Method to Study Gene Expression Patterns During De Novo Root Regeneration from Arabidopsis Leaf Explants

2019 ◽  
pp. 31-38
Author(s):  
Jie Yu ◽  
Ning Zhai ◽  
Lin Xu ◽  
Wu Liu
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Patterns ◽  
Leaf Explants ◽  
Gene Expression Patterns ◽  
Root Regeneration ◽  
Study Gene Expression

Acute Myeloid Leukemia with Translocation t(8;16) Demonstrates Specific Cytomorphological, Cytogenetic, and Gene Expression Characteristics and Can Clearly Be Discriminated from Other AML with Balanced Translocations.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2897-2897
Author(s):  
Torsten Haferlach ◽  
Helmut Loeffler ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Who Classification ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Support Vector ◽  
Gene Expression Patterns ◽  
Down Regulation ◽  
Specific Expression ◽  
Balanced Translocations

Abstract Balanced chromosomal rearrangements leading to fusion genes on the molecular level define distinct biological subsets in AML. The four balanced rearrangements (t(15;17), t(8;21), inv(16), and 11q23/MLL) show a close correlation to cytomorphology and gene expression patterns. We here focused on seven AML with t(8;16)(p11;p13). This translocation is rare (7/3515 cases in own cohort). It is more frequently found in therapy-related AML than in de novo AML (3/258 t-AML, and 4/3287 de novo, p=0.0003). Cytomorphologically, AML with t(8;16) is characterized by striking features: In all 7 cases the positivity for myeloperoxidase on bone marrow smears was >70% and intriguingly, in parallel >80% of blast cells stained strongly positive for non-specific esterase (NSE) in all cases. Thus, these cases can not be classified according to FAB categories. These data suggest that AML-t(8;16) arise from a very early stem cell with both myeloid and monoblastic potential. Furthermore, we detected erythrophagocytosis in 6/7 cases that was described as specific feature in AML with t(8;16). Four pts. had chromosomal aberrations in addition to t(8;16), 3 of these were t-AML all showing aberrations of 7q. Survival was poor with 0, 1, 1, 2, 20 and 18+ (after alloBMT) mo., one lost to follow-up, respectively. We then analyzed gene expression patterns in 4 cases (Affymetrix U133A+B). First we compared t(8;16) AML with 46 AML FAB M1, 41 M4, 9 M5a, and 16 M5b, all with normal karyotype. Hierachical clustering and principal component analyses (PCA) revealed that t(8;16) AML were intercalating with FAB M4 and M5b and did not cluster near to M1. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid. Next we compared the t(8;16) AML with the 4 other balanced subtypes according to the WHO classification (t(15;17): 43; t(8;21): 40; inv(16): 49; 11q23/MLL-rearrangements: 50). Using support vector machines the overall accuracy for correct subgroup assignment was 97.3% (10-fold CV), and 96.8% (2/3 training and 1/3 test set, 100 runs). In PCA and hierarchical cluster analysis the t(8;16) were grouped in the vicinity of the 11q23 cases. However, in a pairwise comparison these two subgroups could be discriminated with an accuracy of 94.4% (10-fold CV). Genes with a specific expression in AML-t(8;16) were further investigated in pathway analyses (Ingenuity). 15 of the top 100 genes associated with AML-t(8;16) were involved in the CMYC-pathway with up regulation of BCOR, COXB5, CDK10, FLI1, HNRPA2B1, NSEP1, PDIP38, RAD50, SUPT5H, TLR2 and USP33, and down regulation of ERG, GATA2, NCOR2 and RPS20. CEBP beta, known to play a role in myelomonocytic differentiation, was also up-regulated in t(8;16)-AML. Ten additional genes out of the 100 top differentially expressed genes were also involved in this pathway with up-regulation of DDB2, HIST1H3D, NSAP1, PTPNS1, RAN, USP4, TRIM8, ZNF278 and down regulation of KIT and MBD2. In conclusion, AML with t(8;16) is a specific subtype of AML with unique characteristics in morphology and gene expression patterns. It is more frequently found in t-AML, outcome is inferior in comparison to other AML with balanced translocations. Due to its unique features, it is a candidate for inclusion into the WHO classification as a specific entity.

Gene Expression Patterns Identify Novel Biologically Relevant Signaling and Transcriptional Pathways Involved in Terminal Erythroid Differentiation and Polycythemia Vera.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3524-3524
Author(s):  
Anil Potti ◽  
Holly K. Dressman ◽  
Murat O. Arcasoy
Keyword(s):  
Gene Expression ◽  
Polycythemia Vera ◽  
Hierarchical Clustering ◽  
Expression Patterns ◽  
Terminal Differentiation ◽  
Erythroid Differentiation ◽  
Gene Expression Patterns ◽  
Biologically Relevant ◽  
Distinct Gene ◽  
Study Gene Expression

Abstract Hematopoietic proliferation, lineage commitment, and terminal differentiation are characterized by the emergence of a cell type-specific gene expression and transcriptional programs that determine the specific phenotype and function of cells in the erythroid lineage. Our objectives in this study were to identify unique gene expression patterns that characterize the transcriptional program of normal primary human erythroid precursors during terminal differentiation, and define the gene expression patterns seen in erythroblasts (EBL) of patients with polycythemia vera (PV). Homogenous populations of primary proEBL were generated from purified liquid cultures of CD34+ cells collected from healthy volunteers and PV patients. All patients with PV were diagnosed based on established criteria and had the JAK2-V617F mutation. Morphologic examination and surface expression of CD71 confirmed the purity of proEBL cell populations. ProEBL from normal individuals were induced to terminally differentiate generating orthochromatic EBL. RNA was extracted from normal proEBL, PV proEBL, and normal orthochromatic EBL. Affymetrix U133 Plus 2.0 arrays representing approximately 39,000 human genes were used for gene expression analysis. Four replicates from four independent primary cell cultures were analyzed for each comparison group (e.g. undifferentiated proEBL versus terminally differentiated orthochromatic EBL). Unsupervised hierarchical clustering showed distinct gene expression profiles in the proEBL and terminally differentiated EBL lineages. 1109 genes (2.0 fold change, P<0.01) were found to be differentially expressed. Numerous erythroid genes were found to be upregulated during terminal differentiation [e.g. globin genes, erythropoietin receptor, heme synthesis enzymes (ferrochelatase, ALAS2) erythrocyte membrane proteins (band 3, ankyrin, protein 4.1) and transcription factors (NFE2, Kruppel-like factors, myb, GATA2)]. As a proof of validation, the differential expression of 7 genes was verified by Northern blotting. To better understand the biologic role of the gene sets identified, using Ingenuity pathway analysis, individual genes were integrated into specific regulatory and signaling pathway networks. A total of 19 networks with significant scores (>23) were identified. Biological functions of the identified networks included RNA post-transcriptional regulation, cell cycle control, translational regulation, DNA replication and repair and cellular assembly/organization. In a proof of principle study, gene expression patterns in PV proEBL (n=6) were compared to normal proEBL (n=5). Unsupervised hierarchical clustering showed a distinct gene expression profile for PV. A binary regression predictive model was also developed to find gene expression patterns predictive for PV. Using this model a 150 gene predictor was found that could predict PV patients from control at 100% accuracy. Ingenuity pathways analysis of a subset of gene subsets demonstrated several biologically relevant networks that were distinct in patients with PV, including myc, CDC2, and JAK2. Deregulation of normal transcriptional mechanisms in hematopoietic cells is associated with the pathogenesis of PV. Further, our data shows that genomic studies provide new insights into transcriptional programs that govern erythroid differentiation, and identify biologically relevant deregulated pathways as potential targets for therapy in PV.

Protein and gene expression patterns of endo-β-mannanase following germination of rice

2008 ◽  
Vol 18 (3) ◽  
pp. 139-149 ◽  
Author(s):  
Yanfang Ren ◽  
J. Derek Bewley ◽  
Xiaofeng Wang
Keyword(s):  
Gene Expression ◽  
Blot Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Aleurone Layer ◽  
Starchy Endosperm ◽  
Rice Grains ◽  
Inactive Form ◽  
Mannanase Activity

AbstractThe rice (Oryza sativa L.) cv. Taichung 65, a japonica subspecies, was used to characterize the isoform, protein and gene expression patterns of endo-β-mannanase during and after seed germination. Activity assays and isoform analyses of whole grains or seed parts (scutellum, aleurone layer and starchy endosperm) revealed that seeds began to express endo-β-mannanase activity at 48 h from the start of imbibition at 25°C, after the completion of germination of most seeds. Three isoforms of endo-β-mannanase (pI 8.86, pI 8.92 and pI 8.98) were detected in the aleurone layer and starchy endosperm, but only two (pI 8.86 and pI 8.92) were present in the scutellum. The endo-β-mannanase in the starchy endosperm was mainly from the aleurone layer. Western blot analysis, using a tomato anti-endo-β-mannanase antibody, indicated that an endo-β-mannanase protein was present in an inactive form in dry grains. The amount of this protein decreased in the scutellum, but increased in the aleurone layer during and after germination. Thus, the increase in endo-β-mannanase activity in rice grains may be due to the activation of extant proteins and/or the de novo synthesis of the enzyme. Northern blot analysis showed that four putative rice endo-β-mannanase genes (OsMAN1, OsMAN2, OsMAN6 and OsMANP) were expressed in germinating and germinated rice grains. However, OsMANP was not expressed in the scutellum. The amount of OsMAN6 mRNA decreased after the completion of germination and paralleled the decline in endo-β-mannanase protein. In the aleurone layer, the increase of OsMAN2, OsMAN6 and OsMANP mRNA was prior to the increase of endo-β-mannanase protein.

scholarly journals Lipid Composition and Associated Gene Expression Patterns during Pollen Germination and Pollen Tube Growth in Olive (Olea europaea L.)

2020 ◽  
Vol 61 (7) ◽  
pp. 1348-1364
Author(s):  
M Luisa Hernández ◽  
Elena Lima-Cabello ◽  
Juan de D Alché ◽  
José M Martínez-Rivas ◽  
Antonio J Castro
Keyword(s):  
Gene Expression ◽  
Fatty Acids ◽  
Fatty Acid ◽  
Lipid Metabolism ◽  
Pollen Tube ◽  
Pollen Germination ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Olive Pollen

Abstract Pollen lipids are essential for sexual reproduction, but our current knowledge regarding lipid dynamics in growing pollen tubes is still very scarce. Here, we report unique lipid composition and associated gene expression patterns during olive pollen germination. Up to 376 genes involved in the biosynthesis of all lipid classes, except suberin, cutin and lipopolysaccharides, are expressed in olive pollen. The fatty acid profile of olive pollen is markedly different compared with other plant organs. Triacylglycerol (TAG), containing mostly C12–C16 saturated fatty acids, constitutes the bulk of olive pollen lipids. These compounds are partially mobilized, and the released fatty acids enter the β-oxidation pathway to yield acetyl-CoA, which is converted into sugars through the glyoxylate cycle during the course of pollen germination. Our data suggest that fatty acids are synthesized de novo and incorporated into glycerolipids by the ‘eukaryotic pathway’ in elongating pollen tubes. Phosphatidic acid is synthesized de novo in the endomembrane system during pollen germination and seems to have a central role in pollen tube lipid metabolism. The coordinated action of fatty acid desaturases FAD2–3 and FAD3B might explain the increase in linoleic and alpha-linolenic acids observed in germinating pollen. Continuous synthesis of TAG by the action of diacylglycerol acyltransferase 1 (DGAT1) enzyme, but not phosphoplipid:diacylglycerol acyltransferase (PDAT), also seems plausible. All these data allow for a better understanding of lipid metabolism during the olive reproductive process, which can impact, in the future, on the increase in olive fruit yield and, therefore, olive oil production.

scholarly journals Algorithmic Reconstruction of GBM Network Complexity

2021 ◽  
Author(s):  
Abicumaran Uthamacumaran ◽  
Morgan Craig
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Data Science ◽  
Expression Patterns ◽  
State Transitions ◽  
Gene Expression Patterns ◽  
Cell Fate Decisions ◽  
Study Gene Expression

Glioblastoma (GBM) is a complex disease that is difficult to treat. Establishing the complex genetic interactions regulating cell fate decisions in GBM can help to shed light on disease aggressivity and improved treatments. Networks and data science offer novel approaches to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with control of differentiation and thus aggressivity. Here, we applied a host of data theoretic techniques, including clustering algorithms, Waddington landscape reconstruction, trajectory inference algorithms, and network approaches, to compare gene expression patterns between pediatric and adult GBM, and those of adult GSCs (glioma-derived stem cells) to identify the key molecular regulators of the complex networks driving GBM/GSC and predict their cell fate dynamics. Using these tools, we identified critical genes and transcription factors coordinating cell state transitions from stem-like to mature GBM phenotypes, including eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as clinically targetable novel putative function interactions differentiating pediatric and adult GBMs from adult GSCs. Our study is among the first to provide strong evidence of the applicability of complex systems approaches for reverse-engineering gene networks from patient-derived single-cell datasets and inferring their complex dynamics, bolstering the search for new clinically- relevant targets in GBM.

scholarly journals A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

2018 ◽  
Author(s):  
Ying Lin ◽  
Anjali M. Rajadhyaksha ◽  
James B. Potash ◽  
Shizhong Han
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Human Brain ◽  
Candidate Genes ◽  
De Novo ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Gene Expression Patterns ◽  
Risk Genes ◽  
Gene Level

AbstractAutism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role ofde novomutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example,TCF20andFBOX11), but also indicated potentially novel candidates, such asDOCK3,MYCBP2andCAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

AML with Translocation T(8;16) Shows Unique Cytomorphological, Cytogenetic, Molecular, and Prognostic Features and Therefore Qualifies as An Own Entity According to WHO Criteria.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1197-1197
Author(s):  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Hans-Ulrich Klein ◽  
Christian Ruckert ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Differentiation Potential ◽  
Who Criteria ◽  
Prognostic Features

Abstract Balanced chromosomal rearrangements define distinct biological subsets in acute myeloid leukemia (AML). It is recognized that recurrent balanced aberrations, such as t(15;17), t(8;21), inv(16), and 11q23/MLL translocations, show a close correlation to cytomorphology and also harbor specific gene expression signatures. We here present a cohort of 13 AML cases with t(8;16)(p11;p13). This translocation is rare with only 13 cases (6 males, 7 females) diagnosed from our overall cohort of 6124 cases of AML over recent years, and is more frequently found in therapy-related AML than in de novo AML (7/438 t-AML, and 6/5686 de novo, p=0.00001). Prognosis was poor with median overall survival of 4.7 months. Five patients deceased within the first month after diagnosis. AML with t(8;16) is characterized by striking cytomorphologic features: In all 13 cases the positivity for myeloperoxidase (MPO) on bone marrow smears was >30% (median: 85%) and intriguingly, in parallel also >40% (median: 88%) of blast cells stained strongly positive for non-specific esterase (NSE) in the same cell, suggesting that AML with t(8;16) arise from a very early stem cell with both myeloid and monoblastic differentiation potential. Therefore, AML with t(8;16) cases can not be classified according to standard FAB categories. Morphologically we also detected erythrophagocytosis in 7/13 cases, a specific feature in AML with t(8;16) that was previously described. With respect to cytogenetics, 6/13 patients had t(8;16)(p11;p13) as sole abnormality. 7/13 patients demonstrated additional non-recurrent abnormalities, 4 cases with single additional aberrations, and 3 cases with two or more additional aberrations. Molecular analyses detected the MYST3- CREBBP fusion transcript in all cases tested (12/12). We then compared gene expression patterns in 7 cases of AML with t(8;16) to: (i) AML FAB subtypes M1 and M4/5 with strong MPO or NSE with normal karyotype and to (ii) distinct AML subtypes with balanced chromosomal aberrations according to WHO classification. In a first series using Affymetrix HG-U133A+B microarrays 4 cases of AML with t(8;16) were compared to FAB M1 (n=46), M4 (n=41), M5a (n=9), and M5b (n=16). Hierarchical clustering and principal component analyses revealed that AML with t(8;16) were intercalating rather with FAB subtypes M4 and M5b and did not cluster near to FAB M1, although strong positivity for MPO was seen in all t(8;16) cases. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid features. When further compared to AML WHO subtypes t(15;17) (n=43), t(8;21) (n=43), inv(16) (n=49), and 11q23/MLL (n=50), AML with t(8;16) samples were repeatedly grouped in the vicinity of the 11q23/MLL cases. This can be explained by a similar expression of genes such as EAF2, HOXA9, HOXA10, PRKCD, or HNMT. Yet, in a subsequent pairwise comparison AML with t(8;16) could also be clearly discriminated from 11q23/MLL with differentially expressed genes including CAPRIN1, RAN, SMARCD2, LRRC41, or H2BFS, higher expressed in AML with t(8;16) and SOCS2, PRAME, RUNX3, or TPT1, lower expressed in AML with t(8;16), respectively. Moreover, the respective FAB-type or WHO-type signatures were validated on a separate cohort of patients (n=3 AML with t(8;16); n=107 other AML subtypes as above), all prospectively analyzed with the successor HG-U133 Plus 2.0 microarray. Again, in direct comparison to FAB-type or WHO-type cases, dominant and unique gene expression patterns were seen for AML with t(8;16), confirming the molecular distinctiveness of this rare AML entity. Using a classification algorithm we were able to correctly predict all AML with t(8;16) cases by their gene expression pattern. This accuracy was observed not only for both FAB-type and WHO-type signatures, but also correctly classified the cases across the different patient cohorts and microarray designs. In conclusion, AML with t(8;16) is a specific subtype of AML with very poor prognosis that often presents as treatment-related AML and with particular characteristics not only in morphology and clinical profile, but also on a molecular level. Due to these unique features, it qualifies as a specific recurrent entity according to WHO criteria.

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