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Abelha TF, Dreiss CA, Green MA, Dailey LA. Conjugated polymers as nanoparticle probes for fluorescence and photoacoustic imaging. J Mater Chem B 2020; 8:592-606. [DOI: 10.1039/c9tb02582k] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this review, the role of conjugated polymer nanoparticles (CPNs) in emerging bioimaging techniques is described.
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Affiliation(s)
- Thais Fedatto Abelha
- King's College London
- Institute of Pharmaceutical Science
- London
- UK
- School of Pharmacy
| | - Cécile A. Dreiss
- King's College London
- Institute of Pharmaceutical Science
- London
- UK
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Pelleri MC, Piovesan A, Caracausi M, Berardi AC, Vitale L, Strippoli P. Integrated differential transcriptome maps of Acute Megakaryoblastic Leukemia (AMKL) in children with or without Down Syndrome (DS). BMC Med Genomics 2014; 7:63. [PMID: 25476127 PMCID: PMC4304173 DOI: 10.1186/s12920-014-0063-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Accepted: 11/12/2014] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The incidence of Acute Megakaryoblastic Leukemia (AMKL) is 500-fold higher in children with Down Syndrome (DS) compared with non-DS children, but the relevance of trisomy 21 as a specific background of AMKL in DS is still an open issue. Several Authors have determined gene expression profiles by microarray analysis in DS and/or non-DS AMKL. Due to the rarity of AMKL, these studies were typically limited to a small group of samples. METHODS We generated integrated quantitative transcriptome maps by systematic meta-analysis from any available gene expression profile dataset related to AMKL in pediatric age. This task has been accomplished using a tool recently described by us for the generation and the analysis of quantitative transcriptome maps, TRAM (Transcriptome Mapper), which allows effective integration of data obtained from different experimenters, experimental platforms and data sources. This allowed us to explore gene expression changes involved in transition from normal megakaryocytes (MK, n=19) to DS (n=43) or non-DS (n=45) AMKL blasts, including the analysis of Transient Myeloproliferative Disorder (TMD, n=20), a pre-leukemia condition. RESULTS We propose a biological model of the transcriptome depicting progressive changes from MK to TMD and then to DS AMKL. The data indicate the repression of genes involved in MK differentiation, in particular the cluster on chromosome 4 including PF4 (platelet factor 4) and PPBP (pro-platelet basic protein); the gene for the mitogen-activated protein kinase MAP3K10 and the thrombopoietin receptor gene MPL. Moreover, comparing both DS and non-DS AMKL with MK, we identified three potential clinical markers of progression to AMKL: TMEM241 (transmembrane protein 241) was the most over-expressed single gene, while APOC2 (apolipoprotein C-II) and ZNF587B (zinc finger protein 587B) appear to be the most discriminant markers of progression, specifically to DS AMKL. Finally, the chromosome 21 (chr21) genes resulted to be the most over-expressed in DS and non-DS AMKL, as well as in TMD, pointing out a key role of chr21 genes in differentiating AMKL from MK. CONCLUSIONS Our study presents an integrated original model of the DS AMLK transcriptome, providing the identification of genes relevant for its pathophysiology which can potentially be new clinical markers.
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Affiliation(s)
- Maria Chiara Pelleri
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy.
| | - Allison Piovesan
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy.
| | - Maria Caracausi
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy.
| | - Anna Concetta Berardi
- Research Laboratory Stem Cells, U.O.C. Immunohematology-Transfusion Medicine and Laboratory of Hematology, Santo Spirito's Hospital, Via del Circuito, 65100, Pescara, Italy.
| | - Lorenza Vitale
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy.
| | - Pierluigi Strippoli
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy. .,Interdepartmental Center for Cancer Research Giorgio Prodi (CIRC), S. Orsola-Malpighi Hospital, University of Bologna, Via Massarenti 9, 40138, Bologna, BO, Italy.
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McGeachie MJ, Chang HH, Weiss ST. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data. PLoS Comput Biol 2014; 10:e1003676. [PMID: 24922310 PMCID: PMC4055564 DOI: 10.1371/journal.pcbi.1003676] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 05/03/2014] [Indexed: 01/08/2023] Open
Abstract
Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.
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Affiliation(s)
- Michael J. McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | - Hsun-Hsien Chang
- Harvard Medical School, Boston, Massachusetts, United States of America
- Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Partners Health Care Center for Personalized Medicine, Boston, Massachusetts, United States of America
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Chang HH, McGeachie M. Phenotype prediction by integrative network analysis of SNP and gene expression microarrays. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6849-52. [PMID: 22255912 DOI: 10.1109/iembs.2011.6091689] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.
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Affiliation(s)
- Hsun-Hsien Chang
- Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA.
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Chang HH, Soderberg K, Skinner JA, Banchereau J, Chaussabel D, Haynes BF, Ramoni M, Letvin NL. Transcriptional network predicts viral set point during acute HIV-1 infection. J Am Med Inform Assoc 2012; 19:1103-9. [PMID: 22700869 DOI: 10.1136/amiajnl-2012-000867] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. METHODS In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. RESULTS The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. CONCLUSIONS Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.
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Affiliation(s)
- Hsun-Hsien Chang
- Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA.
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