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In October 2004, an expert meeting was convened in parallel with the 11th Symposium on Hepatitis C and Related Viruses to discuss how HCV sequence databases could introduce and facilitate a standardized numbering system for HCV nucleotides, proteins and epitopes. Inconsistent and inaccurate numbering of locations in DNA and protein sequences is a problem in the HCV scientific literature. Consistency in numbering is increasingly required for functional and clinical studies of HCV. For example, an unambiguous method for referring to amino acid substitutions at specific positions in NS3 and NS5B coding sequences associated with resistance to specific HCV inhibitors is essential in the investigation of antiviral treatment. This article provides a practical guide to help circumvent these problems in the future, and to bring a common language into discussions in the field. The scope of the current system is limited to the HCV polyprotein and the untranslated regions (UTRs); because of the controversial nature and extreme length variation of the alternate reading frame proteins, numbering for these proteins, if needed, will be decided at a later date. We propose a numbering system adapted from the Los Alamos HIV database,1 with elements from the hepatitis B virus numbering system.2 The system comprises both nucleotides and amino acid sequences and epitopes. It uses the full length genome sequence of isolate H77 (accession number AF009606) as a reference, and includes a method for numbering insertions and deletions relative to this reference sequence. H77 was chosen because it is a commonly used reference strain for many different kinds of functional studies. Furthermore, RNA transcripts from this sequence are of demonstrated infectivity,3, 4 providing evidence that the 5′ and 3′ ends of the sequence are complete. Table 1 lists the boundaries of HCV genomic regions and Fig. 1 provides detailed nucleic acid and amino acid numbering over the complete AF009606 HCV genome sequence. The complete AF009606 HCV genome sequence. HCV, hepatitis C virus; UTR, untranslated region; PPT, poly-pyrimidine tract; CDR, complementarity-determining region. Numbering an amino acid sequence can be absolute, i.e., based on the HCV polyprotein with numbering starting at the first amino acid of the core protein and continuing through the end of NS5B; or it can be relative, i.e., starting over at every protein (core is numbered from 1 to 191, then E1 from 1 to 192). Both of these numbering systems are used in HCV research. While most epitopes are numbered following the polyprotein numbering, drug resistance mutations tend to be numbered using protein numbering, following the practice for HIV-1 and now HBV. All HCV sequence databases incorporate both numbering systems. In order to avoid any confusion between the absolute and the relative numbering, the protein coordinates should be preceded by the protein name when using the relative numbering (e.g., NS3-A156T). Conversion between the relative and absolute numbering can be easily done using either the Locator tool, available at the American HCV database website (http://hcv.lanl.gov/content/hcv-db/LOCATE/locate.html), or the Number tool on the European HCV database website (http://euhcvdb.ibcp.fr/euHCVdb/). There are two different methods for the numbering of the 5′ UTR that each have their adherents and opponents. One system starts numbering the polyprotein coding sequence at 1, and continues on through the end of the 3′ UTR; the 5′ UTR is numbered in the reverse direction, starting at −1 and continuing on to −341. The other system simply starts at 1 for the first nucleotide of the 5′ UTR, and continues on to the last nucleotide of the 3′ UTR. Both systems have advantages and disadvantages, and one can easily be converted to the other. For reasons of simplicity we have decided to adopt the numbering system that sets the start of the 5′ UTR at 1. Nucleotide numbering continues uninterrupted into the coding sequence. In the case of AF009606, the AUG initiation codon would be numbered as 342–344. This system avoids the practical problem encountered in most sequence editors of numbering a sequence non-consecutively (i.e., in the negative numbering system for the 5′ UTR, the numbering at the 5′ UTR/core junction should proceed …−3, −2, −1, +1, +2, +3…, whereas sequence editors tend to number the −1 base as zero). Numbering of the 3′ end of the HCV genome has to accommodate a stretch of pyrimidine residues of highly variable length between the 40 alignable nucleotides at the start of the 3′ UTR and the end of the 3′ UTR, including the highly conserved 3′X-tail. Diagrammatically, the 3′ UTR comprises the following sequential stretches (AF009606 numbering): Coding sequence […] CTC CCC AAC CGA TGA - 9377 3′UTR start (variable region): 9378–AGGTTGGGGTAAACACTCCGGCCTCTTAG GCCATTTCCTG - 9417 Spacer region: 9418 - <60–100 pyrimidines, mainly T> -9548 3′UTR end (conserved region or 3′X): 9549–GGTGGCTCCATCTTAGCCCTAGTCACGGCTAGCTGT GAAAGGTCCGTGAGCCGCATGACTGCAGAGAGTGCTGATA CTGGCCTCTCTGCAGATCATGT - 9646 The length of the poly-pyrimidine tract (PPT) changes rapidly over time within an experimentally infected chimpanzee5 and is known to vary between different clones of the original H77 isolate3, 4 (accessions AF009069-77). The actual numbering of the 3′ end of the 3′ UTR sequence beyond the PPT is therefore of no significance. Furthermore, since the alignment of the PPT is arbitrary, so are any designation of insertions or deletions and any numbering attempt in this region. An additional complication is caused by the choice of H77 as the reference sequence. The 3′X region of H77 has AAT as the first 3 nucleotides. However, this is not the case in most other isolates in which the 3′X is only 98 nucleotides long. To prevent complications arising from this unusual feature of H77, for numbering purposes, the AAT of H77 should be considered as part of the PPT rather than the 3′X region. With this exception, the 3′ UTR follows the numbering of the reference sequence AF009606. If the sequence under consideration has a PPT that is not longer than AF009606 (no sequence currently in the HCV database has a longer PPT), the numbering is straightforward. For a sequence with a longer PPT, we propose that the insertion is ignored for the purpose of numbering. This means that the first nucleotide of the continuation of the alignment after the PPT, the region described in3) and numbered 9418 in their paper, will be numbered 9549, regardless of whether this is the actual position. Positions after that will again be numbered consecutively. Mutations are numbered according to their position, e.g., a NS5A:R217K would be used to indicate that the Arginine in position 217 of the NS5A protein has mutated to a Lysine. The codon involved changes from AGG to AAG (numbering 6906–6908), so the corresponding nucleotide mutation would be denoted G6907A. Insertions can be incorporated by basing the numbering on an alignment that includes all possible insertions, or by devising a special numbering system for insertions relative to a given numbering system. There are several reasons why the second method is preferable. First, sequences occasionally contain very long insertions, meaning that it would be hard to define an alignment that could accommodate all future insertions, and changing the numbering system in a way that would invalidate all previous numbering would be very disruptive. This problem is circumvented by the method outlined below. Second, insertions in HCV are relatively rare, so that this notation does not have to be used often. Deletions are much simpler to deal with than insertions, and we outline a simple naming method for unequivocally designating those. 1. Insertion in sequence relative to the reference sequence. For these cases we propose a residue number/alphabet, where inserted bases or amino acids are indicated by lower case letters following the nucleotide or amino acid position where they occur. For example, three inserted bases in an HCV variant inserted between positions 131 and 132 in the AF009606 reference sequence would be described as 131a, 131b and 131c. (For insertions longer than the length of the alphabet, numbering would proceed 131x, 131y, 131z, 131aa, 131ab, 131ac, …131az, 131ba, 131bb, 131bc…). A similar scheme has been used for numbering amino acids in the immunoglobulin complementarity-determining region (CDR) loops (e.g.,6). Example: in the following NS5A fragment, the location in the variant of the inserted aspartate (D) between AF009606 residues 2412 and 2413 would be referred to as D2412a or NS5A-D440a. 2411 2415 amino acids from start of H77 polyprotein | | GA-DTE - AF009606 EADDTE - variant sequence Table 2 shows the numbering of each variant amino acid in this example. 2. Deletion in sequence relative to the reference sequence. Mentioning the deletions can be useful in cases where the length of the fragment is important, or when referring to the location of an amino acid that is located after a gap (relative to the reference sequence) in an epitope. Example: in the following NS5A fragment, the variant region would be numbered 2410–2415 (del 2413) or NS5A-438-443 (del 441) to make this explicit. 2410 2415 H77 AA position from start of polyprotein | | SGADTE - AF009606 EEA-TE - variant Table 3 shows the numbering of each variant amino acid in this example. Mentioning the deletions would be useful in cases where the length of the fragment is important; in this example, if the deletion were not made explicit it could be assumed that the range 2410–2415 refers to a peptide that is 6 AA long, when it really is only 5 AA. 3. Synthetic sequences. A separate problem is that of artificially engineered sequences, such as replacement of stretches of the HCV genome with extraneous genetic segments. The numbering system should be able to cope with this situation also, by treating the extraneous segment as a special type of HCV, which can be numbered according to the length of the insert. Finding the stretches that correspond to “real” HCV depends on pattern matching, which requires accurate location of the end and re-start of HCV sequences on either side of the insert to retain correct numbering. Using the same example, 2411 2415 amino acids from polyprotein start | | | GADT——-E - AF009606 GADTYNTVATLE - variant sequence ======= insert Table 4 shows the numbering of the insert in this example. While the numbering is based on a genotype 1a reference sequence, both the Sequence Locator tool (US database) and the Number tool (European database) are sufficiently flexible to easily accommodate other genotypes. The tools align sequences of all genotypes described to date unambiguously to the reference sequence, so the numbering will be uniform for other genotypes as well as for genotype 1. This numbering proposal, using the AF009606 (isolate H77) sequence as a reference, should be able to unequivocally number all possible mutations in HCV, both natural and manmade. The HCV sequence databases8 and the Los Alamos HCV immunology database9 (as well as the Los Alamos HIV database) number positions and epitopes according to this system. Moreover, the databases websites provides tools for finding stretches of sequence by their numbers, for assigning start and end coordinates to a sequence, and for converting between the various numbering systems. Numbering HCV nucleotide sequences is done by analogy to H77. The first step is aligning your sequence to H77. If there is no length variation, the numbering is straightforward; nucleotide numbers run from 1 (start of 5′ UTR) to 9646 (end of 3′ UTR). Insertions relative to H77 are labeled with letters. Protein numbering works like the nucleotide numbering, but starts at the start of the polyprotein. The sequence databases will support both systems, but use polyprotein numbering as a basis. Absolute numbering moves across the coding regions, relative numbering starts over at every coding region. Relative numbering is almost exclusively used for proteins, polyprotein numbering mainly in immunology, protein numbering in drug resistance research. The Los Alamos immunology database uses polyprotein numbering. The 5′ UTR numbering starts at 1 and ends at 341; the Core cds starts at 342. The numbering of the 3′ UTR starts at 9378 (after the stop codon), but complications arise due to the variable length of the PPT. The UTR consists of 3 elements: a variable 5′ region, the PPT, and a conserved 3′ region, often called X. The first region is numbered 9378–9410. The PPT consists almost entirely of T's and therefore cannot be meaningfully aligned; it is numbered according to its length in H77, 9411–9545. The X region starts at 9546 (regardless of its actual location, which depends on the length of the PPT) and ends at 9646. The HCV databases can be found at: European site: http://euhcvdb.ibcp.fr Japanese site: http://s2as02.genes.nig.ac.jp/ American site: http://hcv.lanl.gov The European HCV database is funded by HepCVax Cluster (EU FP5 grant QLK2-2002-01329) and VIRGIL Network of Excellence (EU FP6 grant LSHM-CT-2004-503359). The Japanese Hepatitis database is supported by a Grant-in Aid for the Publication of Scientific Research Results (#168111), Grant-in Aid for Scientific Research, Japan Society for the Promotion of Science (JSPS). The American HCV database is funded by the Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases under interagency agreement Y1-A1-1500.

In the present study, capillary liquid chromatography (LC) nano electrospray ionization quadruple time-of-flight (nano-ESI-Q-TOF) mass spectrometry was utilized to identify the unique proteotypic peptides for liquid chromatography-tandem mass spectrometry (LC-MS/MS) mediated breast cancer resistance protein (BCRP/ABCG2) and bile salt export pump (BSEP/ABCG11) quantification, using insect membrane vesicles overexpressing the proteins. The lower limit of quantification was established to be 31.25 pM and 125 nM for BCRP/ABCG2 and BSEP/ABCG11, respectively. The linearity of standard curves was up to 5000 pM. The accuracy and precision of the LC-MS/MS method were evaluated by adding the known amount of synthetic proteotypic peptide or synthetic surrogate peptide substrates in the membrane protein extracts of livers or hepatocytes. The overall relative error (RE) and coefficient of variation (CV) were below 15.9% and 14.2% for BCRP/ABCG2 quantification or below 15.6% and 6.4% for BSEP/ABCG11, respectively. The absolute differences of BCRP/Bcrp and BSEP/Bsep proteins were determined in livers and isolated hepatocytes across species by the newly developed LC-MS/MS methods, with ranking order of dog > rat > monkey approximately = human and rat approximately = monkey > dog approximately = human, respectively (where the uppercase letters identify the human protein, i.e., BSEP and BCRP, and lowercase letters indicate that the transporter derives from a preclinical species, i.e., Bsep and Bcrp). The freshly isolated and cryopreserved hepatocytes conserved the protein levels of BSEP/Bsep and BCRP/Bcrp similarly to those found in liver tissue. We report, for the first time, an absolution quantification method for BCRP/Bcrp and BSEP/Bsep and the differences of the protein expressions across species. The results could serve as supportive information for extrapolation of hepatobiliary elimination from preclinical species to human.

BACKGROUND: Our first predictor of protein disorder was published just over a decade ago in the Proceedings of the IEEE International Conference on Neural Networks (Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK (1997) Identifying disordered regions in proteins from amino acid sequence. Proceedings of the IEEE International Conference on Neural Networks, 1: 90-95). By now more than twenty other laboratory groups have joined the efforts to improve the prediction of protein disorder. While the various prediction methodologies used for protein intrinsic disorder resemble those methodologies used for secondary structure prediction, the two types of structures are entirely different. For example, the two structural classes have very different dynamic properties, with the irregular secondary structure class being much less mobile than the disorder class. The prediction of secondary structure has been useful. On the other hand, the prediction of intrinsic disorder has been revolutionary, leading to major modifications of the more than 100 year-old views relating protein structure and function. Experimentalists have been providing evidence over many decades that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. In addition, experimentalists are also showing that, for many proteins, their functions depend on the unstructured rather than structured state; such results are in marked contrast to the greater than hundred year old views such as the lock and key hypothesis. Despite extensive data on many important examples, including disease-associated proteins, the importance of disorder for protein function has been largely ignored. Indeed, to our knowledge, current biochemistry books don't present even one acknowledged example of a disorder-dependent function, even though some reports of disorder-dependent functions are more than 50 years old. The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins. RESULTS: Disorder prediction has been important for showing that the relatively few experimentally characterized examples are members of a very large collection of related disordered proteins that are wide-spread over all three domains of life. Many significant biological functions are now known to depend directly on, or are importantly associated with, the unfolded or partially folded state. Here our goal is to review the key discoveries and to weave these discoveries together to support novel approaches for understanding sequence-function relationships. CONCLUSION: Intrinsically disordered protein is common across the three domains of life, but especially common among the eukaryotic proteomes. Signaling sequences and sites of posttranslational modifications are frequently, or very likely most often, located within regions of intrinsic disorder. Disorder-to-order transitions are coupled with the adoption of different structures with different partners. Also, the flexibility of intrinsic disorder helps different disordered regions to bind to a common binding site on a common partner. Such capacity for binding diversity plays important roles in both protein-protein interaction networks and likely also in gene regulation networks. Such disorder-based signaling is further modulated in multicellular eukaryotes by alternative splicing, for which such splicing events map to regions of disorder much more often than to regions of structure. Associating alternative splicing with disorder rather than structure alleviates theoretical and experimentally observed problems associated with the folding of different length, isomeric amino acid sequences. The combination of disorder and alternative splicing is proposed to provide a mechanism for easily "trying out" different signaling pathways, thereby providing the mechanism for generating signaling diversity and enabling the evolution of cell differentiation and multicellularity. Finally, several recent small molecules of interest as potential drugs have been shown to act by blocking protein-protein interactions based on intrinsic disorder of one of the partners. Study of these examples has led to a new approach for drug discovery, and bioinformatics analysis of the human proteome suggests that various disease-associated proteins are very rich in such disorder-based drug discovery targets.

Transmissible spongiform encephalopathies (TSE) or "prion diseases" have been related to the "protein-only hypothesis", which suggests that the "scrapie form (PrPSc)" of the prion protein (PrP) is the TSE infectious agent. The nmr structure of the ubiquitous benign cellular form of PrP (PrPC) consists of a globular domain of residues 126-231 with three alpha-helices and a short beta-sheet, and a flexible extended "tail" of residues 23-125. The peptide segment of helix 1 has been implicated in various stages of hypothetical pathways to prion pathology on the basis of the protein-only idea, including that it takes part in the conformation change that leads from PrPC to PrPSc. In this paper we describe solution nmr and circular dichroism studies of the synthetic hexadecapeptide mPrP(143-158), with the sequence H-NDWEDRYYRENMYRYP-NH2, where the bold letters represent the segment that forms helix 1 in murine PrPC. In both H2O and a 1:1 mixture of H2O and trifluoroethanol this polypeptide segment shows high helix propensity, which is a key issue in discussions on potential roles of this molecular region in conformational transitions of PrP.

Through the development and application of a unique approach for producing Re-metallopeptides, a new class of peptide-derived probes that are designed to target beta-amyloid plaques was developed. Derivatives of a class of beta-breaker peptides having the core sequence lvffa or affvl (lower case letters represent D-amino acids) and the single amino acid chelate quinoline (SAACQ) ligand which can bind Re and (99m)Tc were prepared on an automated peptide synthesizer. Both monomeric and dimeric peptides were synthesized in modest to good yields where in select examples a biotin-containing amino acid derivative was included to act as a linker point for further conjugation to carrier proteins. The Re complexes for all reported peptides were prepared similarly and screened for their ability to inhibit fibrillogenesis. Two of the reported compounds showed excellent inhibitory properties (8a: 40 +/- 5% amyloid formation versus control; 16: 40 +/- 4%) and warrant further investigation. For one of these leads, the (99m)Tc analogue was synthesized and the product showed high stability toward histidine and cysteine challenges, making it a viable candidate for in vivo biodistribution studies.

Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or "singleplex" proteins. Actually, multiple-location or "multiplex" proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the "multi-labeled learning" and "accumulation-layer scale", a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.

One of the most important branches of genetic engineering is the expression of recombinant proteins using biological expression systems. Nowadays, different expression systems are used for the production of recombinant proteins including bacteria, yeasts, molds, mammals, plants, and insects. Yeast expression systems such as Saccharomyces cerevisiae (S. cerevisiae) and Pichia pastoris (P. pastoris) are more popular. P. pastoris expression system is one of the most popular and standard tools for the production of recombinant protein in molecular biology. Overall, the benefits of protein production by P. pastoris system include appropriate folding (in the endoplasmic reticulum) and secretion (by Kex2 as signal peptidase) of recombinant proteins to the external environment of the cell. Moreover, in the P. pastoris expression system due to its limited production of endogenous secretory proteins, the purification of recombinant protein is easy. It is also considered a unique host for the expression of subunit vaccines which could significantly affect the growing market of medical biotechnology. Although P. pastoris expression systems are impressive and easy to use with well-defined process protocols, some degree of process optimization is required to achieve maximum production of the target proteins. Methanol and sorbitol concentration, Mut forms, temperature and incubation time have to be adjusted to obtain optimal conditions, which might vary among different strains and externally expressed protein. Eventually, optimal conditions for the production of a recombinant protein in P. pastoris expression system differ according to the target protein.

The apoprotein gene for a chromoprotein antitumor antibiotic, C-1027, was cloned from the producer strain, Streptomyces globisporus C-1027, and sequenced. The process verified that; (1) the sequence included the entire structural gene directing a precursor of the apoprotein (pre-apoprotein having Met1---Ala33 leader peptide ahead of the apoprotein) and flanking regions, (2) the amino acid sequence of the apoprotein deduced from the base sequence perfectly matched the one based on protein analysis, (3) 3rd letters of the codons were 88% G or C, while the 1st plus the 2nd letters were 63% G or C, (4) the structural gene had 57% homology with that of macromomycin apoprotein (mcmA) while the flanking regions had little homology with the corresponding ones of mcmA, except some homology at the -10th and -35th promoter regions, and (5) the gene was transcribed as a monocistronic mRNA in an early growth phase, independent of chromophore production.

We have uncovered new evidence for a significant interaction between divalent sulfur atoms and aromatic rings. Our study involves a statistical analysis of interatomic distances and other geometric descriptors derived from entries in the Cambridge Crystallographic Database (F. H. Allen and O. Kennard, Chem. Design Auto. News, 1993, Vol. 8, pp. 1 and 31-37). A set of descriptors was defined sufficient in number and type so as to elucidate completely the preferred geometry of interaction between six-membered aromatic carbon rings and divalent sulfurs for all crystal structures of nonmetal-bearing organic compounds present in the database. In order to test statistical significance, analogous probability distributions for the interaction of the moiety X-CH(2)-X with aromatic rings were computed, and taken a priori to correspond to the null hypothesis of no significant interaction. Tests of significance were carried our pairwise between probability distributions of sulfur-aromatic interaction descriptors and their CH(2)-aromatic analogues using the Smirnov-Kolmogorov nonparametric test (W. W. Daniel, Applied Nonparametric Statistics, Houghton-Mifflin: Boston, New York, 1978, pp. 276-286), and in all cases significance at the 99% confidence level or better was observed. Local maxima of the probability distributions were used to define a preferred geometry of interaction between the divalent sulfur moiety and the aromatic ring. Molecular mechanics studies were performed in an effort to better understand the physical basis of the interaction. This study confirms observations based on statistics of interaction of amino acids in protein crystal structures (R. S. Morgan, C. E. Tatsch, R. H. Gushard, J. M. McAdon, and P. K. Warme, International Journal of Peptide Protein Research, 1978, Vol. 11, pp. 209-217; R. S. Morgan and J. M. McAdon, International Journal of Peptide Protein Research, 1980, Vol. 15, pp. 177-180; K. S. C. Reid, P. F. Lindley, and J. M. Thornton, FEBS Letters, 1985, Vol. 190, pp. 209-213), as well as studies involving molecular mechanics (G. Nemethy and H. A. Scheraga, Biochemistry and Biophysics Research Communications, 1981, Vol. 98, pp. 482-487) and quantum chemical calculations (B. V. Cheney, M. W. Schulz, and J. Cheney, Biochimica Biophysica Acta, 1989, Vol. 996, pp.116-124; J. Pranata, Bioorganic Chemistry, 1997, Vol. 25, pp. 213-219)-all of which point to the possible importance of the sulfur-aromatic interaction. However, the preferred geometry of the interaction, as determined from our analysis of the small-molecule crystal data, differs significantly from that found by other approaches.

BACKGROUND AND AIMS: Obese and diabetic mice display enhanced intestinal permeability and metabolic endotoxaemia that participate in the occurrence of metabolic disorders. Our recent data support the idea that a selective increase of Bifidobacterium spp. reduces the impact of high-fat diet-induced metabolic endotoxaemia and inflammatory disorders. Here, we hypothesised that prebiotic modulation of gut microbiota lowers intestinal permeability, by a mechanism involving glucagon-like peptide-2 (GLP-2) thereby improving inflammation and metabolic disorders during obesity and diabetes. METHODS: Study 1: ob/ob mice (Ob-CT) were treated with either prebiotic (Ob-Pre) or non-prebiotic carbohydrates as control (Ob-Cell). Study 2: Ob-CT and Ob-Pre mice were treated with GLP-2 antagonist or saline. Study 3: Ob-CT mice were treated with a GLP-2 agonist or saline. We assessed changes in the gut microbiota, intestinal permeability, gut peptides, intestinal epithelial tight-junction proteins ZO-1 and occludin (qPCR and immunohistochemistry), hepatic and systemic inflammation. RESULTS: Prebiotic-treated mice exhibited a lower plasma lipopolysaccharide (LPS) and cytokines, and a decreased hepatic expression of inflammatory and oxidative stress markers. This decreased inflammatory tone was associated with a lower intestinal permeability and improved tight-junction integrity compared to controls. Prebiotic increased the endogenous intestinotrophic proglucagon-derived peptide (GLP-2) production whereas the GLP-2 antagonist abolished most of the prebiotic effects. Finally, pharmacological GLP-2 treatment decreased gut permeability, systemic and hepatic inflammatory phenotype associated with obesity to a similar extent as that observed following prebiotic-induced changes in gut microbiota. CONCLUSION: We found that a selective gut microbiota change controls and increases endogenous GLP-2 production, and consequently improves gut barrier functions by a GLP-2-dependent mechanism, contributing to the improvement of gut barrier functions during obesity and diabetes.

OBJECTIVE: Diabetes and obesity are characterized by a low-grade inflammation whose molecular origin is unknown. We previously determined, first, that metabolic endotoxemia controls the inflammatory tone, body weight gain, and diabetes, and second, that high-fat feeding modulates gut microbiota and the plasma concentration of lipopolysaccharide (LPS), i.e., metabolic endotoxemia. Therefore, it remained to demonstrate whether changes in gut microbiota control the occurrence of metabolic diseases. RESEARCH DESIGN AND METHODS: We changed gut microbiota by means of antibiotic treatment to demonstrate, first, that changes in gut microbiota could be responsible for the control of metabolic endotoxemia, the low-grade inflammation, obesity, and type 2 diabetes and, second, to provide some mechanisms responsible for such effect. RESULTS: We found that changes of gut microbiota induced by an antibiotic treatment reduced metabolic endotoxemia and the cecal content of LPS in both high-fat-fed and ob/ob mice. This effect was correlated with reduced glucose intolerance, body weight gain, fat mass development, lower inflammation, oxidative stress, and macrophage infiltration marker mRNA expression in visceral adipose tissue. Importantly, high-fat feeding strongly increased intestinal permeability and reduced the expression of genes coding for proteins of the tight junctions. Furthermore, the absence of CD14 in ob/ob CD14(-)(/)(-) mutant mice mimicked the metabolic and inflammatory effects of antibiotics. CONCLUSIONS: This new finding demonstrates that changes in gut microbiota controls metabolic endotoxemia, inflammation, and associated disorders by a mechanism that could increase intestinal permeability. It would thus be useful to develop strategies for changing gut microbiota to control, intestinal permeability, metabolic endotoxemia, and associated disorders.

Proteins of the HBV envelope (env) are coded for by two adjacent regions of the HBV env gene: the pre-S and S regions. Antigenic determinants corresponding to amino acid sequences of both regions are recognized by human antibodies and are important in virus-neutralizing responses. Protective immune responses to HBV appear to be linked to the major HLA histocompatibility complex. Inbred and congenic strains of mice represent a model system relevant for studies on the genetic control of immune responsiveness of humans to HBV envelope proteins. Such mouse strains were ranked according to their antibody response to the S protein and divided into high [d,q], intermediate [a,k,b], and low [s] responders (letters in brackets indicate H-2 haplotype.) Selected pre-S antigenic determinants can be mimicked with high fidelity by synthetic peptide analogues that are immunogenic without any carriers. Thus it is possible to study directly the genetic control of immune responsiveness to pre-S epitopes mimicked by these peptides without having to consider the influence of carriers or of S protein. The results presented here show that inbred mouse strains can be ranked according to their antibody responses to the synthetic peptide pre-S(120-145) as follows: A/J[a] approximately equal to SWR/J[q] greater than C57BL/6J[b] approximately equal to AKR/J[k] approximately equal to SJL/J[s] much greater than DBA/2J[d] greater than BALB/cJ[d]. Only SJL/J[s] mice responded well to another synthetic peptide pre-S (12-32). Thus, H-2-linked genes regulating the immune response to S protein and to epitopes on pre-S-coded sequences are distinct. Anti-pre-S(120-145) responses in S protein-nonresponders circumvent this nonresponsiveness. This should be considered in the design of hepatitis B vaccines.

Several cellular proteins are synthesized in the cytosol on free ribosomes and then associate with membranes due to the presence of short peptide sequences. These membrane-targeting sequences contain sites to which lipid chains are attached, which help direct the protein to a particular membrane domain and anchor it firmly in the bilayer. The intracellular concentration of these proteins in particular cellular compartments, where their interacting partners are also concentrated, is essential to their function. This paper reports that the apparently unmodified N-terminal sequence of the Sendai virus C protein (MPSFLKKILKLRGRR . . .; letters in italics represent hydrophobic residues; underlined letters represent basic residues, which has a strong propensity to form an amphipathic alpha-helix in a hydrophobic environment) also function as a membrane targeting signal and membrane anchor. Moreover, the intracellular localization of the C protein at the plasma membrane is essential for inducing the interferon-independent phosphorylation of Stat1 as part of the viral program to prevent the cellular antiviral response.

BACKGROUND: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. RESULTS: This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. CONCLUSION: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.

Antimicrobial peptides are diverse group of biologically active molecules with multidimensional properties. In recent past, a wide variety of AMPs with diverse structures have been reported from different sources such as plants, animals, mammals, and microorganisms. The presence of unusual amino acids and structural motifs in AMPs confers unique structural properties to the peptide that attribute for their specific mode of action. The ability of these active AMPs to act as multifunctional effector molecules such as signalling molecule, immune modulators, mitogen, antitumor, and contraceptive agent makes it an interesting candidate to study every aspect of their structural and biological properties for prophylactic and therapeutic applications. In addition, easy cloning and recombinant expression of AMPs in heterologous plant host systems provided a pipeline for production of disease resistant transgenic plants. Besides these properties, AMPs were also used as drug delivery vectors to deliver cell impermeable drugs to cell interior. The present review focuses on the diversity and broad spectrum antimicrobial activity of AMPs along with its multidimensional properties that could be exploited for the application of these bioactive peptides as a potential and promising drug candidate in pharmaceutical industries.

Coot is a molecular-graphics application for model building and validation of biological macromolecules. The program displays electron-density maps and atomic models and allows model manipulations such as idealization, real-space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers and Ramachandran idealization. Furthermore, tools are provided for model validation as well as interfaces to external programs for refinement, validation and graphics. The software is designed to be easy to learn for novice users, which is achieved by ensuring that tools for common tasks are 'discoverable' through familiar user-interface elements (menus and toolbars) or by intuitive behaviour (mouse controls). Recent developments have focused on providing tools for expert users, with customisable key bindings, extensions and an extensive scripting interface. The software is under rapid development, but has already achieved very widespread use within the crystallographic community. The current state of the software is presented, with a description of the facilities available and of some of the underlying methods employed.