The sulfur controller-2 negative regulatory gene of Neurospora crassa encodes a protein with beta-transducin repeats.

The sulfur regulatory system of Neurospora crassa is composed of a set of structural genes involved in sulfur catabolism controlled by a genetically defined set of trans-acting regulatory genes. These sulfur regulatory genes include cys-3+, which encodes a basic region-leucine zipper transcriptional activator, and the negative regulatory gene scon-2+. We report here that the scon-2+ gene encodes a polypeptide of 650 amino acids belonging to the expanding beta-transducin family of eukaryotic regulatory proteins. Specifically, SCON2 protein contains six repeated G beta-homologous domains spanning the C-terminal half of the protein. SCON2 represents the initial filamentous fungal protein identified in the beta-transducin group. Additionally, SCON2 exhibits a specific amino-terminal domain that potentially defines another subfamily of beta-transducin homologs. Expression of the scon-2+ gene has been examined using RNA hybridization and gel mobility-shift analysis. The dependence of scon-2+ expression on CYS3 function and the binding of CYS3 to the scon-2+ promoter indicate the presence of an important control loop within the N. crassa sulfur regulatory circuit involving CYS3 activation of scon-2+ expression. On the basis of the presence of beta-transducin repeats, the crucial role of SCON2 in the signal-response pathway triggered by sulfur limitation may be mediated by protein-protein interactions.

The prion protein gene: Identifying regulatory signals using marsupial sequence

The function of the prion protein gene (PRNP) and its normal product PrPC is elusive. We used comparative genomics as a strategy to understand the normal function of PRNP. As the reliability of comparisons increases with the number of species and increased evolutionary distance, we isolated and sequenced a 66.5 kb BAC containing the PRNP gene from a distantly related mammal, the model Australian marsupial Macropus eugenii (tammar wallaby). Marsupials are separated from eutherians such as human and mouse by roughly 180 million years of independent evolution. We found that tammar PRNP, like human PRNP, has two exons. Prion proteins encoded by the tammar wallaby and a distantly related marsupial, Monodelphis domestica (Brazilian opossum) PRNP contain proximal PrP repeats with a distinct, marsupial-specific composition and a variable number. Comparisons of tammar wallaby PRNP with PRNPs from human, mouse, bovine and ovine allowed us to identify non-coding gene regions conserved across the marsupial-eutherian evolutionary distance, which are candidates for regulatory regions. In the PRNP 3' UTR we found a conserved signal for nuclear-specific polyadenylation and the putative cytoplasmic polyadenylation element (CPE), indicating that post-transcriptional control of PRNP mRNA activity is important. Phylogenetic footprinting revealed conserved potential binding sites for the MZF-1 transcription factor in both upstream promoter and intron/intron 1, and for the MEF2, MyTI, Oct-1 and NFAT transcription factors in the intron(s). The presence of a conserved NFAT-binding site and CPE indicates involvement of PrPC in signal transduction and synaptic plasticity. (c) 2004 Elsevier B.V. All rights reserved.

The human papillomavirus type 16 E7 protein binds human interferon regulatory factor-9 via a novel PEST domain required for transformation

It is critical that viruses are able to avoid the antiviral activities of interferon (IFN). We have shown previously that the human papillomavirus (HPV) is able to avoid IFN-alpha via interaction of the HPV-16 E7 protein with IFN regulatory factor-9 (IRF-9). Here, we investigated the details of the interaction using HPV-16 E7 peptide mapping to show that IRF-9 binds HPV-16 E7 in a domain encompassing amino acids 25-36. A closer examination of this region indicates this is a novel proline, glutamate, serine, and threonine-rich (PEST) domain, with a PEST score of 8.74. We have also mapped the region of interaction within IRF-9 and found that amino acids 354-393 play an important role in binding to HPV-16 E7. This region of IRF-9 encompasses the IRF association domain (IAD), a region important for protein-protein interaction central to IRF function. Finally, we used alanine-scanning mutagenesis to determine if E7-IRF-9 interaction was important for E7-mediated cellular transformation and found that the HPV-16 E7 mutants Y25A, E26A, S31A, S32A, and E35A, but not L28A and N29A, caused loss of transformation ability. Preliminary data suggest loss of IRF-9 interaction with E7 mutants correlated with transformation. Our work suggests E7- IRF- 9 interaction is important for the transforming ability of HPV-16 E7 and that HPV-16 E7 may interact with other IRF proteins that have IAD domains.

Regulatory crosstalk by protein kinases on CFTR trafficking and activity

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a member of the ATP binding cassette (ABC) transporter superfamily that functions as a cAMP-activated chloride ion channel in fluid-transporting epithelia. There is abundant evidence that CFTR activity (i.e., channel opening and closing) is regulated by protein kinases and phosphatases via phosphorylation and dephosphorylation. Here, we review recent evidence for the role of protein kinases in regulation of CFTR delivery to and retention in the plasma membrane. We review this information in a broader context of regulation of other transporters by protein kinases because the overall functional output of transporters involves the integrated control of both their number at the plasma membrane and their specific activity. While many details of the regulation of intracellular distribution of CFTR and other transporters remain to be elucidated, we hope that this review will motivate research providing new insights into how protein kinases control membrane transport to impact health and disease.

Stem cell regulatory gene expression in human adult dental pulp and periodontal ligament cells undergoing odontogenic/osteogenic differentiation

Introduction During development and regeneration, odontogenesis and osteogenesis are initiated by a cascade of signals driven by several master regulatory genes. Methods In this study, we investigated the differential expression of 84 stem cell–related genes in dental pulp cells (DPCs) and periodontal ligament cells (PDLCs) undergoing odontogenic/osteogenic differentiation. Results Our results showed that, although there was considerable overlap, certain genes had more differential expression in PDLCs than in DPCs. CCND2, DLL1, and MME were the major upregulated genes in both PDLCs and DPCs, whereas KRT15 was the only gene significantly downregulated in PDLCs and DPCs in both odontogenic and osteogenic differentiation. Interestingly, a large number of regulatory genes in odontogenic and osteogenic differentiation interact or crosstalk via Notch, Wnt, transforming growth factor β (TGF-β)/bone morphogenic protein (BMP), and cadherin signaling pathways, such as the regulation of APC, DLL1, CCND2, BMP2, and CDH1. Using a rat dental pulp and periodontal defect model, the expression and distribution of both BMP2 and CDH1 have been verified for their spatial localization in dental pulp and periodontal tissue regeneration. Conclusions This study has generated an overview of stem cell–related gene expression in DPCs and PDLCs during odontogenic/osteogenic differentiation and revealed that these genes may interact through the Notch, Wnt, TGF-β/BMP, and cadherin signalling pathways to play a crucial role in determining the fate of dental derived cell and dental tissue regeneration. These findings provided a new insight into the molecular mechanisms of the dental tissue mineralization and regeneration

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Subcellular localization of proteasomes and their regulatory complexes in mammalian cells

Proteasomes can exist in several different molecular forms in mammalian cells. The core 20S proteasome, containing the proteolytic sites, binds regulatory complexes at the ends of its cylindrical structure. Together with two 19S ATPase regulatory complexes it forms the 26S proteasome, which is involved in ubiquitin-dependent proteolysis. The 20S proteasome can also bind 11S regulatory complexes (REG, PA28) which play a role in antigen processing, as do the three variable c-interferoninducible catalytic b-subunits (e.g. LMP7). In the present study, we have investigated the subcellular distribution of the different forms of proteasomes using subunit speci®c antibodies. Both 20S proteasomes and their 19S regulatory complexes are found in nuclear, cytosolic and microsomal preparations isolated from rat liver. LMP7 was enriched approximately two-fold compared with core a-type proteasome subunits in the microsomal preparations. 20S proteasomes were more abundant than 26S proteasomes, both in liver and cultured cell lines. Interestingly, some signi®cant differences were observed in the distribution of different subunits of the 19S regulatory complexes. S12, and to a lesser extent p45, were found to be relatively enriched in nuclear fractions from rat liver, and immuno¯uorescent labelling of cultured cells with anti-p45 antibodies showed stronger labelling in the nucleus than in the cytoplasm. The REG was found to be localized predominantly in the cytoplasm. Three- to six-fold increases in the level of REG were observed following cinterferon treatment of cultured cells but c-interferon had no obvious effect on its subcellular distribution. These results demonstrate that different regulatory complexes and subpopulations of proteasomes have different distributions within mammalian cells and, therefore, that the distribution is more complex than has been reported for yeast proteasomes.

Early inflammatory and myogenic responses to resistance exercise in the elderly

Introduction and Methods: This study compared changes in myokine and myogenic genes following resistance exercise (3 sets of 12 repetitions of maximal unilateral knee extension) in 20 elderly men (67.8 ± 1.0 years) and 15 elderly women (67.2 ± 1.5 years). Results: Monocyte chemotactic protein (MCP)-1, macrophage inhibitory protein (MIP)-1β, interleukin (IL)-6 and MyoD mRNA increased significantly (P < 0.05), whereas myogenin and myostatin mRNA decreased significantly after exercise in both groups. Macrophage-1 (Mac-1) and MCP-3 mRNA did not change significantly after exercise in either group. MIP-1β, Mac-1 and myostatin mRNA were significantly higher before and after exercise in men compared with women. In contrast, MCP-3 and myogenin mRNA were significantly higher before and after exercise in the women compared with the men. Conclusions: In elderly individuals, gender influences the mRNA expression of certain myokines and growth factors, both at rest and after resistance exercise. These differences may influence muscle regeneration following muscle injury

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Small RNA sequencing of potato leafroll virus-infected plants reveals an additional subgenomic RNA encoding a sequence-specific RNA-binding protein

Potato leafroll virus (PLRV) is a positive-strand RNA virus that generates subgenomic RNAs (sgRNA) for expression of 3' proximal genes. Small RNA (sRNA) sequencing and mapping of the PLRV-derived sRNAs revealed coverage of the entire viral genome with the exception of four distinctive gaps. Remarkably, these gaps mapped to areas of PLRV genome with extensive secondary structures, such as the internal ribosome entry site and 5' transcriptional start site of sgRNA1 and sgRNA2. The last gap mapped to ~500. nt from the 3' terminus of PLRV genome and suggested the possible presence of an additional sgRNA for PLRV. Quantitative real-time PCR and northern blot analysis confirmed the expression of sgRNA3 and subsequent analyses placed its 5' transcriptional start site at position 5347 of PLRV genome. A regulatory role is proposed for the PLRV sgRNA3 as it encodes for an RNA-binding protein with specificity to the 5' of PLRV genomic RNA. © 2013.

Identification of a cis-regulatory element by transient analysis of co-ordinately regulated genes

Background Transcription factors (TFs) co-ordinately regulate target genes that are dispersed throughout the genome. This co-ordinate regulation is achieved, in part, through the interaction of transcription factors with conserved cis-regulatory motifs that are in close proximity to the target genes. While much is known about the families of transcription factors that regulate gene expression in plants, there are few well characterised cis-regulatory motifs. In Arabidopsis, over-expression of the MYB transcription factor PAP1 (PRODUCTION OF ANTHOCYANIN PIGMENT 1) leads to transgenic plants with elevated anthocyanin levels due to the co-ordinated up-regulation of genes in the anthocyanin biosynthetic pathway. In addition to the anthocyanin biosynthetic genes, there are a number of un-associated genes that also change in expression level. This may be a direct or indirect consequence of the over-expression of PAP1. Results Oligo array analysis of PAP1 over-expression Arabidopsis plants identified genes co-ordinately up-regulated in response to the elevated expression of this transcription factor. Transient assays on the promoter regions of 33 of these up-regulated genes identified eight promoter fragments that were transactivated by PAP1. Bioinformatic analysis on these promoters revealed a common cis-regulatory motif that we showed is required for PAP1 dependent transactivation. Conclusion Co-ordinated gene regulation by individual transcription factors is a complex collection of both direct and indirect effects. Transient transactivation assays provide a rapid method to identify direct target genes from indirect target genes. Bioinformatic analysis of the promoters of these direct target genes is able to locate motifs that are common to this sub-set of promoters, which is impossible to identify with the larger set of direct and indirect target genes. While this type of analysis does not prove a direct interaction between protein and DNA, it does provide a tool to characterise cis-regulatory sequences that are necessary for transcription activation in a complex list of co-ordinately regulated genes.

Bimolecular interaction of Insulin-Like Growth Factor (IGF) binding protein-2 with αvβ3 negatively modulates IGF-I-Mediated migration and tumor growth

Both the integrin and insulin-like growth factor binding protein (IGFBP) families independently play important roles in modulating tumor cell growth and progression. We present evidence for a specific cell surface localization and a bimolecular interaction between the αvβ3 integrin and IGFBP-2. The interaction, which could be specifically perturbed using vitronectin and αvβ3 blocking antibodies, was shown to modulate IGF-mediated cellular migration responses. Moreover, this interaction was observed in vivo and correlated with reduced tumor size of the human breast cancer cells, MCF-7β3, which overexpressed the αvβ3 integrin. Collectively, these results indicate that αvβ3 and IGFBP-2 act cooperatively in a negative regulatory manner to reduce tumor growth and the migratory potential of breast cancer cells.

A variant of the breast cancer type 2 susceptibility protein (BRC) repeat is essential for the RECQL5 Helicase to interact with RAD51 Recombinase for genome stabilization

The BRC repeat is a structural motif in the tumor suppressor BRCA2 (breast cancer type 2 susceptibility protein), which promotes homologous recombination (HR) by regulating RAD51 recombinase activity. To date, the BRC repeat has not been observed in other proteins, so that its role in HR is inferred only in the context of BRCA2. Here, we identified a BRC repeat variant, named BRCv, in the RECQL5 helicase, which possesses anti-recombinase activity in vitro and suppresses HR and promotes cellular resistance to camptothecin-induced replication stress in vivo. RECQL5-BRCv interacted with RAD51 through two conserved motifs similar to those in the BRCA2-BRC repeat. Mutations of either motif compromised functions of RECQL5, including association with RAD51, inhibition of RAD51-mediated D-loop formation, suppression of sister chromatid exchange, and resistance to camptothecin-induced replication stress. Potential BRCvs were also found in other HR regulatory proteins, including Srs2 and Sgs1, which possess anti-recombinase activities similar to that of RECQL5. A point mutation in the predicted Srs2-BRCv disrupted the ability of the protein to bind RAD51 and to inhibit D-loop formation. Thus, BRC is a common RAD51 interaction module that can be utilized by different proteins to either promote HR, as in the case of BRCA2, or to suppress HR, as in RECQL5.

Drosophila 3′ UTRs are more complex than protein-coding sequences

The 3′ UTRs of eukaryotic genes participate in a variety of post-transcriptional (and some transcriptional) regulatory interactions. Some of these interactions are well characterised, but an undetermined number remain to be discovered. While some regulatory sequences in 3′ UTRs may be conserved over long evolutionary time scales, others may have only ephemeral functional significance as regulatory profiles respond to changing selective pressures. Here we propose a sensitive segmentation methodology for investigating patterns of composition and conservation in 3′ UTRs based on comparison of closely related species. We describe encodings of pairwise and three-way alignments integrating information about conservation, GC content and transition/transversion ratios and apply the method to three closely related Drosophila species: D. melanogaster, D. simulans and D. yakuba. Incorporating multiple data types greatly increased the number of segment classes identified compared to similar methods based on conservation or GC content alone. We propose that the number of segments and number of types of segment identified by the method can be used as proxies for functional complexity. Our main finding is that the number of segments and segment classes identified in 3′ UTRs is greater than in the same length of protein-coding sequence, suggesting greater functional complexity in 3′ UTRs. There is thus a need for sustained and extensive efforts by bioinformaticians to delineate functional elements in this important genomic fraction. C code, data and results are available upon request.

Co-regulatory potential of vascular endothelial growth factor–A and vascular endothelial growth factor–C in thyroid carcinoma

Vascular endothelial growth factor (VEGF) promotes growth of blood or lymphatic vessels. The aim of the current study is to identify relationships between VEGF-A and VEGF-C, and their impact in angiogenesis and metastases in thyroid cancers. VEGF-A and VEGF-C mRNA and protein expression was investigated in 136 thyroid cancers (123 papillary thyroid carcinomas and 13 undifferentiated thyroid carcinomas) and 40 matched lymph node metastases with papillary thyroid carcinoma using reverse transcription polymerase chain reaction and immunohistochemistry. VEGF-A and VEGF-C mRNA expression was significantly different between conventional papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, and undifferentiated thyroid carcinomas (P = 1 x 10(-6) and 1 x 10(-5), respectively). In undifferentiated carcinoma, VEGF-A and VEGF-C protein overexpression was noted in all cases. VEGF-A and VEGF-C mRNA overexpression was noted in 51% (n = 62) and 27% (n = 33) of the papillary thyroid carcinomas, whereas VEGF-A and VEGF-C protein overexpression was also identified in 70% (n = 86) and 62% (n = 76) of the carcinomas. VEGF-A mRNA was significantly higher in cancers with lymph node metastases compared with nonmetastatic cancers (P = .001), whereas most metastatic cancers underexpressed VEGF-C (P = .0002), with a similar trend for protein. The expression of VEGF-A and VEGF-C correlated with each other at both mRNA and protein levels (P = .00004 and .003, respectively). In summary, VEGF-A and -C expressions correlate with the pathological parameters and metastatic status of thyroid carcinomas. The significant correlations between the expressions of these genes add weight to hypotheses concerning VEGF-A and -C interaction in cancer progression.