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At least 1,315 records · Page 73Linked to original sources
PubMed2004

Large scale gene expression profiling of metabolic shift of mammalian cells in culture.

The metabolic state of hybridoma cells in continuous culture varies with the cultivation condition from which the culture is initiated. At a metabolically shifted state, cells have markedly reduced glucose and other nutrient consumption and lactate production as compared to cells in batch culture or in continuous culture without a metabolic shift. Taking a combined genomics and proteomics approach, we investigated the molecular mechanism of metabolic shift. Cells from continuous cultures at two different steady states with a glucose consumption to lactate production molar ratio (DeltaL/DeltaG) of 0.08 and 1.4 were studied. Affymetrix GeneChips as well as cDNA microarrays were employed to identify differentially expressed mRNA transcripts, and the differentially expressed proteins were identified using the 2D gel electrophoresis-mass spectrometry approach. The decrease in glucose metabolism upon metabolic shift is accompanied by a decrease in gene expression of a number of genes involved in its metabolism. However, the number of genes differentially expressed and the extent of differential expression upon metabolic shift are relatively moderate. The change in the expression of metabolic genes at the transcriptional level was confirmed by real time PCR. The results suggest that metabolic shift is a combined effect of both biochemical events at reaction level and gene expression at transcription and translation level. This approach of integrating transcriptional profiling, proteomic techniques and biochemical analysis provides a more global view of the metabolism of mammalian cells in culture.

Animals↗
PubMed2005

The transcriptome's drugable frequenters.

Microarray studies are widely employed in the exploratory phase of the drug discovery process. Expectations raised by the genomics revolution led to the belief that they would rapidly lead to the identification of novel drug targets. However, a few basic questions were often overlooked. Are members of drugable gene families properly represented in the transcriptome? Or are they poorly expressed and below the detection limit of the microarray technology? This review explores the representation of drug targets and components of downstream cellular signaling pathways in the transcriptome. It appears that members of drugable gene families are underrepresented in the transcriptomes of non-pathological human tissues. But, they are represented at or above the expected frequency in the differential transcriptome (i.e. the set of genes that changes expression upon a change in cellular environment). Analysis of differential gene expression on a genome-wide scale will therefore give a comprehensive overview of cellular pathways and possible drug targets.

Drug Design↗
PubMed2006

Data processing and classification analysis of proteomic changes: a case study of oil pollution in the mussel, Mytilus edulis.

BACKGROUND: Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. The challenges associated with the analysis of large-scale multivariate proteomic datasets have been widely discussed in medical research and biomarker discovery. This concept has been introduced to ecotoxicology only recently, so data processing and classification analysis need to be refined before they can be readily applied in biomarker discovery and monitoring studies. RESULTS: Data sets obtained from a case study of oil pollution in the Blue mussel were investigated for differential protein expression by retentate chromatography-mass spectrometry and decision tree classification. Different tissues and different settings were used to evaluate classifiers towards their discriminatory power. It was found that, due the intrinsic variability of the data sets, reliable classification of unknown samples could only be achieved on a broad statistical basis (n > 60) with the observed expression changes comprising high statistical significance and sufficient amplitude. The application of stringent criteria to guard against overfitting of the models eventually allowed satisfactory classification for only one of the investigated data sets and settings. CONCLUSION: Machine learning techniques provide a promising approach to process and extract informative expression signatures from high-dimensional mass-spectrometry data. Even though characterisation of the proteins forming the expression signatures would be ideal, knowledge of the specific proteins is not mandatory for effective class discrimination. This may constitute a new biomarker approach in ecotoxicology, where working with organisms, which do not have sequenced genomes render protein identification by database searching problematic. However, data processing has to be critically evaluated and statistical constraints have to be considered before supervised classification algorithms are employed.

Journal Article↗
PubMed2004

[Toxicoproteomics: first experiences in a BMBF-study].

The rapid development of molecular toxicology is providing innovative approaches to an improved investigation and recognition of toxic substances. Proteome analysis offers, with 2DE/MS (two-dimensional gel electrophoresis and mass spectrometry) and SELDI (surface enhanced laser desorption/ionisation), a promising discipline to classify molecular changes caused by toxic exposure. The Rat Liver Foci Bioassay (RLFB) is a detailed, well-described model for the investigation of liver carcinogenesis induced by chemical substances. Based on this model, we examined whether proteomic methods of molecular toxicology can be used for the early recognition of toxic and/or carcinogenic characteristics of toxic substances. In addition, identification and subsequent prevalidation of new hepatocellular biomarkers was performed, enabling better prediction of toxic and/or carcinogenic effects. This could lead to a more meaningful RLFB and thus to an improved risk assessment of chemicals. 2DE analysis in this study showed that deregulated proteins are assigned to mainly anabolic and catabolic metabolism pathways in the cell. Beyond this, individual proteins were identified which play a key role in the carcinogenic process. A comparison of the differentially expressed proteins in tissue from tumour-bearing animals and tissue derived from the start of the study revealed that protein expression changes (biomarkers) were already detectable shortly after exposure. In addition, analysis by SELDI clearly showed several differentially expressed proteins and/or derived masses. The spectra represented specific differences in tissues, which could be assigned to the same histopathological endpoints. With bioinformatics analysis it was possible to identify individual discriminating mass peaks, which were indicative of tumour formation. Group specific changes can be illustrated and/or represented in more detail with further cluster analysis methods. These results give hope for an improved prediction of hepatotoxicity and carcinogenicity by means of protein markers, which could in the future lead to a shortening of carcinogenicity studies and to a reduction in the use of experimental animals.

Animals↗
PubMed2007

Innate immune response by ficolin binding in apoptotic placenta is associated with the clinical syndrome of preeclampsia.

BACKGROUND: Unidentified circulating factors derived from placenta are thought to be responsible for the exaggerated systemic inflammation leading to preeclampsia. Our aim was to identify the circulating factors present in preeclampsia and to investigate their relationship to the underlying systemic immune response responsible for the associated clinical manifestations. METHODS: We obtained blood samples from pregnant women with and without preeclampsia and performed comparative proteomic analyses to identify the abnormal circulating factors by 2-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption ionization time of flight for protein separation and identification. In placentas from preeclamptic pregnancies, we evaluated the potential role of the candidate proteins identified by Western and immunohistochemical analysis. We also used proinflammatory cytokine antibody arrays to investigate local and systemic immune responses. RESULTS: We found that ficolins, the pattern-recognition proteins involved in the lectin-complement pathway, were differentially expressed in plasma from preeclamptic pregnancies. Ficolins were present in low concentrations in plasma but at high concentrations in the placenta, particularly in syncytiotrophoblasts undergoing apoptosis. The binding of ficolins in apoptotic trophoblasts induced innate immunity through local and systemic cytokine activation and correlated with the clinical manifestation of preeclampsia. CONCLUSIONS: We identified specific in vivo circulating factors derived from the placenta that are responsible for the local immune recognition and systemic inflammatory response in the development of clinical manifestations of preeclampsia. These findings may have predictive value and also therapeutic applications to lessen adverse clinical outcomes of preeclampsia.

Apoptosis↗
PubMed2007

Proteomic analysis of cervical-vaginal fluid: identification of novel biomarkers for detection of intra-amniotic infection.

Intra-amniotic infection (IAI) is associated with preterm birth and perinatal mortality. To identify potential biomarkers, we performed a comprehensive survey of the cervical-vaginal fluid (CVF) proteome from a primate IAI model utilizing multidimensional protein identification technology (LC/LC-MS/MS) and MALDI-TOF-MS analyses. Analyses of CVF proteome identified 205 unique proteins and differential expression of 27 proteins in controls and IAI samples. Protein expression signatures and immunodetection of specific biomarkers identified can be employed for noninvasive detection of IAI.

Amniotic Fluid↗
PubMed2004

Burkholderia phenoliruptrix sp. nov., to accommodate the 2,4,5-trichlorophenoxyacetic acid and halophenol-degrading strain AC1100.

Strain AC1100 is well-known for its ability to degrade a variety of recalcitrant xenobiotics, including 2,4,5-trichlorophenoxyacetic acid. We performed a polyphasic-taxonomic study to determine its taxonomic position. The G+C content of strain AC1100 was 62.6 mol%. On the basis of 16S rRNA gene sequence similarity, strain AC1100 belonged to the beta-Proteobacteria and was most closely related to Burkholderia fungorum (98.3% similarity). DNA-DNA hybridisations, comparison of protein profiles, cellular fatty acid analysis and biochemical tests allowed genotypic and phenotypic differentiation of strain AC1100 from other Burkholderia species. Our data show that strain AC1100 represents a novel species for which the name Burkholderia phenoliruptrix sp. nov. is proposed. The type strain is AC1100T (= LMG 22037T = CCUG 48558T).

2,4,5-Trichlorophenoxyacetic Acid↗
PubMed2026

RNF43 Mutations Are Associated With the Classical Molecular Subtype, Vigorous Antitumor Immune Responses, and Prolonged Survival in Pancreatic Adenocarcinoma.

RNF43 mutations were correlated with microsatellite status in colorectal cancer and with fewer and later recurrences in pancreatic ductal adenocarcinoma (PDAC). Here, we undertake a detailed assessment of RNF43 mutations in PDAC. A total of 313 PDACs (308 microsatellite stable [MSS] and 5 microsatellite-instable [MSI] cases) underwent next-generation sequencing (Oncomine Tumor Mutation Load assay; Thermo Fisher). Spatial analyses (NanoString) classified PDACs according to their transcriptomic and proteomic immune signaling. Fluorescent imaging was used to define spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+). Each of 20 PDACs with RNF43 mutations (RNF43mut) and without RNF43 mutations (RNF43wt) underwent multiplex immunofluorescence analysis to determine immune status. A total of 153 PDACs (22 RNF43mut and 131 RNF43wt cases) underwent bulk RNA sequencing to assign into molecular subtypes. Overall, 24 RNF43 mutations were identified (22 MSS PDACs and 2 MSI PDACs). The incidence of RNF43 mutations in MSS PDACs (7.1%) was consistent with The Cancer Genome Atlas (6.7%). However, RNF43 mutations were more frequent among MSI PDACs (40%). Additionally, RNF43mut had differential frequencies of other mutations (including Wnt pathway genes), higher tumor mutational burden values (5.5 mut/mb vs 1.67 mut/mb; P < .01), and significantly longer overall survival (47 vs 18 months; P < .0001) than RNF43wt. Moreover, RNF43mut exhibited significantly higher densities of CD8+ T lymphocytes, dendritic cells, and B lymphocytes (P < .001) and an upregulation of ITGAX, CD11c, CD8, and HLA-DR compared with RNF43wt. Patients with RNF43mut PDACs were more often of the classical molecular subtype (20/22, 90.9%). RNF43mut PDACs showed high tumor mutational burden values, suggesting increased neoantigen load coupled with an abundance of antigen-presenting immune cells and an upregulation of immune determinants promoting antigen presentation. All this contributes to stronger antitumor immune responses and improved clinical outcomes.

Humans↗
PubMed2004

Proteomic analysis of mouse mammary terminal end buds identifies axonal growth cone proteins.

Ductal morphogenesis in the mouse mammary gland occurs mainly postnatally and is driven by specialized structures at the ends of the developing ducts, the terminal end buds (TEBs), which later regress once ductal growth is complete. To identify proteins that are specifically associated with migration of TEBs we developed a novel method of isolating TEBs, which eliminated the mammary stroma. The protein expression profile of the TEBs was then compared with that of isolates taken from the 4th inguinal mammary gland of adult virgin mice using two-dimensional (2-D) gel electrophoresis and mass spectrometry (MS) analysis (matrix-assisted laser desorption/ionization and quadrupole time of flight). Following construction of an integrated protein expression database, 44 protein features which showed differential expression levels between the two sets were chosen for MS analysis. Of these, 24 gave protein annotations whereas the other 20 produced unidentified peptides. Fourteen unequivocal proteins were identified from these 24, whereas the remaining 10 matched more than one protein within a single 2-D gel feature. Several of the identified proteins were associated with the cytoskeleton and have previously been reported in axonal growth cones, suggesting that they may influence cell shape and motility within the advancing TEBs, in a similar fashion to migrating axons.

Animals↗
PubMed2004

Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice.

In the present study, the effect of a high fat diet on the expression of proteins in insulin target tissues was analyzed using a proteomic approach. Gastrocnemius muscle, white and brown adipose tissue, and liver were taken from C57BL/6 mice either fed on a high-fat or a chow diet. Expression levels of approximately 10 000 polypeptides for all the four tissues were assessed by two-dimensional gel electrophoresis (2-DE). Computer-assisted image analysis allowed the detection of 50 significantly (p < 0.05) differentially expressed proteins between obese and lean mice. Interestingly, more than half of these proteins were detected in the brown adipose tissue. The differentially expressed proteins were identified by tandem mass spectrometry. Several stress and redox proteins were modulated in response to the high-fat diet. A key glycolytic enzyme was found to be downregulated in adipose tissues and muscle, suggesting that at elevated plasma fatty acid concentrations, fatty acids compete with glucose as an oxidative fuel source. Furthermore, in brown adipose tissue there were significant changes in mitochondrial enzymes involved in the Krebs tricarboxylic acid (TCA) cycle and in the respiratory chain in response to the high-fat diet. The brown adipose tissue is an energy-dissipating tissue. Our data suggest that the high-fat diet treated mice were increasing energy expenditure to defend against weight gain.

Adipose Tissue↗
PubMed2005

Down-regulation of the tumor suppressor protein 14-3-3sigma is a sporadic event in cancer of the breast.

14-3-3 proteins comprise a family of highly conserved and broadly expressed multifunctional regulatory proteins that are involved in various cellular processes such as cell cycle progression, cell growth, differentiation, and apoptosis. Transcriptional expression of the sigma isoform of 14-3-3 is frequently impaired in human cancers, including carcinomas of the breast, which has led to the suggestion that this protein might be involved in the neoplastic transformation of breast epithelial cells. Here we report on the analysis of 14-3-3sigma expression in primary breast tumors using a proteomic approach complemented by immunohistochemical analysis by means of specific antibodies against this isoform. We show that the levels of expression of 14-3-3sigma were similar in non-malignant breast epithelial tissue and matched malignant tissue with only sporadic loss of expression observed in 3 of the 68 tumors examined. Moreover we show that 14-3-3sigma immunoreactivity was restricted to epithelial cells and significantly stronger in the myoepithelial cells that line the mammary ducts and lobules. The lack of expression of 14-3-3sigma in the three breast carcinomas was not associated with high levels of expression of the dominant-negative transcriptional regulator DeltaNp63 or with increased expression of estrogen-responsive finger protein, a ubiquitin-protein ligase (E3) that targets 14-3-3sigma for proteolysis. Validation of the results was performed retrospectively on an independent clinical tumor sample set using a tissue microarray containing 65 primary tumors. Our data suggest that, contrary to what was previously thought, loss of expression of 14-3-3sigma protein is not a frequent event in breast tumorigenesis.

14-3-3 Proteins↗
PubMed2005

Proteomic analysis of somatic embryogenesis in Medicago truncatula. Explant cultures grown under 6-benzylaminopurine and 1-naphthaleneacetic acid treatments.

The Medicago truncatula line 2HA has a 500-fold greater capacity to regenerate plants in culture by somatic embryogenesis than wild-type Jemalong. We have compared proteomes of tissue cultures from leaf explants of these two lines. Both 2HA and Jemalong explants were grown on media containing the auxin 1-naphthaleneacetic acid and the cytokinin 6-benzylaminopurine. Proteins were extracted from the cultures at different time points (2, 5, and 8 weeks), separated by two-dimensional gel electrophoresis, and detected by silver staining. More than 2,000 proteins could be reproducibly resolved and detected on each gel. Statistical analysis showed that 54 protein spots were significantly (P < 0.05) changed in expression (accumulation) during the 8 weeks of culture, and most of these spots were extracted from colloidal Coomassie-stained two-dimensional gel electrophoresis gels and were subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry or liquid chromatography-tandem mass spectrometry analysis. Using a publicly available expressed sequence tag database and the Mascot search engine, we were able to identify 16 differentially expressed proteins. More than 60% of the differentially expressed protein spots had very different patterns of gene expression between 2HA and Jemalong during the 8 weeks of culture.

Adenine↗
PubMed2003

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Algorithms↗
PubMedDate not supplied

Visual analysis of gel-free proteome data.

We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.

Chromatography, Gel↗
PubMed2004

Protein profiling of microdissected prostate tissue links growth differentiation factor 15 to prostate carcinogenesis.

Identification of proteomic alterations associated with early stages in the development of prostate cancer may facilitate understanding of progression of this highly variable disease. Matched normal, high-grade prostatic intraepithelial neoplasia (hPIN) and prostate cancer cells of predominantly Gleason grade 3 were procured by laser capture microdissection from serial sections obtained from snap-frozen samples dissected from 22 radical prostatectomy specimens. From these cells, protein profiles were generated by surface-enhanced laser desorption/ionization-time of flight mass spectrometry. A 24-kDa peak was observed at low or high intensity in profiles of prostate cancer cells in 19 of 27 lesions and at low intensity in 3 of 8 hPIN lesions but was not detectable in matched normal cells. SDS-PAGE analysis of prostate cancer and matched normal epithelium confirmed expression of a prostate cancer-specific 24-kDa protein. Mass spectrometry and protein data-based analysis identified the protein as the dimeric form of mature growth differentiation factor 15 (GDF15). The increased expression of mature GDF15 protein in prostate cancer cells cannot be explained on the basis of up-regulation of GDF15 mRNA because reverse transcription-PCR analysis showed similar amounts of transcript in normal, hPIN, and prostate cancer cells that were obtained by laser capture microdissection in the same set of serial sections from which the protein profiles were obtained. Our findings suggest that early prostate carcinogenesis is associated with expression of mature GDF15 protein.

Adenocarcinoma↗
PubMedDate not supplied

The use of microarrays for studying the pathogenesis of Helicobacter pylori.

At present, the genomes of various microorganisms have been completely sequenced, and many others are in progress. The availability of this level of information and the computational analysis of the described sequences have led to the development of new genomic areas such as: analysis in silico, comparative genomics, functional genomics, transcriptomics, proteomics, and pharmacogenomics. Microarray technology is a powerful tool for analyzing the expression profile of thousands of genes in a global way and can be applied to the study of various biological systems. Using the complete sequences for both the H. pylori and human genome that are available in the data bases, a number of researchers have revealed important information. Some of these data offer a glimpse into the great genetic diversity of H. pylori, the differential genetic expression between the strains that shows the complexity of the response of microorganisms to different conditions of development, and into the association of gene cluster expression with clinical outcome. Other groups have examined the global transcriptional response of gastric epithelial cells to H. pylori. The majority of these studies report an alteration in gene expression related to transcription functions, transduction signals, cell cycle regulation and differentiation, development factors, proliferation/apoptosis balance, expression of membrane proteins, and inflammatory response.

Animals↗
PubMed2002

Application of zwitterionic detergents to the solubilization of integral membrane proteins for two-dimensional gel electrophoresis and mass spectrometry.

Comparative analysis has long been utilized in biological research to interpret protein interactions in both drug naïve versus drug challenged and normal versus diseased tissues. The technology of proteomics today allows researchers to provide insight into old and still open questions related to biological mechanisms while offering the opportunity to discover novel details in cellular lifecycles. Perhaps the most powerful way to execute these differential displays is in the combination of two-dimensional (2-D) gel electrophoresis and mass spectrometry. While these two techniques together are well suited for abundant and soluble proteins found in cells, rare proteins and integral membrane proteins are still problematic. Recently, a series of novel zwitterionic detergents has been reported in the literature that shows a substantial improvement in solubilizing integral membrane proteins. We show that the amidosulfobetaine, 4-octylbenzol amidosulfobetaine, is better than 3-[(3-cholamidopropyl)dimethylamino]-1-propanesulfonate (CHAPS) at solubilizing both an ion channel and a G-protein coupled receptor (GPCR), while another amidosulfobetaine, myristic amidosulfobetaine (ASB-14), was better than CHAPS at solubilizing a GPCR. Neither membrane protein was visible after staining with colloidal Coomassie blue, silver nor Sypro Ruby. However, a comparison against a duplicate immunoblot allowed for the localization and identification of the ion channel from a 2-D gel by liquid chromatography-tandem mass spectrometry.

Amino Acid Sequence↗
PubMed2006

Comparative proteome analyses of maize (Zea mays L.) primary roots prior to lateral root initiation reveal differential protein expression in the lateral root initiation mutant rum1.

The embryonically preformed primary root is the first root type of maize that emerges after germination. In this study the abundant soluble proteins of 2.5-day-old primary roots of wild-type and lateral root mutant rum1 seedlings were compared before the initiation of lateral roots. In CBB-stained 2-D gels, among 350 detected proteins 14 were identified as differentially accumulated (>twofold change; t-test: 95% significance) in wild-type versus rum1 primary roots. These proteins which were identified via ESI MS/MS are encoded by 12 different genes. Functionally, these proteins are involved in lignin biosynthesis, defense, and the citrate cycle. Nine of these genes were further analyzed at the RNA expression level. This study represents the first comparative proteomic analysis of maize primary roots prior to lateral root initiation and will contribute to a better understanding of the molecular basis of root development in cereals.

Blotting, Northern↗