Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Progressive clustering based method for protein function prediction

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.

Studies of OPA1 pathogenic mechanisms in Dominant Optic Atrophy and novel protein function in mitochondrial DNA stability

The mitochondrion is an essential cytoplasmic organelle that provides most of the energy necessary for eukaryotic cell physiology. Mitochondrial structure and functions are maintained by proteins of both mitochondrial and nuclear origin. These organelles are organized in an extended network that dynamically fuses and divides. Mitochondrial morphology results from the equilibrium between fusion and fission processes, controlled by a family of “mitochondria-shaping” proteins. It is becoming clear that defects in mitochondrial dynamics can impair mitochondrial respiration, morphology and motility, leading to apoptotic cell death in vitro and more or less severe neurodegenerative disorders in vivo in humans. Mutations in OPA1, a nuclear encoded mitochondrial protein, cause autosomal Dominant Optic Atrophy (DOA), a heterogeneous blinding disease characterized by retinal ganglion cell degeneration leading to optic neuropathy (Delettre et al., 2000; Alexander et al., 2000). OPA1 is a mitochondrial dynamin-related guanosine triphosphatase (GTPase) protein involved in mitochondrial network dynamics, cytochrome c storage and apoptosis. This protein is anchored or associated on the inner mitochondrial membrane facing the intermembrane space. Eight OPA1 isoforms resulting from alternative splicing combinations of exon 4, 4b and 5b have been described (Delettre et al., 2001). These variants greatly vary among diverse organs and the presence of specific isoforms has been associated with various mitochondrial functions. The different spliced exons encode domains included in the amino-terminal region and contribute to determine OPA1 functions (Olichon et al., 2006). It has been shown that exon 4, that is conserved throughout evolution, confers functions to OPA1 involved in maintenance of the mitochondrial membrane potential and in the fusion of the network. Conversely, exon 4b and exon 5b, which are vertebrate specific, are involved in regulation of cytochrome c release from mitochondria, and activation of apoptosis, a process restricted to vertebrates (Olichon et al., 2007). While Mgm1p has been identified thanks to its role in mtDNA maintenance, it is only recently that OPA1 has been linked to mtDNA stability. Missense mutations in OPA1 cause accumulation of multiple deletions in skeletal muscle. The syndrome associated to these mutations (DOA-1 plus) is complex, consisting of a combination of dominant optic atrophy, progressive external ophtalmoplegia, peripheral neuropathy, ataxia and deafness (Amati- Bonneau et al., 2008; Hudson et al., 2008). OPA1 is the fifth gene associated with mtDNA “breakage syndrome” together with ANT1, PolG1-2 and TYMP (Spinazzola et al., 2009). In this thesis we show for the first time that specific OPA1 isoforms associated to exon 4b are important for mtDNA stability, by anchoring the nucleoids to the inner mitochondrial membrane. Our results clearly demonstrate that OPA1 isoforms including exon 4b are intimately associated to the maintenance of the mitochondrial genome, as their silencing leads to mtDNA depletion. The mechanism leading to mtDNA loss is associated with replication inhibition in cells where exon 4b containing isoforms were down-regulated. Furthermore silencing of exon 4b associated isoforms is responsible for alteration in mtDNA-nucleoids distribution in the mitochondrial network. In this study it was evidenced that OPA1 exon 4b isoform is cleaved to provide a 10kd peptide embedded in the inner membrane by a second transmembrane domain, that seems to be crucial for mitochondrial genome maintenance and does correspond to the second transmembrane domain of the yeasts orthologue encoded by MGM1 or Msp1, which is also mandatory for this process (Diot et al., 2009; Herlan et al., 2003). Furthermore in this thesis we show that the NT-OPA1-exon 4b peptide co-immuno-precipitates with mtDNA and specifically interacts with two major components of the mitochondrial nucleoids: the polymerase gamma and Tfam. Thus, from these experiments the conclusion is that NT-OPA1- exon 4b peptide contributes to the nucleoid anchoring in the inner mitochondrial membrane, a process that is required for the initiation of mtDNA replication and for the distribution of nucleoids along the network. These data provide new crucial insights in understanding the mechanism involved in maintenance of mtDNA integrity, because they clearly demonstrate that, besides genes implicated in mtDNA replications (i.e. polymerase gamma, Tfam, twinkle and genes involved in the nucleotide pool metabolism), OPA1 and mitochondrial membrane dynamics play also an important role. Noticeably, the effect on mtDNA is different depending on the specific OPA1 isoforms down-regulated, suggesting the involvement of two different combined mechanisms. Over two hundred OPA1 mutations, spread throughout the coding region of the gene, have been described to date, including substitutions, deletions or insertions. Some mutations are predicted to generate a truncated protein inducing haploinsufficiency, whereas the missense nucleotide substitutions result in aminoacidic changes which affect conserved positions of the OPA1 protein. So far, the functional consequences of OPA1 mutations in cells from DOA patients are poorly understood. Phosphorus MR spectroscopy in patients with the c.2708delTTAG deletion revealed a defect in oxidative phosphorylation in muscles (Lodi et al., 2004). An energetic impairment has been also show in fibroblasts with the severe OPA1 R445H mutation (Amati-Bonneau et al., 2005). It has been previously reported by our group that OPA1 mutations leading to haploinsufficiency are associated in fibroblasts to an oxidative phosphorylation dysfunction, mainly involving the respiratory complex I (Zanna et al., 2008). In this study we have evaluated the energetic efficiency of a panel of skin fibroblasts derived from DOA patients, five fibroblast cell lines with OPA1 mutations causing haploinsufficiency (DOA-H) and two cell lines bearing mis-sense aminoacidic substitutions (DOA-AA), and compared with control fibroblasts. Although both types of DOA fibroblasts maintained a similar ATP content when incubated in a glucose-free medium, i.e. when forced to utilize the oxidative phosphorylation only to produce ATP, the mitochondrial ATP synthesis through complex I, measured in digitonin-permeabilized cells, was significantly reduced in cells with OPA1 haploinsufficiency only, whereas it was similar to controls in cells with the missense substitutions. Furthermore, evaluation of the mitochondrial membrane potential (DYm) in the two fibroblast lines DOA-AA and in two DOA-H fibroblasts, namely those bearing the c.2819-2A>C mutation and the c.2708delTTAG microdeletion, revealed an anomalous depolarizing response to oligomycin in DOA-H cell lines only. This finding clearly supports the hypothesis that these mutations cause a significant alteration in the respiratory chain function, which can be unmasked only when the operation of the ATP synthase is prevented. Noticeably, oligomycin-induced depolarization in these cells was almost completely prevented by preincubation with cyclosporin A, a well known inhibitor of the permeability transition pore (PTP). This results is very important because it suggests for the first time that the voltage threshold for PTP opening is altered in DOA-H fibroblasts. Although this issue has not yet been addressed in the present study, several are the mechanisms that have been proposed to lead to PTP deregulation, including in particular increased reactive oxygen species production and alteration of Ca2+ homeostasis, whose role in DOA fibroblasts PTP opening is currently under investigation. Identification of the mechanisms leading to altered threshold for PTP regulation will help our understanding of the pathophysiology of DOA, but also provide a strategy for therapeutic intervention.

Identification of a new genomic hot spot of evolutionary diversification of protein function.

Establishment of phylogenetic relationships remains a challenging task because it is based on computational analysis of genomic hot spots that display species-specific sequence variations. Here, we identify a species-specific thymine-to-guanine sequence variation in the Glrb gene which gives rise to species-specific splice donor sites in the Glrb genes of mouse and bushbaby. The resulting splice insert in the receptor for the inhibitory neurotransmitter glycine (GlyR) conveys synaptic receptor clustering and specific association with a particular synaptic plasticity-related splice variant of the postsynaptic scaffold protein gephyrin. This study identifies a new genomic hot spot which contributes to phylogenetic diversification of protein function and advances our understanding of phylogenetic relationships.

Molecular mechanisms of signal transducer and activator of transcription (STAT) protein function in hematopoiesis

Hematopoietic growth factors play important roles in regulating blood cell growth and development in vivo. In this work, we investigated the signaling mechanisms of two growth factors with clinical significance, erythropoietin (Epo) and granulocyte colony-stimulating factor (G-CSF). Epo is essential for the survival, proliferation and differentiation of red blood cell progenitors, while G-CSF plays an important role in controlling mature neutrophil production. To identify which amino acid(s) and/or motif in EpoR is responsible for cell survival, wild type or mutant EpoR isoforms were transfected into the growth factor-dependent 32D cell line. Proliferation and apoptosis assays demonstrated that an EpoR isoform that lacks intracellular tyrosine residues and is truncated after 321 amino acids in the cytoplasmic tail (EpoR 1-321) mediates Epo-dependent cell survival. Furthermore, in absence of fetal calf serum (FCS), Epo signaling through wild type or mutant receptors supported anti-apoptosis, but not proliferation during 72 hours in response to Epo. To investigate the signaling pathway by which EpoR regulates cell survival, a dominant negative Stat5b (dnStat5b) isoform was generated and coexpressed with EpoR in stable cell lines. Expression of dnStat5b causes a significant induction of apoptosis in the presence of Epo in cells expressing EpoR 1-321, indicating that Stat5 is essential for survival signaling through tyrosine independent sequences in the EpoR. In a second project to investigate G-CSF signaling, we studied mechanisms by which G-CSF regulates the expression of PU.1, an important transcription factor in myeloid and B cell development. We demonstrated, by immunoblot and real time RT-PCR, that PU.1 is induced by G-CSF ex vivo as well as in vivo. To test whether G-CSF signaling through Stat3 is required for PU.1 regulation, the upstream region of the PU.1 gene was analyzed for potential Stat3 binding motifs. Four potential sites were identified; chromatin immunoprecipitations demonstrated that G-CSF activated Stat3 binds to 3 of the 4 binding motifs. In addition, PU.1 induction by G-CSF was completely abrogated in bone marrow from hematopoietic conditional Stat3 knockout mice. These results indicate an important role for Stat3 in G-CSF-dependent PU.1 gene regulation. Collectively, our works demonstrate that Stat protein play important and diverse roles in hematopoietic growth factor signaling. ^

Low cost 3D global instability analysis and flow sensitivity based on dynamic mode decomposition and high-order numerical tools

We explore the recently developed snapshot-based dynamic mode decomposition (DMD) technique, a matrix-free Arnoldi type method, to predict 3D linear global flow instabilities. We apply the DMD technique to flows confined in an L-shaped cavity and compare the resulting modes to their counterparts issued from classic, matrix forming, linear instability analysis (i.e. BiGlobal approach) and direct numerical simulations. Results show that the DMD technique, which uses snapshots generated by a 3D non-linear incompressible discontinuous Galerkin Navier?Stokes solver, provides very similar results to classical linear instability analysis techniques. In addition, we compare DMD results issued from non-linear and linearised Navier?Stokes solvers, showing that linearisation is not necessary (i.e. base flow not required) to obtain linear modes, as long as the analysis is restricted to the exponential growth regime, that is, flow regime governed by the linearised Navier?Stokes equations, and showing the potential of this type of analysis based on snapshots to general purpose CFD codes, without need of modifications. Finally, this work shows that the DMD technique can provide three-dimensional direct and adjoint modes through snapshots provided by the linearised and adjoint linearised Navier?Stokes equations advanced in time. Subsequently, these modes are used to provide structural sensitivity maps and sensitivity to base flow modification information for 3D flows and complex geometries, at an affordable computational cost. The information provided by the sensitivity study is used to modify the L-shaped geometry and control the most unstable 3D mode.

Protein function prediction based on protein – protein interaction networks.

This research has expanded the knowledge in Bioinformatics and Data mining. It makes an influential contribution to the future research in this area.

Development of a new signal processing diagnostic tool for vibration signals acquired in transient conditions

The diagnostics of mechanical components operating in transient conditions is still an open issue, in both research and industrial field. Indeed, the signal processing techniques developed to analyse stationary data are not applicable or are affected by a loss of effectiveness when applied to signal acquired in transient conditions. In this paper, a suitable and original signal processing tool (named EEMED), which can be used for mechanical component diagnostics in whatever operating condition and noise level, is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED) and the analytical approach of the Hilbert transform. The proposed tool is able to supply diagnostic information on the basis of experimental vibrations measured in transient conditions. The tool has been originally developed in order to detect localized faults on bearings installed in high speed train traction equipments and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on spectral kurtosis or envelope analysis, which represent until now the landmark for bearings diagnostics.

Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD and MED

The signal processing techniques developed for the diagnostics of mechanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transient conditions. In this chapter, an original signal processing tool is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert transform. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.

Predictability of nonstationary time series using wavelet and EMD based ARMA models

Research has been undertaken to ascertain the predictability of non-stationary time series using wavelet and Empirical Mode Decomposition (EMD) based time series models. Methods have been developed in the past to decompose a time series into components. Forecasting of these components combined with random component could yield predictions. Using this ideology, wavelet and EMD analyses have been incorporated separately which decomposes a time series into independent orthogonal components with both time and frequency localizations. The component series are fit with specific auto-regressive models to obtain forecasts which are later combined to obtain the actual predictions. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability is checked for six and twelve months ahead forecasts across both the methodologies. Based on performance measures, it is observed that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm can be used to model events such as droughts with reasonable accuracy. Also, some modifications that can be made in the model have been discussed that could extend the scope of applicability to other areas in the field of hydrology. (C) 2013 Elesvier B.V. All rights reserved.

多尺度分解与相位反褶积提高海上地震资料分辨率和信噪比

Offshore seismic exploration is full of high investment and risk. And there are many problems, such as multiple. The technology of high resolution and high S/N ratio on marine seismic data processing is becoming an important project. In this paper, the technology of multi-scale decomposition on both prestack and poststack seismic data based on wavelet and Hilbert-Huang transform and the theory of phase deconvolution is proposed by analysis of marine seismic exploration, investigation and study of literatures, and integration of current mainstream and emerging technology. Related algorithms are studied. The Pyramid algorithm of decomposition and reconstruction had been given by the Mallat algorithm of discrete wavelet transform In this paper, it is introduced into seismic data processing, the validity is shown by test with field data. The main idea of Hilbert-Huang transform is the empirical mode decomposition with which any complicated data set can be decomposed into a finite and often small number of intrinsic mode functions that admit well-behaved Hilbert transform. After the decomposition, a analytical signal is constructed by Hilbert transform, from which the instantaneous frequency and amplitude can be obtained. And then, Hilbert spectrum. This decomposition method is adaptive and highly efficient. Since the decomposition is based on the local characteristics of the time scale of data, it is applicable to nonlinear and non-stationary processes. The phenomenons of fitting overshoot and undershoot and end swings are analyzed in Hilbert-Huang transform. And these phenomenons are eliminated by effective method which is studied in the paper. The technology of multi-scale decomposition on both prestack and poststack seismic data can realize the amplitude preserved processing, enhance the seismic data resolution greatly, and overcome the problem that different frequency components can not restore amplitude properly uniformly in the conventional method. The method of phase deconvolution, which has overcome the minimum phase limitation in traditional deconvolution, approached the base fact well that the seismic wavelet is phase mixed in practical application. And a more reliable result will be given by this method. In the applied research, the high resolution relative amplitude preserved processing result has been obtained by careful analysis and research with the application of the methods mentioned above in seismic data processing in four different target areas of China Sea. Finally, a set of processing flow and method system was formed in the paper, which has been carried on in the application in the actual production process and has made the good progress and the huge economic benefit.

A novel spectral representation of electromyographic signals

Time/frequency and temporal analyses have been widely used in biomedical signal processing. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analysed in order to understand or model the physiological system. Historically, Fourier spectral analyses have provided a general method for examining the global energy/frequency distributions. However, an assumption inherent to these methods is the stationarity of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in the analysis of electromyographic signals. The results show that this method may provide not only an increase in the spectral resolution but also an insight into the underlying process of the muscle contraction.

EMD: A novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency-energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Application of EMD as a novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.