Haut PDF In silico analysis of mitochondrial proteins

In silico analysis of mitochondrial proteins

In silico analysis of mitochondrial proteins

Orthologs of subfamilies were identified by a two-round procedure combining BLAST search with phylogenetic inference. Each round included BLAST searches and data selection followed by phylogenetic analysis. The difference between round one and two was the set of seed sequences used for BLAST searches. In round one, the genome- deduced protein sequences from each species were com- pared by BLAST with known ACAD proteins (seeds listed in Table 2), at a threshold of e = 1  10 20 . In total, 2258 sequences matched at least one seed under this condition. From each species, we selected up to three top matches for each ACAD subfamily and preliminarily anno- tated the corresponding proteins as potential homologs of the corresponding subfamily. As certain query sequences matched multiple different subfamilies, we analyzed these a second time by applying the following rule: if a given sequence matched multiple subfamilies and the e-values of the matches differed by more than 10-fold, then the sequence was assigned to the subfamily with the lowest e-value. This case applied to 1572 sequences. Otherwise, if e-values of the multiple matches differed less than 10-fold, all preliminary subfamily assignments were retained and the final annotation was based on the sub- sequent phylogenetic analysis. This category included 341 sequences. We built a maximum likelihood phylogenetic tree for each protein subfamily (see procedure below) using all sequences assigned to this subfamily. From these trees, we selected slowly evolving and unambiguous ortho- logs of the initial mammalian seed sequences, i.e. proteins from Monosiga, the closest unicellular relative of animals (36), and Batrachochytrium, a member of the earliest diver- gence in fungi (36). These, combined with the first set of seeds, formed the second set of seeds used for round two of BLAST searches (Table 2). The same screening procedure was applied as in round one, followed by construction of a phylogenetic tree for each subfamily
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Mitochondrial DNA analysis of the geographic structure of Indian scad mackerel in the Indo-Malay archipelago

Mitochondrial DNA analysis of the geographic structure of Indian scad mackerel in the Indo-Malay archipelago

We are grateful to P.H. Barber for helpful advice and corrections on this manuscript; to V. Castric, C. Poux, A. Rohfritsch, and Suprapto for help with laboratory work; to R. Andamari, S.B. Atmaja, A.S. Cabanban, G. Lesage, S. Nurhakim, M. Potier, Rumini, B. Sadhotomo, Suprapto, and Suwarso for help in collecting fish samples; to A.S. Cabanban and J.-M. Ecoutin for hospitality in Malaysia and Indonesia. We gratefully acknowledge the support of J.-R. Durand, J.-M. Ecoutin, M. Fatuchri Sukadi, P. Levang, S. Nurhakim, J. Roch, and J. Widodo (PELFISH: IRD and BPPL), F. Bonhomme, and J.-P. Féral. Funded by AB 632740 of IRD/DRV, IFREMER URM 16 and Réseau Diversité Marine.
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In Silico Survey of the Mitochondrial Protein Uptake and Maturation Systems in the Brown Alga Ectocarpus siliculosus

In Silico Survey of the Mitochondrial Protein Uptake and Maturation Systems in the Brown Alga Ectocarpus siliculosus

Methods Identification and annotation of mitochondrial proteins Mitochondrial proteins encoded by the Ectocarpus nuclear genome were identified using several different approaches. First, proteins with N-terminal mitochondrial targeting sequences were identified by applying the HECTAR program [30] to the complete set of proteins encoded by the genome [31]. Additional analyses (Table 1 and S1) were carried out with the mitochondrial localisation predictors listed in Table 2. Note that although most of these programs (including HECTAR) only recognise N-terminal targeting sequences, some, such as MitoPred, Cello and SubLoc, also take into account other features [32]. Second, additional mitochondrial proteins were identified by searching for genes that had been assigned either definition descriptions containing the word mitochondrial or gene ontology annotations with mitochondria as the subcellular localisation during the automatic annotation of genome sequence (which was based on matches to existing sequence databases). Finally, detailed manual searches were carried out for specific mitochondrial proteins involved in protein import and maturation. During the manual functional annotation process, particular attention was paid to the identification of protein motifs, transmembrane domains, shared structural characteristic and key amino acids as a means to validate protein function (Tables 2, S2 and S3, Figs. S1 and S2).
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RNA interference screen reveals a high proportion of mitochondrial proteins essential for correct cell cycle progress in Trypanosoma brucei

RNA interference screen reveals a high proportion of mitochondrial proteins essential for correct cell cycle progress in Trypanosoma brucei

Protocol and criteria for definition of cell cycle defects At day 5 post-induction, cells were fixed in 4% parafor- maldehyde and stained with 4′,6-diamidino2-phenylindole (DAPI), air-dried on microscope immunofluorescence slides, and then analysed on a Zeiss Axioplan 2 micro- scope with a 100× objective equipped with a Photomet- rics CoolSNAP charge-coupled device camera (Roper Scientific®) driven by Metamorph Software (Molecular Devices®). The numbers of nuclei (N) and kinetoplasts (K) per cell were then counted in 200 cells, allowing de- termining the position of these cells in the cell cycle progress. Morphological abnormalities of the nuclei and/or kinetoplasts, as well as of the whole cell, were also recorded. All primary phenotypic data were collected blind. These data were then compared to those obtained for the reference cell line described above (T280), where DAPI analysis indicated ~68% cells with one nucleus (N) and one kinetoplast (K) (1N1K cells), ~20% 1N2K cells, and ~7% 2N2K cells, i.e. 95% normal phenotypes. ‘Abnormal’ phenotypes were rare in the reference line: 2.5% 2N1K, 2% zoids (0N1K), <1% multinucleated cells and none for the rest of them. Phenotypes were consid- ered as abnormal in the mutants when they were increased/reduced by a proportion of ± 2.5 SDs, as compared to the induced control (T280). These limits were defined in order to ensure statistical significance to the differences observed. Therefore, an abnormal phenotype was recorded if the following conditions were met: 1N1K <66.1% or >70.9%; 1N2K <13.4% or >29.4%; 2N2K <4.7% or >10.9%; 2N1K >3.8%; multinucleated cells (>2N) >2%; >2N > 2K >0.6%; >2K, 0K and 0N0K >0; and ‘zoids’ (0N) >3.2%.
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Comprehensive functional analysis of large lists of genes and proteins

Comprehensive functional analysis of large lists of genes and proteins

Conclusions Publicly available data are now widely used in research, often integrated with new data generated by researchers. Software that perform such type of analyses to select candidate markers are more and more needed. ClueGO performs up to date functional analyses for large lists of genes and proteins revealing new biological insights that add to previous knowledge. Compared to many other tools that represent the results as long lists of terms, ClueGO visualizes GO terms and pathways as networks, and groups them based on their biological role. Several lists of markers can be simultaneously analyzed to underline their common or specific functions. To facilitate the analysis, ClueGO provides predefined settings for the selection of the terms. In addition, pathways and terms representative for the investigated genes/proteins, their significance and interrelations in functional groups are directly visualized on the network. Several ClueGO visual styles can be used to highlight different important information on nodes and edges. The network and other figures can be exported and saved as high quality images. Data sources, including the date when the ontology files were created, the version of the software used as well as the parameters applied to select pathways should be mentioned to ensure the reproducibility of the results. Many species are now supported by ClueGO, and additional organisms will be included on demand. ClueGO is extended by CluePedia, that enables the analysis of experimental data and the visualization of protein- protein interrelations within a pathway. Based on experimental derived or in silico scores, new genes/proteins potentially associated with a pathway can be found. New versions of the software will continue to support the community of ClueGO and Cytoscape with new features and visualizations.
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Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Local conformational analysis Secondary structures were assigned with the most widely used algorithm, namely DSSP (CMBI version 2000) with default parameters (Kabsch et Sander 83). Protein Blocks (PBs) were also used. PBs are a structural alphabet composed of 16 local prototypes ( Joseph et al., 2010 ) five residues in length. PBs give a reasonable approximation of all local protein 3D structures ( de Brevern, Etchebest & Hazout, 2000 ) and are very efficient in protein superimpositions ( Joseph, Srinivasan & de Brevern, 2012 ) and MD (Molecular Dynamics) analyses ( de Brevern et al., 2005 ). They are labelled from a to p. PBs m and d can be roughly described as prototypes for α-helix and central β-strand, respectively. PBs a to c primarily represent β-strand N-caps and PBs e and f represent β-strand C-caps; PBs g to j are specific to coils; PBs k and l to α-helix N-caps, while PBs n to p to α-helix C-caps. PB ( de Brevern, 2005 ) assignment was carried out using our PBxplore tool (available at GitHub) ( Barnoud et al., 2017 ).
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Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson’s disease

Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson’s disease

In this work, we have studied the effect of PD-related perturbations on brain cells metabolism. Metabolites were measured in brain tissue from Parkin KO (knockout) mice, as well as in brain tissue exposed to the complex I antagonist CCCP (carbonyl cyanide m-chloro phenyl hydrazone). The genetic model of Parkinson’s disease selected for the present study was Parkin KO mice that present mitochondrial efficiency reduction. This gene is situated on locus 6q25.2–q27 [15] and codes for a E3-ubiquitin protein ligase [16] involved in the degradation of damaged proteins through the Ubiquitin Proteasome System (UPS) [17]. Parkin is also involved with Pink1 in mitophagy, a quality control mechanism removing damaged mitochondria [18]. Impaired mitochondrial and protein degradation may lead to protein aggregation and perturbed cellular energetics. In the present study, animals were produced by mating heterozygote mice to obtain KO and wild-type (WT) littermates. Because a number of toxins such as MPTP, and PD gene mutations including those in the Parkin gene, perturb mitochondrial function and in particular the complex I, we compared tissues obtained from Parkin KO mice to WT tissues treated with CCCP. This ionophore is known to dissipate the pH gradient across the mitochondrial membrane, leading to the loss of ATP production, an energetic shuttle critical for cellular metabolism [19].
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In silico analysis of the binding of anthelmintics to Caenorhabditis elegansP-glycoprotein 1.

In silico analysis of the binding of anthelmintics to Caenorhabditis elegansP-glycoprotein 1.

a b s t r a c t Macrocyclic lactones (ML) are important anthelmintics used in animals and humans against parasite nematodes, but their therapeutic success is compromised by the spread of ML resistance. Some ABC transporters, such as P -glycoproteins (Pgps), are selected and overexpressed in ML-resistant nematodes, supporting a role for some drug efflux proteins in ML resistance. However, the role of such proteins in ML transport remains to be clari fied at the molecular level. Recently, Caenorhabditis elegans Pgp-1 (Cel-Pgp- 1) has been crystallized, and its drug-modulated ATPase function characterized in vitro revealed Cel-Pgp- 1 as a multidrug transporter. Using this crystal structure, we have developed an in silico drug docking model in order to study the binding of ML and other anthelmintic drugs to Cel-Pgp-1. All tested ML bound with high affinity in a unique site, within the inner chamber of the protein, supporting that ML may be transported by Cel-Pgp-1. Interestingly, interacting residues delineate a ML specific fingerprint involving H-bonds, including T1028. In particular, benzofurane and spiroketal moieties bound to specific sub-sites. When compared with the aglycone ML, such as moxidectin and ivermectin aglycone, aver- mectin anthelmintics have significant higher affinity for Cel-Pgp-1, likely due to the sugar substituent(s) that bind to a specific area involving H-bonds at Y771. Triclabendazole, closantel and emodepside bound with good affinities to different sub-sites in the inner chamber, partially overlapping with the ML binding site, suggesting that they could compete for Cel-Pgp-1-mediated ML transport. In conclusion, this work provides novel information on the role of nematode Pgps in transporting anthelmintics, and a valuable tool to predict drug-drug interactions and to rationally design new competitive inhibitors of clinically- relevant nematode Pgps, to improve anthelmintic therapeutics.
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Mitochondrial proteomics : analysis of a whole mitochondrial extract with two-dimensional electrophoresis.

Mitochondrial proteomics : analysis of a whole mitochondrial extract with two-dimensional electrophoresis.

Dysfunction of mitochondria can lead to several disorders of varied severity, ranging from intolerance to an intense effort to perinataly fatal diseases. Progressive mitochondrial dysfunction has also been implicated in the aging process. This has led to interest in comparative mitochondrial proteomics. As many mitochondrial proteins are assembled into complexes of defined stoichiometry (e.g. the respiratory complexes whose structure is sometimes known [2, 3]) it is interesting to reach a fine quantification level which allows to investigate mis-stoichimetries caused by deficient complex assembly. Not all proteomics techniques allow reaching this precision level, and two-dimensional electrophoresis is among the few available choices nowadays. However, this technique is not without drawbacks, especially for hydrophobic proteins [4] and adequate protein solubilization conditions must be used to visualize at least part of the inner membrane-embedded proteins.
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Ancient and conserved functional interplay between Bcl-2 family proteins in the mitochondrial pathway of apoptosis

Ancient and conserved functional interplay between Bcl-2 family proteins in the mitochondrial pathway of apoptosis

We next examined the ability of recombinant trBcl-2L1 and trBcl-2L2 to engage peptides spanning the BH3 motif of trBak and trBax using isothermal titration calorimetry (ITC) (Fig. 4A). Our analysis revealed that trBcl-2L1 only engaged trBax with nanomolar affinity (K D of 426 nM), while trBak was bound with micromolar affinity (K D of 1.43 M). In contrast, trBcl-2L2 bound both trBak and trBax with nanomolar affinities (K D of 563 and 728 nM, respec- tively). We then established the structural basis of apoptosis inhibi- tion by determining the crystal structure of a trBcl-2L2:trBak BH3 complex. The structure was refined to a resolution of 1.6 Å, with clear and continuous electron density obtained for trBcl-2L2 residues 34 to 182 and trBak BH3 103 to 127 (table S1). trBcl-2L2 adopts the classical Bcl-2 globular helical bundle fold comprising eight  -helices, which is also found in human and sponge Bcl-2 homologs (Fig. 4, B to D). trBcl-2L2 uses the canonical Bcl-2 ligand binding groove formed by helices 2 to 5 to engage BH3 motif ligands (Fig. 4B). In the trBcl-2L2:trBak BH3 complex (Fig. 4E), trBak resi- dues I109, L113, L116, and Y120 protrude into four hydrophobic pockets of trBcl-2L2. In addition, the hallmark ionic interaction observed across the Bcl-2 family is formed between trBcl-2L2 R97 and trBak D118. Two additional ionic interactions are found be- tween trBcl-2L2 R69 and trBak E108 and trBcl-2L2 E54 and trBak R123. These three ionic interactions are supplemented by an addi- tional two hydrogen bonds between the trBcl-2L1 E54 carboxyl group and the trBak Y120 hydroxyl group and trBcl-2L2 N94 aspartate group and trBak D118 carboxyl group.
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In silico local structure approach: A case study on Outer Membrane Proteins.

In silico local structure approach: A case study on Outer Membrane Proteins.

We use an approach of HMM comparison [47] to further analyze the differences between SA20-OMP and SA20-GB. This analysis is based on the likelihood of the structures under three different alphabets: SA20-OMP, SA20-GB and the alphabet previously introduced by Camproux et al [34]. This latter alphabet, called SA27, is composed of 27 structural letters, four of them describing α-helices and five of them describing β-strands. It was learned on a large number of globular structures and provides a satisfying local approximation. The structures of the OMPset and the GBset are encoded under the three alphabets using the Viterbi algorithm and the repartition of the resulting log-likelihoods are analyzed. The log-likelihood can be seen as the compatibility between a structure and a structural alphabet. The results are shown on Figure 4. When comparing SA20-OMP and SA20-GB, as expected, each alphabet gives higher likelihood to the structures used for learning (Figure 4a). When log-likelihoods obtained under SA27 are compared with those obtained under SA20-OMP (Figure 4b), the discrimination is clear: OMP structures have higher likelihoods under SA20-OMP and almost all GB structures have higher likelihoods under SA27. It means that GBset structures, which have the same secondary structure content than OMPset structures, are better represented by SA27 than SA20-OMP. On the contrary, no clear distinction can be seen when SA27 is compared with SA20-GB (Figure 4c): some GBset structures have higher log-likelihood under SA27, and some OMPset structures have higher likelihoods under SA20- GB. These results indicate that OMP structures have characteristic features in terms of local structures, and are better represented by a specific alphabet. This analysis shows the interest and necessity to learn a specific alphabet of OMP structures.
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Systematic Identification of Mitochondrial Proteins by LC−MS/MS

Systematic Identification of Mitochondrial Proteins by LC−MS/MS

acquired during the analysis of one digested gel slice and stated in particular that 40% of the spectra were automatically identified, whereas 41% were unusable. In an attempt to improve the quality of MS/MS spectra of ions giving weak signals, we then increased the MS/MS analysis time when working on [400, 720] and [680, 1300]. For dark blue gel slices, MS/MS fragmentation lasted 6 s when working on [400, 1300], and 10 s on [400, 720] or [680, 1300]; for pale gel slices, MS/MS durations were 10 and 15 s, respectively. The 15-s duration was unlikely to be effective for all ions:  when more than five or six coeluted peptides were fragmented, some of them could not be detected long enough to benefit from the 15 s of MS/MS analysis. But this duration could be effective for peptides eluting at the beginning or the end of the gradient, when candidates for fragmentation were less numerous.
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Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson's disease.

Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson's disease.

mitochondrial transcripts were up-regulated in the PD, with none passing the multiple comparisons test. Only seven of these were up-regulated in both the SNm and SN1 (supplemental Table 1 in "Appendix"). Genes that were up-regulated transcribed proteins with a variety of functions including mitochondrial electron transport, catecholamine and fatty acid metabolism, and cell death. Supplemental Table 2 in "Appendix" shows the genes that were up-regulated in the SN1 but not in the SNm. There were several transcripts encoding proteins involved in the oxidative stress response, glycine metabolism and proliferation that were only up-regulated in the SN1. In the cortex, eight mitochondrial genes were up-regulated in PD, these include monoamine oxidase A (MAOA) and dihydrolipoamide branched-chain transacylase that were up-regulated in both SNm and SN1 and A kinase anchor protein 10 that was also up-regulated in the SN1. The expression of solute carrier family 25 member 21, acetyl-coenzyme A acyltransferase 2, mitochondrial ribosomal protein 63, dimethylglycine dehydrogenase precursor and surfeit 1 was increased only in the cortex. Only five mitochondrial genes were down-regulated in the PD cortex (p<0.01); of these, nipsnap homolog 1 and translocase of inner mitochondrial membrane 10 homolog were also down-regulated in the SNm. The expression of cytochrome b-561, translocase of inner mitochondrial membrane 10 homolog and polymerase (RNA) mitochondrial (DNA directed) (POLRMT) was decreased in the cortex but not in the nigra. POLRMT may be of special interest as it is essential for the initiation of mitochondrial gene transcription [39]. It should be mentioned that the transcripts differentially expressed in the cortex did not pass the multiple-comparisons correction.
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Chemical targeting of NEET proteins reveals their function in mitochondrial morphodynamics

Chemical targeting of NEET proteins reveals their function in mitochondrial morphodynamics

90°C. For mitochondrial and cytosolic fraction preparations, after treatment HeLa cells were washed twice and gently scraped in cold PBS. Cell pellets were recovered via centrifugation at 600g for 5 min. Cells were gently re-suspended in M buffer (440 mM mannitol, 140mM sucrose, 40mM HEPES, 1mM EDTA, 2mg/ml fatty acid free BSA and protease/phosphatase inhibitor cocktail) and placed on ice for 10 minutes. After homogenization with Dounce homogenizer (20 stroke), the lysate was centrifuged at 600g for 5min to pellet nuclei and recover the post nuclear supernatant (PNS). The PNS was than centrifuged at 7200 g for 15 min at 4°C to collect the mitochondrial enriched fraction and the supernatant (cytosolic fraction). For routine SDS-PAGE, precast gradient gels (4-20% Tris-Glycine, Invitrogen) were used and home-made 7.5% Tris-Glycine gels for OPA-1 detection. Separated proteins were transferred onto PVDF membranes. Membranes were blocked with BSA 3%/ TBS/0.1% tween for 1h and incubated with primary antibodies overnight at 4° in 2% BSA/TBS/0.1%tween. Immunoblot analysis was performed by chemiluminescence (Millipore) in a ChemiDoc MP Imaging System (Bio-Rad). Quantification of band intensities were carried using Image J software.
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An in-silico analysis of information sharing systems for adaptable resources management: A case study of oyster farmers

An in-silico analysis of information sharing systems for adaptable resources management: A case study of oyster farmers

5.4 Heterogeneity vs information sharing Heterogeneity is known to have a decisive influence on innovation diffusion (Bohlmann et al., 2010), on environmental consequences of consumption (Raihanian Mashhadi and Behdad, 2018), or in financial markets (Schmitt and Westerhoff, 2017). Modelling heterogeneity is even a key strength of such models (An, 2012). Our results strengthen this observation: while information sharing allows for a quicker convergence of beliefs, heterogeneity has the greater impact in terms of overall production and results in more of the available strategies being explored (Figure 11). In this case, the mere existence of an external representation (the information sharing system structure is similar whatever the population) is not enough for agents to “think”, and even act “the previously unthinkable” (Kirsh 2010). Economicus agents on their own are convinced that cultivating triploids is the best choice and never explore other potentially successful strategies, such as raising natural oysters, the sole goal of conscious farmers. When oyster farmers are heterogeneous, total production for the same number of agents, increases by 150%, regardless of the information sharing scenario.
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Advances in the quantification of mitochondrial function in primary human immune cells through extracellular flux analysis

Advances in the quantification of mitochondrial function in primary human immune cells through extracellular flux analysis

The cell number requirements and mitochondrial perturbation concentrations determined herein are consistent with published work that showed XF analysis requires 75,000–600,000/ well of freshly isolated human peripheral blood cells to measure oxygen consumption profiles [ 42 – 44 ], with similar numbers analyzed in experiments on activated immune cells [ 32 , 35 , 42 , 43 ]. Given the sensitivity of oxygen sensors, it is often necessary to run 3 replicate wells to generate meaningful data. Taken together, the number of cells and technical replicates needed for XF significantly increases the analytical burden of combining data from technical replicate wells over the multiple plates required for biological replicates, or due to the practicalities of run timing. Our cell number titration and linearity of response over multiple numbers of cells confirms the need for relatively large numbers of cells/well, and the need for fewer cells follow- ing metabolic activation with standard stimuli. Analysis of impacts of T cell activation methods and cellular storage conditions over the short-term (on ice versus room temperature) or long- term (-170˚C) solve some of the previously identified practical problems with human PBMC analysis [ 44 ]. The studies on time course of metabolic action showed unexpected non-linear patterns of oxygen consumption after stimulation of our mixed naïve/memory CD4 + T cell population. The potentially cyclical nature of maximal respiration and SRC strikingly differed from the logarithmic increase in T cell function, at least as measured by IL-6 accumulation. Taken together, these data indicate that more detailed longitudinal combinations of XF and cytokine production (rather than cytokine accumulation as measured herein), combined by SHORE and the multivariate cytokine analyses that we previously published [ 14 ], may reveal novel insights into the dynamic nature of lymphocyte regulation by respiratory pathways.
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Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Lastly, abnormal mitochondrial distribution and trafficking have been described in many neurodegenerative disease models such as Alzheimer’s disease (Pigino et al., 2003; Rui et al., 2006; Thies and Mandelkow, 2007), Huntington’s disease (Trushina et al., 2004), Parkinson’s disease (Blesa et al., 2015), Hereditary Spastic Paraplegia (Ferreirinha et al., 2004; McDermott et al., 2003), Charcot-Marie-Tooth (Baloh et al., 2007) and Amyotrophic Lateral Sclerosis (De Vos et al., 2007). Using cultured neurons as a model system, mitochondria were found to move at speeds ranging from 0.05 µm/s (representing a possible threshold to define motile mitochondria (Chen et al., 2016) to 1-1.5 µm/s (Stepkowski et al., 2016), consistent with previous studies (Ashrafi et al., 2014; Wang and Schwarz, 2009). Therefore, observed average velocities vary across a wide range. For example, astrocyte mitochondria move slower than neuronal ones, perhaps pertaining to cell type-specific differences in the trafficking machinery (Fiacco and McCarthy, 2004). However astrocyte mitochondria are still capable of covering the same distances as neuronal mitochondria (Stephen et al., 2015). Hence, in addition to mitochondrial velocity, assessment of the distance covered by moving mitochondria may be of interest. Run times (Miller et al., 2015) and percent of time spent in motion (‘processivity’) (Caino et al., 2016) represents other numerical parameters that could be used to classify mitochondria on the basis of their movement. Other factors that may influence measured mitochondrial speeds are: (i) fission-fusion (Liu et al., 2009); (ii) experimental conditions, (iii) local cytoskeletal environment (Moore et al., 2016); (iv) mitochondrial morphology and network complexity (Caino et al., 2016; Rafelski, 2013).
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Evolutionary relationships and expression analysis of EUL domain proteins in rice (Oryza sativa)

Evolutionary relationships and expression analysis of EUL domain proteins in rice (Oryza sativa)

Protein sequences encoding the reference members of the different lectin families [Agaricus bisporus agglu- tinin (ABA), Q00022.3; Amaranthus caudatus agglu- tinin (amaranthin), AAL05954.1; Robinia pseudoacacia chitinase-related agglutinin (CRA), ABL98074.1; Nostoc ellipsosporum agglutinin (cyanovirin), P81180.2; Euony- mus europaeus agglutinin (EUL), ABW73993.1; Galanthus nivalis agglutinin (GNA), P30617.1; Hevea brasiliensis agglutinin (hevein), ABW34946.1; Artocarpus integer agglutinin (JRL), AAA32680.1; Glycine max agglutinin (legume lectin), P05046.1; Brassica juncea LysM domain (LysM), BAN83772.1; Nicotiana tabacum agglutinin (nictaba), AAK84134.1; Ricinus communis agglutinin lectin chain (ricin-B), 2AAI_B] were used to perform BLAST searches against the Oryza sativa subsp. ja- ponica genome (RGAP release 7) available from NCBI (https://blast.ncbi.nlm.nih.gov), MSU (Kawahara et al. 2013) and phytozome (https://phytozome.jgi.doe.gov), as described previously by Van Holle and Van Damme (2015). Top hits were used for a consecutive BLAST search. In addition the MSU database (Kawahara et al. 2013) was searched using the Pfam domain identifier [ABA: PF07367 (fungal fruit body lectin); amaranthin: PF07468 (agglutinin domain); CRA: PF00704 (glycol- hydro 18); cyanovirin: PF08881 (CVNH); EUL: PF14200 (ricin-lectin 2); GNA: PF01453 (B-lectin); hevein: PF00187 (chitin bind 1); JRL: PF01419 (jacalin); legume lectin: PF00139 (lectin legB); LysM: PF01476 (LysM domain); nictaba: PF14299 (PP2); ricin-B: PF00652 (ricin-B lectin)] of the different lectin domains. Protein se- quences were downloaded from MSU (Kawahara et al. 2013) and screened for the presence of conserved protein domains using interproscan 5 (Mitchell et al. 2015). The program was downloaded (https://www.ebi.ac.uk/ interpro/download/) and locally installed. Indica lec- tins were identified by BLAST searches with the lectin domains of the japonica hits against the indica rice genome (ASM465v1) available from EnsemblPlants (http://plants.ensembl.org). As for the japonica se- quences, these protein sequences were analyzed for the presence of conserved protein domains using Interproscan 5 (Mitchell et al. 2015). Only sequences with at least one lectin domain were retained. The protein sequences of the lectins were analyzed for the presence of signal peptides using SignalP 4.1 (Petersen et al. 2011) and the presence of transmembrane domains was analyzed using TMHMM 2.0 (Krogh et al. 2001).
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In Situ Analysis of Weakly Bound Proteins Reveals Molecular Basis of Soft Corona Formation

In Situ Analysis of Weakly Bound Proteins Reveals Molecular Basis of Soft Corona Formation

pH 9 (Sigma C8210). The SNPs tend to buffer the solution to- wards basic pH, especially at high concentration. A minimum concentration of 50 mM phosphate buffer was required to maintain the solution at pH 7 under our experimental condi- tions. Because of the buffering effect of SNPs, a smaller con- centration of CHES or NH4Ac buffer was required at pH 9. All buffers are stable in the given temperature range. The SRCD experiments were conducted on the DISCO beamline at SOLEIL Synchrotron 76 (Saint-Aubin, France). 1 mM oxyHb was mixed with 100 mg/mL SNPs in 0.5 mL Eppendorf tubes and gently mixed on a wheel for 1 hour at 22°C. The final pH of the solution was checked using a pH microelectrode (Bio- trode, Metrohm). All the samples were vortexed at low speed for a few seconds before analysis. 4 µL of the solution were deposited in a CaF 2 round cell with a 12 µm pathlength 77 (Hellma). The pathlength was measured by light interference of an empty cell on a spectrophotometer in the visible domain. The spectra were recorded from 170 nm to 260 nm. Tempera- ture experiments were conducted from 22°C to 97°C with a 3°C step and an equilibration time of 5 min. 3 scans were recorded at each temperature step. The experimental spectra were av- eraged, baseline subtracted and smoothed using CDtoolX software 78 . The intensity of the SRCD signal was calibrated at 192 nm and at 290 nm with a standard solution of camphor- sulfonic acid (CSA) analyzed in a 100 µm pathlength cell at 22°C at a concentration of 6.19 g.L-1. The experimental spectra were converted from θ (millidegrees) to Δε (L.mol-1.cm-1) us- ing a Mean Residue Weight (MRW) of 113.15 for porcine ox- yHb. The melting curves represented as Δε(194 nm) and Δε(222 nm) as a function of temperature were fitted by one or two successive sigmoids using Igor software. The same results were obtained by applying a two-sigmoidal curve equation to the melting curve following work by Rodnin et al. 79
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Kernel methods for in silico chemogenomics

Kernel methods for in silico chemogenomics

We extracted compound interaction data from the KEGG BRITE Database (Kanehisa et al., 2002, 2004) concerning enzyme, GPCR and ion channel, three target classes particularly relevant for novel drug development. For each family, the database provides a list of known compounds for each target. Depending on the target families, various categories of compounds are defined to indicate the type of interaction between each target and each compound. These are for example inhibitor, cofactor and effector for enzyme ligands, antagonist or (full/partial) agonist for GPCR and pore blocker, (positive/negative) allosteric modulator, agonist or antagonist for ion channels. The list is not exhaustive for the latter since numerous categories exist. Although different types of interactions on a given target might correspond to different binding sites, it is theoretically possible for a non-linear classifier like SVM with non-linear kernels to learn classes consisting of several disconnected sets. Therefore, for the sake of clarity of our analysis, we do not differentiate between the categories of compounds.
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