Functional divergence within class B MADS-box genes TfGLO and TfDEF in Torenia fournieri Lind

Homeotic class B genes GLOBOSA (GLO)/PISTILLATA (PI) and DEFICIENS (DEF)/APETALA3 (AP3) are involved in the development of petals and stamens in Arabidopsis. However, functions of these genes in the development of floral organs in torenia are less well known. Here, we demonstrate the unique floral phenotypes of transgenic torenia formed due to the modification of class B genes, TfGLO and TfDEF. TfGLO-overexpressing plants showed purple-stained sepals that accumulated anthocyanins in a manner similar to that of petals. TfGLO-suppressed plants showed serrated petals and TfDEF-suppressed plants showed partially decolorized petals. In TfGLO-overexpressing plants, cell shapes on the surfaces of sepals were altered to petal-like cell shapes. Furthermore, TfGLO- and TfDEF-suppressed plants partially had sepal-like cells on the surfaces of their petals. We isolated putative class B gene-regulated genes and examined their expression in transgenic plants. Three xyloglucan endo-1,4-beta-d-glucanase genes were up-regulated in TfGLO- and TfDEF-overexpressing plants and down-regulated in TfGLO- and TfDEF-suppressed plants. In addition, 10 anthocyanin biosynthesis-related genes, including anthocyanin synthase and chalcone isomerase, were up-regulated in TfGLO-overexpressing plants and down-regulated in TfGLO-suppressed plants. The expression patterns of these 10 genes in TfDEF transgenic plants were diverse and classified into several groups. HPLC analysis indicated that sepals of TfGLO-overexpressing plants accumulate the same type of anthocyanins and flavones as wild-type plants. The difference in phenotypes and expression patterns of the 10 anthocyanin biosynthesis-related genes between TfGLO and TfDEF transgenic plants indicated that TfGLO and TfDEF have partial functional divergence, while they basically work synergistically in torenia

Functional Analysis of the Two Brassica AP3 Genes Involved in Apetalous and Stamen Carpelloid Phenotypes

The Arabidopsis homeotic genes APETALA3 (AP3) and PISTILLATA (PI) are B genes which encode MADS-box transcription factors and specify petal and stamen identities. In the current study, the stamen carpelloid (SC) mutants, HGMS and AMS, of B. rapa and B. napus were investigated and two types of AP3 genes, B.AP3.a and B.AP3.b, were functional characterized. B.AP3.a and B.AP3.b share high similarity in amino acid sequences except for 8 residues difference located at the C-terminus. Loss of this 8 residues in B.AP3.b led to the change of PI-derived motifs. Meanwhile, B.AP3.a specified petal and stamen development, whereas B.AP3.b only specified stamen development. In B. rapa, the mutations of both genes generated the SC mutant HGMS. In B. napus that contained two B.AP3.a and two B.AP3.b, loss of the two B.AP3.a functions was the key reason for the apetalous mutation, however, the loss-of-function in all four AP3 was related to the SC mutant AMS. We inferred that the 8 residues or the PI-derived motif in AP3 gene probably relates to petal formation

Rapid Etiological Classification of Meningitis by NMR Spectroscopy Based on Metabolite Profiles and Host Response

Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis. We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites. Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6-8% of samples were classified as indeterminate. This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques

Use of 1H and 31P HRMAS to evaluate the relationship between quantitative alterations in metabolite concentrations and tissue features in human brain tumour biopsies

[EN] Quantitative multinuclear high-resolution magic angle spinning (HRMAS) was performed in order to determine the tissue pH values of and the absolute metabolite concentrations in 33 samples of human brain tumour tissue. Metabolite concentrations were quantified by 1D 1 H and 31P HRMAS using the electronic reference to in vivo concentrations (ERETIC) synthetic signal. 1 H–1 H homonuclear and 1 H–31P heteronuclear correlation experiments enabled the direct assessment of the 1 H–31P spin systems for signals that suffered from overlapping in the 1D 1 H spectra, and linked the information present in the 1D 1 H and 31P spectra. Afterwards, the main histological features were determined, and high heterogeneity in the tumour content, necrotic content and nonaffected tissue content was observed. The metabolite profiles obtained by HRMAS showed characteristics typical of tumour tissues: rather low levels of energetic molecules and increased concentrations of protective metabolites. Nevertheless, these characteristics were more strongly correlated with the total amount of living tissue than with the tumour cell contents of the samples alone, which could indicate that the sampling conditions make a significant contribution aside from the effect of tumour development in vivo. The use of methylene diphosphonic acid as a chemical shift and concentration reference for the 31P HRMAS spectra of tissues presented important drawbacks due to its interaction with the tissue. Moreover, the pH data obtained from 31P HRMAS enabled us to establish a correlation between the pH and the distance between the N(CH3)3 signals of phosphocholine and choline in 1 H spectra of the tissue in these tumour samples.The authors acknowledge the SCSIE-University of Valencia Microscopy Service for the histological preparations. They also acknowledge Martial Piotto (Bruker BioSpin, France) for providing the ERETIC synthetic signal. Furthermore, they acknowledge financial support from the Spanish Government project SAF2007-6547, the Generalitat Valenciana project GVACOMP2009-303, and the E.U.'s VI Framework Programme via the project "Web accessible MR decision support system for brain tumor diagnosis and prognosis, incorporating in vivo and ex vivo genomic and metabolomic data" (FP6-2002-LSH 503094). CIBER-BBN is an initiative funded by the VI National R&D&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions, and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.Esteve Moya, V.; Celda, B.; Martínez Bisbal, MC. (2012). Use of 1H and 31P HRMAS to evaluate the relationship between quantitative alterations in metabolite concentrations and tissue features in human brain tumour biopsies. Analytical and Bioanalytical Chemistry. 403:2611-2625. https://doi.org/10.1007/s00216-012-6001-zS26112625403Cheng LL, Chang IW, Louis DN, Gonzalez RG (1998) Cancer Res 58:1825–1832Opstad KS, Bell BA, Griffiths JR, Howe FA (2008) Magn Reson Med 60:1237–1242Sjobakk TE, Johansen R, Bathen TF, Sonnewald U, Juul R, Torp SH, Lundgren S, Gribbestad IS (2008) NMR Biomed 21:175–185Martinez-Bisbal MC, Marti-Bonmati L, Piquer J, Revert A, Ferrer P, Llacer JL, Piotto M, Assemat O, Celda B (2004) NMR Biomed 17:191–205Erb G, Elbayed K, Piotto M, Raya J, Neuville A, Mohr M, Maitrot D, Kehrli P, Namer IJ (2008) Magn Reson Med 59:959–965Wilson M, Davies NP, Brundler MA, McConville C, Grundy RG, Peet AC (2009) Mol Cancer 8:6Martinez-Bisbal MC, Monleon D, Assemat O, Piotto M, Piquer J, Llacer JL, Celda B (2009) NMR Biomed 22:199–206Martínez-Granados B, Monleón D, Martínez-Bisbal MC, Rodrigo JM, del Olmo J, Lluch P, Ferrández A, Martí-Bonmatí L, Celda B (2006) NMR Biomed 19:90–100Hubesch B, Sappey-Marinier D, Roth K, Meyerhoff DJ, Matson GB, Weiner MW (1990) Radiology 174:401–409Albers MJ, Krieger MD, Gonzalez-Gomez I, Gilles FH, McComb JG, Nelson MD Jr, Bluml S (2005) Magn Reson Med 53:22–29Wijnen JP, Scheenen TW, Klomp DW, Heerschap A (2010) NMR Biomed 23:968–976Podo F (1999) NMR Biomed 12:413–439Griffiths JR, Cady E, Edwards RH, McCready VR, Wilkie DR, Wiltshaw E (1983) Lancet 1:1435–1436Robitaille PL, Robitaille PA, Gordon Brown G, Brown GG (1991) J Magn Reson 92:73–84, 1969Griffiths JR (1991) Br J Cancer 64:425–427Payne GS, Troy H, Vaidya SJ, Griffiths JR, Leach MO, Chung YL (2006) NMR Biomed 19:593–598De Silva SS, Payne GS, Thomas V, Carter PG, Ind TE, deSouza NM (2009) NMR Biomed 22:191–198Wang Y, Cloarec O, Tang H, Lindon JC, Holmes E, Kochhar S, Nicholson JK (2008) Anal Chem 80:1058–1066Lehnhardt FG, Rohn G, Ernestus RI, Grune M, Hoehn M (2001) NMR Biomed 14:307–317Srivastava NK, Pradhan S, Gowda GA, Kumar R (2010) NMR Biomed 23:113–122Akoka S, Barantin L, Trierweiler M (1999) Anal Chem 71:2554–2557Albers MJ, Butler TN, Rahwa I, Bao N, Keshari KR, Swanson MG, Kurhanewicz J (2009) Magn Reson Med 61:525–532Ben Sellem D, Elbayed K, Neuville A, Moussallieh FM, Lang-Averous G, Piotto M, Bellocq JP, Namer IJ (2011) J Oncol 2011:174019Bourne R, Dzendrowskyj T, Mountford C (2003) NMR Biomed 16:96–101Martinez-Bisbal MC, Esteve V, Martinez-Granados B, Celda B (2011) J Biomed Biotechnol 2011:763684, Epub 2010 Sep 5Celda B, Montelione GT (1993) J Magn Reson B 101:189–193Esteve V, Celda B (2008) Magn Reson Mater Phys MAGMA 21:484–484Collins TJ (2007) Biotechniques 43:25–30Govindaraju V, Young K, Maudsley AA (2000) NMR Biomed 13:129–153Fan TW-M (1996) Prog Nucl Magn Reson Spectrosc 28:161–219Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte CF, Tolmie DE, Kent Wenger R, Yao H, Markley JL (2008) Nucleic Acids Res 36:D402–D408Kriat M, Vion-Dury J, Confort-Gouny S, Favre R, Viout P, Sciaky M, Sari H, Cozzone PJ (1993) J Lipid Res 34:1009–1019Subramanian A, Shankar Joshi B, Roy AD, Roy R, Gupta V, Dang RS (2008) NMR Biomed 21:272–288Daykin CA, Corcoran O, Hansen SH, Bjornsdottir I, Cornett C, Connor SC, Lindon JC, Nicholson JK (2001) Anal Chem 73:1084–1090Griffin JL, Lehtimaki KK, Valonen PK, Grohn OH, Kettunen MI, Yla-Herttuala S, Pitkanen A, Nicholson JK, Kauppinen RA (2003) Cancer Res 63:3195–3201Petroff OAC, Prichard JW (1995) In: Kraicer J, Dixon SJ (eds) Methods in neurosciences. Academic, San DiegoBarton S, Howe F, Tomlins A, Cudlip S, Nicholson J, Anthony Bell B, Griffiths J (1999) Magn Reson Mater Phys Biol Med 8:121–128Sitter B, Sonnewald U, Spraul M, Fjosne HE, Gribbestad IS (2002) NMR Biomed 15:327–337Coen M, Hong YS, Cloarec O, Rhode CM, Reily MD, Robertson DG, Holmes E, Lindon JC, Nicholson JK (2007) Anal Chem 79:8956–8966Russell D, Rubinstein LJ (1998) Russel and Rubinstein's pathology of tumors of the nervous system. Arnold, LondonTynkkynen T, Tiainen M, Soininen P, Laatikainen R (2009) Anal Chim Acta 648:105–112Kjaergaard M, Brander S, Poulsen F (2011) J Biomol NMR 49:139–149Robert O, Sabatier J, Desoubzdanne D, Lalande J, Balayssac S, Gilard V, Martino R, Malet-Martino M (2011) Anal Bioanal Chem 399:987–999Chadzynski GL, Bender B, Groeger A, Erb M, Klose U (2011) J Magn Reson 212:55–63Weljie AM, Jirik FR (2011) Int J Biochem Cell Biol 43:981–989Barba I, Cabanas ME, Arus C (1999) Cancer Res 59:1861–1868Liimatainen T, Hakumaki JM, Kauppinen RA, Ala-Korpela M (2009) NMR Biomed 22:272–279Opstad KS, Bell BA, Griffiths JR, Howe FA (2008) NMR Biomed 21:677–685Schmitz JE, Kettunen MI, Hu D, Brindle KM (2005) Magn Reson Med 54:43–50Glunde K, Artemov D, Penet MF, Jacobs MA, Bhujwalla ZM (2010) Chem Rev 110:3043–3059Hertz L (2008) Neuropharmacology 55:289–309Takahashi T, Otsuguro K, Ohta T, Ito S (2010) Br J Pharmacol 161:1806–181

Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution

Probabilistic fire spread forecast as a management tool in an operational setting

Background: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. Results: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. Conclusion: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these firesinfo:eu-repo/semantics/publishedVersio

Continuous-time modeling of cell fate determination in Arabidopsis flowers

<p>Abstract</p> <p>Background</p> <p>The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored.</p> <p>Results</p> <p>We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant <it>Arabidopsis thaliana</it>. In this model, the dimerization of MADS-box transcription factors is incorporated explicitly. The unknown parameters are estimated from (known) experimental expression data. The model is validated by simulation studies of known mutant plants.</p> <p>Conclusions</p> <p>The proposed model gives realistic predictions with respect to independent mutation data. A simulation study is carried out to predict the effects of a new type of mutation that has so far not been made in <it>Arabidopsis</it>, but that could be used as a severe test of the validity of the model. According to our predictions, the role of dimers is surprisingly important. Moreover, the functional loss of any dimer leads to one or more phenotypic alterations.</p

An IGF-I promoter polymorphism modifies the relationships between birth weight and risk factors for cardiovascular disease and diabetes at age 36

OBJECTIVE: To investigate whether IGF-I promoter polymorphism was associated with birth weight and risk factors for cardiovascular disease (CVD) and type 2 diabetes (T2DM), and whether the birth weight – risk factor relationship was the same for each genotype. DESIGN AND PARTICIPANTS: 264 subjects (mean age 36 years) had data available on birth weight, IGF-I promoter polymorphism genotype, CVD and T2DM risk factors. Student's t-test and regression analyses were applied to analyse differences in birth weight and differences in the birth weight – risk factors relationship between the genotypes. RESULTS: Male variant carriers (VCs) of the IGF-I promoter polymorphism had a 0.2 kg lower birth weight than men with the wild type allele (p = 0.009). Of the risk factors for CVD and T2DM, solely LDL concentration was associated with the genotype for the polymorphism. Most birth weight – risk factor relationships were stronger in the VC subjects; among others the birth weight – systolic blood pressure relationship: 1 kg lower birth weight was related to an 8.0 mmHg higher systolic blood pressure CONCLUSION: The polymorphism in the promoter region of the IGF-I gene is related to birth weight in men only, and to LDL concentration only. Furthermore, the genotype for this polymorphism modified the relationships between birth weight and the risk factors, especially for systolic and diastolic blood pressure

Ageing, adipose tissue, fatty acids and inflammation

A common feature of ageing is the alteration in tissue distribution and composition, with a shift in fat away from lower body and subcutaneous depots to visceral and ectopic sites. Redistribution of adipose tissue towards an ectopic site can have dramatic effects on metabolic function. In skeletal muscle, increased ectopic adiposity is linked to insulin resistance through lipid mediators such as ceramide or DAG, inhibiting the insulin receptor signalling pathway. Additionally, the risk of developing cardiovascular disease is increased with elevated visceral adipose distribution. In ageing, adipose tissue becomes dysfunctional, with the pathway of differentiation of preadipocytes to mature adipocytes becoming impaired; this results in dysfunctional adipocytes less able to store fat and subsequent fat redistribution to ectopic sites. Low grade systemic inflammation is commonly observed in ageing, and may drive the adipose tissue dysfunction, as proinflammatory cytokines are capable of inhibiting adipocyte differentiation. Beyond increased ectopic adiposity, the effect of impaired adipose tissue function is an elevation in systemic free fatty acids (FFA), a common feature of many metabolic disorders. Saturated fatty acids can be regarded as the most detrimental of FFA, being capable of inducing insulin resistance and inflammation through lipid mediators such as ceramide, which can increase risk of developing atherosclerosis. Elevated FFA, in particular saturated fatty acids, maybe a driving factor for both the increased insulin resistance, cardiovascular disease risk and inflammation in older adults

An Analysis of Enzyme Kinetics Data for Mitochondrial DNA Strand Termination by Nucleoside Reverse Transcription Inhibitors

Nucleoside analogs used in antiretroviral treatment have been associated with mitochondrial toxicity. The polymerase-γ hypothesis states that this toxicity stems from the analogs' inhibition of the mitochondrial DNA polymerase (polymerase-γ) leading to mitochondrial DNA (mtDNA) depletion. We have constructed a computational model of the interaction of polymerase-γ with activated nucleoside and nucleotide analog drugs, based on experimentally measured reaction rates and base excision rates, together with the mtDNA genome size, the human mtDNA sequence, and mitochondrial dNTP concentrations. The model predicts an approximately 1000-fold difference in the activated drug concentration required for a 50% probability of mtDNA strand termination between the activated di-deoxy analogs d4T, ddC, and ddI (activated to ddA) and the activated forms of the analogs 3TC, TDF, AZT, FTC, and ABC. These predictions are supported by experimental and clinical data showing significantly greater mtDNA depletion in cell culture and patient samples caused by the di-deoxy analog drugs. For zidovudine (AZT) we calculated a very low mtDNA replication termination probability, in contrast to its reported mitochondrial toxicity in vitro and clinically. Therefore AZT mitochondrial toxicity is likely due to a mechanism that does not involve strand termination of mtDNA replication