Yeast Features: Identifying Significant Features Shared Among Yeast Proteins for Functional Genomics

Background
High throughput yeast functional genomics experiments are revealing associations among tens to hundreds of genes using numerous experimental conditions. To fully understand how the identified genes might be involved in the observed system, it is essential to consider the widest range of biological annotation possible. Biologists often start their search by collating the annotation provided for each protein within databases such as the Saccharomyces Genome Database, manually comparing them for similar features, and empirically assessing their significance. Such tasks can be automated, and more precise calculations of the significance can be determined using established probability measures. 
Results
We developed Yeast Features, an intuitive online tool to help establish the significance of finding a diverse set of shared features among a collection of yeast proteins. A total of 18,786 features from the Saccharomyces Genome Database are considered, including annotation based on the Gene Ontology’s molecular function, biological process and cellular compartment, as well as conserved domains, protein-protein and genetic interactions, complexes, metabolic pathways, phenotypes and publications. The significance of shared features is estimated using a hypergeometric probability, but novel options exist to improve the significance by adding background knowledge of the experimental system. For instance, increased statistical significance is achieved in gene deletion experiments because interactions with essential genes will never be observed. We further demonstrate the utility by suggesting the functional roles of the indirect targets of an aminoglycoside with a known mechanism of action, and also the targets of an herbal extract with a previously unknown mode of action. The identification of shared functional features may also be used to propose novel roles for proteins of unknown function, including a role in protein synthesis for YKL075C.
Conclusions
Yeast Features (YF) is an easy to use web-based application (http://software.dumontierlab.com/yeastfeatures/) which can identify and prioritize features that are shared among a set of yeast proteins. This approach is shown to be valuable in the analysis of complex data sets, in which the extracted associations revealed significant functional relationships among the gene products.
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Relevant distance between two different instances of the same potential energy in protein folding

In the context of complex systems and, particularly, of protein folding, a physically meaningful distance is defined which allows to make useful statistical statements about the way in which energy differences are modified when two different instances of the same potential-energy function are used. When the two instances arise from the fact that different algorithms or different approximations are used, the distance herein defined may be used to evaluate the relative accuracy of the two methods. When the difference is due to a change in the free parameters of which the potential depends on, the distance can be used to quantify, in each region of parameter space, the robustness of the modeling to such a change and this, in turn, may be used to assess the significance of a parameters' fit. Both cases are illustrated with a practical example: the study of the Poisson-based solvation energy in the Trp-Cage protein (PDB code 1L2Y).Comment: 20 pages, 6 figures, LaTeX file, elsart style. v1: Aknowledgments modified. v2: y-values of fig. 5 and 6 corrected. v3: Journal-ref added, aknowledgements modified and fig. 1 and 2 correcte

Finding the "Dark Matter'' in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter'' of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

The Proteasomal Deubiquitinating Enzyme PSMD14 Regulates Macroautophagy by Controlling Golgi-to-ER Retrograde Transport

Ubiquitination regulates several biological processes, however the role of specific members of the ubiquitinome on intracellular membrane trafficking is not yet fully understood. Here, we search for ubiquitin-related genes implicated in protein membrane trafficking performing a High-Content siRNA Screening including 1187 genes of the human “ubiquitinome” using amyloid precursor protein (APP) as a reporter. We identified the deubiquitinating enzyme PSMD14, a subunit of the 19S regulatory particle of the proteasome, specific for K63-Ub chains in cells, as a novel regulator of Golgi-to-endoplasmic reticulum (ER) retrograde transport. Silencing or pharmacological inhibition of PSMD14 with Capzimin (CZM) caused a robust increase in APP levels at the Golgi apparatus and the swelling of this organelle. We showed that this phenotype is the result of rapid inhibition of Golgi-to-ER retrograde transport, a pathway implicated in the early steps of the autophagosomal formation. Indeed, we observed that inhibition of PSMD14 with CZM acts as a potent blocker of macroautophagy by a mechanism related to the retention of Atg9A and Rab1A at the Golgi apparatus. As pharmacological inhibition of the proteolytic core of the 20S proteasome did not recapitulate these effects, we concluded that PSMD14, and the K63-Ub chains, act as a crucial regulatory factor for macroautophagy by controlling Golgi-to-ER retrograde transport

OPA1 mutation and late-onset cardiomyopathy: mitochondrial dysfunction and mtDNA instability.

BackgroundMitochondrial fusion protein mutations are a cause of inherited neuropathies such as Charcot-Marie-Tooth disease and dominant optic atrophy. Previously we reported that the fusion protein optic atrophy 1 (OPA1) is decreased in heart failure.Methods and resultsWe investigated cardiac function, mitochondrial function, and mtDNA stability in a mouse model of the disease with OPA1 mutation. The homozygous mutation is embryonic lethal. Heterozygous OPA(+/-) mice exhibit reduced mtDNA copy number and decreased expression of nuclear antioxidant genes at 3 to 4 months. Although initial cardiac function was normal, at 12 months the OPA1(+/-) mouse hearts had decreased fractional shortening, cardiac output, and myocyte contraction. This coincided with the onset of blindness. In addition to small fragmented mitochondria, aged OPA1(+/-) mice had impaired cardiac mitochondrial function compared with wild-type littermates.ConclusionsOPA1 mutation leads to deficiency in antioxidant transcripts, increased reactive oxygen species, mitochondrial dysfunction, and late-onset cardiomyopathy

Force Distribution Reveals Signal Transduction in E. coli Hsp90

AbstractHeat-shock protein 90 (Hsp90) is an ubiquitous chaperone that is essential for cell function in that it promotes client-protein folding and stabilization. Its function is tightly controlled by an ATP-dependent large conformational transition between the open and closed states of the Hsp90 dimer. The underlying allosteric pathway has remained largely unknown, but it is revealed here in atomistic detail for the Escherichia coli homolog HtpG. Using force-distribution analysis based on molecular-dynamics simulations (>1 μs in total), we identify an internal signaling pathway that spans from the nucleotide-binding site to an ∼2.3-nm-distant region in the HtpG middle domain, that serves as a dynamic hinge region, and to a putative client-protein-binding site in the middle domain. The force transmission is triggered by ATP capturing a magnesium ion and thereby rotating and bending a proximal long α-helix, which represents the major force channel into the middle domain. This allosteric mechanism is, with statistical significance, distinct from the dynamics in the ADP and apo states. Tracking the distribution of forces is likely to be a promising tool for understanding and guiding experiments of complex allosteric proteins in general

Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

Energy metabolism in human pluripotent stem cells and their differentiated counterparts

Background: Human pluripotent stem cells have the ability to generate all cell types present in the adult organism, therefore harboring great potential for the in vitro study of differentiation and for the development of cell-based therapies. Nonetheless their use may prove challenging as incomplete differentiation of these cells might lead to tumoregenicity. Interestingly, many cancer types have been reported to display metabolic modifications with features that might be similar to stem cells. Understanding the metabolic properties of human pluripotent stem cells when compared to their differentiated counterparts can thus be of crucial importance. Furthermore recent data has stressed distinct features of different human pluripotent cells lines, namely when comparing embryo-derived human embryonic stem cells (hESCs) and induced pluripotent stem cells (IPSCs) reprogrammed from somatic cells. Methodology/Principal Findings: We compared the energy metabolism of hESCs, IPSCs, and their somatic counterparts. Focusing on mitochondria, we tracked organelle localization and morphology. Furthermore we performed gene expression analysis of several pathways related to the glucose metabolism, including glycolysis, the pentose phosphate pathway and the tricarboxylic acid (TCA) cycle. In addition we determined oxygen consumption rates (OCR) using a metabolic extracellular flux analyzer, as well as total intracellular ATP levels by high performance liquid chromatography (HPLC). Finally we explored the expression of key proteins involved in the regulation of glucose metabolism. Conclusions/Findings: Our results demonstrate that, although the metabolic signature of IPSCs is not identical to that of hESCs, nonetheless they cluster with hESCs rather than with their somatic counterparts. ATP levels, lactate production and OCR revealed that human pluripotent cells rely mostly on glycolysis to meet their energy demands. Furthermore, our work points to some of the strategies which human pluripotent stem cells may use to maintain high glycolytic rates, such as high levels of hexokinase II and inactive pyruvate dehydrogenase (PDH). © 2011 Varum et al