effects , DNA Damage/genetics , DNA Repair/drug effects , DNA Repair/genetics , Datasets as Topic , Drug Screening Assays, Antitumor/methods , High-Throughput Screening Assays/methods , Humans , Mutagenesis , Mutation , Osteosarcoma/genetics , Poly(ADP-ribose) Polymerase Inhibitors/pharmacology , Rad51 Recombinase/metabolism
5.
Sci Signal ; 8(408): ra130, 2015 Dec 22.
Article in English | MEDLINE | ID: mdl-26696630

ABSTRACT

Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics.


Subject(s)
Biomarkers, Tumor/metabolism , MAP Kinase Kinase 4/metabolism , Models, Biological , Neuroblastoma/metabolism , Neuroblastoma/mortality , Nuclear Proteins/metabolism , Oncogene Proteins/metabolism , Signal Transduction , Adolescent , Animals , Cell Line, Tumor , Child , Child, Preschool , Disease-Free Survival , Female , Follow-Up Studies , Humans , Infant , Male , N-Myc Proto-Oncogene Protein , Neoplasms, Experimental/metabolism , Predictive Value of Tests , Survival Rate , Zebrafish/metabolism , Zebrafish Proteins/metabolism
6.
PLoS Comput Biol ; 10(7): e1003763, 2014 Jul.
Article in English | MEDLINE | ID: mdl-25077791

ABSTRACT

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.


Subject(s)
Evolution, Molecular , Models, Biological , Signal Transduction/genetics , Signal Transduction/physiology , Algorithms , Drug Discovery , Humans
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