A standard z-score was used to identify hits from the RNAi screen

A standard z-score was used to identify hits from the RNAi screen. The z-score was based on a raw score defined as z = (x-μ)/σ, where x is a reporter gene activity from a single well,

μ is the mean reporter gene activity calculated for entire plate including non-silencing shRNA MK-8776 concentration samples, and σ is the standard deviation of the entire plate. Acknowledgements We thank Hongzhao Tian for technical assistance. This work was supported by a LANL Laboratory-Directed Research and Development Exploratory Research Grant and by the National Center for Research Resources and the National Institute of General Medical Sciences of the National Institutes of Health through Grant Number P41-RR01315, “The National Flow Cytometry Resource”. The funding agencies had no role in the design of the experiments, analysis of the data, or writing of the manuscript. References 1. Cornelis G: Yersinia type III secretion: send in

the effectors. J Cell Biol 2002, 158:401–8.PubMedCrossRef 2. Pettersson J, Nordfelth R, Dubinina E, Bergman T, Gustafsson M, Magnusson K, Wolf-Watz H: Modulation of virulence factor expression by pathogen target cell contact. Science 1996, 273:12–31. 1233CrossRef 3. Simonet M, Richard S, Berche P: Electron microscopic evidence for in vivo extracellular localization of Yersinia pseudotuberculosis harboring the pYV plasmid. Infect Immun 1990, 58:841–5.PubMed 4. Nakajima R, Motin VL, Brubaker RR: Suppression of cytokines S3I-201 in mice by protein A-V antigen fusion peptide and restoration of synthesis by active immunization. Infect Immun 1995, 63:3021–9.PubMed 5. Cornelis GR: The type III secretion injectisome. Nat Rev Microbiol 2006, 4:811–25.PubMedCrossRef 6. Straley SC, Harmon PA: Growth in mouse peritoneal macrophages of Yersinia pestis lacking established virulence determinants. Infect Immun 1984, 45:649–54.PubMed 7. Pujol C, Bliska JB: The ability to replicate in macrophages is conserved between Yersinia pestis and Yersinia pseudotuberculosis . Infect Immun 2003, 71:5892–9.PubMedCrossRef 8. Perry RD, Fetherston JD: Yersinia pestis–etiologic agent of plague. Bay 11-7085 Clin Microbiol Rev 1997, 10:35–66.PubMed

9. Mittal R, Peak-Chew SY, McMahon HT: Acetylation of MEK2 and I kappa B MG-132 datasheet kinase (IKK) activation loop residues by YopJ inhibits signaling. Proc Natl Acad Sci U S A 2006, 103:18574–9.PubMedCrossRef 10. Mukherjee S, Keitany G, Li Y, Wang Y, Ball HL, Goldsmith EJ, Orth K: Yersinia YopJ acetylates and inhibits kinase activation by blocking phosphorylation. Science 2006, 312:1211–4.PubMedCrossRef 11. Sweet CR, Conlon J, Golenbock DT, Goguen J, Silverman N: YopJ targets TRAF proteins to inhibit TLR-mediated NF-kappaB, MAPK and IRF3 signal transduction. Cell Microbiol 2007, 9:2700–15.PubMedCrossRef 12. Hannon GJ, Rossi JJ: Unlocking the potential of the human genome with RNA interference. Nature 2004, 431:371–8.PubMedCrossRef 13.

Results and discussion Results of optimization for DNA sensor mod

Results and discussion Results of optimization for DNA sensor model The parameters to be optimized in this model were A, B and C in Equation 2 which create a solution space of four dimensions with three variables and one

function known as fitness function. The best results obtained out of 20 runs are shown in Table 1 which introduce the lowest fitness values. PF-02341066 nmr Table 1 The best values of the optimizing parameters over the 20 runs The best fitness value obtained Optimized value for A Optimized value for B Optimized value for C 6.742e-07 2.138e10 8.9921e9 -5.680e3 The experimental waveform of the DNA sensor is used for obtaining the optimized values for parameters A, B and C. The optimized model and the experimental waveforms are shown in Figure 3. Figure 3 DNA sensor characteristics. The experimental selleck chemicals and optimized model waveforms for DNA sensor in the presence of probe DNA. The mean absolute percentage error (MAPE) index is used to assess the quality of the MM-102 modelled waveform (see Equation 7). (7) The optimized model is evaluated

using the MAPE index for different concentrations of the DNA sensor. Table 2 shows the accuracy of the proposed optimized model for six different concentrations of the DNA sensor covering a range from 0.01 to 500 nM. The lowest accuracy obtained is 98.46% for the concentration of 0.01 nM while the highest accuracy is 99.41% belonging to the concentration of 100 nM. Overall, the accuracy of more than 98% represents an overall error of less than 2% which is quite acceptable for the optimized model. Table 2 The Dichloromethane dehalogenase MAPE value for different concentrations of DNA sensor ( F ) Concentration F (nM) MAPE value (%) Accuracy based on MAPE (%) F = 0.01 1.54 98.46 F = 0.1

0.90 99.10 F = 1 1.03 98.97 F = 10 0.77 99.23 F = 100 0.59 99.41 F = 500 0.93 99.07 In the next section, it is demonstrated that the optimized model of solution-gated graphene-based DNA sensors can be utilized for electrical detection of DNA hybridization application. DNA hybridization detection using the optimized model The detection of DNA hybridization has been a topic of central importance owing to a wide variety of applications such as diagnosis of pathogenic and genetic disease, gene expression analysis and the genotyping of mutations and polymorphisms [46, 47]. Technologies in DNA biosensing [48] have received special appeal not only for their low cost and simplicity but also for their ultimate capabilities in detecting single-nucleotide polymorphisms (SNP) which have been correlated to several diseases and genetic disorders such as Alzheimer and Parkinson diseases. The DNA hybridization event is the basis of many existing DNA detection techniques. In DNA hybridization as depicted in Figure 4, the target, unknown single-stranded DNA (ssDNA), is identifid and formed by a probe ssDNA and a double-stranded (dsDNA) helix structure with two complementary strands.

20 Driskell JA: Sports nutrition London: CRC Press; 2000 21 B

20. Driskell JA: Sports nutrition. London: CRC Press; 2000. 21. Baysal A: Beslenme. Ankara: Hatiboğlu Yayınevi; 2007. 22. Burns RD, Schiller MR, Merrick MA, Wolf KN: Intercollegiate student athlete use of nutritional supplements and the role of athletic trainers and dietitians in nutrition counseling. Journal of the American Dietetic Association 2004,104(2):246–249.PubMedCrossRef 23. Heredeen F, Fellers RB: Nutrition knovvledge of college football linemen:

Implications for nutrition education. J Am Diet Assoc 1999,9(1):A-38. 24. Wilson ED, Fisher KH, Garcia PA: Principles of nutrition. 4th edition. Wiley; 1979. 25. Merdol TK, Başoğlu S, Örer N: Beslenme ve diyetetik açıklamalı sözlük. Ankara: Vactosertib ic50 Hatiboğlu Yayınları; 1997. 26. Perron M, Endres J: Knowledge, attitudes, and dietary practices of female athletes. J Am Diet Assoc 1985, 85:573–576.PubMed

27. Coyle E: Fluid and fuel intake during exercise. Journal of Sports Sciences 2004,22(1):39–55.PubMedCrossRef 28. Charles SL: Relationships between Nutrition, Alcohol Use and Liver Disease [http://​pubs.​niaaa.​nih.​gov/​publications/​arh27~3/​220~231.​htm] Alcohol Research and Health; 2003. 29. Abood DA, Black DR, Birnbaum RD: Nutrition education intervention for college female athletes. J Nutr Educ Behav 2004,36(3):135–137.PubMedCrossRef 30. Dunn D, Turner LW, Denny G: Nutrition knowledge and attitudes of college athletes. The Smoothened Agonist purchase Sport Journal 2007.,10(4): 31. Douglas PD, Douglas JG: Nutrition knowledge and food practices of high school athletes. J Am Diet Assoc 1984,84(10):1198–1202.PubMed 32. Wong SH, HaAmy SC, RAD001 cost Yuanzhen L, Benli Xu: Nutrition Knowledge and Attitudes of Athletes and Coaches in Hong Kong, Beijing, and Shanghai. Medicine and Science in Sports and Exercise 2004,36(5):349. 33. Reading KJ, McCargar LJ, Marriage BJ: Adolescent and young adult male hockey players: nutrition knowledge and education. Can J Diet Pract Res 1999, 60:166–169.PubMed 34. Corley G, Demarest-Litchford

M, Bazzarre TL: Nutrition Histidine ammonia-lyase knowledge and dietary practices of college coaches. J Am Diet Assoc 1990,90(5):705–709.PubMed 35. Smith-Rockwell M, Nickols-Richardson SM, Thye FW: Nutrition knowledge, opinions and practices of coaches and athletic trainers at a division 1 university. Int J Sport Nutr Exerc Metab 2001, 11:174–85.PubMed 36. Contento IR: Nutrition education: linking research, theory, and practice. Sudbury: Mass. Jones and Bartlett Publishers; 2007. Competing interests The authors declare that they have no competing interests. Authors’ contributions AOO wrote the analysis plan with input from other author and drafted the manuscript, YO conducted the analysis and participated in the interpretation of the results and provided critical comments. Both authors were involved in the implementation of the study as well as read and approved the final manuscript.

The initiative pursues the principles of comprehensive transparen

The initiative pursues the principles of comprehensive transparency and publicity. Dr. Dotson introduced some of the working group’s recent recommendations on genetic variants which have potential benefit for common disease prevention or which predict response to drug treatment. He also drew attention to GAPP Finder, launched by OPHG

in 2010, which provides a continually updated database, tracking the growing number of genetic tests and genomic applications under development or available for use in clinical and public health practice. Robert Green (Harvard-Partners Center for Personalized Genetic Medicine, USA) first gave some insights into the Risk Evaluation and Education for Alzheimer’s Disease (REVEAL) study, in which adult offspring of Alzheimer’s disease patients were offered testing for the apolipoprotein E (Apo E) polymorphism. VX-680 solubility dmso At this point, Dr. Green addressed the major issue of the symposium—the perception and behavioral outcome of predictive genetic testing. The REVEAL study showed that testing had minimal psychological impact and even HDAC inhibitor provoked behavioral changes (for example, intake of vitamins and other supplements or the taking out of new health insurances)

in persons to whom the information that they were carriers of the high-risk Apo E 4-allele had been disclosed, although no buy NSC23766 effective preventive measures for Alzheimer’s disease exist today. Dr. Green pointed out that, in the public and scientists’ view, the road to “healthy aging” starts with self-awareness and self-responsibility towards disease prevention. To this end, action is needed early in life. However, solid scientific evidence must be presented to support the recommendations and actions chosen. Dr. Green also mentioned ongoing intervention trials to establish the effect of attained

genetic risks information for common diseases, e.g., type 2 diabetes or obesity. He also mentioned the forthcoming MedSeq study, which is the first clinical trial ever funded by the National Institutes of Health (NIH) to empirically study the use of whole genome sequencing in the practice of medicine and which is expected to meet the challenges of disclosure of large-scale genomic data. the Dr. Green finalized by citing a statement given by the US Preventive Services Task Force (Petitti et al. 2009): “Decision makers do not have the luxury of waiting for certain evidence. Even though evidence is insufficient, the clinician must still provide advice, patients must make choices, and policymakers must establish policies.” Martina Cornel (VU University Medical Center Amsterdam, The Netherlands) spoke about the problems facing the application of genomics in the prevention of common diseases and focused on recently published policy statements by the European Society of Human Genetics regarding direct-to-consumer genetic testing and genetic testing for common disorders. Dr.

J Anim Sci 2010,88(9):3041–3046

J Anim Sci 2010,88(9):3041–3046.PubMedCrossRef 14. Edwards JE, Huws SA, Kim EJ, Kingston-Smith AH: Characterization of the dynamics of initial bacterial colonization of nonconserved forage in the bovine rumen. FEMS Microbiol

Ecol 2007,62(3):323–335.PubMedCrossRef 15. Stevenson DM, Weimer PJ: Dominance of Prevotella and low abundance of GS-1101 chemical structure classical ruminal bacterial NSC 683864 cost species in the bovine rumen revealed by relative quantification real-time PCR. ApplMicrobiolBiotechnol 2007,75(1):165–174. 16. Furet J-P, Firmesse O, Gourmelon M, Bridonneau C, Tap J, Mondot S, Doré J, Corthier G: Comparative assessment of human and farm animal faecal microbiota using real-time quantitative PCR. FEMS Microbiol Ecol 2009,68(3):351–362.PubMedCrossRef 17. Jones S, Lennon J: Evidence for limited microbial transfer of methane in a planktonic food web. AquatMicrobEcol 2009,58(1):45–53. 18. Kim YG, Lee TH, Park TJ, Park HS, Lee SH: Identification of dominant microbial community in aerophilic biofilm reactors by fluorescence in situ hybridization and PCR-denaturing gradient gel electrophoresis. Korean J Chem Eng 2009,26(3):685–690.CrossRef 19. Walter J, Tannock GW, Tilsala-Timisjarvi A, Rodtong S, Loach DM, Munro K, Alatossava T: Detection and identification of

gastrointestinal Lactobacillus species by using denaturing gradient gel electrophoresis and species-specific PCR primers. Appl Environ Microbiol 2000,66(1):297–303.PubMedCrossRef 20. Smith AH, Mackie RI: Effect of condensed tannins on bacterial check details diversity and metabolic activity in the rat gastrointestinal tract. Appl Environ Microbiol 2004,70(2):1104–1115.PubMedCrossRef 21. Fromin N, Hamelin J, Tarnawski S, Roesti D, Jourdain-Miserez IMP dehydrogenase K, Forestier N, Teyssier-Cuvelle S, Gillet

F, Aragno M, Rossi P: Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns. Environ Microbiol 2002,4(11):634–643.PubMedCrossRef 22. Jouany J-P, Senaud J: Influence des ciliés du rumen sur l’utilisation digestive de différents régimes riches en glucides solubles et sur les produits terminaux formés dans le rumen. Il. — Régimes contenant de l’inuline, du saccharose et du lactose. ReprodNutrDévelop 1983,23(3):607–623. 23. Martin C, Michalet-Doreau B: Variations in mass and enzyme activity of rumen microorganisms: Effect of barley and buffer supplements. J Sci Food Agric 1995,67(3):407–413.CrossRef 24. Lever M: Carbohydrate determination with 4-hydroxybenzoic acid hydrazide (PAHBAH): Effect of bismuth on the reaction. Anal Biochem 1977,81(1):21–27.PubMedCrossRef 25. Pierce J, Suelter CH: An evaluation of the Coomassie brilliant blue G-250 dye-binding method for quantitative protein determination. Anal Biochem 1977,81(2):478–480.PubMedCrossRef 26. Park G, Oh H, Ahn S: Improvement of the ammonia analysis by the phenate method in water and wastewater. Bull Korean Chem Soc 2009, 30:2032–2038.CrossRef 27.

8227 0 0127 0 9091 AUC0–inf 0 8255 0 0099 0 9010 C max 0 5835 0 1

8227 0.0127 0.9091 AUC0–inf 0.8255 0.0099 0.9010 C max 0.5835 0.1291 0.8606 AUC 0–inf area under the serum concentration–time curve from time zero to infinity AUC 0–t area under the serum concentration–time curve from time zero to time of last measurable concentration, C max maximum serum concentration Fig. 2 Mean plasma ibandronic acid concentrations obtained for the test and reference formulations following a 150-mg dose (log scale). N = 146 for ibandronic acid, N = 146 for

Bonviva® (first administration), N = 142 for Bonviva® (second administration), EDTA Ethylene diaminetetraacetic acid The CVWR for AUC0–t , AUC0–inf and C max were 39.77, 39.45 and 43.23 %, respectively. The limits of the acceptance range PLK inhibitor based upon the within-subject variability seen in the bioequivalence study using scaled Thiazovivin research buy average bioequivalence were 73.01–136.97 %. No statistical outliers were detected for the reference formulation following examination ARRY-438162 ic50 of the distribution of the ln-transformed C max. The 90 % confidence intervals were 95.05–110.67 for

C max, 94.35–107.94 for AUC0–t and 94.37–107.88 for AUC0–inf, which are within the predefined bioequivalence acceptance range of 80.00–125.00 %. For C max, the observed ratio and confidence intervals were also within the limits of acceptance obtained using the scaled average bioequivalence BCKDHB approach. Wilcoxon’s test performed on the

t max data showed no statistically significant difference between treatments (p = 0.1382). The least-squares means ratios, the 90 % geometric confidence intervals, and the CVWR for the reference product are presented in Table 4. Table 4 Ibandronic acid: ratios, 90 % geometric confidence intervals (CI) for AUC0–t , AUC0–inf and C max and intra-subject CV for Bonviva® Variable Treatment comparisons Ratioa (%) 90 % CIb (%) Intra-subject CV (Bonviva®) (%) AUC0–t Test (A)—reference (B) 100.92 94.35–107.94 39.77 AUC0–inf Test (A)—reference (B) 100.90 94.37–107.88 39.45 C max c Test (A)—reference (B) 102.56 95.05–110.67 43.23 aCalculated using least-squares means b90 % geometric confidence interval using ln-transformed data cThe scaled average bioequivalence approach was used for C max and the widened limits obtained were 73.01–136.97 % AUC 0–inf area under the serum concentration–time curve from time zero to infinity AUC 0–t area under the serum concentration–time curve from time zero to time of last measurable concentration, C max maximum serum concentration, CV coefficient of variance 3.

The classification of IMPDHs was further substantiated with IMPDH

The classification of IMPDHs was further substantiated with IMPDH sequences obtained from more Penicillium species as described in the following. P. brevicompactum and P. chrysogenum belong to Penicillium subgenus Penicillium and are closely related [16]. To investigate if the presence of two IMPDHs is a general phenomenon in Penicillium subgenus Penicillium, we created degenerate

primers designed to amplify the genes coding for the two types of IMPDHs, IMPDH-A and IMPDH-B. These primers were used to amplify IMPDH-encoding genes by using gDNA from four additional Penicillium strains as PCR templates (Table 1). Interestingly, despite the fact that strains tested included both MPA www.selleckchem.com/products/carfilzomib-pr-171.html producers and non-producers, we found two IMPDH copies in all four strains (Table 1). We then performed a cladistic analysis including these new genes, which showed that mpaF and its orthologs clearly form a separate group (Figure 3). Table 1 Strains and sequences Taxon name IBT number Other collection numbers MPA prod.* Sequences (Accession #)         IMPDH-A IMPDH-B β-tubulin P. bialowiezense 21578 CBS 112477 ++ JF302658 JF302662 JF302653 P.

brevicompactum 23078 – ++ JF302657 HQ731031+ JF302652 P. carneum 3472 CBS 466.95 ++ JF302656 JF302660 selleckchem JF302650 P. chrysogenum 5857 NRRL 1951 – XM_002562313 XM_002559146 XM_002559715 P. paneum 21729 CBS 112296 – JF302654 JF302661 JF302651 P. from roqueforti 16406 NRRL 849 + JF302655 JF302659 JF302649 *) MPA production on CYA media. FK228 purchase – means no production, + medium and ++ high production (Frisvad and Samson, 2004)). +) The MPA gene cluster sequence from P. brevicompactum which contains the gene encoding MpaFp.

Figure 3 Identification and cladistic analysis of IMPDH-A and IMPDH-B coding genes from different fungi. A) Gene organization of imdA from A. nidulans and mpaF (coding for IMPDH-B in P. brevicompactum). The sequence region used for creating the cladograms in B is marked by a square. Introns are marked by a thin open line. B) and C): Rooted cladograms based on, B) IMPDH cDNA sequences (651-654 bp); and C) β-tubulin cDNA sequences (981 bp) from species from Penicillium subgenus Penicillium and from five fungi with sequenced genomes including the outgroup. P.: Penicillium and A.: Aspergillus. Bootstrap values (expressed as percentage of 1000 replications) are shown at the branch points. MPA production is indicated by “”+”" or “”-”". The clades with Penicillium subgenus Penicillium genes are boxed; red, IMPDH-A; blue, IMPDH-B; green, β-tubulin. Coccidioides immitis has been used as outgroup in both analyses B and C. Scale bars correspond to 0.130 and 0.060 nucleotide changes per site in cladograms B) and C) respectively.

Mature biofilms contained living bacteria and were structurally,

Mature biofilms contained living bacteria and were structurally, chemically, and physiologically heterogeneous. These remarkable structures

are formed in the laboratory without unusual culturing conditions (i.e., beyond the choice of medium, temperature, and incubating conditions) and the C188-9 organism does not appear to lose the ability to form biofilm, even after a six or more subcultures. The principal architectural elements observed by electron microscopy may be useful morphological identifiers for classifying PARP inhibitor review bacterial biofilms in vivo. The complexity and reproducibility of the structural motifs in the observed biofilms suggest that they are the result of organized assembly and not a result of ad hoc associations. These results suggest possible ecological advantages of the P. fluorescens EvS4-B1 strain.

Cooperation among microbes currently is generating much interest within both the evolutionary and microbial communities [47]. The matrix of cross-linked polymers observed in the studied biofilms is being produced in copious amounts with high associated costs to the bacteria, while causing large separations between cells. These are relevant and impressive observations, especially within the context of recent theoretical studies [48], which have demonstrated that polymer production in biofilms can be a competitive trait allowing EPS-producing bacteria to occupy more favorable locations in the biofilm while “”suffocating”" strains of non-polymer producers. Conversely, biofilm EPS may provide a protective microenvironment fostering mutualism, such selleck chemicals llc as encountered among endophytic bacteria that colonize intercellular spaces in various interior plant tissues and in the rhizosphere without causing

damage. It has been suggested that biofilms produced by facultative endophytes may be involved in protecting plants from vascular pathogens and may have applications in pesticide phytoremediation [49]. Begun et al. showed that EPS from staphylococcal biofilms protected the enclosed bacterial communities against the immune defenses of the Dehydratase model nematode Caenorhabditis elegans [50]. Methods Bacterial isolation and culture conditions The bacteria used in this study were isolated from soil (T = 31.6°C) directly adjacent to a tar seep at a location on Sulphur Mountain (Ventura County, CA). The soil isolate EvS4-B1 was obtained following enrichment on solid media containing 10 μM thioanisole using the minimum number of passes required to obtain a pure culture. Working cultures of the EvS4-B1 isolate were maintained as slants on complex media inoculated directly from cryostocks. Slants were discarded two weeks following inoculation. Strain EvS4-B1 was cultured using a freshwater medium lacking essential vitamins and minerals (10 mL, see below) in 20 mm culture tubes. Cultures were maintained at 30°C and were shaken at 250 rev min-1. The same growth medium was used throughout.

J Immunol 2001, 166:7477–7485 PubMed 26 Pathak SK, Basu S, Bhatt

J Immunol 2001, 166:7477–7485.PubMed 26. Pathak SK, Basu S, Bhattacharyya selleck screening library A, Kundu M, Basu J: Mycobacterium tuberculosis lipoarabinomannan-mediated IRAK-M

induction negatively regulates Toll-like receptor-dependent interleukin-12 p40 production in macrophages. J Biol Chem 2005, 280:42794–42800.PubMedCrossRef 27. Lowe DM, Redford PS, Wilkinson RJ, O’Garra A, Martineau AR: Neutrophils in tuberculosis: friend or foe? Trends Immunol 2012, 1:14–25.CrossRef 28. Weischenfeldt J, Porse B: Bone Marrow-Derived Macrophages (BMM): Isolation and Applications. Cold Spring Harb Protoc 2008. Competing interests The authors declare that they have no competing interests. Authors’ contributions MRMA performed the experiments and prepared the figures; EPA evaluated growth curves of mycobacteria in MΦ and broth; VL cultured and characterized the mycobacterial strains; TVP established the in vitro model of BMDM infection; EPA, SCMR and FMA carried out the immunoassays; EBL, MRIL and MRMA analyzed the data; EL and MRMA conceived of, designed the study and wrote the manuscript, MREL revised the manuscript critically. Entinostat price All authors read and approved the final manuscript.”
Selleckchem PFT�� Background Mycobacterium tuberculosis is one of the leading causes of death due to a single infectious agent. Its success is based on perfect adaptation to the human host

and the conditions prevailing in infected cells and tissues such as hypoxia, nutrient starvation, low pH and the presence of antimicrobial substances. By adapting their gene expression, growth and metabolism to these environmental conditions, the bacteria are able to persist over long periods of time inside immune cells within granuloma in a latent Carbohydrate state until possible reactivation and outbreak of disease. To be able to combat the disease, it is necessary to understand the molecular mechanisms regulating mycobacterial intracellular persistence, latency

and reactivation. A class of proteins implicated in regulating latency are the mycobacterial histone-like proteins (Hlp) [1]. Hlp have been identified in pathogenic as well as environmental mycobacteria [2, 3]. Proteins belonging to this class have been given different designations in different mycobacterial species such as HLPMt or HupB in M. tuberculosis[3, 4], MDP1 (mycobacterial DNA-binding protein 1) in Mycobacterium bovis BCG [5], Hlp in Mycobacterium smegmatis[2] and ML-LBP21 in Mycobacterium leprae[6]. They are composed of an extremely basic C-terminal part homologous to eukaryotic histone H1 and an N-terminal region similar to HU from Escherichia coli[3, 5]. Hlp expression is developmentally regulated and up-regulation was observed in dormant M. smegmatis[2] and stationary cultures from M. bovis BCG [5]. It is an immunogenic protein detectable in tuberculosis patients [7].

BMC Microbiol 2009, 9:50 PubMedCrossRef 34 Tindall BJ, Rosselló-

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Kanz C, Kanapin A, Das U, Michoud K, Phan I, Gattiker A, Kulikova T, Faruque N, Duggan K, Mclaren P, Reimholz B, Duret L, Penel S, Reuter I, Apweiler R: Integrted and Genome Reviews: integrated views of complete genomes and proteomes. Nucleic Acids Res 2005, (33 Database):D297–302. 38. Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science 1997,278(5338):631–637.PubMedCrossRef 39. Tatusov RL, Galperin MY, Natale DA, Koonin EV: The COG PLX-4720 solubility dmso database: a tool for genome-scale analysis of protein functions and

evolution. Nucleic Acids Res 2000, 28:33–36.PubMedCrossRef 40. Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV: The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 2001, 29:22–28.PubMedCrossRef 41. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA: The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003, 4:41.PubMedCrossRef 42. Fulton DL, Li YY, Laird MR, Horsman BG, Roche FM, Brinkman FS: Improving the Ribose-5-phosphate isomerase specificity of high-throughput ortholog prediction. BMC Bioinformatics 2006, 7:270.PubMedCrossRef 43. Chiu JC, Lee EK, Egan MG, Sarkar IN, Coruzzi GM, DeSalle R: OrthologID: automation of genome-scale ortholog identification within a parsimony framework. Bioinformatics 2006,22(6):699–707.PubMedCrossRef 44. Zmasek CM, Eddy SR: RIO: analyzing BMS345541 proteomes by automated phylogenomics using resampled inference of orthologs. BMC Bioinformatics 2002, 3:14.PubMedCrossRef 45. Storm CEV, Sonnhammer ELL: Automated ortholog inference from phylogenetic trees and calculation of orthology reliability. Bioinformatics 2002, 18:92–99.PubMedCrossRef 46.