Osteomielitis tuberculosa tibial aislada: a propósito de un caso

Se presenta un caso infrecuent e de osteomielitis tuberculosa aislada tibial sin evidencia de focos tuberculosos en otras localizaciones óseas o extraóseas. Con éste motivo, se revisa la bibliografía y se destaca el valor diagnóstico de la biopsia así como la importancia de asociar el curetaje al tratamiento tuberculostátic o y el bue n pronóstico a distancia de estas lesiones.An infrequent cas e of tuberculous isolated tibial osteomyelitis, without any evidenc e of tuberculous focus in eithe r bone or extrabone locations is presented. With this motive the bibliography is reviewed and the diagnostic value of the biopsy is enhance d as well as the importanc e of associating the curettage to he specific treatment and the good prediction in the long run of these lesions

Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data

[EN] Background: Porcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach. Results: The single model analyses, linkage and GWAS, revealed over 30 and 20 chromosomal regions, 24 of them identified here for the first time, specifically associated to the content of diverse fatty acids in BF and IMF, respectively. The analyses with multitrait models allowed identifying for the first time with a formal statistical approach seven different regions with pleiotropic effects on particular fatty acids in both fat deposits. The most relevant were found on SSC8 for C16:0 and C16:1(n-7) fatty acids, detected by both linkage and GWAS approaches. Other detected pleiotropic regions included one on SSC1 for C16:0, two on SSC4 for C16:0 and C18:2, one on SSC11 for C20:3 and the last one on SSC17 for C16:0. Finally, a targeted eQTL scan focused on regions showing tissue consistent effects was conducted with Longissimus and fat gene expression data. Some powerful candidate genes and regions were identified such as the PBX1, RGS4, TRIB3 and a transcription regulatory element close to ELOVL6 gene to be further studied. Conclusions: Complementary genome scans have confirmed several chromosome regions previously associated to fatty acid composition in backfat and intramuscular fat, but even more, to identify new ones. Although most of the detected regions were tissue-specific, supporting the hypothesis that the major part of genes affecting fatty acid composition differs among tissues, seven chromosomal regions showed tissue-consistent effects. Additional gene expression analyses have revealed powerful target regions to carry the mutation responsible for the pleiotropic effects.This work was funded by the MICINN project AGL2011-29821-C02 (Ministerio de Economia y Competitividad). We thank to Fabian Garcia, Anna Mercade and Carmen Barragan for their assistance in DNA preparation and SNP genotyping.Muñoz, M.; Rodríguez, MC.; Alves, E.; Folch, J.; Ibañez Escriche, N.; Silió, L.; Fernández, A. (2013). Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics. 14. https://doi.org/10.1186/1471-2164-14-845S14Lichtenstein, A. H. (2003). Dietary Fat and Cardiovascular Disease Risk: Quantity or Quality? Journal of Women’s Health, 12(2), 109-114. doi:10.1089/154099903321576493Jiménez-Colmenero, F., Ventanas, J., & Toldrá, F. (2010). Nutritional composition of dry-cured ham and its role in a healthy diet. Meat Science, 84(4), 585-593. doi:10.1016/j.meatsci.2009.10.029Webb, E. C., & O’Neill, H. A. (2008). The animal fat paradox and meat quality. Meat Science, 80(1), 28-36. doi:10.1016/j.meatsci.2008.05.029Wood, J. D., Enser, M., Fisher, A. V., Nute, G. R., Sheard, P. R., Richardson, R. I., … Whittington, F. M. (2008). Fat deposition, fatty acid composition and meat quality: A review. Meat Science, 78(4), 343-358. doi:10.1016/j.meatsci.2007.07.019Martı́n, L., Timón, M. L., Petrón, M. J., Ventanas, J., & Antequera, T. (2000). Evolution of volatile aldehydes in Iberian ham matured under different processing conditions. Meat Science, 54(4), 333-337. doi:10.1016/s0309-1740(99)00107-2Fernández, A., de Pedro, E., Núñez, N., Silió, L., Garcı́a-Casco, J., & Rodrı́guez, C. (2003). Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs. Meat Science, 64(4), 405-410. doi:10.1016/s0309-1740(02)00207-3Sellier, P., Maignel, L., & Bidanel, J. P. (2009). Genetic parameters for tissue and fatty acid composition of backfat, perirenal fat and longissimus muscle in Large White and Landrace pigs. animal, 4(4), 497-504. doi:10.1017/s1751731109991261Suzuki, K., Ishida, M., Kadowaki, H., Shibata, T., Uchida, H., & Nishida, A. (2006). Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs. Journal of Animal Science, 84(8), 2026-2034. doi:10.2527/jas.2005-660Clop, A., Ovilo, C., Perez-Enciso, M., Cercos, A., Tomas, A., Fernandez, A., … Noguera, J. L. (2003). Detection of QTL affecting fatty acid composition in the pig. Mammalian Genome, 14(9), 650-656. doi:10.1007/s00335-002-2210-7Nii, M., Hayashi, T., Tani, F., Niki, A., Mori, N., Fujishima-Kanaya, N., … Mikawa, S. (2006). Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar × Large White intercross. Animal Genetics, 37(4), 342-347. doi:10.1111/j.1365-2052.2006.01485.xRamayo-Caldas, Y., Mercadé, A., Castelló, A., Yang, B., Rodríguez, C., Alves, E., … Folch, J. M. (2012). Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross1. Journal of Animal Science, 90(9), 2883-2893. doi:10.2527/jas.2011-4900Uemoto, Y., Soma, Y., Sato, S., Ishida, M., Shibata, T., Kadowaki, H., … Suzuki, K. (2011). Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Animal Genetics, 43(1), 27-34. doi:10.1111/j.1365-2052.2011.02218.xGuo, T., Ren, J., Yang, K., Ma, J., Zhang, Z., & Huang, L. (2009). Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2population. Animal Genetics, 40(2), 185-191. doi:10.1111/j.1365-2052.2008.01819.xRamos, A. M., Crooijmans, R. P. M. A., Affara, N. A., Amaral, A. J., Archibald, A. L., Beever, J. E., … Groenen, M. A. M. (2009). Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS ONE, 4(8), e6524. doi:10.1371/journal.pone.0006524Corominas, J., Ramayo-Caldas, Y., Puig-Oliveras, A., Pérez-Montarelo, D., Noguera, J. L., Folch, J. M., & Ballester, M. (2013). Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs. PLoS ONE, 8(1), e53687. doi:10.1371/journal.pone.0053687Ponsuksili, S., Jonas, E., Murani, E., Phatsara, C., Srikanchai, T., Walz, C., … Wimmers, K. (2008). Trait correlated expression combined with expression QTL analysis reveals biological pathways and candidate genes affecting water holding capacity of muscle. BMC Genomics, 9(1), 367. doi:10.1186/1471-2164-9-367Steibel, J. P., Bates, R. O., Rosa, G. J. M., Tempelman, R. J., Rilington, V. D., Ragavendran, A., … Ernst, C. W. (2011). Genome-Wide Linkage Analysis of Global Gene Expression in Loin Muscle Tissue Identifies Candidate Genes in Pigs. PLoS ONE, 6(2), e16766. doi:10.1371/journal.pone.0016766C�novas, A., Quintanilla, R., Amills, M., & Pena, R. N. (2010). Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness traits. BMC Genomics, 11(1), 372. doi:10.1186/1471-2164-11-372Uemoto, Y., Sato, S., Ohnishi, C., Terai, S., Komatsuda, A., & Kobayashi, E. (2009). The effects of single and epistatic quantitative trait loci for fatty acid composition in a Meishan × Duroc crossbred population. Journal of Animal Science, 87(11), 3470-3476. doi:10.2527/jas.2009-1917Muñoz, M., Alves, E., Ramayo-Caldas, Y., Casellas, J., Rodríguez, C., Folch, J. M., … Fernández, A. I. (2011). Recombination rates across porcine autosomes inferred from high-density linkage maps. Animal Genetics, 43(5), 620-623. doi:10.1111/j.1365-2052.2011.02301.xQuintanilla, R., Pena, R. N., Gallardo, D., Cánovas, A., Ramírez, O., Díaz, I., … Amills, M. (2011). Porcine intramuscular fat content and composition are regulated by quantitative trait loci with muscle-specific effects1. Journal of Animal Science, 89(10), 2963-2971. doi:10.2527/jas.2011-3974Liaubet, L., Lobjois, V., Faraut, T., Tircazes, A., Benne, F., Iannuccelli, N., … Cherel, P. (2011). Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism. BMC Genomics, 12(1). doi:10.1186/1471-2164-12-548Mitchell-Olds, T. (2010). Complex-trait analysis in plants. Genome Biology, 11(4), 113. doi:10.1186/gb-2010-11-4-113Scoggan, K. A., Jakobsson, P.-J., & Ford-Hutchinson, A. W. (1997). Production of Leukotriene C4in Different Human Tissues Is Attributable to Distinct Membrane Bound Biosynthetic Enzymes. Journal of Biological Chemistry, 272(15), 10182-10187. doi:10.1074/jbc.272.15.10182JAKOBSSON, A., WESTERBERG, R., & JACOBSSON, A. (2006). Fatty acid elongases in mammals: Their regulation and roles in metabolism. Progress in Lipid Research, 45(3), 237-249. doi:10.1016/j.plipres.2006.01.004Iankova, I., Chavey, C., Clapé, C., Colomer, C., Guérineau, N. C., Grillet, N., … Fajas, L. (2008). Regulator of G Protein Signaling-4 Controls Fatty Acid and Glucose Homeostasis. Endocrinology, 149(11), 5706-5712. doi:10.1210/en.2008-0717Angyal, A., & Kiss-Toth, E. (2012). The tribbles gene family and lipoprotein metabolism. Current Opinion in Lipidology, 23(2), 122-126. doi:10.1097/mol.0b013e3283508c3bÓvilo, C., Pérez-Enciso, M., Barragán, C., Clop, A., Rodríguez, C., Oliver, M. A., … Noguera, J. L. (2000). A QTL for intramuscular fat and backfat thickness is located on porcine Chromosome 6. Mammalian Genome, 11(4), 344-346. doi:10.1007/s003350010065Veroneze, R., Lopes, P. S., Guimarães, S. E. F., Silva, F. F., Lopes, M. S., Harlizius, B., & Knol, E. F. (2013). Linkage disequilibrium and haplotype block structure in six commercial pig lines. 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Genetics, 171(2), 783-790. doi:10.1534/genetics.104.03669

New insight into the SSC8 genetic determination of fatty acid composition in pigs

[EN] Background:Fat content and fatty acid composition in swine are becoming increasingly studied because of their effect on sensory and nutritional quality of meat. A QTL (quantitative trait locus) for fatty acid composition in backfat was previously detected on porcine chromosome 8 (SSC8) in an Iberian x Landrace F-2 intercross. More recently, a genome-wide association study detected the same genomic region for muscle fatty acid composition in an Iberian x Landrace backcross population. ELOVL6, a strong positional candidate gene for this QTL, contains a polymorphism in its promoter region (ELOVL6:c.-533C < T), which is associated with percentage of palmitic and palmitoleic acids in muscle and adipose tissues. Here, a combination of single-marker association and the haplotype-based approach was used to analyze backfat fatty acid composition in 470 animals of an Iberian x Landrace F2 intercross genotyped with 144 SNPs (single nucleotide polymorphisms) distributed along SSC8. Results:Two trait-associated SNP regions were identified at 93 Mb and 119 Mb on SSC8. The strongest statistical signals of both regions were observed for palmitoleic acid (C16:1(n-7)) content and C18:0/C16:0 and C18:1(n-7)/C16:1 (n-7) elongation ratios. MAML3 and SETD7 are positional candidate genes in the 93 Mb region and two novel microsatellites in MAML3 and nine SNPs in SETD7 were identified. No significant association for the MAML3 microsatellite genotypes was detected. The SETD7:c. 700G > T SNP, although statistically significant, was not the strongest signal in this region. In addition, the expression of MAML3 and SETD7 in liver and adipose tissue varied among animals, but no association was detected with the polymorphisms in these genes. In the 119 Mb region, the ELOVL6:c.-533C > T polymorphism showed a strong association with percentage of palmitic and palmitoleic fatty acids and elongation ratios in backfat. Conclusions:Our results suggest that the polymorphisms studied in MAML3 and SETD7 are not the causal mutations for the QTL in the 93 Mb region. However, the results for ELOVL6 support the hypothesis that the ELOVL6:c.-533C > T polymorphism has a pleiotropic effect on backfat and intramuscular fatty acid composition and that it has a role in the determination of the QTL in the 119 Mb region.This work was funded by MICINN AGL2008-04818-C03/GAN and MINECO AGL2011-29821-C02 and the Innovation Programme Consolider-Ingenio 2010 (CSD2007-00036). M. Revilla is a Master's student of Animal Breeding and Biotechnology of Reproduction (Polytechnical University of Valencia and Autonomous University of Barcelona). Y. Ramayo-Caldas was funded by a FPU grant (AP2008-01450), J. Corominas by a FPI scholarship from the Ministry of Education (BES-2009-018223) and A. Puig-Oliveras by a PIF scholarship (458-01-1/2011). This manuscript has been proofread by Chuck Simons, a native English speaking university instructor in English.Revilla, M.; Ramayo-Caldas, Y.; Castelló, A.; Corominas, J.; Puig-Oliveras, A.; Ibañez Escriche, N.; Muñoz, M.... (2014). New insight into the SSC8 genetic determination of fatty acid composition in pigs. Genetics Selection Evolution. 46. https://doi.org/10.1186/1297-9686-46-28S46Clarke, R., Frost, C., Collins, R., Appleby, P., & Peto, R. (1997). Dietary lipids and blood cholesterol: quantitative meta-analysis of metabolic ward studies. BMJ, 314(7074), 112-112. doi:10.1136/bmj.314.7074.112Mensink, R. P., & Katan, M. B. (1992). Effect of dietary fatty acids on serum lipids and lipoproteins. A meta-analysis of 27 trials. Arteriosclerosis and Thrombosis: A Journal of Vascular Biology, 12(8), 911-919. doi:10.1161/01.atv.12.8.911Hunter, J. E., Zhang, J., & Kris-Etherton, P. M. (2009). Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review. The American Journal of Clinical Nutrition, 91(1), 46-63. doi:10.3945/ajcn.2009.27661Astrup, A., Dyerberg, J., Elwood, P., Hermansen, K., Hu, F. B., Jakobsen, M. U., … Willett, W. C. (2011). The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? The American Journal of Clinical Nutrition, 93(4), 684-688. doi:10.3945/ajcn.110.004622Harris, W. S., Poston, W. C., & Haddock, C. K. (2007). Tissue n−3 and n−6 fatty acids and risk for coronary heart disease events. Atherosclerosis, 193(1), 1-10. doi:10.1016/j.atherosclerosis.2007.03.018Lopez-Huertas, E. (2010). Health effects of oleic acid and long chain omega-3 fatty acids (EPA and DHA) enriched milks. A review of intervention studies. Pharmacological Research, 61(3), 200-207. doi:10.1016/j.phrs.2009.10.007Guo, T., Ren, J., Yang, K., Ma, J., Zhang, Z., & Huang, L. (2009). Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc × Erhualian intercross F2population. Animal Genetics, 40(2), 185-191. doi:10.1111/j.1365-2052.2008.01819.xUemoto, Y., Soma, Y., Sato, S., Ishida, M., Shibata, T., Kadowaki, H., … Suzuki, K. (2011). Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Animal Genetics, 43(1), 27-34. doi:10.1111/j.1365-2052.2011.02218.xClop, A., Ovilo, C., Perez-Enciso, M., Cercos, A., Tomas, A., Fernandez, A., … Noguera, J. L. (2003). Detection of QTL affecting fatty acid composition in the pig. Mammalian Genome, 14(9), 650-656. doi:10.1007/s00335-002-2210-7Ramayo-Caldas, Y., Mercadé, A., Castelló, A., Yang, B., Rodríguez, C., Alves, E., … Folch, J. M. (2012). Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross1. Journal of Animal Science, 90(9), 2883-2893. doi:10.2527/jas.2011-4900Muñoz, M., Rodríguez, M. C., Alves, E., Folch, J. M., Ibañez-Escriche, N., Silió, L., & Fernández, A. I. (2013). Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics, 14(1), 845. doi:10.1186/1471-2164-14-845Ramos, A. M., Crooijmans, R. P. M. A., Affara, N. A., Amaral, A. J., Archibald, A. L., Beever, J. E., … Groenen, M. A. M. (2009). Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS ONE, 4(8), e6524. doi:10.1371/journal.pone.0006524Estellé, J., Mercadé, A., Pérez-Enciso, M., Pena, R. N., Silió, L., Sánchez, A., & Folch, J. M. (2009). Evaluation ofFABP2as candidate gene for a fatty acid composition QTL in porcine chromosome 8. Journal of Animal Breeding and Genetics, 126(1), 52-58. doi:10.1111/j.1439-0388.2008.00754.xEstellé, J., Fernández, A. I., Pérez-Enciso, M., Fernández, A., Rodríguez, C., Sánchez, A., … Folch, J. M. (2009). A non-synonymous mutation in a conserved site of theMTTPgene is strongly associated with protein activity and fatty acid profile in pigs. Animal Genetics, 40(6), 813-820. doi:10.1111/j.1365-2052.2009.01922.xPurcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795Pérez-Enciso, M., & Misztal, I. (2011). Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics, 12(1). doi:10.1186/1471-2105-12-202Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. 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Covalent Histone Modifications Underlie the Developmental Regulation of Insulin Gene Transcription in Pancreatic β Cells. Journal of Biological Chemistry, 278(26), 23617-23623. doi:10.1074/jbc.m303423200Ramayo-Caldas, Y., Mach, N., Esteve-Codina, A., Corominas, J., Castelló, A., Ballester, M., … Folch, J. M. (2012). Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics, 13(1), 547. doi:10.1186/1471-2164-13-547Corominas, J., Ramayo-Caldas, Y., Puig-Oliveras, A., Pérez-Montarelo, D., Noguera, J. L., Folch, J. M., & Ballester, M. (2013). Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs. PLoS ONE, 8(1), e53687. doi:10.1371/journal.pone.005368

Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip

[EN] Background: The traditional strategy to map QTL is to use linkage analysis employing a limited number of markers. These analyses report wide QTL confidence intervals, making very difficult to identify the gene and polymorphisms underlying the QTL effects. The arrival of genome-wide panels of SNPs makes available thousands of markers increasing the information content and therefore the likelihood of detecting and fine mapping QTL regions. The aims of the current study are to confirm previous QTL regions for growth and body composition traits in different generations of an Iberian x Landrace intercross (IBMAP) and especially identify new ones with narrow confidence intervals by employing the PorcineSNP60 BeadChip in linkage analyses. Results: Three generations (F3, Backcross 1 and Backcross 2) of the IBMAP and their related animals were genotyped with PorcineSNP60 BeadChip. A total of 8,417 SNPs equidistantly distributed across autosomes were selected after filtering by quality, position and frequency to perform the QTL scan. The joint and separate analyses of the different IBMAP generations allowed confirming QTL regions previously identified in chromosomes 4 and 6 as well as new ones mainly for backfat thickness in chromosomes 4, 5, 11, 14 and 17 and shoulder weight in chromosomes 1, 2, 9 and 13; and many other to the chromosome-wide signification level. In addition, most of the detected QTLs displayed narrow confidence intervals, making easier the selection of positional candidate genes. Conclusions: The use of higher density of markers has allowed to confirm results obtained in previous QTL scans carried out with microsatellites. Moreover several new QTL regions have been now identified in regions probably not covered by markers in previous scans, most of these QTLs displayed narrow confidence intervals. Finally, prominent putative biological and positional candidate genes underlying those QTL effects are listed based on recent porcine genome annotation.This work was funded by MICINN projects AGL2008-04818-C03/GAN and CSD2007-00036. DPM was funded by a FPI Ph.D grant from the Spanish Ministerio de Educacion (BES-2009-025417). YR was funded by a FPU Ph.D grant from the Spanish Ministerio de Educacion (AP2008-01450). We want to thanks to Dr. Martien Groenen (Wageningen, NL) for the SNP annotation on porcine genome assembly, to Anna Mercade for her technical assistance with the SNPs genotyping and to Rita Benitez and Fabian Garcia for technical support.Fernández, A.; Pérez-Montarelo, D.; Barragan, C.; Ramayo-Caldas, Y.; Ibáñez-Escriche, N.; Castelló, A.; Noguera, J.... (2012). Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip. 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Early socialization and environmental enrichment of lactating piglets affects the caecal microbiota and metabolomic response after weaning

The aim of this study was to determine the possible impact of early socialization and an enriched neonatal environment to improve adaptation of piglets to weaning. We hypothesized that changes in the microbiota colonization process and in their metabolic response and intestinal functionality could help the animals face weaning stress. A total of 48 sows and their litters were allotted into a control (CTR) or an enriched treatment (ENR), in which piglets from two adjacent pens were combined and enriched with toys. The pattern of caecal microbial colonization, the jejunal gene expression, the serum metabolome and the intestinal physiology of the piglets were assessed before (-2 d) and after weaning (+ 3d). A differential ordination of caecal microbiota was observed after weaning. Serum metabolome suggested a reduced energetic metabolism in ENR animals, as evidenced by shifts in triglycerides and fatty acids, VLDL/LDL and creatine regions. The TLR2 gene showed to be downregulated in the jejunum of ENR pigs after weaning. The integration of gene expression, metabolome and microbiota datasets confirmed that differences between barren and enriched neonatal environments were evident only after weaning. Our results suggest that improvements in adaptation to weaning could be mediated by a better response to the post-weaning stress.info:eu-repo/semantics/publishedVersio

Electromagnetic navigation bronchoscopy in the European cohort of the prospective, multicenter NAVIGATE study

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Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media

[EN] Within the emergent field of Systems Biology, mathematical models obtained from physical chemical laws (the so-called first principles-based models) of microbial systems are employed to discern the principles that govern cellular behaviour and achieve a predictive understanding of cellular functions. The reliance on this biochemical knowledge has the drawback that some of the assumptions (specific kinetics of the reaction system, unknown dynamics and values of the model parameters) may not be valid for all the metabolic possible states of the network. In this uncertainty context, the combined use of fundamental knowledge and data measured in the fermentation that describe the behaviour of the microorganism in the manufacturing process is paramount to overcome this problem. In this paper, a grey modelling approach is presented combining data-driven and first principles information at different scales, developed for Pichia pastoris cultures grown on different carbon sources. This approach will allow us to relate patterns of recombinant protein production to intracellular metabolic states and correlate intra and extracellular reactions in order to understand how the internal state of the cells determines the observed behaviour in P. pastoris cultivations.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants DPI2011-28112-C04-01 and DPI2011-28112-C04-02. The authors are also grateful to Biopolis SL for supporting this research. We also gratefully acknowledge Associate Professor Jose Camacho for providing the Exploratory Data Analysis Toolbox.González Martínez, JM.; Folch-Fortuny, A.; Llaneras Estrada, F.; Tortajada Serra, M.; Picó Marco, JA.; Ferrer, A. (2014). Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media. Chemometrics and Intelligent Laboratory Systems. 134:89-99. https://doi.org/10.1016/j.chemolab.2014.02.003S899913

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

[EN] With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in González-Martínez et al. (2014). In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants DPI2011-28112-C04-01 and DPI2011-28112-C04-02. The authors are also grateful to Biopolis SL for supporting this research.Folch-Fortuny, A.; Tortajada Serra, M.; Prats-Montalbán, JM.; Llaneras Estrada, F.; Picó Marco, JA.; Ferrer Riquelme, AJ. (2015). MCR-ALS on metabolic networks: Obtaining more meaningful pathways. Chemometrics and Intelligent Laboratory Systems. 142:293-303. https://doi.org/10.1016/j.chemolab.2014.10.004S29330314