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Murtaza S, Tabassum B, Tariq M, Riaz S, Yousaf I, Jabbar B, Khan A, Samuel AO, Zameer M, Nasir IA. Silencing a Myzus persicae Macrophage Inhibitory Factor by Plant-Mediated RNAi Induces Enhanced Aphid Mortality Coupled with Boosted RNAi Efficacy in Transgenic Potato Lines. Mol Biotechnol 2022; 64:1152-1163. [PMID: 35460447 DOI: 10.1007/s12033-022-00498-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/08/2022] [Indexed: 11/28/2022]
Abstract
Myzus persicae causes considerable losses to crops as a major pest. The damage is direct by feeding and also partly indirect because it vectors plant viruses. The currently available control strategies rely on unsafe and nonecofriendly chemical pesticide applications. Plant-mediated RNA interference (RNAi) has emerged as a powerful tool in crop protection from insect pests. Aphid salivary proteins are essential for phloem feeding and act as mediators of the complex interactions between aphids and their host plants. We documented the efficacy of dsRNA directed against macrophage inhibitory factor (MIF1) of M. persicae to induce aphid mortality and gene silencing through the generation of transgenic potato lines. A binary construct harbouring dsMIF1 driven by the CaMV35S promoter was introduced into the local potato variety 'AGB-white' by Agrobacterium-mediated transformation. PCR and Southern blotting validated the transgene presence and genomic integration in seven transgenic potato lines. An in vitro detached leaf assay revealed a significantly high aphid mortality of 65% in the transgenic potato line sDW-2, while the aphid mortality was 77% in the sDW-2 transgenic line during the in planta bioassay in comparison with 19% aphid mortality in the control nontransgenic potato line. A significantly high silencing effect was observed in the mRNA expression of MIF1, which was reduced to 21% in aphids fed on the transgenic potato line sDW-2. However, variable knockdown effects were found among six other transgenic potato lines, ranging from 30 to 62%. The study concluded that plant-mediated silencing of aphid RNA induces significant RNAi in M. persicae, along with enhanced aphid mortality.
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Affiliation(s)
- Shahid Murtaza
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Bushra Tabassum
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan. .,School of Biological Sciences, University of the Punjab Quaid-I-Azam Campus, Lahore, 54590, Pakistan.
| | - Muhammad Tariq
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Saman Riaz
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Iqra Yousaf
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Basit Jabbar
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Anwar Khan
- Department of Microbiology, BUITEMS, Quetta, Pakistan
| | | | - Mariam Zameer
- Institute of Molecular Biology & Biotechnology, University of Lahore, Lahore, Pakistan
| | - Idrees Ahmad Nasir
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
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Peak learning of mass spectrometry imaging data using artificial neural networks. Nat Commun 2021; 12:5544. [PMID: 34545087 PMCID: PMC8452737 DOI: 10.1038/s41467-021-25744-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 08/18/2021] [Indexed: 02/07/2023] Open
Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
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Pineda AL, Ogoe HA, Balasubramanian JB, Rangel Escareño C, Visweswaran S, Herman JG, Gopalakrishnan V. On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue. BMC Cancer 2016; 16:184. [PMID: 26944944 PMCID: PMC4778315 DOI: 10.1186/s12885-016-2223-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 02/28/2016] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. METHODS Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. RESULTS All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method. CONCLUSIONS The results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.
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Affiliation(s)
- Arturo López Pineda
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA.
| | - Henry Ato Ogoe
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA.
| | - Jeya Balaji Balasubramanian
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA.
| | - Claudia Rangel Escareño
- Department of Computational Genomics, National Institute of Genomic Medicine, Periferico Sur No. 4809, Col. Arenal Tepepan, Tlalpan, 14610, Mexico City, Mexico.
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA.
| | - James Gordon Herman
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh School of Medicine, UPMC Cancer Pavilion, 5150 Centre Avenue, 15232, Pittsburgh, PA, USA.
| | - Vanathi Gopalakrishnan
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA.
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The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management. BMC Cancer 2015; 15:918. [PMID: 26581891 PMCID: PMC4652365 DOI: 10.1186/s12885-015-1917-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 11/06/2015] [Indexed: 12/30/2022] Open
Abstract
Background Papillary thyroid cancer is the most common endocrine malignancy. The most sensitive and specific diagnostic tool for thyroid nodule diagnosis is fine-needle aspiration (FNA) biopsy with cytological evaluation. Nevertheless, FNA biopsy is not always decisive leading to “indeterminate” or “suspicious” diagnoses in 10 %–30 % of cases. BRAF V600E detection is currently used as molecular test to improve the diagnosis of thyroid nodules, yet it lacks sensitivity. The aim of the present study was to identify novel molecular markers/computational models to improve the discrimination between benign and malignant thyroid lesions. Methods We collected 118 pre-operative thyroid FNA samples. All 118 FNA samples were characterized for the presence of the BRAF V600E mutation (exon15) by pyrosequencing and further assessed for mRNA expression of four genes (KIT, TC1, miR-222, miR-146b) by quantitative polymerase chain reaction. Computational models (Bayesian Neural Network Classifier, discriminant analysis) were built, and their ability to discriminate benign and malignant tumors were tested. Receiver operating characteristic (ROC) analysis was performed and principal component analysis was used for visualization purposes. Results In total, 36/70 malignant samples carried the V600E mutation, while all 48 benign samples were wild type for BRAF exon15. The Bayesian neural network (BNN) and discriminant analysis, including the mRNA expression of the four genes (KIT, TC1, miR-222, miR-146b) showed a very strong predictive value (94.12 % and 92.16 %, respectively) in discriminating malignant from benign patients. The discriminant analysis showed a correct classification of 100 % of the samples in the malignant group, and 95 % by BNN. KIT and miR-146b showed the highest diagnostic accuracy of the ROC curve, with area under the curve values of 0.973 for KIT and 0.931 for miR-146b. Conclusions The four genes model proposed in this study proved to be highly discriminative of the malignant status compared with BRAF assessment alone. Its implementation in clinical practice can help in identifying malignant/benign nodules that would otherwise remain suspicious. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1917-2) contains supplementary material, which is available to authorized users.
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Combined analysis identifies six genes correlated with augmented malignancy from non-small cell to small cell lung cancer. Tumour Biol 2015; 37:2193-207. [PMID: 26349752 DOI: 10.1007/s13277-015-3938-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 08/17/2015] [Indexed: 12/30/2022] Open
Abstract
With increased malignancy, lung cancer can be classified into adenocarcinoma (ADC), squamous cell carcinoma (SQC), large cell carcinoma (LCC), and the small cell subtype (SCLC); yet, elucidations to this augmented malignancy has not been addressed. In this study, we elucidated the molecular diversity among these subtypes by investigating large-scale sequencing datasets. Among genes upregulated from normal, ADC, SQC, LCC to SCLC, six hub genes were found closely correlated with adverse clinical outcome and were testified on cellular or tissue level with quantitative RT-PCR. Cox regression model was then built to generate a risk signature. The possible linkages among these genes were also explored. Transcript levels of BUB1, E2F1, ESPL1, GTSE1, RAB3B, and U2AF2 were found significantly elevated from normal, ADC, SQC, LCC to SCLC. Overexpression of one or multiple of these genes was correlated with adverse overall survival (OS) and relapse-free survival (RFS) in the whole patient cohort or groups stratified according to clinical variables, while most of all six genes were independent prognostic factors. When used as a six-gene risk signature, patients with high signature score displayed more unfavorable clinical variables and poorer outcome. Tight regulative relationships were found within these genes, while BUB1 and E2F1 were likely to be the drivers. We considered the augmented malignancy from non-small cell lung cancer (NSCLC) to SCLC might be due to the elevation of these six genes. We believe these genes were powerful cancer prognostic markers and potential therapeutic targets in lung cancer; moreover, changes of their level might be correlated with lung cancer phenotype plasticity.
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Nur SM, Hasan MA, Amin MA, Hossain M, Sharmin T. Design of Potential RNAi (miRNA and siRNA) Molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Gene Silencing by Computational Method. Interdiscip Sci 2015. [PMID: 26223545 PMCID: PMC7090891 DOI: 10.1007/s12539-015-0266-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF1ab replicase polyprotein may be used as a suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence-specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In the current study, four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.
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Affiliation(s)
- Suza Mohammad Nur
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Md Anayet Hasan
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh.
| | - Mohammad Al Amin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Mehjabeen Hossain
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Tahmina Sharmin
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
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Nur SM, Hasan MA, Amin MA, Hossain M, Sharmin T. Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method. Interdiscip Sci 2014. [PMID: 25373633 DOI: 10.1007/s12539-014-0208-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 08/17/2014] [Accepted: 09/22/2014] [Indexed: 06/04/2023]
Abstract
The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.
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Affiliation(s)
- Suza Mohammad Nur
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
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Nur SM, Hasan MA, Amin MA, Hossain M, Sharmin T. Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method. Interdiscip Sci 2014. [PMID: 25519155 DOI: 10.1007/s12539-014-0233-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 08/17/2014] [Accepted: 09/22/2014] [Indexed: 06/04/2023]
Abstract
The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.
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Affiliation(s)
- Suza Mohammad Nur
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong, 4331, Bangladesh
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Functional dissection of regulatory models using gene expression data of deletion mutants. PLoS Genet 2013; 9:e1003757. [PMID: 24039601 PMCID: PMC3764135 DOI: 10.1371/journal.pgen.1003757] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 07/13/2013] [Indexed: 11/19/2022] Open
Abstract
Genome-wide gene expression profiles accumulate at an alarming rate, how to integrate these expression profiles generated by different laboratories to reverse engineer the cellular regulatory network has been a major challenge. To automatically infer gene regulatory pathways from genome-wide mRNA expression profiles before and after genetic perturbations, we introduced a new Bayesian network algorithm: Deletion Mutant Bayesian Network (DM_BN). We applied DM_BN to the expression profiles of 544 yeast single or double deletion mutants of transcription factors, chromatin remodeling machinery components, protein kinases and phosphatases in S. cerevisiae. The network inferred by this method identified causal regulatory and non-causal concurrent interactions among these regulators (genetically perturbed genes) that are strongly supported by the experimental evidence, and generated many new testable hypotheses. Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms, the network inferred by DM_BN excels in both precision and recall. To facilitate its application in other systems, we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www.picb.ac.cn/hanlab/DM_BN.html.
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Nunes-Miranda JD, Igrejas G, Araujo E, Reboiro-Jato M, Capelo JL. Mass Spectrometry-Based Fingerprinting of Proteins & Peptides in Wine Quality Control: A Critical Overview. Crit Rev Food Sci Nutr 2013; 53:751-9. [DOI: 10.1080/10408398.2011.557514] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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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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Schlage WK, Westra JW, Gebel S, Catlett NL, Mathis C, Frushour BP, Hengstermann A, Van Hooser A, Poussin C, Wong B, Lietz M, Park J, Drubin D, Veljkovic E, Peitsch MC, Hoeng J, Deehan R. A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC SYSTEMS BIOLOGY 2011; 5:168. [PMID: 22011616 PMCID: PMC3224482 DOI: 10.1186/1752-0509-5-168] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2011] [Accepted: 10/19/2011] [Indexed: 11/25/2022]
Abstract
Background Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate. Results We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung. Conclusions The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.
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Affiliation(s)
- Walter K Schlage
- Philip Morris International R&D, Philip Morris Research Laboratories GmbH, Fuggerstr.3, 51149 Koeln, Germany
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Chang HH, McGeachie M, Alterovitz G, Ramoni MF. Mapping transcription mechanisms from multimodal genomic data. BMC Bioinformatics 2010; 11 Suppl 9:S2. [PMID: 21044360 PMCID: PMC2967743 DOI: 10.1186/1471-2105-11-s9-s2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. RESULTS We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. CONCLUSIONS The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.
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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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Fu X, Xue C, Huang Y, Xie Y, Li Y. The activity and expression of microRNAs in prostate cancers. MOLECULAR BIOSYSTEMS 2010; 6:2561-72. [PMID: 20957285 DOI: 10.1039/c0mb00100g] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Recent studies have shown that microRNA (miRNA) inhibitory activity can be quantified by examining their target mRNA expression levels. The accumulated evidence of differential miRNA activities between cancer subtypes necessitates the systematical comparison of miRNA expressions and activities. In this study, we integrated 8 mRNA microarray datasets to infer and compare the miRNA activities between prostate cancers (PCs) and normal tissues (NTs). Gene expression analyses show that miRNA activity is stronger in PCs. This conclusion is consolidated by target protein expression. We simultaneously collected 6 independent miRNA expression datasets, where great inconsistency is present in the expression difference between PCs and NTs. The overall correlation between miRNA activity and expression is very weak. However, meta-analysis demonstrated that the expressions of 114 individual miRNAs agree with their activities. Additionally, we detected two other factors associated with higher miRNA activity in PCs. One is deregulation of some key miRNA-repression related genes, such as the over-expression of Dicer, TRBP and Ago2, and the under-expression of IRP1 in PCs. The other is that miRNA-mRNA binding site efficacy has significant positive correlation with miRNA activity, whereas no correlation with miRNA expression. Furthermore, miRNA activity is more reproducible than miRNA expression across different datasets, which allows miRNA activity to be a good feature for the classification of cancer subtypes. We expect our analysis can improve the methods for inferring miRNA activity and further, provide some clues to the role of miRNA in tumorigenesis.
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Affiliation(s)
- XuPing Fu
- State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
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Chang HH, Dreyfuss JM, Ramoni MF. A transcriptional network signature characterizes lung cancer subtypes. Cancer 2010; 117:353-60. [PMID: 20839314 DOI: 10.1002/cncr.25592] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2010] [Revised: 07/20/2010] [Accepted: 07/20/2010] [Indexed: 11/07/2022]
Abstract
BACKGROUND Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent. RESULTS The model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy. CONCLUSIONS These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles.
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Affiliation(s)
- Hsun-Hsien Chang
- Children's Hospital Informatics Program, Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts 02115, USA.
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Lussier YA, Sarkar IN. Selected proceedings of the 2009 Summit on Translational Bioinformatics. BMC Bioinformatics 2009; 10 Suppl 9:I1. [PMID: 19761562 PMCID: PMC2745679 DOI: 10.1186/1471-2105-10-s9-i1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
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