microRNA-mediated R gene regulation: molecular scabbards for double-edged swords

Plant resistance(R) proteins are immune receptors that recognize pathogen effectors and trigger rapid defense responses, namely effector-triggered immunity. R protein-mediated pathogen resistance is usually race specific. During plant-pathogen coevolution,plant genomes accumulated large numbers of R genes. Even though plant R genes provide important natural resources for breeding disease-resistant crops, their presence in the plant genome comes at a cost. Misregulation of R genes leads to developmental defects, such as stunted growth and reduced fertility. In the past decade, many microRNAs(miRNAs) have been identified to target various R genes in plant genomes. miRNAs reduce R gene levels under normal conditions and allow induction of R gene expression under various stresses. For these reasons, we consider R genes to be double-edged "swords" and miRNAs as molecular "scabbards". In the present review, we summarize the contributions and potential problems of these "swords" and discuss the features and production of the "scabbards", as well as the mechanisms used to pull the "sword" from the "scabbard"when needed.     

Biological complexity and drug discovery: a practical systems biology approach

Drugs fail in clinical studies most often from lack of efficacy or unexpected toxicities. These failures result from an inadequate understanding of drug action and follow, in part, from our dependence on drug discovery technologies that do not take into account the complexity of human disease biology. Biological systems exhibit many features of complex engineering systems, including modularity, redundancy, robustness, and emergent properties. Addressing these features has contributed to the successful design of an improved biological assay technology for inflammation drug discovery. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), involves the statistical analysis of protein datasets generated from novel complex primary human cell-based assay systems. Compound profiling in these systems has revealed that a surprisingly large number of biological mechanisms can be detected and distinguished. Features of these assays relevant to the behaviour of complex systems are described.     

Evolution of complexity in miRNA-mediated gene regulation systems

Using Arabidopsis microRNA (miRNA)-mediated gene regulation system as a model, we investigated how complex systems evolve with special attention to selection to maintain the systems. We found that the copy number of miRNA genes within each system is a key factor to determine the complexity of the system, indicating a crucial role of gene duplication to increase the complexity. Furthermore, we show that the mode of selection to maintain the systems depend on their complexity levels.     

Mechanisms of microRNA-mediated gene regulation in animal cells

MicroRNAs are a large family of regulatory molecules found in all multicellular organisms. Even though their functions are only beginning to be understood, it is evident that microRNAs have important roles in a wide range of biological processes, including developmental timing, growth control, and differentiation. Indeed, recent bioinformatic and experimental evidence suggests that a remarkably large proportion of genes (>30%) are subject to microRNA-mediated regulation. Although it is clear that microRNAs function by suppressing protein production from targeted mRNAs, there is, at present, no consensus about how such downregulation is accomplished. In this review, I describe the evidence that there are multiple mechanisms of microRNA-mediated repression and discuss the possible connections between these mechanisms.     

Scaling of biological community structure: a systems approach to community complexity

Local community dynamics are determined by the interaction of environmental variation and the biotic properties of communities. This interaction occurs on many spatial and temporal scales, hence the expectation is that community dynamics will be complex. Previous theoretical approaches to communities have assumed linear, near equilibrium dynamics. An alternative approach suggests that community dynamics are the result of the balance between energy use by the community and its tendency to move towards thermodynamic equilibrium, in this case extinction of all species in the community. Because this balance will be imprecise, community dynamics should be oscillatory. Furthermore, because energy use by a community can be broken down into a hierarchical set of processes occurring on different time scales, community dynamics should reflect multiple periodicities. The above theoretical treatment suggests that since community dynamics are scaled, a hierarchical observational approach should help resolve important aspects of community structure. This approach of scaling community observations provides a technique for evaluation of community responses to environmental change, including human induced perturbations. A thermodynamic approach to community dynamics can also provide the basis for new theoretical and empricial discoveries about biological communities.     

Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach

BackgroundOvarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed.ObjectiveTo perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer.MethodThe gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction.ResultsWe identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer.ConclusionOur study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.     

Stability of multienzyme systems with feedback regulation: a graph theoretical approach

Stability of multienzyme systems with feedback regulation has been analyzed on the basis of the Lienard-Chipart criteria. The rules governing the topological graph construction for multienzyme systems have been developed. A theorem about correspondence of the graph constructed and coefficients of the characteristic polynomial of linearized kinetic equations is proved. The graph-theoretical stability analysis proposed is illustrated by a number of examples of multienzyme systems with feedback regulation.     

Evidence for protein kinase C regulation of ovarian theca-interstitial cell androgen biosynthesis

The hypothesis that protein kinase C may be an important regulator of ovarian theca-interstitial cell steroidogenesis was tested by using phorbol-12-myristate-13-acetate (PMA) and phorbol-12, 13-dibutyrate (PDB) to directly stimulate protein kinase C activity. Collagenase-dispersed cells (4 x 10(5) viable cells/dish) form ovaries of hypophysectomized immature rats were cultured in serum-free medium in the presence and absence of 0-100 ng/ml of luteinizing hormone (LH), PMA (0-100 nM), and/or PDB (0-100 nM). Treatment with 100 ng/ml LH stimulated androsterone production 100-fold at Day 4 of culture. The presence of 100 nM PMA or PDB had no effect on basal androsterone production; however, treatment with increasing concentrations of PMA or PDB (0-100 nM) caused a dose-related inhibition (maximum 70%) of LH-stimulated androsterone synthesis (ID50 = 1.8 nM and 2.4 nM, respectively). PMA and PDB did not significantly alter DNA, protein, or cell viability, indicating that their inhibitory effects were not due to changes in cell number or viability. Cells treated with LH and 100 nM 4 alpha-phorbol didecanoate (4 alpha-PDD; a phorbol ester that does not activate protein kinase C) failed to show significant decreases in androsterone production. Time-course studies revealed that when PMA treatment was delayed until Day 2 or 4 of culture, dramatic inhibitory effects on LH-stimulated androsterone production were still observed. These results suggest that the biological activity of protein kinase C is retained after the cells have expressed their differentiated state.(ABSTRACT TRUNCATED AT 250 WORDS)     

Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.     

Shared immunoproteome for ovarian cancer diagnostics and immunotherapy: potential theranostic approach to cancer

Elimination of cancer through early detection and treatment is the ultimate goal of cancer research and is especially critical for ovarian and other forms of cancer typically diagnosed at very late stages that have very poor response rates. Proteomics has opened new avenues for the discovery of diagnostic and therapeutic targets. Immunoproteomics, which defines the subset of proteins involved in the immune response, holds considerable promise for providing a better understanding of the early-stage immune response to cancer as well as important insights into antigens that may be suitable for immunotherapy. Early administration of immunotherapeutic vaccines can potentially have profound effects on prevention of metastasis and may potentially cure through efficient and complete tumor elimination. We developed a mass-spectrometry-based method to identify novel autoantibody-based serum biomarkers for the early diagnosis of ovarian cancer that uses native tumor-associated proteins immunoprecipitated by autoantibodies from sera obtained from cancer patients and from cancer-free controls to identify autoantibody signatures that occur at high frequency only in cancer patient sera. Interestingly, we identified a subset of more than 50 autoantigens that were also processed and presented by MHC class I molecules on the surfaces of ovarian cancer cells and thus were common to the two immunological processes of humoral and cell-mediated immunity. These shared autoantigens were highly representative of families of proteins with roles in key processes in carcinogenesis and metastasis, such as cell cycle regulation, cell proliferation, apoptosis, tumor suppression, and cell adhesion. Autoantibodies appearing at the early stages of cancer suggest that this detectable immune response to the developing tumor can be exploited as early-stage biomarkers for the development of ovarian cancer diagnostics. Correspondingly, because the T-cell immune response depends on MHC class I processing and presentation of peptides, proteins that go through this pathway are potential candidates for the development of immunotherapeutics designed to activate a T-cell immune response to cancer. To the best of our knowledge, this is the first comprehensive study that identifies and categorizes proteins that are involved in both humoral and cell-mediated immunity against ovarian cancer, and it may have broad implications for the discovery and selection of theranostic molecular targets for cancer therapeutics and diagnostics in general.     

Application of a systems approach to study developmental gene regulation

All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.     

Drugging the undruggable proteins in cancer: A systems biology approach

In recent years, the research community has, with comprehensive systems biology approaches and related technologies, gained insight into the vast complexity of numerous cancers. These approaches allow an in-depth exploration that cannot be achieved solely using conventional low-throughput methods, which do not closely mimic the natural cellular environment. In this review, we discuss recent integrative multiple omics approaches for understanding and modulating previously identified ‘undruggable’ targets such as members of the RAS family, MYC, TP53, and various E3 ligases and deubiquitinases. We describe how these technologies have revolutionized drug discovery by overcoming an array of biological and technological challenges and how, in the future, they will be pivotal in assessing cancer states in individual patients, allowing for the prediction and application of personalized disease treatments.