Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with a poor response t... more Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with a poor response to chemotherapy and low survival rate. This unfavorable treatment response is likely to derive from both late diagnosis and from complex, incompletely understood biology, and heterogeneity among NSCLC subtypes. To define the relative contributions of major cellular pathways to the biogenesis of NSCLC and highlight major differences between NSCLC subtypes, we studied the molecular signatures of lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), based on analysis of gene expression and comparison of tumor samples with normal lung tissue. Our results suggest the existence of specific molecular networks and subtype-specific differences between lung ADC and SCC subtypes, mostly found in cell cycle, DNA repair, and metabolic pathways. However, we also observed similarities across major gene interaction networks and pathways in ADC and SCC. These data provide a new insight into the bio...
Genetic suppressor elements (GSEs) are short biologically active gene fragments that encode domin... more Genetic suppressor elements (GSEs) are short biologically active gene fragments that encode dominantly acting peptides or inhibitory antisense RNAs. GSEs can be isolated from a single gene or from a multigene complex by constructing a library of short random fragments of the target gene(s) in an expression vector, followed by expression selection for the desired phenotype in a suitable cellular system. GSE selection from a single gene allows one to develop efficient and specific inhibitors of the gene function and to identify functional protein domains. GSE selection from a multigene complex, such as a normalized (uniform abundance) cDNA population from mammalian cells, makes it possible to identify genes that are involved in selectable cellular phenotypes. The potential of GSE selection for uncovering novel gene functions was first demonstrated using bacteriophage lambda as a model system. GSE selection in retroviral expression vectors has been applied in mammalian cells to identif...
Journal of Bioinformatics and Computational Biology, 2007
Microarray-based characterization of tissues, cellular and disease states, and environmental cond... more Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.
2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
This study is aimed at elucidating putative transcription regulators (TRs) responsible for the ob... more This study is aimed at elucidating putative transcription regulators (TRs) responsible for the observed differential expression pattern. Combined direct promoter binding and indirect transcriptional regulation networks were used, and the expression levels of each TR's downstream targets were collectively analyzed, as a sample, for significance. Statistical procedure was also developed that takes into account the sign of the expression change,
The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and process... more The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
Proceedings of the National Academy of Sciences, 1994
We describe a general strategy for cloning mammalian genes whose downregulation results in a sele... more We describe a general strategy for cloning mammalian genes whose downregulation results in a selectable phenotype. This strategy is based on expression selection of genetic suppressor elements (GSEs), cDNA fragments encoding either specific peptides that act as dominant inhibitors of protein function or antisense RNA segments that efficiently inhibit gene expression. Since GSEs counteract the gene from which they are derived, they can be used as dominant selectable markers for the phenotype associated with downregulation of the corresponding gene. A retroviral library containing random fragments of normalized (uniform abundance) cDNA expressed in mouse NIH 3T3 cells was used to select for GSEs inducing resistance to the anticancer drug etoposide. Three GSEs were isolated, two of which are derived from unknown genes and the third encodes antisense RNA for the heavy chain of a motor protein kinesin. The kinesin-derived GSE induces resistance to several DNA-damaging drugs and immortalizes senescent mouse embryo fibroblasts, indicating that kinesin is involved in the mechanisms of drug sensitivity and in vitro senescence. Expression of the human kinesin heavy-chain gene was decreased in four of four etoposide-resistant HeLa cell lines, derived by conventional drug selection, indicating that downregulation of kinesin represents a natural mechanism of drug resistance in mammalian cells.
One of the main challenges in modern medicine is to stratify different patient groups in terms of... more One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with a poor response t... more Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with a poor response to chemotherapy and low survival rate. This unfavorable treatment response is likely to derive from both late diagnosis and from complex, incompletely understood biology, and heterogeneity among NSCLC subtypes. To define the relative contributions of major cellular pathways to the biogenesis of NSCLC and highlight major differences between NSCLC subtypes, we studied the molecular signatures of lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), based on analysis of gene expression and comparison of tumor samples with normal lung tissue. Our results suggest the existence of specific molecular networks and subtype-specific differences between lung ADC and SCC subtypes, mostly found in cell cycle, DNA repair, and metabolic pathways. However, we also observed similarities across major gene interaction networks and pathways in ADC and SCC. These data provide a new insight into the bio...
Genetic suppressor elements (GSEs) are short biologically active gene fragments that encode domin... more Genetic suppressor elements (GSEs) are short biologically active gene fragments that encode dominantly acting peptides or inhibitory antisense RNAs. GSEs can be isolated from a single gene or from a multigene complex by constructing a library of short random fragments of the target gene(s) in an expression vector, followed by expression selection for the desired phenotype in a suitable cellular system. GSE selection from a single gene allows one to develop efficient and specific inhibitors of the gene function and to identify functional protein domains. GSE selection from a multigene complex, such as a normalized (uniform abundance) cDNA population from mammalian cells, makes it possible to identify genes that are involved in selectable cellular phenotypes. The potential of GSE selection for uncovering novel gene functions was first demonstrated using bacteriophage lambda as a model system. GSE selection in retroviral expression vectors has been applied in mammalian cells to identif...
Journal of Bioinformatics and Computational Biology, 2007
Microarray-based characterization of tissues, cellular and disease states, and environmental cond... more Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.
2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
This study is aimed at elucidating putative transcription regulators (TRs) responsible for the ob... more This study is aimed at elucidating putative transcription regulators (TRs) responsible for the observed differential expression pattern. Combined direct promoter binding and indirect transcriptional regulation networks were used, and the expression levels of each TR's downstream targets were collectively analyzed, as a sample, for significance. Statistical procedure was also developed that takes into account the sign of the expression change,
The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and process... more The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
Proceedings of the National Academy of Sciences, 1994
We describe a general strategy for cloning mammalian genes whose downregulation results in a sele... more We describe a general strategy for cloning mammalian genes whose downregulation results in a selectable phenotype. This strategy is based on expression selection of genetic suppressor elements (GSEs), cDNA fragments encoding either specific peptides that act as dominant inhibitors of protein function or antisense RNA segments that efficiently inhibit gene expression. Since GSEs counteract the gene from which they are derived, they can be used as dominant selectable markers for the phenotype associated with downregulation of the corresponding gene. A retroviral library containing random fragments of normalized (uniform abundance) cDNA expressed in mouse NIH 3T3 cells was used to select for GSEs inducing resistance to the anticancer drug etoposide. Three GSEs were isolated, two of which are derived from unknown genes and the third encodes antisense RNA for the heavy chain of a motor protein kinesin. The kinesin-derived GSE induces resistance to several DNA-damaging drugs and immortalizes senescent mouse embryo fibroblasts, indicating that kinesin is involved in the mechanisms of drug sensitivity and in vitro senescence. Expression of the human kinesin heavy-chain gene was decreased in four of four etoposide-resistant HeLa cell lines, derived by conventional drug selection, indicating that downregulation of kinesin represents a natural mechanism of drug resistance in mammalian cells.
One of the main challenges in modern medicine is to stratify different patient groups in terms of... more One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.
Uploads
Papers by Ilya Mazo