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Rad9 associates with Aft1 in vivo with its BRCT domain interacting with N-Aft1 in vitro. (A) In rad9 background, Aft1 was tagged with 9Myc epitopes. Rad9 tagged with flag epitope was inserted in the high copy plasmid pDB20 and this construct was inserted to rad9 strain in order to get rad9 Aft1–9Myc pDB20-Rad9-flag strain. rad9 Aft1–9Myc pDB20-flag strain was used as control. In SDS-PAGE and immunoblotting analysis, anti-Flag was used as a bait and probing was done with anti-Myc. Input and IP samples are shown. The co-IP experiment was repeated twice. (B) Protein extracts were prepared from wt cells grown in YPD / BPS-BCS, endogenously expressing Rad9–9Myc or Rad9–9Myc along with Aft1–3HA. Portions (1 / 40) of each extract (WCE) were analyzed by SDS-PAGE and immunoblotting using anti-HA or anti-Myc to detect Aft1–3HA and Rad9–9Myc, respectively. Remaining extracts were incubated with EZview anti-HA agarose beads and then flow-through (FT) as well as pulled-down (PD) proteins were analyzed by SDS-PAGE and immunoblotting using anti-Myc to detect Rad9–9Myc. (C) Bacterially expressed GST (negative control), GST-Nhp6a (positive control) (32), GST-N-Rad9 or GST-BRCT-Rad9 proteins, bound on glutathione agarose beads, were incubated with bacterially expressed 6xHis-N-Aft1 or 6xHis- C-Aft1 derivatives eluted from Ni-NTA agarose beads. Glutathione beads were then washed and proteins bound on them were analysed by SDS-PAGE and immunoblotting using anti-His antibody. The input lane contains 20% of the total amount of each 6xHis-tagged protein incubated with the beads. Left panel: coomassie blue gel showing the electrophoretic pattern of the GST-tagged (total amounts) as well as the 6xHis-tagged (input amounts) proteins used in the assay. 

Rad9 associates with Aft1 in vivo with its BRCT domain interacting with N-Aft1 in vitro. (A) In rad9 background, Aft1 was tagged with 9Myc epitopes. Rad9 tagged with flag epitope was inserted in the high copy plasmid pDB20 and this construct was inserted to rad9 strain in order to get rad9 Aft1–9Myc pDB20-Rad9-flag strain. rad9 Aft1–9Myc pDB20-flag strain was used as control. In SDS-PAGE and immunoblotting analysis, anti-Flag was used as a bait and probing was done with anti-Myc. Input and IP samples are shown. The co-IP experiment was repeated twice. (B) Protein extracts were prepared from wt cells grown in YPD / BPS-BCS, endogenously expressing Rad9–9Myc or Rad9–9Myc along with Aft1–3HA. Portions (1 / 40) of each extract (WCE) were analyzed by SDS-PAGE and immunoblotting using anti-HA or anti-Myc to detect Aft1–3HA and Rad9–9Myc, respectively. Remaining extracts were incubated with EZview anti-HA agarose beads and then flow-through (FT) as well as pulled-down (PD) proteins were analyzed by SDS-PAGE and immunoblotting using anti-Myc to detect Rad9–9Myc. (C) Bacterially expressed GST (negative control), GST-Nhp6a (positive control) (32), GST-N-Rad9 or GST-BRCT-Rad9 proteins, bound on glutathione agarose beads, were incubated with bacterially expressed 6xHis-N-Aft1 or 6xHis- C-Aft1 derivatives eluted from Ni-NTA agarose beads. Glutathione beads were then washed and proteins bound on them were analysed by SDS-PAGE and immunoblotting using anti-His antibody. The input lane contains 20% of the total amount of each 6xHis-tagged protein incubated with the beads. Left panel: coomassie blue gel showing the electrophoretic pattern of the GST-tagged (total amounts) as well as the 6xHis-tagged (input amounts) proteins used in the assay. 

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... Although Aft1 and Aft2 share a core, Fe-S cluster-regulated DNA binding domain, Aft1 has extended sequences at both the N and C termini that are absent from Aft2 [20]. In addition to activating the iron regulon, Aft1 plays central roles in seemingly unrelated processes: it interacts with the kinetochore protein Iml3 to promote peri-centromeric cohesion [21], with Sit1 (Arn3) to regulate ferroxamine B uptake [22], and with the DNA damage checkpoint protein Rad9 to monitor fragile genomic sites [23]. ...
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Genes in linear proximity often share regulatory inputs, expression and evolutionary patterns, even in complex eukaryote genomes with extensive intergenic sequences. Gene regulation, on the other hand, is effected through the co-ordinated activation (or suppression) of genes participating in common biological pathways, which are often transcribed from distant loci. Existing approaches for the study of gene expression focus on the functional aspect, taking positional constraints into account only marginally. In this work we propose a novel concept for the study of gene expression, through the combination of topological and functional information into bipartite networks. Starting from genome-wide expression profiles, we define extended chromosomal regions with consistent patterns of differential gene expression and then associate these domains with enriched functional pathways. By analyzing the resulting networks in terms of size, connectivity and modularity we can draw conclusions on the way genome organization may underlie the gene regulation program. Implementation of this approach in a detailed RNASeq profiling of sustained Tnf stimulation of mouse synovial fibroblasts, allowed us to identify unexpected regulatory changes taking place in the cells after 24 h of stimulation. Bipartite network analysis suggests that the cytokine response set by Tnf, progresses through two distinct transitions. An early generalization of the inflammatory response, that is followed by a late shutdown of immune-related functions and the redistribution of expression to developmental and cell adhesion pathways and distinct chromosomal regions. We show that the incorporation of topological information may provide additional insights in the complex propagation of Tnf activation.
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