PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of jinflammBioMed CentralBiomed Central Web Sitesearchsubmit a manuscriptregisterthis articleJournal of Inflammation (London, England)Journal Front Page
 
J Inflamm (Lond). 2010; 7: 50.
Published online 2010 September 30. doi:  10.1186/1476-9255-7-50
PMCID: PMC2959006

TNFRSF11B computational development network construction and analysis between frontal cortex of HIV encephalitis (HIVE) and HIVE-control patients

Abstract

Background

TNFRSF11B computational development network construction and analysis of frontal cortex of HIV encephalitis (HIVE) is very useful to identify novel markers and potential targets for prognosis and therapy.

Methods

By integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we identified and constructed significant molecule TNFRSF11B development network from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726.

Results

Our result verified TNFRSF11B developmental process only in the downstream of frontal cortex of HIVE-control patients (BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1 inhibition), whereas in the upstream of frontal cortex of HIVE (DGKG, PDCD4 activation) and downstream (CFDP1, DGKG, GAS1, PAX6 activation; BST2, PDCD4, TGFBR3, VEZF1 inhibition). Importantly, we datamined that TNFRSF11B development cluster of HIVE is involved in T-cell mediated immunity, cell projection organization and cell motion (only in HIVE terms) without apoptosis, plasma membrane and kinase activity (only in HIVE-control patients terms), the condition is vital to inflammation, brain morphology and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE include developmental process, signal transduction, negative regulation of cell proliferation, RNA-binding, zinc-finger, cell development, positive regulation of biological process and cell differentiation.

Conclusions

We deduced the stronger TNFRSF11B development network in HIVE consistent with our number computation. It would be necessary of the stronger TNFRSF11B development function to inflammation, brain morphology and cognition of HIVE.

Background

The neurodegenerative process in HIV encephalitis (HIVE) is associated with cognitive impairment with extensive damage to the dendritic and synaptic structure. Several mechanisms might be involved in including release of neurotoxins, oxidative stress and decreased activity of neurotrophic factors [1]. The effect of HIV on brain has been studied by several researchers. Such as, decreased brain dopamine transporters are related to cognitive deficits in HIV patients with or without cocaine abuse; Magnetic resonance imaging and spectroscopy of the brain in HIV disease; Analysis of the effects of injecting drug use and HIV-1 infection on 18F-FDG PET brain development [2-4]. TNFRSF11B computational development network construction and analysis of the frontal cortex of HIV encephalitis (HIVE) is very useful to identify novel markers and potential targets for prognosis and therapy.

TNFRSF11B is one out of 50 genes identified as high expression in frontal cortex of HIV encephalitis (HIVE) vs HIVE-control patients. TNFRSF11B has been proved to be concerned with molecular function of receptor, and biological process of developmental processes, skeletal development and mesoderm development (DAVID database). TNFRSF11B's relational study also can be seen in these papers [5-10]. However, the molecular mechanism concerning TNFRSF11B development construction in HIVE has little been addressed.

In this paper, by integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we identified and constructed significant molecule TNFRSF11B development network from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726. Our result verified TNFRSF11B developmental process only in the downstream of frontal cortex of HIVE-control patients (BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1 inhibition), whereas in the upstream of frontal cortex of HIVE (DGKG, PDCD4 activation) and downstream (CFDP1, DGKG, GAS1, PAX6 activation; BST2, PDCD4, TGFBR3, VEZF1 inhibition). Importantly, we datamined that TNFRSF11B development cluster of HIVE is involved in T-cell mediated immunity, cell projection organization and cell motion (only in HIVE terms) without apoptosis, plasma membrane and kinase activity (only in HIVE-control patients terms), the condition is vital to inflammation, brain morphology and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE include developmental process, signal transduction, negative regulation of cell proliferation, RNA-binding, zinc-finger, cell development, positive regulation of biological process and cell differentiation, therefore we deduced the stronger TNFRSF11B development network in HIVE consistent with our number computation. It would be necessary of the stronger TNFRSF11B development function to inflammation, brain morphology and cognition of HIVE. TNFRSF11B development interaction module construction in HIVE can be a new route for studying the pathogenesis of HIVE. Our construction of TNFRSF11B development network may be useful to identify novel markers and potential targets for prognosis and therapy of HIVE.

Methods

Microarray Data

We used microarrays containing 12558 genes from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726 [1]. HIVE-control patients mean normal adjacent frontal cortex tissues of HIV encephalitis (HIVE) and no extensive damage to the dendritic and synaptic structure.

Gene Selection Algorithms

50 molecular markers of the frontal cortex of HIVE were identified using significant analysis of microarrays (SAM). SAM is a statistical technique for finding significant genes in a set of microarray experiments. The input to SAM is gene expression measurements from a set of microarray experiments, as well as a response variable from each experiment. The response variable may be a grouping like untreated, treated, and so on. SAM computes a statistic di for each gene i, measuring the strength of the relationship between gene expression and the response variable. It uses repeated permutations of the data to determine if the expression of any genes is significantly related to the response. The cutoff for significance is determined by a tuning parameter delta, chosen by the user based on the false positive rate. We normalized data by log2, and selected two class unpaired and minimum fold change = 1.52. Here we chose the 50 top-fold significant (high expression genes of HIVE compared with HIVE-control patients) genes under the false-discovery rate and q-value as 9.12%. The q-value (invented by John Storey [11]) for each gene is the lowest false discovery rate at which that gene is called significant. It is like the well-known p-value, but adapted to multiple-testing situations.

Network Establishment of Candidate Genes

The entire network was constructed using GRNInfer [12] and GVedit tools. GRNInfer is a novel mathematic method called GNR (Gene Network Reconstruction tool) based on linear programming and a decomposition procedure for inferring gene networks. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the reconstruction reliability. The following Equation (1) represents all of the possible networks for the same dataset.

J=(X'A)UΛ1VT+YVT=J^+YVT
(1)

We established network based on the 50 top-fold distinguished genes and selected parameters as lambda 0.0 because we used one dataset, threshold 0.000001. Lambda is a positive parameter, which balances the matching and sparsity terms in the objective function. Using different thresholds, we can predict various networks with different edge density.

Functional Annotation Clustering

The DAVID Gene Functional Clustering Tool provides typical batch annotation and gene-GO term enrichment analysis for highly throughput genes by classifying them into gene groups based on their annotation term co-occurrence [13,14]. The grouping algorithm is based on the hypothesis that similar annotations should have similar gene members. The functional annotation clustering integrates the same techniques of Kappa statistics to measure the degree of the common genes between two annotations, and fuzzy heuristic clustering to classify the groups of similar annotations according to kappa values.

Results

Identification of HIVE Molecular Markers

TNFRSF11B is one out of 50 genes identified as high expression in frontal cortex of HIV encephalitis (HIVE) vs HIVE-control patients. We normalized data by log2, and selected two class unpaired and minimum fold change = 1.52. Here we chose the 50 top-fold significant (high expression genes of HIVE compared with HIVE-control patients) genes under the false-discovery rate and q-value as 9.12%. We identified potential HIVE molecular markers and chose the 50 top-fold significant positive genes from 12558 genes from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726 including tumor necrosis factor receptor superfamily member 11b (TNFRSF11B), programmed cell death 4 (PDCD4), diacylglycerol kinase gamma (DGKG), craniofacial development protein 1 (CFDP1), growth arrest-specific 1 (GAS1), paired box 6 (PAX6), bone marrow stromal cell antigen 2 (BST2), transforming growth factor beta receptor III (TGFBR3), vascular endothelial zinc finger 1 (VEZF1), etc. (see appendix).

Identification of TNFRSF11B Up- and Down-stream Development Cluster in Frontal Cortex of HIVE-Control Patients and HIVE by DAVID

We first datamined 4 lists of TNFRSF11B up- and down-stream genes from 12 frontal cortex of HIVE-control patients and 16 HIVE by GRNInfer respectively. With inputting 4 lists into DAVID, we further identified TNFRSF11B up- and down-stream development cluster of HIVE-control patients and HIVE. TNFRSF11B development cluster terms only in frontal cortex of HIVE-control patients cover apoptosis, plasma membrane and kinase activity, as shown in (Figure 1A, C). However, TNFRSF11B development cluster terms only in frontal cortex of HIVE contain T-cell mediated immunity, cell projection organization and cell motion, as shown in (Figure 1B, D). TNFRSF11B development cluster terms both in frontal cortex of HIVE-control patients and HIVE include developmental process, signal transduction, negative regulation of cell proliferation, RNA-binding, zinc-finger, cell development, positive regulation of biological process and cell differentiation, as shown in (Figure 1A, B, C, D).

Figure 1
TNFRSF11B up- and down-stream development cluster in frontal cortex of HIVE-control patients by DAVID (A, C). TNFRSF11B up- and down-stream development cluster by DAVID in frontal cortex of HIVE (B, D). Gray color represents gene-term association positively ...

In frontal cortex of HIVE-control patients, TNFRSF11B upstream showed little results without developmental process, as shown in (Figure (Figure1A).1A). In frontal cortex of HIVE, TNFRSF11B upstream modules mainly cover developmental process (DGKG, PDCD4, TNFRSF11B), etc., as shown in (Figure (Figure1B).1B). In frontal cortex of HIVE-control patients, TNFRSF11B downstream modules mainly consist of developmental process (BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1, TNFRSF11B), etc., as shown in (Figure (Figure1C).1C). In frontal cortex of HIVE, TNFRSF11B downstream modules mainly contain developmental process (CFDP1, DGKG, BST2, PDCD4, GAS1, PAX6, TGFBR3, VEZF1, TNFRSF11B), etc., as shown in (Figure (Figure1D1D).

TNFRSF11B Up- and Down-stream Development Network Construction in Frontal Cortex of HIVE-Control Patients and HIVE

In frontal cortex of HIVE-control patients, TNFRSF11B upstream development network appeared no result, as shown in (Figure (Figure2A),2A), whereas in frontal cortex of HIVE, TNFRSF11B upstream development network showed that DGKG, PDCD4 activate TNFRSF11B, as shown in (Figure (Figure2B2B).

Figure 2
TNFRSF11B up- and down-stream development network construction in frontal cortex of HIVE-control patients by infer (A, C). TNFRSF11B up- and down-stream development network construction in frontal cortex of HIVE by infer (B, D). Arrowhead represents activation, ...

In frontal cortex of HIVE-control patients, TNFRSF11B downstream development network reflected that TNFRSF11B inhibits BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1, as shown in (Figure (Figure2C),2C), whereas in frontal cortex of HIVE, TNFRSF11B downstream development network appeared that TNFRSF11B activates CFDP1, DGKG, GAS1, PAX6 and inhibits BST2, PDCD4, TGFBR3, VEZF1, as shown in (Figure (Figure2D2D).

Discussion

We have already done some works in this relative field about gene network construction and analysis presented in our published papers [15-19]. By integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we constructed significant molecule TNFRSF11B development network and compared TNFRSF11B up- and down-stream gene numbers of activation and inhibition between HIVE-control patients and HIVE (Table (Table11).

Table 1
Up- and down-stream gene numbers of activation and inhibition of each module with TNFRSF11B gene in TNFRSF11B development cluster between frontal cortex of HIVE-control patients and HIVE.

In TNFRSF11B developmental process of upstream network of frontal cortex of HIVE-control patients there was no result, whereas in that of HIVE, our integrative result reflected that DGKG, PDCD4 activate TNFRSF11B. In TNFRSF11B developmental process of downstream network of HIVE-control patients, our integrative result illustrated that TNFRSF11B inhibits BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1, whereas in that of HIVE, TNFRSF11B activates CFDP1, DGKG, GAS1, PAX6 and inhibits BST2, PDCD4, TGFBR3, VEZF1 (Figure (Figure1,1, ,2;2; Table Table2).2). PAX6 is identified by molecular function of transcription factor, homeobox transcription factor, nucleic acid binding and DNA-binding protein, and it is involved in biological process of nucleoside, nucleotide and nucleic acid metabolism, mRNA transcription, mRNA transcription regulation, developmental processes, neurogenesis, segment specification and ectoderm development (DAVID database). PAX6's relational study also can be presented in these papers [20-25]. DGKG has been proved to be concerned with molecular function of kinase, and biological process of lipid, fatty acid and steroid metabolism, signal transduction, intracellular signaling cascade and lipid metabolism (DAVID). DGKG's relational study also can be presented in these papers [26-29]. GAS1's molecular function consists of mRNA processing factor, mRNA splicing factor, kinase modulator, dehydrogenase and kinase activator, and it is concerned with biological process of glycolysis, amino acid catabolism, pre-mRNA processing, mRNA splicing, cell proliferation and differentiation (DAVID database). GAS1's relational study also can be presented in these papers [30-33]. PDCD4 is relevant to molecular function of nucleic acid binding, translation factor, translation elongation factor and miscellaneous function, and biological process of protein metabolism and modification, protein biosynthesis, apoptosis, induction of apoptosis (DAVID). PDCD4's relational study also can be presented in these papers [34-39]. CFDP1 has been reported to have molecular function of mRNA splicing factor, select calcium binding proteins and KRAB box transcription factor, and to be concerned with biological process of mRNA transcription regulation and cell motility (DAVID database). CFDP1's relational study also can be presented in these papers [40-44]. We gained the positive result of TNFRSF11B developmental process through the net numbers of activation minus inhibition compared with HIVE-control patients and predicted possibly the increase of TNFRSF11B developmental process in HIVE.

Table 2
Activation and inhibition gene names of TNFRSF11B up- and down-stream development cluster in frontal cortex of HIVE-control patients and HIVE.

Importantly, we datamined that TNFRSF11B development cluster of HIVE is involved in T-cell mediated immunity, cell projection organization and cell motion (only in HIVE terms) without apoptosis, plasma membrane and kinase activity (only in HIVE-control patients terms), the condition is vital to inflammation, brain morphology and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE include developmental process, signal transduction, negative regulation of cell proliferation, RNA-binding, zinc-finger, cell development, positive regulation of biological process and cell differentiation, therefore we deduced the stronger TNFRSF11B development network in HIVE consistent with our number computation. Some researchers indicated that tumor necrosis factor receptor studied to relate with inflammation, brain morphology and cognition [45,46]. Therefore, we predicted the stronger TNFRSF11B development function in HIVE. It would be necessary of the stronger TNFRSF11B development function to inflammation, brain morphology and cognition of HIVE.

Conclusions

In summary, we deduced the stronger TNFRSF11B developmental process in HIVE. It would be necessary of the stronger TNFRSF11B development function to inflammation, brain morphology and cognition of HIVE. TNFRSF11B development interaction module construction in HIVE can be a new route for studying the pathogenesis of HIVE.

Abbreviations

TNFRSF11B: tumor necrosis factor receptor superfamily member 11b; IFI44L: interferon-induced protein 44-like; ADH1B: alcohol dehydrogenase 1B (class I) beta polypeptide; RASGRP3: RAS guanyl releasing protein 3; MAPKAPK3: mitogen-activated protein kinase-activated protein kinase 3; CREB5: cAMP responsive element binding protein 5; MX1: myxovirus resistance 1 interferon-inducible protein p78; IFITM1: interferon induced transmembrane protein 1; MYBPC1: myosin binding protein C slow type; ROR1: receptor tyrosine kinase-like orphan receptor 1; IFI35: interferon-induced protein 35; LCAT: lecithin-cholesterol acyltransferase; ZC3HAV1: zinc finger CCCH-type antiviral 1; LY96: lymphocyte antigen 96; TSPAN4: tetraspanin 4; C10orf116: chromosome 10 open reading frame 116; DGKG: diacylglycerol kinase gamma; STAT1: signal transducer and activator of transcription 1; IFI27: interferon alpha-inducible protein 27; BST2: bone marrow stromal cell antigen 2; TGFBR3: transforming growth factor, beta receptor III; SLC16A4: solute carrier family 16 member 4; FER1L3: myoferlin; ZNF652: zinc finger protein 652; HLA-B: hypothetical protein LOC441528; PDCD4: programmed cell death 4; SF1: splicing factor 1; CFHR1: complement factor H-related 1; CFB: complement factor B; LGALS3BP: lectin galactoside-binding soluble 3 binding protein; RDX: radixin; MT1G: metallothionein 1G; RBBP6: retinoblastoma binding protein 6; TENC1: tensin like C1 domain containing phosphatase; PAX6: paired box 6; NFAT5: nuclear factor of activated T-cells 5 tonicity-responsive; DGKG: diacylglycerol kinase, gamma; CFDP1: craniofacial development protein 1; VEZF1: vascular endothelial zinc finger 1; GAS1: growth arrest-specific 1; ATP6V0E1: ATPase H+ transporting lysosomal 9 kDa V0 subunit e1.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors participated in design and performance of the study, interpreted the result and contributed to writing the paper. All authors read and approved the final version of the manuscript.

Acknowledgements

This work was supported by the National Natural Science Foundation in China (No.60871100) and the Teaching and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. State Key Lab of Pattern Recognition Open Foundation, Key project of philosophical and social science of MOE (07JZD0005).

References

  • Masliah E, Roberts ES, Langford D, Everall I, Crews L, Adame A, Rockenstein E, Fox HS. Patterns of gene dysregulation in the frontal cortex of patients with HIV encephalitis. J Neuroimmunol. 2004;157:163–175. doi: 10.1016/j.jneuroim.2004.08.026. [PubMed] [Cross Ref]
  • Chang L, Wang GJ, Volkow ND, Ernst T, Telang F, Logan J, Fowler JS. Decreased brain dopamine transporters are related to cognitive deficits in HIV patients with or without cocaine abuse. Neuroimage. 2008;42:869–878. doi: 10.1016/j.neuroimage.2008.05.011. [PMC free article] [PubMed] [Cross Ref]
  • Descamps M, Hyare H, Stebbing J, Winston A. Magnetic resonance imaging and spectroscopy of the brain in HIV disease. J HIV Ther. 2008;13:55–58. [PubMed]
  • Georgiou MF, Gonenc A, Waldrop-Valverde D, Kuker RA, Ezuddin SH, Sfakianakis GN, Kumar M. Analysis of the effects of injecting drug use and HIV-1 infection on 18F-FDG PET brain metabolism. J Nucl Med. 2008;49:1999–2005. doi: 10.2967/jnumed.108.052688. [PubMed] [Cross Ref]
  • Beyens G, Daroszewska A, de Freitas F, Fransen E, Vanhoenacker F, Verbruggen L, Zmierczak HG, Westhovens R, Van Offel J, Ralston SH. et al. Identification of sex-specific associations between polymorphisms of the osteoprotegerin gene, TNFRSF11B, and Paget's disease of bone. J Bone Miner Res. 2007;22:1062–1071. doi: 10.1359/jbmr.070333. [PubMed] [Cross Ref]
  • Chong B, Hegde M, Fawkner M, Simonet S, Cassinelli H, Coker M, Kanis J, Seidel J, Tau C, Tuysuz B. et al. Idiopathic hyperphosphatasia and TNFRSF11B mutations: relationships between phenotype and genotype. J Bone Miner Res. 2003;18:2095–2104. doi: 10.1359/jbmr.2003.18.12.2095. [PubMed] [Cross Ref]
  • Cundy T, Hegde M, Naot D, Chong B, King A, Wallace R, Mulley J, Love DR, Seidel J, Fawkner M. et al. A mutation in the gene TNFRSF11B encoding osteoprotegerin causes an idiopathic hyperphosphatasia phenotype. Hum Mol Genet. 2002;11:2119–2127. doi: 10.1093/hmg/11.18.2119. [PubMed] [Cross Ref]
  • Janssens K, de Vernejoul MC, de Freitas F, Vanhoenacker F, Van Hul W. An intermediate form of juvenile Paget's disease caused by a truncating TNFRSF11B mutation. Bone. 2005;36:542–548. doi: 10.1016/j.bone.2004.12.004. [PubMed] [Cross Ref]
  • Vidal C, Brincat M, Xuereb Anastasi A. TNFRSF11B gene variants and bone mineral density in postmenopausal women in Malta. Maturitas. 2006;53:386–395. doi: 10.1016/j.maturitas.2005.11.003. [PubMed] [Cross Ref]
  • Whyte MP, Singhellakis PN, Petersen MB, Davies M, Totty WG, Mumm S. Juvenile Paget's disease: the second reported, oldest patient is homozygous for the TNFRSF11B "Balkan" mutation (966_969delTGACinsCTT), which elevates circulating immunoreactive osteoprotegerin levels. J Bone Miner Res. 2007;22:938–946. doi: 10.1359/jbmr.070307. [PubMed] [Cross Ref]
  • Storey. JD. A direct approach to false discovery rates. J Roy Stat Soc, Ser B. 2002;64:479–498. doi: 10.1111/1467-9868.00346. [Cross Ref]
  • Wang Y, Joshi T, Zhang XS, Xu D, Chen L. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 2006;22:2413–2420. doi: 10.1093/bioinformatics/btl396. [PubMed] [Cross Ref]
  • Huang da W, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8:R183. doi: 10.1186/gb-2007-8-9-r183. [PMC free article] [PubMed] [Cross Ref]
  • Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4:P3. doi: 10.1186/gb-2003-4-5-p3. [PubMed] [Cross Ref]
  • Sun Y, Wang L, Jiang M, Huang J, Liu Z, Wolfl S. Secreted Phosphoprotein 1 Upstream Invasive Network Construction and Analysis of Lung Adenocarcinoma Compared with Human Normal Adjacent Tissues by Integrative Biocomputation. Cell Biochem Biophys. 2009. ISSN: 1085-9195 (Print) 1559-0283 (Online) [PubMed]
  • Sun Y, Wang L, Lui L. Integrative decomposition procedure and Kappa statistics set up ATF2 ion binding module in Malignant Pleural Mesothelioma (MPM) Frontiers of Electrical and Electronic Engineering in China. 2008;3:381–387. doi: 10.1007/s11460-008-0086-3. [Cross Ref]
  • Wang L, Huang J, Jiang M, Zheng X. AFP computational secreted network construction and analysis between human hepatocellular carcinoma (HCC) and no-tumor hepatitis/cirrhotic liver tissues. Tumour Biol. [PubMed]
  • Wang L, Sun Y, Jiang M, Zhang S, Wolfl S. FOS proliferating network construction in early colorectal cancer (CRC) based on integrative significant function cluster and inferring analysis. Cancer Invest. 2009;27:816–824. doi: 10.1080/07357900802672753. [PubMed] [Cross Ref]
  • Wang L, Sun Y, Jiang M, Zheng X. Integrative decomposition procedure and Kappa statistics for the distinguished single molecular network construction and analysis. J Biomed Biotechnol. 2009. p. 726728. [PMC free article] [PubMed]
  • Rath MF, Bailey MJ, Kim JS, Coon SL, Klein DC, Moller M. Developmental and daily expression of the Pax4 and Pax6 homeobox genes in the rat retina: localization of Pax4 in photoreceptor cells. J Neurochem. 2008. [PubMed]
  • Sjodal M, Gunhaga L. Expression patterns of Shh, Ptc2, Raldh3, Pitx2, Isl1, Lim3 and Pax6 in the developing chick hypophyseal placode and Rathke's pouch. Gene Expr Patterns. 2008;8:481–485. doi: 10.1016/j.gep.2008.06.007. [PubMed] [Cross Ref]
  • Suter DM, Tirefort D, Julien S, Krause KH. A Sox1 to Pax6 switch drives neuroectoderm to radial glia progression during differentiation of mouse embryonic stem cells. Stem Cells. 2008. [PubMed]
  • Tuoc TC, Stoykova A. Trim11 modulates the function of neurogenic transcription factor Pax6 through ubiquitin-proteosome system. Genes Dev. 2008;22:1972–1986. doi: 10.1101/gad.471708. [PubMed] [Cross Ref]
  • Villarroel CE, Villanueva-Mendoza C, Orozco L, Alcantara-Ortigoza MA, Jimenez DF, Ordaz JC, Gonzalez-del Angel A. Molecular analysis of the PAX6 gene in Mexican patients with congenital aniridia: report of four novel mutations. Mol Vis. 2008;14:1650–1658. [PMC free article] [PubMed]
  • Wen JH, Chen YY, Song SJ, Ding J, Gao Y, Hu QK, Feng RP, Liu YZ, Ren GC, Zhang CY, Paired box 6 (PAX6) regulates glucose metabolism via proinsulin processing mediated by prohormone convertase 1/3 (PC1/3) Diabetologia. 2008. [PubMed]
  • Hozumi Y, Fukaya M, Adachi N, Saito N, Otani K, Kondo H, Watanabe M, Goto K. Diacylglycerol kinase beta accumulates on the perisynaptic site of medium spiny neurons in the striatum. Eur J Neurosci. 2008;28:2409–2422. doi: 10.1111/j.1460-9568.2008.06547.x. [PubMed] [Cross Ref]
  • Hozumi Y, Watanabe M, Otani K, Goto K. Diacylglycerol kinase beta promotes dendritic outgrowth and spine maturation in developing hippocampal neurons. BMC Neurosci. 2009;10:99. doi: 10.1186/1471-2202-10-99. [PMC free article] [PubMed] [Cross Ref]
  • Nakano T, Hozumi Y, Ali H, Saino-Saito S, Kamii H, Sato S, Kayama T, Watanabe M, Kondo H, Goto K. Diacylglycerol kinase zeta is involved in the process of cerebral infarction. Eur J Neurosci. 2006;23:1427–1435. doi: 10.1111/j.1460-9568.2006.04685.x. [PubMed] [Cross Ref]
  • Nakano T, Iseki K, Hozumi Y, Kawamae K, Wakabayashi I, Goto K. Brain trauma induces expression of diacylglycerol kinase zeta in microglia. Neurosci Lett. 2009;461:110–115. doi: 10.1016/j.neulet.2009.06.001. [PubMed] [Cross Ref]
  • Gobeil S, Zhu X, Doillon CJ, Green MR. A genome-wide shRNA screen identifies GAS1 as a novel melanoma metastasis suppressor gene. Genes Dev. 2008;22:2932–2940. doi: 10.1101/gad.1714608. [PubMed] [Cross Ref]
  • Hirayama H, Fujita M, Yoko-o T, Jigami Y. O-mannosylation is required for degradation of the endoplasmic reticulum-associated degradation substrate Gas1*p via the ubiquitin/proteasome pathway in Saccharomyces cerevisiae. J Biochem. 2008;143:555–567. doi: 10.1093/jb/mvm249. [PubMed] [Cross Ref]
  • Lopez-Ramirez MA, Dominguez-Monzon G, Vergara P, Segovia J. Gas1 reduces Ret tyrosine 1062 phosphorylation and alters GDNF-mediated intracellular signaling. Int J Dev Neurosci. 2008;26:497–503. doi: 10.1016/j.ijdevneu.2008.02.006. [PubMed] [Cross Ref]
  • Seppala M, Depew MJ, Martinelli DC, Fan CM, Sharpe PT, Cobourne MT. Gas1 is a modifier for holoprosencephaly and genetically interacts with sonic hedgehog. J Clin Invest. 2007;117:1575–1584. doi: 10.1172/JCI32032. [PMC free article] [PubMed] [Cross Ref]
  • Lankat-Buttgereit B, Muller S, Schmidt H, Parhofer KG, Gress TM, Goke R. Knockdown of Pdcd4 results in induction of proprotein convertase 1/3 and potent secretion of chromogranin A and secretogranin II in a neuroendocrine cell line. Biol Cell. 2008;100:703–715. doi: 10.1042/BC20080052. [PubMed] [Cross Ref]
  • Schmid T, Jansen AP, Baker AR, Hegamyer G, Hagan JP, Colburn NH. Translation inhibitor Pdcd4 is targeted for degradation during tumor promotion. Cancer Res. 2008;68:1254–1260. doi: 10.1158/0008-5472.CAN-07-1719. [PubMed] [Cross Ref]
  • Suzuki C, Garces RG, Edmonds KA, Hiller S, Hyberts SG, Marintchev A, Wagner G. PDCD4 inhibits translation initiation by binding to eIF4A using both its MA3 domains. Proc Natl Acad Sci USA. 2008;105:3274–3279. doi: 10.1073/pnas.0712235105. [PubMed] [Cross Ref]
  • Talotta F, Cimmino A, Matarazzo MR, Casalino L, De Vita G, D'Esposito M, Di Lauro R, Verde P. An autoregulatory loop mediated by miR-21 and PDCD4 controls the AP-1 activity in RAS transformation. Oncogene. 2008. [PubMed]
  • Wang Q, Sun Z, Yang HS. Downregulation of tumor suppressor Pdcd4 promotes invasion and activates both beta-catenin/Tcf and AP-1-dependent transcription in colon carcinoma cells. Oncogene. 2008;27:1527–1535. doi: 10.1038/sj.onc.1210793. [PubMed] [Cross Ref]
  • Wang X, Wei Z, Gao F, Zhang X, Zhou C, Zhu F, Wang Q, Gao Q, Ma C, Sun W. et al. Expression and prognostic significance of PDCD4 in human epithelial ovarian carcinoma. Anticancer Res. 2008;28:2991–2996. [PubMed]
  • Diekwisch TG, Luan X. CP27 function is necessary for cell survival and differentiation during tooth morphogenesis in organ culture. Gene. 2002;287:141–147. doi: 10.1016/S0378-1119(01)00868-X. [PubMed] [Cross Ref]
  • Diekwisch TG, Luan X, McIntosh JE. CP27 localization in the dental lamina basement membrane and in the stellate reticulum of developing teeth. J Histochem Cytochem. 2002;50:583–586. [PubMed]
  • Iwashita S, Itoh T, Takeda H, Sugimoto Y, Takahashi I, Nobukuni T, Sezaki M, Masui T, Hashimoto K. Gene organization of bovine BCNT that contains a portion corresponding to an endonuclease domain derived from an RTE-1 (Bov-B LINE), non-LTR retrotransposable element: duplication of an intramolecular repeat unit downstream of the truncated RTE-1. Gene. 2001;268:59–66. doi: 10.1016/S0378-1119(01)00422-X. [PubMed] [Cross Ref]
  • Iwashita S, Osada N, Itoh T, Sezaki M, Oshima K, Hashimoto E, Kitagawa-Arita Y, Takahashi I, Masui T, Hashimoto K, Makalowski W. A transposable element-mediated gene divergence that directly produces a novel type bovine Bcnt protein including the endonuclease domain of RTE-1. Mol Biol Evol. 2003;20:1556–1563. doi: 10.1093/molbev/msg168. [PubMed] [Cross Ref]
  • Luan X, Diekwisch TG. CP27 affects viability, proliferation, attachment and gene expression in embryonic fibroblasts. Cell Prolif. 2002;35:207–219. doi: 10.1046/j.1365-2184.2002.00238.x. [PubMed] [Cross Ref]
  • Lanzrein AS, Johnston CM, Perry VH, Jobst KA, King EM, Smith AD. Longitudinal study of inflammatory factors in serum, cerebrospinal fluid, and brain tissue in Alzheimer disease: interleukin-1beta, interleukin-6, interleukin-1 receptor antagonist, tumor necrosis factor-alpha, the soluble tumor necrosis factor receptors I and II, and alpha1-antichymotrypsin. Alzheimer Dis Assoc Disord. 1998;12:215–227. doi: 10.1097/00002093-199809000-00016. [PubMed] [Cross Ref]
  • Wassink TH, Crowe RR, Andreasen NC. Tumor necrosis factor receptor-II: heritability and effect on brain morphology in schizophrenia. Mol Psychiatry. 2000;5:678–682. doi: 10.1038/sj.mp.4000807. [PubMed] [Cross Ref]

Articles from Journal of Inflammation (London, England) are provided here courtesy of BioMed Central