Directing Experimental Biology: A Case Study in Mitochondrial Biogenesis

Computational approaches have promised to organize collections of functional genomics data into testable predictions of gene and protein involvement in biological processes and pathways. However, few such predictions have been experimentally validated on a large scale, leaving many bioinformatic methods unproven and underutilized in the biology community. Further, it remains unclear what biological concerns should be taken into account when using computational methods to drive real-world experimental efforts. To investigate these concerns and to establish the utility of computational predictions of gene function, we experimentally tested hundreds of predictions generated from an ensemble of three complementary methods for the process of mitochondrial organization and biogenesis in Saccharomyces cerevisiae. The biological data with respect to the mitochondria are presented in a companion manuscript published in PLoS Genetics (doi:10.1371/journal.pgen.1000407). Here we analyze and explore the results of this study that are broadly applicable for computationalists applying gene function prediction techniques, including a new experimental comparison with 48 genes representing the genomic background. Our study leads to several conclusions that are important to consider when driving laboratory investigations using computational prediction approaches. While most genes in yeast are already known to participate in at least one biological process, we confirm that genes with known functions can still be strong candidates for annotation of additional gene functions. We find that different analysis techniques and different underlying data can both greatly affect the types of functional predictions produced by computational methods. This diversity allows an ensemble of techniques to substantially broaden the biological scope and breadth of predictions. We also find that performing prediction and validation steps iteratively allows us to more completely characterize a biological area of interest. While this study focused on a specific functional area in yeast, many of these observations may be useful in the contexts of other processes and organisms

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype

Identification of a Negative Allosteric Site on Human α4β2 and α3β4 Neuronal Nicotinic Acetylcholine Receptors

Acetylcholine-based neurotransmission is regulated by cationic, ligand-gated ion channels called nicotinic acetylcholine receptors (nAChRs). These receptors have been linked to numerous neurological diseases and disorders such as Alzheimer's disease, Parkinson's disease, and nicotine addiction. Recently, a class of compounds has been discovered that antagonize nAChR function in an allosteric fashion. Models of human α4β2 and α3β4 nicotinic acetylcholine receptor (nAChR) extracellular domains have been developed to computationally explore the binding of these compounds, including the dynamics and free energy changes associated with ligand binding. Through a blind docking study to multiple receptor conformations, the models were used to determine a putative binding mode for the negative allosteric modulators. This mode, in close proximity to the agonist binding site, is presented in addition to a hypothetical mode of antagonism that involves obstruction of C loop closure. Molecular dynamics simulations and MM-PBSA free energy of binding calculations were used as computational validation of the predicted binding mode, while functional assays on wild-type and mutated receptors provided experimental support. Based on the proposed binding mode, two residues on the β2 subunit were independently mutated to the corresponding residues found on the β4 subunit. The T58K mutation resulted in an eight-fold decrease in the potency of KAB-18, a compound that exhibits preferential antagonism for human α4β2 over α3β4 nAChRs, while the F118L mutation resulted in a loss of inhibitory activity for KAB-18 at concentrations up to 100 µM. These results demonstrate the selectivity of KAB-18 for human α4β2 nAChRs and validate the methods used for identifying the nAChR modulator binding site. Exploitation of this site may lead to the development of more potent and subtype-selective nAChR antagonists which may be used in the treatment of a number of neurological diseases and disorders

The PtdIns 3-Kinase/Akt Pathway Regulates Macrophage-Mediated ADCC against B Cell Lymphoma

Macrophages are important effectors in the clearance of antibody-coated tumor cells. However, the signaling pathways that regulate macrophage-induced ADCC are poorly defined. To understand the regulation of macrophage-mediated ADCC, we used human B cell lymphoma coated with Rituximab as the tumor target and murine macrophages primed with IFNγ as the effectors. Our data demonstrate that the PtdIns 3-kinase/Akt pathway is activated during macrophage-induced ADCC and that the inhibition of PtdIns 3-kinase results in the inhibition of macrophage-mediated cytotoxicity. Interestingly, downstream of PtdIns 3-kinase, expression of constitutively active Akt (Myr-Akt) in macrophages significantly enhanced their ability to mediate ADCC. Further analysis revealed that in this model, macrophage-mediated ADCC is dependent upon the release of nitric oxide (NO). However, the PtdIns 3-kinase/Akt pathway does not appear to regulate NO production. An examination of the role of the PtdIns 3-kinase/Akt pathway in regulating conjugate formation indicated that macrophages treated with an inhibitor of PtdIns 3-kinase fail to polarize the cytoskeleton at the synapse and show a significant reduction in the number of conjugates formed with tumor targets. Further, inhibition of PtdIns 3-kinase also reduced macrophage spreading on Rituximab-coated surfaces. On the other hand, Myr-Akt expressing macrophages displayed a significantly greater ability to form conjugates with tumor cells. Taken together, these findings illustrate that the PtdIns 3-kinase/Akt pathway plays a critical role in macrophage ADCC through its influence on conjugate formation between macrophages and antibody-coated tumor cells

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology