Linear fuzzy gene network models obtained from microarray data by exhaustive search

BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems

Adaptations of Avian Flu Virus Are a Cause for Concern

We are in the midst of a revolutionary period in the life sciences. Technological capabilities have dramatically expanded, we have a much improved understanding of the complex biology of selected microorganisms, and we have a much improved ability to manipulate microbial genomes. With this has come unprecedented potential for better control of infectious diseases and significant societal benefit. However, there is also a growing risk that the same science will be deliberately misused and that the consequences could be catastrophic. Efforts to describe or define life-sciences research of particular concern have focused on the possibility that knowledge or products derived from such research, or new technologies, could be directly misapplied with a sufficiently broad scope to affect national or global security. Research that might greatly enhance the harm caused by microbial pathogens has been of special concern (1–3). Until now, these efforts have suffered from a lack of specificity and a paucity of concrete examples of “dual use research of concern” (3). Dual use is defined as research that could be used for good or bad purposes. We are now confronted by a potent, real-world example

Genomic Signature-Based Identification of Influenza A Viruses Using RT-PCR/Electro-Spray Ionization Mass Spectrometry (ESI-MS) Technology

BACKGROUND: The emergence and rapid spread of the 2009 H1N1 pandemic influenza A virus (H1N1pdm) in humans highlights the importance of enhancing the capability of existing influenza surveillance systems with tools for rapid identification of emerging and re-emerging viruses. One of the new approaches is the RT-PCR electrospray ionization mass spectrometry (RT-PCR/ESI-MS) technology, which is based on analysis of base composition (BC) of RT-PCR amplicons from influenza "core" genes. Combination of the BC signatures represents a "genomic print" of an influenza A virus. METHODOLOGY/PRINCIPAL FINDINGS: Here, 757 samples collected between 2006 and 2009 were tested, including 302 seasonal H1N1, 171 H3N2, 7 swine triple reassortants, and 277 H1N1pdm viruses. Of the 277 H1N1pdm samples, 209 were clinical specimens (throat, nasal and nasopharyngeal swabs, nasal washes, blood and sputum). BC signatures for the clinical specimen from one of the first cases of the 2009 pandemic, A/California/04/2009, confirmed it as an unusual, previously unrecognized influenza A virus, with "core" genes related to viruses of avian, human and swine origins. Subsequent analysis of additional 276 H1N1pdm samples revealed that they shared the genomic print of A/California/04/2009, which differed from those of North American swine triple reassortant viruses, seasonal H1N1 and H3N2 and other viruses tested. Moreover, this assay allowed distinction between "core" genes of co-circulating groups of seasonal H1N1, such as clades 2B, 2C, and their reassortants with dual antiviral resistance to adamantanes and oseltamivir. CONCLUSIONS/SIGNIFICANCE: The RT-PCR/ESI-MS assay is a broad range influenza identification tool that can be used directly on clinical specimens for rapid and accurate detection of influenza virus genes. The assay differentiates the H1N1pdm from seasonal and other nonhuman hosts viruses. Although not a diagnostic tool, this assay demonstrates its usefulness and robustness in influenza virus surveillance and detection of novel and unusual viruses with previously unseen genomic prints

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations