Top PDF Interaction between plate make and protein in protein crystallisation screening.

Interaction between plate make and protein in protein crystallisation screening.

Interaction between plate make and protein in protein crystallisation screening.

Crystallisation. Three crystallisation conditions were used for each protein: the precipitant at the published crystallisation concentration, the precipitant at a concentration 20% lower than the published condition and the precipitant at a concentration 20% higher than the reported precipitant concentration (buffer and other additives conditions remained the same in these three test conditions). Four replicates were used for each of these three conditions. All the crystallisation experiments were set up on the same day, in parallel using the same protein batches (incubated on ice) for each plate. Protein buffers and crystallisation conditions for each protein are given in Table 2. Plates were removed from their plastic sleeves just prior to setting up the experiments to minimise contamination. The crystallisation condition (85 m l) was placed in the reservoirs of the crystallisation plates using a Biomek H 2000 Laboratory Automation workstation (Beckman Coulter, California, USA). The protein drops constituted 200 nL of protein and 200 nL of crystallisation solution and were prepared using a MosquitoH robot (TTP LabTech, Melbourn, UK) at room temperature. All sitting-drop plates were sealed using tape (Qiagen, California, USA) and all plates were incubated in the same incubator (Thermoline, Queensland, Australia) set to 20 uC. In total, 792 crystallisation drops were prepared in one day. Each of the 11 plates used in this pilot study held 4 replicates of 3 crystallisation conditions for 6 proteins. Images of the crystallisation experiments were captured using a Crystal Monitor TM workstation (Emerald Biosystems, Washington, USA) at the standard settings of 1.0 brightness, 1.0 gamma adjustment and auto exposure for the highest image resolution (10 s per image). The brightness was adjusted to 1.25 or 1.5 for plates with dark shadowing around the crystallisation drop to improve the image quality.
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Direct interaction between EgFABP1, a fatty acid binding protein from Echinococcus granulosus, and phospholipid membranes.

Direct interaction between EgFABP1, a fatty acid binding protein from Echinococcus granulosus, and phospholipid membranes.

evident between apo-EgFABP1 and the different complexes (Figure 2). Results show that binding of FAs gives EgFABP1 significant relative protection against cleavage. After 5 minutes of proteolysis the apo-protein shows several bands corresponding to proteolytic fragments, while the holo-forms show mainly the band corresponding to full-length EgFABP1 and less intense bands corresponding to proteolytic fragments (Figure 2A). This suggests that ligand-binding results in a different exposure of proteolytic sites. It is interesting to note that after 16 hours of proteolysis the holo-proteins do not seem to be further proteolysed while the apo- protein is almost completely degraded (Figure 2B). Previous results obtained for other members of the family of FABPs have suggested that binding of ligands involves conformational changes, especially on the portal region of FABPs [10,31,42]. Furthermore, in silico simulations show that, upon ligand binding, subtle conformational changes can be detected inside the cavity, in the surface and in the portal region of EgFABP1 (Esteves, unpublished data). These changes could make cleavage sites less accessible to the protease.
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Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.

method for identifying CRC related genes by integrating gene expression profile and a weighted functional protein association network constructed with PPI data from STRING. This method can make up the defect of only using high-throughput data. Meanwhile, the mRMR (maximum relevance minimum re- dundancy) algorithm [17] was utilized to identify six promising candidate genes distinguishing tumor and the normal colorectal samples. The Dijkstra’s algorithm [18] was used to construct the shortest paths between each pair of the six genes. Moreover, additional 35 genes on these shortest paths were also identified and analyzed. For such (6z35)~41 gene thus identified, it was observed that they contained more cancer genes than the genes identified from the gene expression profiles alone. Furthermore, the 41 genes also had greater functional similarity with the reported CRC genes than the genes identified from gene expression profiles alone. It is anticipated that some of the 41 genes thus identified might belong to novel CRC related genes.
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Paracoccidoides brasiliensis 30 kDa adhesin: identification as a 14-3-3 protein, cloning and subcellular localization in infection models.

Paracoccidoides brasiliensis 30 kDa adhesin: identification as a 14-3-3 protein, cloning and subcellular localization in infection models.

Paracoccidoides brasiliensis adhesion to lung epithelial cells is considered an essential event for the establishment of infection and different proteins participate in this process. One of these proteins is a 30 kDa adhesin, pI 4.9 that was described as a laminin ligand in previous studies, and it was more highly expressed in more virulent P. brasiliensis isolates. This protein may contribute to the virulence of this important fungal pathogen. Using Edman degradation and mass spectrometry analysis, this 30 kDa adhesin was identified as a 14-3-3 protein. These proteins are a conserved group of small acidic proteins involved in a variety of processes in eukaryotic organisms. However, the exact function of these proteins in some processes remains unknown. Thus, the goal of the present study was to characterize the role of this protein during the interaction between the fungus and its host. To achieve this goal, we cloned, expressed the 14-3-3 protein in a heterologous system and determined its subcellular localization in in vitro and in vivo infection models. Immunocytochemical analysis revealed the ubiquitous distribution of this protein in the yeast form of P. brasiliensis, with some concentration in the cytoplasm. Additionally, this 14-3-3 protein was also present in P. brasiliensis cells at the sites of infection in C57BL/6 mice intratracheally infected with P. brasiliensis yeast cells for 72 h (acute infections) and 30 days (chronic infection). An apparent increase in the levels of the 14-3-3 protein in the cell wall of the fungus was also noted during the interaction between P. brasiliensis and A549 cells, suggesting that this protein may be involved in host-parasite interactions, since inhibition assays with the protein and this antibody decreased P. brasiliensis adhesion to A549 epithelial cells. Our data may lead to a better understanding of P. brasiliensis interactions with host tissues and paracoccidioidomycosis pathogenesis.
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DUF581 is plant specific FCS-like zinc finger involved in protein-protein interaction.

DUF581 is plant specific FCS-like zinc finger involved in protein-protein interaction.

corresponds to amino acids from 89 to 140. The C-terminal fragment comprised of amino acids from 141 to 177 of whole protein. We repeated the Y2H with deletion fragments of FLZ1 with PLANT AND FUNGI ATYPICAL DUAL-SPECIFICITY PHOSPHATASE 3 (PFA-DSP3) and SALT TOLERANCE HOMOLOG2 (STH2) which are earlier found to be interacting with full-length FLZ1 (Figure 4A). In Y2H with deletion constructs, we found that only FLZ domain can mediate the protein-protein interaction with the prey proteins suggesting their role in protein-protein interaction (Figure 4C). In beta-galactosi- dase assay, FLZ domain showed nearly half strength of interaction compared to full length bait while N-terminal and C-terminal fragments showed very minimal enzyme activity proving that FLZ domain alone is responsible for interaction of FLZ1 with other proteins (Figure 4D, E). However, the strength of the interaction is reduced to almost half when FLZ domain alone interacted with prey proteins suggesting that the other parts of the protein may be helping in providing a strong interaction between both proteins.
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Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

As shown here, CYP2C18 and CYP2C9 were associ- ated with the development of gastric cancer through the metabolism of xenobiotics by cytochrome P450, arachi- donic acid metabolism, retinol metabolism and the linoleic acid metabolism pathway. CYP2C9 is one of the predomi- nant epoxygenase isoforms involved in the metabolism of arachidonic acid into 12-epoxyeicosatrienoic acid (EEF). CYP2C9 epoxygenases are upregulated in human tumors and promote tumor progression and metastasis (Xu et al., 2011). Retinol may influence gastric carcinogenesis through its essential role in controlling cell proliferation and differentiation. High intakes of retinol from foods or a combination of foods and supplements are associated with a lower risk of gastric cancer (Larsson et al., 2007). CYP2C18 and CYP2C9 are related to retinol metabolism in human through their ability to transform retinol into 4- OH-retinoic acid and 18-OH-retinoic acid (Marill et al., 2000). These all-trans-retinoic acids are associated with G0/G1 phase arrest and decreased VEGF expression in hu- man gastric cancer cell lines (Zhang et al., 2007). However, dietary linoleic acid stimulates the invasion and peritoneal metastasis of gastric carcinoma cells through COX-cata- lyzed metabolism and the activation of ERK (Matsuoka et al., 2010). CYP2C9 is involved in linoleic acid epoxy- genation and the major product of this reaction is leuko- toxin that increases oxidative stress and subsequent pro- inflammatory events (Viswanathan et al., 2003), leading to tumor cell progression. We therefore suggest that P450 family genes are involved in gastric cancer by metabolizing exogenous anti-cancer drugs, stimulating arachidonic acid and linoleic acid metabolism and inhibiting retinol metabo- lism.
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Still stratus not altocumulus: further evidence against the date/party hub distinction.

Still stratus not altocumulus: further evidence against the date/party hub distinction.

Bertin et al. fi nd that party hubs evolve more slowly. As originally noted [3], the question is whether party hubs evolve slower than date hubs when controlling for important covariates, most notably protein abundance [8]. We showed previously that any weak tendency for party hubs to evolve slower was accounted for by their abundance [2]. Unlike our prior analysis, Bertin et al. do not ask if party and date hubs evolve at different rates controlling for abundance but, instead, ask if PCC is related to evolutionary rate controlling for abundance. However, they inappropriately apply a parametric test (Pearson product-moment correlation) that requires the distribution of all variables to be normally distributed. Although the method is robust to some degree of deviation from normality, the extent to which the abundance data is non- normal is extreme (Shaprio-Wilks tests for null of normality, W = 0.2, p << 0.0001, W = 1 implying normality, W << 1 implying deviation from normality). This leaves two avenues: either to transform the data to make them approximately normal or to perform the equivalent non-parametric test.
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IDENTIFICATION OF PHARMACOLOGICAL TARGETS COMBINING DOCKING AND MOLECULAR DYNAMICS SIMULATIONS

IDENTIFICATION OF PHARMACOLOGICAL TARGETS COMBINING DOCKING AND MOLECULAR DYNAMICS SIMULATIONS

Variations in the interacting rules, for instance, decreasing the van der Waals repulsion to increase the atom-atom proximity between the receptor and ligand, result in flexibility in the receptor (Jiang and Kim, 1991; Gschwend et al., 1996). Furthermore, the use of rotamer libraries provides a set of side chain conformations with experimental origins that improve the sampling and prevent the minimization barriers, consequently conferring flexibility to the system (Leach, 1994). An ensemble of proteins is also considered as a conformational preselected set that provides more options for adjustability in the molecular recognition, although it contains rigid targets (Knegtel et al., 1997; Cavasotto and Abagyan, 2004). Changes in the protein backbone, giving rise to alternative conformations, can also be used to generate an average structure that maintains its most conserved features, which can be considered when the docking is subsequently performed. In this pretreatment of the set of coordinates, some loop movements that are involved reveal their participation (Alonso et al., 2006). Some methods focusing around the binding site have been developed that form an ensemble- based grid or employ precalculated two-body potentials to determine the interaction energy of the ligand. Particularly, ensemble-based grids reduce the effect of steric clashes in the interaction and therefore, lead to the selection of reliable conformations (Alonso et al., 2006).
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Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

be involved in already known protein complexes associated with many biological processes. Out of the 37 markers, eight (CSNK2A1, CLTC, PARD3, IQGAP1, ACTB, ACTG1, CTNNA1 and GSN) were significantly involved in the core functional modules and showed significant change in co-expres- sion levels between disease and control state. Furthermore, proteins encoded by 4 genes (ARRB2, STX1A, TFRC, MARCKS) showed involvement with several neurotransmitters including dopamine, which plays a significant role in PD. These 12 proteins may be considered as biologically significant with respect to PD. Our study represents a novel investigation of the PPI networks for PD. The 37 network biomarkers identified in our study may provide as potential therapeutic targets for PD applications developments.
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Forage intake and performance of heifers fed rice bran on Alexandergrass pasture

Forage intake and performance of heifers fed rice bran on Alexandergrass pasture

1989). The total dry matter intake (FIf; kg DM day -1 ) was evaluated by the formula: FIf= FO/(1- digestibility). From these data, we calculated the forage intake, total dry matter intake, crude protein intake, neutral detergent fiber intake and total digestible nutrient intake, expressed in %BW. The coefficient of substitution (reduction in forage DM intake per kg DM of supplement consumed) and the addition rate (increase in total DM intake per kg DM of supplement consumed) were estimated according to Hodgson (1990). Grazing simulation was made according as proposed by Euclideset al. (1992). Forage samples were oven-dried at 55ºC for 72 hours and ground in a Wiley mill for later laboratory analysis. The dry matter content of the samples was determined by drying in an oven at 105°C for at least eight hours. The ash content was determined by combustion at 600°C for four hours and the organic matter content was obtained by mass difference. The protocols followed in laboratory analyses were: Kjeldahl method (Official… 1997) for the determination of total nitrogen; Sengeret al. (2008) for analysis of neutral detergent fiber; Demarquillyet al. (1969) for the in situ dry matter digestibility of forage and supplement; Bligh and Dyer (1959) to determine the ether extract content of the rice bran; Kunkle and Bates (1998) to calculate the TDN.
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Sao Paulo Med. J.  vol.123 número6

Sao Paulo Med. J. vol.123 número6

CONTEXT AND OBJECTIVE: Very low birth weight (VLBW) infants have special nutritional needs. There is a current tendency to individualize their protein needs. The objective of this study was to determine the suitability of serum and urinary urea as indicators for protein intake in adequate-for-gestational-age (AGA) and small- for-gestational-age (SGA) VLBW infants. DESIGN AND SETTING: Prospective study in the nursery attached to the Maternity Ward of the “Prof. Pedro de Alcântara” Children’s Institute, Hospital das Clínicas, Department of Pediatrics, Faculdade de Medicina da Universidade de São Paulo, Brazil.
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Understanding ion exchange chromatography adsorption mechanisms under different conditions

Understanding ion exchange chromatography adsorption mechanisms under different conditions

Structural differences between the studied solid phases according to the supplier are shown in Table 8. Static binding capacity and affinity constant present in the table were obtained based on isotherm data by a linear regression of the Langmuir linearized equation. As stated by the provider, a traditional ligand attachment method (TSKgel SP and Toyopearl DEAE) consists of attaching the ligand directly to the resin through a spacer arm. The second generation attachment (TSKgel SuperQ) adds a carbon spacer network between the bead surface and the ligand. Ligands are also attached along the length of the spacer network, improving capacity. The third generation (Toyopearl GigaCap Q) ligand is just like the second but moves the charged groups to the larger pores where protein has better access to them, with the aim of increasing mass transfer speed and promoting faster desorption. The absence of ligands in smaller pores is not necessarily a disadvantage for protein separation, because induces a reduced elution volume 73 .
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Subcompartmentalisation of proteins in the rhoptries correlates with ordered events of erythrocyte invasion by the blood stage malaria parasite.

Subcompartmentalisation of proteins in the rhoptries correlates with ordered events of erythrocyte invasion by the blood stage malaria parasite.

In spite of its distinct nature to date only a few proteins have been described that are specific to the process of tight junction dependent invasion and conserved across apicomplexan species that invade by this means. Drawing an analogy with terminology for bacterial host-cell entry [21], we refer to these here as invasins, to distinguish them from conserved proteins involved in either general apicomplexan cell motility [22] or adhesins involved in species or lifecycle dependent host-cell recognition [23]. Of the invasins described, several have been localised definitively to follow the tight junction constriction during entry, and therefore likely constitute the core structural basis for its formation. First identified in T. gondii these include the RhOptry Neck proteins, RON2, RON4, RON5 and RON8 (the latter restricted to Coccidia), which form a macromolecular complex [24,25,26,27]. In support of a key role for this complex, PfRON4 has recently been shown to be important, and likely essential, for zoite invasion [28]. The micronemal protein Apical Membrane Antigen 1 (AMA1) has been shown to interact with the RON complex in both Toxoplasma and Plasmodium spp. [24,29,30,31] with the combined RON-AMA1 interaction believed to span both sides of the junction. AMA1 is anchored to the zoite surface and the RON complex anchored in the host cell membrane via RON2 [32,33,34] with RON4 secreted inside the target host cell [8,25]. This embedded molecular interaction between an extracellular loop of RON2 with AMA1 has been proposed as the basis for the traction potential of the junction for invasion [33,34,35]. However, recent demonstration of the nonessentiality of AMA1 to invasion during tachyzoite and malaria parasite liver stage invasion of host cells [28] suggests that whilst involved, this interaction may not be integral to junction structure and function. Although several groups have confirmed conservation of the RON-AMA1 complex interaction in Plasmodium zoites [30,31,36,37], imaging of parasites (only merozoites to date) during invasion has only convincingly localised the invasins RON4 (specifically) and AMA1 (in part) to the junction constriction during invasion [8]. Preliminary evidence suggests that RON2 may also follow this path [32,37,38]. Whilst parallels with Toxoplasma data support the importance of each of these components, and by inference the other conserved member
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Bioinformatics Analysis of the Effects of Tobacco Smoke on Gene Expression.

Bioinformatics Analysis of the Effects of Tobacco Smoke on Gene Expression.

Furthermore, in the transcriptional regulatory network, the TFs f MAX and USF1 regulated many overlapped DEGs. MAX is a member of the basic helix-loop-helix leucine zipper (bHLHZ) family of transcription factors. It can form heterodimers with other family members, including MYC, which is an oncoprotein implicated in cell growth, proliferation, differentia- tion and apoptosis [33]. The dysregulated expression of MYC has been reported in a wide range of human malignancies [34]. On the other hand, USF1 also encodes a member of the bHLHZ family and functions as a cellular transcription factor that regulates the expression of numerous genes involved in cellular proliferation and the cell cycle [35, 36]. Importantly, Wu et al. [37] have demonstrated that nicotine, a component of tobacco smoke, can enhance USF1 translocation from the cytoplasm to the nucleus. As a result, we speculated that tobacco smoke might induce cancer by targeting genes such as MAX and USF1.
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Bromatological characteristics and in vitro digestibility of four sugarcane varieties subjected or not to the application of quicklime

Bromatological characteristics and in vitro digestibility of four sugarcane varieties subjected or not to the application of quicklime

Fresh samples from the mounts, with approximately 0.5 kg, were pre-dried in a forced-air circulation oven at 55°C for 72 hours and then ground in a mill with a 1 mm sieve to determine dry matter (DM) in a 105°C oven, ether extract (EE), organic matter (OM), acid detergent fiber (ADF) and lignin, as described by Silva and Queiroz (2002). Crude protein (CP) level was determined in a LECO nitrogen auto-analyzer (WILES et al., 1998). Neutral detergent fiber (NDF) was determined by methodology adapted by Van Soest et al. (1991), without α-amylase and using an autoclave for 40 minutes. Total carbohydrates (TCH) levels were obtained by difference, according to Sniffen et al. (1992), in which TCH (%) = 100 - (%CP + %EE + %MM). Non-fibrous carbohydrates (NFC) levels were calculated according to Hall (2003), by subtracting de NDF from total carbohydrates, in which NFC CNF% = 100% – (NDF% + CP% + EE% + MM%).
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A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

To compute the precision and recall, the relevant items and irrelevant items should be constructed. First and foremost, the known disease-gene associations are relevant items here. As for the irrelevant items, we associated the genes that are not known to any disease with a disease artificially, and these disease-gene associations are considered as irrelevant items and constitute the irrelevant control set. It should be known that the genes, which are not associated with any disease in our disease-gene association set and called ‘‘unassociated genes’’, are not ‘‘irrelevant’’ and just ‘‘unknown to us’’. In our method, three types of irrelevant control set are constructed. One is the whole genome wide control set, another is the random control set and the last one is the artificial linkage interval control set. As for the random control set in 10- fold cross-validation, we divided all the 1428 disease-gene associations into ten subdivisions averagely, with about 142 diseases and 142 disease-gene associations in each subdivision. For each subdivision, we randomly selected n genes from the set of ‘‘unassociated genes’’. For each disease involved in the subdivision, we constructed n disease-gene associations with the n random selected genes. So, there are about 142  n disease-gene associa- tions constructed artificially, which constitute the random control set and are considered as irrelevant items. There are about 142 known disease-gene associations, which are considered as relevant items. Both the irrelevant and relevant items are measured by their ranks in the whole genome to compute precision and recall. All ten subdivisions are done separately in the same way as above. For a given rank k, the final precision and recall are the average results of all ten subdivisions. As for the whole genome wide control set, all the ‘‘unassociated genes’’ in the protein interaction network rather than random selected n genes are used, and the irrelevant items and the irrelevant control set are constructed in the same way as above. As regard to simulating the real-life situation in which one or more susceptible linkage intervals rather than specific genes have been associated with some disease, an artificial linkage interval around a known disease-causing gene is constructed according to the genes’ coordinates on the whole genome, and this is motivated by the method used in Lage et al. [4]. We extracted no more than 100 genes around the known disease gene on the same chromosome, and these genes are used to construct the irrelevant items and the irrelevant control set as above. The tests were performed on the three irrelevant control sets, and the results will be described in detail later.
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Cross-link guided molecular modeling with ROSETTA.

Cross-link guided molecular modeling with ROSETTA.

prior to the incorporation of XL-MS distances. First, cross-linker molecules are flexible and can covalently link lysine residues over a large range of inter-residue distances [23]. In the current work, all calculations are based on data using disuccinimidyl suberate (DSS) as a reagent. DSS has a spacer length of approximately 11.4 A ˚ , but was experimentally found to bridge lysine residues of up to 30.0 A ˚ and more (calculated as Ca-Ca protein backbone carbon atom Euclidean distance). This distance restraint takes into account the length of two extended lysine side chains (,5.5 A ˚ each) and some conformational flexibility of the protein complex [6]. Second, cross-linker molecules can be assumed to not penetrate the protein surface and be located on solvent accessible surface patches [24]. In case cross-links are simulated as distance restraints in modeling calculations, the linear Euclidean distance measure becomes inappropriate, as it will penetrate the protein surface. Both limitations can either be solved by explicitly modeling the cross-linker molecule [25] or by implementing a non-linear distance measure [24]. We previously introduced the Xwalk (‘‘Crosswalk’’) algorithm [26] to calculate the shortest path between two cross-linked amino acids, where the path must not penetrate the protein surface and only lead through solvent occupied space. The algorithm is based on a cubic grid around the cross-linked amino acids, a distance calculator that fills the grid cells with distances following a breadth-first search algorithm, and a trace-back method that selects the shortest path through the grid between cross-linked amino acids. The length of the shortest path is a distance measure that we termed Solvent Accessible Surface (SAS) distance, which represents a more reasonable measure of cross-link distances in modeling calculations.
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Effects of changes in food supply at the time of sex differentiation on the gonadal transcriptome of juvenile fish. Implications for natural and farmed populations.

Effects of changes in food supply at the time of sex differentiation on the gonadal transcriptome of juvenile fish. Implications for natural and farmed populations.

The relationship between growth and sex has long been known for the European sea bass at the time of gonadal differentiation, where the largest fish are essentially all females whereas both sexes are found among the smallest fish, although males predominate. Early size-grading experiments (between 66–143 dph) have confirmed this [13,15,17] by obtaining ,90% females among the largest selected fish. Moreover, in a previous study we found that altering growth rates during the sex differentiation period in both size-graded and non-size-graded populations did not alter the sex ratios [19]. Here, it is presented for the first time a microarray analysis of undifferentiated gonads from 4-month-old sea bass with opposite growing rates just after size-grading (T1), and on differentiated testis (11-month-old juveniles, T2). To the best of our knowledge, the question of whether naturally occurring differences in somatic growth are somehow translated in observed transcriptomic differences in the gonads during sex differentiation has never been explored in fish. Nevertheless, what could be called a related type of work was performed in mitten crabs [36], where it was separately analyzed the relationship between nutrition and reproduction, by examining the hepatopancreas and testes transcriptomes, respectively. Interestingly, and regardless the differences among experimental designs and the model organisms used, some traits found in the study with crabs, as the differential expression of some heat shock proteins, cell death suppressors, RNA-dependent DNA polymerases or controllers of splicing, were also found in our study. Similarly to what has been previously reported in fish liver and muscle transcriptomes [22,24,26,52], it is then clear that the juvenile testis is also affected by changes in food supply. Interestingly, there are then common transcriptomic responses with the above mentioned tissues, but not with the brain transcriptomic responses [52].
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Protein-protein interaction analysis highlights additional loci of interest for multiple sclerosis.

Protein-protein interaction analysis highlights additional loci of interest for multiple sclerosis.

interactors) [12]. The non-randomness of the network and the significance of the interaction parameters are tested using a permutation method that compares the original network with thousands of networks created by randomly re-assigning the protein names while keeping the overall structure (size and number of interactions) of the original network. Those genes that participate in the network more than expected by chance are defined as genes to prioritize (corrected p,0.05) [12]. Expression data were gathered from BioGPS, an online gene annotation database that reports individual gene expression levels for a number of human tissues and cell types [17]. Analyses were performed using non-parametric tests (Kruskal-Wallis and Mann- Whitney tests). Gene ontology terms were investigated using The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7, an online tool that is able to identify the functional categories and biological processes which are most represented within a list of genes [18,19].
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Trypanothione reductase: a target protein for a combined in vitro and in silico screening approach.

Trypanothione reductase: a target protein for a combined in vitro and in silico screening approach.

The screening compound library and also the diverse subset used in the first target-based in vitro screening campaign contained structurally diverse compounds. It was applied as general library in many other target-based screening approaches. Nitro compounds and especially nitroheterocyclic compounds were only represented in a minor extend. Interestingly, with TR as target, this kind of compound class was found with an over-averaged frequency compared to other target-based approaches [48], [49]. Moreover, all best hits including the four compounds studied in detail belong to this specific compound class. Compounds containing nitro groups are usually underrepresented in known drugs. This is related to at least two reasons: a) the nitro group is often not involved in the interactions with the target protein and therefore not essential, b) nitro groups are undesired functional groups that are usually replaced in the lead/ drug optimization phase. In contrast, nitroheterocyclic compounds show an over-averaged chemotherapeutical potential in pathogens causing neglected diseases. The interest in this com- pound class has grown since the success of the nifurtimox/eflornithine combination against Af- rican trypanosomiasis and has prompted in many subsequent screening and research activities until today [48], [49]. In addition, the nitro-heterocyclic pro-drug fexinidazole is the first drug candidate in 30 years that entered clinical phase II/III against African trypanosomiasis for both stages of the disease [50], [51]. The compound also shows activity against T. cruzi and L. dono- vani and therefore is evaluated against Chagas disease [50], [52] and Leishmaniasis [53] in clin- ical proof-of-concept studies. Despite these research efforts the role of the nitro group
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