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1.
Cancers (Basel) ; 16(4)2024 Feb 18.
Artículo en Inglés | MEDLINE | ID: mdl-38398213

RESUMEN

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

2.
Biomolecules ; 13(6)2023 05 27.
Artículo en Inglés | MEDLINE | ID: mdl-37371475

RESUMEN

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.


Asunto(s)
Inteligencia Artificial , Aprendizaje Profundo , Perfilación de la Expresión Génica , Transcriptoma
3.
Front Immunol ; 13: 920669, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35911770

RESUMEN

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.


Asunto(s)
Neoplasias de la Mama , Descubrimiento del Conocimiento , Neoplasias de la Mama/genética , Neoplasias de la Mama/terapia , Femenino , Humanos , Aprendizaje , Redes Neurales de la Computación , Neuronas/fisiología , Microambiente Tumoral
4.
Front Cell Dev Biol ; 10: 752326, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35359437

RESUMEN

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.

5.
Sci Rep ; 11(1): 11198, 2021 05 27.
Artículo en Inglés | MEDLINE | ID: mdl-34045642

RESUMEN

Glioblastomas (GBMs) are the most common and lethal primary brain malignancy in adults. Oncolytic virus (OV) immunotherapies selectively kill GBM cells in a manner that elicits antitumor immunity. Cellular communication network factor 1 (CCN1), a protein found in most GBM microenvironments, expression predicts resistance to OVs, particularly herpes simplex virus type 1 (HSV-1). This study aims to understand how extracellular CCN1 alters the GBM intracellular state to confer OV resistance. Protein-protein interaction network information flow analyses of LN229 human GBM transcriptomes identified 39 novel nodes and 12 binary edges dominating flow in CCN1high cells versus controls. Virus response programs, notably against HSV-1, and cytokine-mediated signaling pathways are highly enriched. Our results suggest that CCN1high states exploit IDH1 and TP53, and increase dependency on RPL6, HUWE1, and COPS5. To validate, we reproduce our findings in 65 other GBM cell line (CCLE) and 174 clinical GBM patient sample (TCGA) datasets. We conclude through our generalized network modeling and system level analysis that CCN1 signals via several innate immune pathways in GBM to inhibit HSV-1 OVs before transduction. Interventions disrupting this network may overcome immunovirotherapy resistance.


Asunto(s)
Neoplasias Encefálicas/terapia , Proteína 61 Rica en Cisteína/metabolismo , Glioblastoma/terapia , Herpesvirus Humano 1 , Viroterapia Oncolítica/métodos , Virus Oncolíticos , Línea Celular Tumoral , Humanos , Microambiente Tumoral
6.
J Bioinform Syst Biol ; 4(1): 13-32, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33842927

RESUMEN

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.

7.
Cancer Res ; 81(11): 2995-3007, 2021 06 01.
Artículo en Inglés | MEDLINE | ID: mdl-33602789

RESUMEN

One of the greatest barriers to curative treatment of neuroblastoma is its frequent metastatic outgrowth prior to diagnosis, especially in cases driven by amplification of the MYCN oncogene. However, only a limited number of regulatory proteins that contribute to this complex MYCN-mediated process have been elucidated. Here we show that the growth arrest-specific 7 (GAS7) gene, located at chromosome band 17p13.1, is preferentially deleted in high-risk MYCN-driven neuroblastoma. GAS7 expression was also suppressed in MYCN-amplified neuroblastoma lacking 17p deletion. GAS7 deficiency led to accelerated metastasis in both zebrafish and mammalian models of neuroblastoma with overexpression or amplification of MYCN. Analysis of expression profiles and the ultrastructure of zebrafish neuroblastoma tumors with MYCN overexpression identified that GAS7 deficiency led to (i) downregulation of genes involved in cell-cell interaction, (ii) loss of contact among tumor cells as critical determinants of accelerated metastasis, and (iii) increased levels of MYCN protein. These results provide the first genetic evidence that GAS7 depletion is a critical early step in the cascade of events culminating in neuroblastoma metastasis in the context of MYCN overexpression. SIGNIFICANCE: Heterozygous deletion or MYCN-mediated repression of GAS7 in neuroblastoma releases an important brake on tumor cell dispersion and migration to distant sites, providing a novel mechanism underlying tumor metastasis in MYCN-driven neuroblastoma.See related commentary by Menard, p. 2815.


Asunto(s)
Biomarcadores de Tumor/metabolismo , Neoplasias de la Médula Ósea/secundario , Deleción Cromosómica , Regulación Neoplásica de la Expresión Génica , Proteína Proto-Oncogénica N-Myc/metabolismo , Proteínas del Tejido Nervioso/deficiencia , Neuroblastoma/patología , Animales , Apoptosis , Biomarcadores de Tumor/genética , Neoplasias de la Médula Ósea/genética , Neoplasias de la Médula Ósea/metabolismo , Proliferación Celular , Humanos , Ratones , Ratones SCID , Proteína Proto-Oncogénica N-Myc/genética , Proteínas del Tejido Nervioso/genética , Neuroblastoma/genética , Neuroblastoma/metabolismo , Pronóstico , Tasa de Supervivencia , Células Tumorales Cultivadas , Ensayos Antitumor por Modelo de Xenoinjerto , Pez Cebra
8.
Nucleic Acids Res ; 47(14): e82, 2019 08 22.
Artículo en Inglés | MEDLINE | ID: mdl-31114928

RESUMEN

With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify 'regulostat' constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of fine-tuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug-regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.


Asunto(s)
Algoritmos , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Regulación de la Expresión Génica , Redes Reguladoras de Genes/genética , Línea Celular Tumoral , Simulación por Computador , Humanos , Fenotipo
9.
Cancer Cell ; 32(3): 310-323.e5, 2017 09 11.
Artículo en Inglés | MEDLINE | ID: mdl-28867147

RESUMEN

A genome-wide association study identified LMO1, which encodes an LIM-domain-only transcriptional cofactor, as a neuroblastoma susceptibility gene that functions as an oncogene in high-risk neuroblastoma. Here we show that dßh promoter-mediated expression of LMO1 in zebrafish synergizes with MYCN to increase the proliferation of hyperplastic sympathoadrenal precursor cells, leading to a reduced latency and increased penetrance of neuroblastomagenesis. The transgenic expression of LMO1 also promoted hematogenous dissemination and distant metastasis, which was linked to neuroblastoma cell invasion and migration, and elevated expression levels of genes affecting tumor cell-extracellular matrix interaction, including loxl3, itga2b, itga3, and itga5. Our results provide in vivo validation of LMO1 as an important oncogene that promotes neuroblastoma initiation, progression, and widespread metastatic dissemination.


Asunto(s)
Carcinogénesis/patología , Proteínas de Unión al ADN/metabolismo , Proteínas con Dominio LIM/metabolismo , Proteína Proto-Oncogénica N-Myc/metabolismo , Neuroblastoma/metabolismo , Neuroblastoma/patología , Factores de Transcripción/metabolismo , Animales , Animales Modificados Genéticamente , Carcinogénesis/metabolismo , Línea Celular Tumoral , Movimiento Celular/genética , Matriz Extracelular/metabolismo , Regulación Neoplásica de la Expresión Génica , Humanos , Hiperplasia , Modelos Biológicos , Invasividad Neoplásica , Metástasis de la Neoplasia , Neuroblastoma/genética , Transducción de Señal/genética , Transgenes , Pez Cebra
10.
Sci Rep ; 7(1): 6993, 2017 08 01.
Artículo en Inglés | MEDLINE | ID: mdl-28765560

RESUMEN

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.


Asunto(s)
Redes Reguladoras de Genes , Aprendizaje Automático , Neoplasias/genética , Neoplasias/fisiopatología , Biología Computacional , Humanos
11.
Cell Rep ; 18(12): 2932-2942, 2017 03 21.
Artículo en Inglés | MEDLINE | ID: mdl-28329685

RESUMEN

Growing evidence suggests a major role for Src-homology-2-domain-containing phosphatase 2 (SHP2/PTPN11) in MYCN-driven high-risk neuroblastoma, although biologic confirmation and a plausible mechanism for this contribution are lacking. Using a zebrafish model of MYCN-overexpressing neuroblastoma, we demonstrate that mutant ptpn11 expression in the adrenal gland analog of MYCN transgenic fish promotes the proliferation of hyperplastic neuroblasts, accelerates neuroblastomagenesis, and increases tumor penetrance. We identify a similar mechanism in tumors with wild-type ptpn11 and dysregulated Gab2, which encodes a Shp2 activator that is overexpressed in human neuroblastomas. In MYCN transgenic fish, Gab2 overexpression activated the Shp2-Ras-Erk pathway, enhanced neuroblastoma induction, and increased tumor penetrance. We conclude that MYCN cooperates with either GAB2-activated or mutant SHP2 in human neuroblastomagenesis. Our findings further suggest that combined inhibition of MYCN and the SHP2-RAS-ERK pathway could provide effective targeted therapy for high-risk neuroblastoma patients with MYCN amplification and aberrant SHP2 activation.


Asunto(s)
Proteínas Portadoras/metabolismo , Proteína Proto-Oncogénica N-Myc/metabolismo , Neuroblastoma/metabolismo , Neuroblastoma/patología , Proteína Tirosina Fosfatasa no Receptora Tipo 11/metabolismo , Proteínas de Pez Cebra/metabolismo , Pez Cebra/metabolismo , Animales , Animales Modificados Genéticamente , Apoptosis/efectos de los fármacos , Apoptosis/genética , Carbazoles/farmacología , Carcinogénesis/efectos de los fármacos , Carcinogénesis/genética , Carcinogénesis/patología , Proliferación Celular/efectos de los fármacos , Amplificación de Genes/efectos de los fármacos , Perfilación de la Expresión Génica , Humanos , Sistema de Señalización de MAP Quinasas/efectos de los fármacos , Mutación/genética , Factores de Riesgo
12.
Int J Mol Sci ; 18(1)2016 Dec 27.
Artículo en Inglés | MEDLINE | ID: mdl-28035989

RESUMEN

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.


Asunto(s)
Simulación por Computador , Modelos Biológicos , Neuroblastoma/patología , Niño , Humanos , Neuroblastoma/epidemiología , Neuroblastoma/genética , Neuroblastoma/terapia , Análisis de Supervivencia
13.
Sci Rep ; 6: 37003, 2016 11 14.
Artículo en Inglés | MEDLINE | ID: mdl-27841317

RESUMEN

To better address the problem of drug resistance during cancer chemotherapy and explore the possibility of manipulating drug response phenotypes, we developed a network-based phenotype mapping approach (P-Map) to identify gene candidates that upon perturbed can alter sensitivity to drugs. We used basal transcriptomics data from a panel of human lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for conferring response phenotypes for anthracycline and taxane, two common anticancer agents use in clinics. We further tested selected gene candidates that interact with phenotypic differentially expressed genes (PDEGs), which are up-regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer (TNBC) cells. Our results indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or vice versa, by perturbing gene candidates in DRNs and suggest plausible mechanisms regulating directionality of drug response sensitivity. More important, the current work highlights a new way to formulate systems-based therapeutic design: supplementing therapeutics that aim to target disease culprits with phenotypic modulators capable of altering DRN properties with the goal to re-sensitize resistant phenotypes.


Asunto(s)
Antineoplásicos/toxicidad , Resistencia a Antineoplásicos/genética , Redes Reguladoras de Genes/efectos de los fármacos , Proteínas Adaptadoras Transductoras de Señales/genética , Proteínas Adaptadoras Transductoras de Señales/metabolismo , Algoritmos , Antraciclinas/toxicidad , Hidrocarburos Aromáticos con Puentes/toxicidad , Línea Celular Tumoral , Humanos , Nucleósido-Difosfato Quinasa/genética , Nucleósido-Difosfato Quinasa/metabolismo , Fenotipo , Interferencia de ARN , ARN Interferente Pequeño/metabolismo , Receptores Tipo I de Interleucina-1/genética , Receptores Tipo I de Interleucina-1/metabolismo , Taxoides/toxicidad
14.
Nucleic Acids Res ; 44(10): e100, 2016 06 02.
Artículo en Inglés | MEDLINE | ID: mdl-26975659

RESUMEN

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.


Asunto(s)
Biología Computacional/métodos , Mapas de Interacción de Proteínas , Programas Informáticos , Transcriptoma , Algoritmos , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Neoplasias de la Mama/genética , Neoplasias de la Mama/metabolismo , Bases de Datos Factuales , Dislipidemias/genética , Dislipidemias/metabolismo , Femenino , Redes Reguladoras de Genes , Humanos , Transducción de Señal
15.
J Vis Exp ; (97)2015 Mar 31.
Artículo en Inglés | MEDLINE | ID: mdl-25867597

RESUMEN

Comprehensive genomic analysis has uncovered surprisingly large numbers of genetic alterations in various types of cancers. To robustly and efficiently identify oncogenic "drivers" among these tumors and define their complex relationships with concurrent genetic alterations during tumor pathogenesis remains a daunting task. Recently, zebrafish have emerged as an important animal model for studying human diseases, largely because of their ease of maintenance, high fecundity, obvious advantages for in vivo imaging, high conservation of oncogenes and their molecular pathways, susceptibility to tumorigenesis and, most importantly, the availability of transgenic techniques suitable for use in the fish. Transgenic zebrafish models of cancer have been widely used to dissect oncogenic pathways in diverse tumor types. However, developing a stable transgenic fish model is both tedious and time-consuming, and it is even more difficult and more time-consuming to dissect the cooperation of multiple genes in disease pathogenesis using this approach, which requires the generation of multiple transgenic lines with overexpression of the individual genes of interest followed by complicated breeding of these stable transgenic lines. Hence, use of a mosaic transient transgenic approach in zebrafish offers unique advantages for functional genomic analysis in vivo. Briefly, candidate transgenes can be coinjected into one-cell-stage wild-type or transgenic zebrafish embryos and allowed to integrate together into each somatic cell in a mosaic pattern that leads to mixed genotypes in the same primarily injected animal. This permits one to investigate in a faster and less expensive manner whether and how the candidate genes can collaborate with each other to drive tumorigenesis. By transient overexpression of activated ALK in the transgenic fish overexpressing MYCN, we demonstrate here the cooperation of these two oncogenes in the pathogenesis of a pediatric cancer, neuroblastoma that has resisted most forms of contemporary treatment.


Asunto(s)
Transformación Celular Neoplásica/genética , Técnicas de Transferencia de Gen , Neuroblastoma/genética , Oncogenes , Pez Cebra/genética , Quinasa de Linfoma Anaplásico , Animales , Animales Modificados Genéticamente , Transformación Celular Neoplásica/metabolismo , Modelos Animales de Enfermedad , Humanos , Proteínas Luminiscentes/biosíntesis , Proteínas Luminiscentes/genética , Neuroblastoma/metabolismo , Proteínas Tirosina Quinasas Receptoras/biosíntesis , Proteínas Tirosina Quinasas Receptoras/genética , Transgenes , Proteína Fluorescente Roja
16.
J Biosci ; 39(3): 493-504, 2014 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-24845512

RESUMEN

DARPP-32 (dopamine and adenosine 3', 5'-monophosphate-regulated phosphoprotein of 32 kDa), which belongs to PPP1R1 gene family, is known to act as an important integrator in dopamine-mediated neurotransmission via the inhibition of protein phosphatase-1 (PP1). Besides its neuronal roles, this protein also behaves as a key player in pathological and pharmacological aspects. Use of bioinformatics and phylogenetics approaches to further characterize the molecular features of DARPP-32 can guide future works. Predicted phosphorylation sites on DARPP-32 show conservation across vertebrates. Phylogenetics analysis indicates evolutionary strata of phosphorylation site acquisition at the C-terminus, suggesting functional expansion of DARPP-32, where more diverse signalling cues may involve in regulating DARPP-32 in inhibiting PP1 activity. Moreover, both phylogenetics and synteny analyses suggest de novo origination of PPP1R1 gene family via chromosomal rearrangement and exonization.


Asunto(s)
Fosfoproteína 32 Regulada por Dopamina y AMPc/química , Secuencia de Aminoácidos , Animales , Sitios de Unión , Biología Computacional , Secuencia Conservada , Evolución Molecular , Humanos , Fosforilación , Filogenia , Alineación de Secuencia , Análisis de Secuencia de Proteína , Sintenía , Vertebrados
17.
Biochim Biophys Acta ; 1830(10): 4778-89, 2013 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-23791553

RESUMEN

BACKGROUND: 4-Nitrophenol (4-NP) is a prioritized environmental pollutant and its toxicity has been investigated using zebrafish, advocated as an alternative toxicological model. However, molecular information of 4-NP induced hepatotoxicity is still limited. This study aimed to obtain molecular insights into 4-NP-induced hepatotoxicity using zebrafish as a model. METHODS: Adult male zebrafish were exposed to 4-NP for 8, 24, 48 and 96h. Livers were sampled for microarray experiment, qRT-PCR and various histological analyses. RESULTS: Transcriptomic analysis revealed that genes associated with oxidative phosphorylation and electron transport chain were significantly up-regulated throughout early and late stages of 4-NP exposure due to oxidative phosphorylation uncoupling by 4-NP. This in turn induced oxidative stress damage and up-regulated pathways associated with tumor suppressors Rb and p53, cell cycle, DNA damage, proteasome degradation and apoptosis. Pathways associated with cell adhesion and morphology were deregulated. Carbohydrate and lipid metabolisms were down-regulated while methionine and aromatic amino acid metabolisms as well as NFKB pathway associated with chronic liver conditions were up-regulated. Up-regulation of NFKB, NFAT and interleukin pathways suggested hepatitis. Histological analyses with specific staining methods and qRT-PCR analysis of selected genes corroborated with the transcriptomic analysis suggesting 4-NP induced liver injury. CONCLUSION: Our findings allowed us to propose a plausible model and provide a broader understanding of the molecular events leading to 4-NP induced acute hepatotoxicity for future studies involving other nitrophenol derivatives. GENERAL SIGNIFICANCE: This is the first transcriptomic report on 4-NP induced hepatotoxicity.


Asunto(s)
Perfilación de la Expresión Génica , Hígado/efectos de los fármacos , Nitrofenoles/toxicidad , Transcriptoma , Animales , Fosforilación Oxidativa , Pez Cebra
18.
PLoS One ; 6(12): e28273, 2011.
Artículo en Inglés | MEDLINE | ID: mdl-22194820

RESUMEN

Bisphenol-A is an important environmental contaminant due to the increased early-life exposure that may pose significant health-risks to various organisms including humans. This study aimed to use zebrafish as a toxicogenomic model to capture transcriptomic and phenotypic changes for inference of signaling pathways, biological processes, physiological systems and identify potential biomarker genes that are affected by early-life exposure to bisphenol-A. Phenotypic analysis using wild-type zebrafish larvae revealed BPA early-life exposure toxicity caused cardiac edema, cranio-facial abnormality, failure of swimbladder inflation and poor tactile response. Fluorescent imaging analysis using three transgenic lines revealed suppressed neuron branching from the spinal cord, abnormal development of neuromast cells, and suppressed vascularization in the abdominal region. Using knowledge-based data mining algorithms, transcriptome analysis suggests that several signaling pathways involving ephrin receptor, clathrin-mediated endocytosis, synaptic long-term potentiation, axonal guidance, vascular endothelial growth factor, integrin and tight junction were deregulated. Physiological systems with related disorders associated with the nervous, cardiovascular, skeletal-muscular, blood and reproductive systems were implicated, hence corroborated with the phenotypic analysis. Further analysis identified a common set of BPA-targeted genes and revealed a plausible mechanism involving disruption of endocrine-regulated genes and processes in known susceptible tissue-organs. The expression of 28 genes were validated in a separate experiment using quantitative real-time PCR and 6 genes, ncl1, apoeb, mdm1, mycl1b, sp4, U1SNRNPBP homolog, were found to be sensitive and robust biomarkers for BPA early-life exposure toxicity. The susceptibility of sp4 to BPA perturbation suggests its role in altering brain development, function and subsequently behavior observed in laboratory animals exposed to BPA during early life, which is a health-risk concern of early life exposure in humans. The present study further established zebrafish as a model for toxicogenomic inference of early-life chemical exposure toxicity.


Asunto(s)
Exposición a Riesgos Ambientales , Fenoles/toxicidad , Toxicogenética , Pez Cebra/crecimiento & desarrollo , Pez Cebra/genética , Animales , Animales Modificados Genéticamente , Compuestos de Bencidrilo , Biomarcadores/metabolismo , Regulación hacia Abajo/efectos de los fármacos , Regulación hacia Abajo/genética , Perfilación de la Expresión Génica , Regulación del Desarrollo de la Expresión Génica/efectos de los fármacos , Redes Reguladoras de Genes/efectos de los fármacos , Redes Reguladoras de Genes/genética , Proteínas Fluorescentes Verdes/metabolismo , Humanos , Larva/efectos de los fármacos , Análisis de Secuencia por Matrices de Oligonucleótidos , Fenotipo , Reacción en Cadena en Tiempo Real de la Polimerasa , Reproducibilidad de los Resultados , Estadística como Asunto , Transcriptoma/efectos de los fármacos , Transcriptoma/genética , Regulación hacia Arriba/genética , Proteínas de Pez Cebra/genética , Proteínas de Pez Cebra/metabolismo
19.
Zebrafish ; 6(4): 425-31, 2009 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-19916844

RESUMEN

In an effort of validating the zebrafish model for studies of human tumors, our previous transcriptome analyses revealed striking molecular similarities between zebrafish and human liver neoplasia. However, as biological processes function at modular levels such as pathways and cascades, it is also important to capture conservation at the modular levels and in regulatory program(s) controlling these modules. In this study, we performed comparative transcriptome analyses with two modules, biological modules and transcription factor target modules, using gene set enrichment analysis against carcinogen-induced liver tumors in zebrafish with four tissue types of human tumors. We observed conservation of enriched modules that are associated to tumorigenesis such as cell cycle, metastasis, and hypoxia in these tumors. More importantly, we identified conserved regulatory programs linking these cancer-related modules with transcription factors and oncogenes such as Myc, E2F, STAT, and YY1. Taken together, our analyses revealed that carcinogen-induced liver tumors in zebrafish capture cancerous hallmarks observed in human tumors not only for cancer-related biological modules but also at transcriptional program, further implicating conserved tumorigenesis mechanism in both zebrafish and human.


Asunto(s)
Biomarcadores de Tumor/genética , Regulación Neoplásica de la Expresión Génica , Neoplasias/genética , Factores de Transcripción/metabolismo , Pez Cebra/genética , Animales , Perfilación de la Expresión Génica , Humanos , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/metabolismo , Neoplasias/metabolismo , Neoplasias/patología , Pez Cebra/metabolismo
20.
Cancer ; 115(18): 4246-63, 2009 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-19551902

RESUMEN

BACKGROUND: Mathematic models and sensitivity analyses of biologic pathways have been used for exploring the dynamics and for detecting the key components of signaling pathways. METHODS: The authors previously developed a mathematic model of the epidermal growth factor receptor-extracellular signal-regulated protein kinase (EGFR-ERK) pathway using ordinary differential equations from existing EGFR-ERK pathway models. By using prolonged ERK activation as an indicator that may lead to cell proliferation under certain circumstances, in the current study, a pathway sensitivity analysis was performed to test its capability of detecting pro-proliferative activities through altered protein levels to examine the effects on ERK activation. RESULTS: The analysis revealed that 12 of 20 oncoproteins and 4 of 5 tumor suppressors were detected, consistent with reported experimental works. Because pathway dynamics depend on many factors, some of which were not included in the current models, failure to detect all known oncogenes and tumor suppressors can be because of the failure to include relevant crosstalk to other pathway components. CONCLUSIONS: Overall, the current results indicated that pathway sensitivity analysis is a useful approach for detecting and distinguishing pro-proliferation activities of oncoproteins and suppressed proliferative activities of tumor suppressors at altered protein levels at least in the EGFR-ERK model.


Asunto(s)
Proliferación Celular , Receptores ErbB/metabolismo , Quinasas MAP Reguladas por Señal Extracelular/metabolismo , Genes Supresores de Tumor , Modelos Teóricos , Neoplasias/metabolismo , Proteínas Oncogénicas/metabolismo , Transducción de Señal , Humanos
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