Protein encoded by oncogene 6b from Agrobacterium tumefaciens has a reprogramming potential and histone chaperone-like activity

Crown gall tumors are formed mainly by actions of a group of genes in the T-DNA that is transferred from Agrobacterium tumefaciens and integrated into the nuclear DNA of host plants. These genes encode enzymes for biosynthesis of auxin and cytokinin in plant cells. Gene 6b in the T-DNA affects tumor morphology and this gene alone is able to induce small tumors on certain plant species. In addition, unorganized calli are induced from leaf discs of tobacco that are incubated on phytohormone-free media; shooty teratomas and morphologically abnormal plants, which might be due to enhanced competence of cell division and meristematic states, are regenerated from the calli. Thus, the 6b gene appears to stimulate a reprogramming process in plants. To uncover mechanisms behind this process, various approaches including the yeast-two-hybrid system have been exploited and histone H3 was identified as one of the proteins that interact with 6b. It has been also demonstrated that 6b acts as a histone H3 chaperon in vitro and affects the expression of various genes related to cell division competence and the maintenance of meristematic states. We discuss current views on a role of 6b protein in tumorigenesis and reprogramming in plants

Meta-Analyses of Microarrays of Arabidopsis asymmetric leaves1 (as1), as2 and Their Modifying Mutants Reveal a Critical Role for the ETT Pathway in Stabilization of Adaxial-Abaxial Patterning and Cell Division During Leaf Development

It is necessary to use algorithms to analyze gene expression data from DNA microarrays, such as in clustering and machine learning. Previously, we developed the knowledge-based fuzzy adaptive resonance theory (KB-FuzzyART), a clustering algorithm suitable for analyzing gene expression data, to find clues for identifying gene networks. Leaf primordia form around the shoot apical meristem (SAM), which consists of indeterminate stem cells. Upon initiation of leaf development, adaxial-abaxial patterning is crucial for lateral expansion, via cellular proliferation, and the formation of flat symmetric leaves. Many regulatory genes that specify such patterning have been identified. Analysis by the KB-FuzzyART and subsequent molecular and genetic analyses previously showed that ASYMMETRIC LEAVES1 (AS1) and AS2 repress the expression of some abaxial-determinant genes, such as AUXIN RESPONSE FACTOR3 (ARF3)/ETTIN (ETT) and ARF4, which are responsible for defects in leaf adaxial-abaxial polarity in as1 and as2. In the present study, genetic analysis revealed that ARF3/ETT and ARF4 were regulated by modifier genes, BOBBER1 (BOB1) and ELONGATA3 (ELO3), together with AS1-AS2. We analyzed expression arrays with as2 elo3 and as2 bob1, and extracted genes downstream of ARF3/ETT by using KB-FuzzyART and molecular analyses. The results showed that expression of Kip-related protein (KRP) (for inhibitors of cyclin-dependent protein kinases) and Isopentenyltransferase (IPT) (for biosynthesis of cytokinin) genes were controlled by AS1-AS2 through ARF3/ETT and ARF4 functions, which suggests that the AS1-AS2-ETT pathway plays a critical role in controlling the cell division cycle and the biosynthesis of cytokinin around SAM to stabilize leaf development in Arabidopsis thalian

ASYMMETRIC-LEAVES2 and an ortholog of eukaryotic NudC domain proteins repress expression of AUXIN-RESPONSE-FACTOR and class 1 KNOX homeobox genes for development of flat symmetric leaves in Arabidopsis

Summary Leaf primordia form around the shoot apical meristem, which consists of indeterminate stem cells. Upon initiation of leaf development, adaxial-abaxial patterning is crucial for appropriate lateral expansion, via cellular proliferation, and the formation of flat symmetric leaves. Many genes that specify such patterning have been identified, but regulation by upstream factors of the expression of relevant effector genes remains poorly understood. In Arabidopsis thaliana, ASYMMETRIC LEAVES2 (AS2) and AS1 play important roles in repressing transcription of class 1 KNOTTED1-like homeobox (KNOX) genes and leaf abaxial-determinant effector genes. We report here a mutation, designated enhancer of asymmetric leaves2 and asymmetric leaves1 (eal), that is associated with efficient generation of abaxialized filamentous leaves on the as2 or as1 background. Levels of transcripts of many abaxial-determinant genes, including ETTIN (ETT)/AUXIN RESPONSE FACTOR3 (ARF3), and all four class 1 KNOX genes were markedly elevated in as2 eal shoot apices. Rudimentary patterning in as2 eal leaves was suppressed by the ett mutation. EAL encodes BOBBER1 (BOB1), an Arabidopsis ortholog of eukaryotic NudC domain proteins. BOB1 was expressed in plant tissues with division potential and bob1 mutations resulted in lowered levels of transcripts of some cell-cycle genes and decreased rates of cell division in shoot and root apices. Coordinated cellular proliferation, supported by BOB1, and repression of all class 1 KNOX genes, ETT/ARF3 by AS2 (AS1) and BOB1 might be critical for repression of the indeterminate state and of aberrant abaxialization in the presumptive adaxial domain of leaf primordia, which might ensure the formation of flat symmetric leaves

A genetic link between epigenetic repressor AS1-AS2 and a putative small subunit processome in leaf polarity establishment of Arabidopsis

Although the DEAD-box RNA helicase family is ubiquitous in eukaryotes, its developmental role remains unelucidated. Here, we report that cooperative action between the Arabidopsis nucleolar protein RH10, an ortholog of human DEAD-box RNA helicase DDX47, and the epigenetic repressor complex of ASYMMETRIC-LEAVES1 (AS1) and AS2 (AS1-AS2) is critical to repress abaxial (ventral) genes ETT/ARF3 and ARF4, which leads to adaxial (dorsal) development in leaf primordia at shoot apices. Double mutations of rh10-1 and as2 (or as1) synergistically up-regulated the abaxial genes, which generated abaxialized filamentous leaves with loss of the adaxial domain. DDX47 is part of the small subunit processome (SSUP) that mediates rRNA biogenesis. In rh10-1 we found various defects in SSUP-related events, such as: accumulation of 35S/33S rRNA precursors; reduction in the 18S/25S ratio; and nucleolar hypertrophy. Double mutants of as2 with mutations of genes that encode other candidate SSUP-related components such as nucleolin and putative rRNA methyltransferase exhibited similar synergistic defects caused by up-regulation of ETT/ARF3 and ARF4. These results suggest a tight link between putative SSUP and AS1-AS2 in repression of the abaxial-determining genes for cell fate decisions for adaxial development

Analysis of Gene Expression Profiles of Soft Tissue Sarcoma Using a Combination of Knowledge-Based Filtering with Integration of Multiple Statistics

<div><p>The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., <i>MIF</i>, <i>SCD1</i>, <i>P4HA1</i>, <i>ENO1</i>, and <i>STAT1</i>) and cell cycle- and DNA repair-related genes (e.g., <i>TACC3</i>, <i>PRDX1</i>, <i>PRKDC</i>, and <i>H2AFY</i>). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. <i>STAT1</i> showed a strong association with overall survival in UPS patients (logrank <i>p</i> = 1.84×10⁻⁶ and adjusted <i>p</i> value 2.99×10⁻³ after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.</p></div

Pairwise comparison between histological types using Welch’s t test for 29 probe sets.

<p>*<i>q</i> <0.05. The <i>p</i> value was calculated using Welch’s t test, and the <i>q</i> value was calculated from the <i>p</i> value by means of the Benjamini-Hochberg method for the correction of multiple testing problems.</p><p>Pairwise comparison between histological types using Welch’s t test for 29 probe sets.</p

A hypothetical regulation model of metabolic and signaling control in highly malignant STS.

<p>(A) Signaling pathways, excluding cell cycle and DNA repair. (B) Cell cycle and DNA repair pathways. The pink oval indicates the genes selected in the present study. MUFA, monounsaturated fatty acid; SFA, saturated fatty acid; SCD1, stearoyl-CoA desaturase 1; MIF, macrophage migration inhibitory factor; CXCR, CXC chemokine receptor; PI3K, phosphoinositide 3-kinase; MAPK, extracellular signal-regulated kinase; ERK, mitogen-activated protein kinase; PTTG1, pituitary tumor-transforming 1; ASPM, abnormal spindle-like microcephaly-associated protein; CDC20, cell division cycle protein 20; KIF20A, kinesin family member 20A; ENO1, enolase 1; P4HA, prolyl 4-hydroxylase subunit α; PRDX1, peroxiredoxin 1; FAM162A, family with sequence similarity 162, member A; STAT1, signal transducer and activator of transcription 1; CDK1, cyclin-dependent kinase 1; TACC3, transforming, acidic coiled-coil containing protein 3; PRKDC, protein kinase, DNA-activated, catalytic polypeptide; H2AFY, H2A histone family, member Y; SLC16A1, solute carrier family 16, member 1; VEGF, vascular endothelial growth factor; HIF, hypoxia inducible factor; PLOD2, procollagen-lysine,2-oxoglutarate 5-dioxygenase 2; NF-κB, nuclear factor-kappa B.</p

The Kaplan-Meier curve and the logrank test for <i>STAT1</i> in UPS patients.

<p>The <i>STAT1</i>-positive group (<i>STAT1</i> expression level >4871.5) consisted of 14 patients (blue line), and the <i>STAT1</i>-negative group consisted of 6 patients (red line). A hazard ratio (exp(B) = 30.2) was calculated using the Cox proportional hazards model.</p

A Venn diagram of gene classification based on pairwise comparisons of histological types using Welch’s <i>t</i> test.

<p>Genes inside the red circle were statistically significant (<i>q</i> <0.05 calculated using Welch’s <i>t</i> test and the BH method) in the comparison of UPS with SS. Genes inside the green oval were statistically significant (<i>q</i> <0.05) in the comparison of UPS with MLS. Genes inside the blue oval were statistically significant (<i>q</i> <0.05) in the comparison of UPS and MFS. Genes inside the pink oval are common to CINSARC and our 25-gene set. For PCA of the 9-probe set, <i>MIF</i> and <i>CD34</i> highlighted in red were the first and third largest contributing coefficients to PC1, respectively. <i>PTK7</i> and <i>PRDX1</i> highlighted in blue were the first and second largest contributing coefficients to PC2, respectively. <i>ENO1</i>/<i>MBP1</i> highlighted in purple was the second largest contributing coefficient to PC1 and the third largest contributing coefficient to PC2. <i>SCD1</i> highlighted in green was the largest contributing coefficient to PC3.</p

Correlation analysis based on Spearman’s rank correlation coefficient between gene expression data and the histological grade (or metastasis status).

<p>Correlation analysis based on Spearman’s rank correlation coefficient between gene expression data and the histological grade (or metastasis status).</p