A pathway impact analysis approach to identify context‐specific signaling networks consistent with differential gene expression data

Gene Expression Profiles During Hepatic Stellate Cell Activation in Culture and In Vivo

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Replication of the genome must be coordinated with gene transcription and cellular metabolism, especially following replication stress in the presence of limiting deoxyribonucleotides. The Saccharomyces cerevisiae Rad53 (CHEK2 in mammals) checkpoint kinase plays a major role in cellular responses to DNA replication stress. Cell cycle regulated, genome-wide binding of Rad53 to chromatin was examined. Under replication stress, the kinase bound to sites of active DNA replication initiation and fork progression, but unexpectedly to the promoters of about 20% of genes encoding proteins involved in multiple cellular functions. Rad53 promoter binding correlated with changes in expression of a subset of genes. Rad53 promoter binding to certain genes was influenced by sequence-specific transcription factors and less by checkpoint signaling. However, in checkpoint mutants, untimely activation of late-replicating origins reduces the transcription of nearby genes, with concomitant localization of Rad53 to their gene bodies. We suggest that the Rad53 checkpoint kinase coordinates genome-wide replication and transcription under replication stress conditions. Editor's evaluation The unexpected localization of a cell cycle checkpoint kinase, Rad53, to promoters in response to replication stress suggests that Rad53 may help coordinate transcription in response to disrupted replication. This work will be of interest to those interested in the interplay between genome stability and gene expression. https://doi.org/10.7554/eLife.84320.sa0 Decision letter eLife's review process Introduction Rad53, the homolog of the mammalian CHEK2 kinase present in the budding yeast Saccharomyces cerevisiae, is an essential kinase involved in a multitude of cellular processes. The most well-established function of Rad53 is its role as an effector kinase in response to various sources of DNA damage. To maintain genome stability during S-phase, a DNA replication checkpoint (DRC) is activated in response to replication stress via the sensor kinase Mec1 (the mammalian ATM/ATR), the replication fork protein Mrc1 (Claspin in mammals), and other fork proteins (Lanz et al., 2019; Osborn and Elledge, 2003; Pardo et al., 2017; Paulovich and Hartwell, 1995; Saldivar et al., 2017). A second DNA damage checkpoint mediated by Rad9 (TP53BP1 in mammals) responds to double strand DNA breaks. Both branches converge on the effector kinase Rad53, which triggers a wide range of downstream events, including slowing or halting cell cycle progression, transiently preventing initiation of DNA replication at origins that have not yet replicated, activating DNA repair pathways and elevating the synthesis of deoxyribonucleoside triphosphates (dNTPs) by upregulating genes encoding the ribonucleotide reductase (RNR). The checkpoint signaling also promotes widespread changes in gene expression (Jaehnig et al., 2013; Pardo et al., 2017). Unlike most of the checkpoint genes, both Mec1 and Rad53 kinases are essential for cell viability in unperturbed cells. This can be partly explained by their role in regulating dNTP pools (Desany et al., 1998; Forey et al., 2020; Zhao et al., 2000). However, under the bypass conditions in cells without Sml1, the inhibitor of the RNR enzyme, cells lacking either kinases are viable. Yet, it is important to note that these kinase-null mutants are extremely sick and sensitive to various types of exogenous stress. Furthermore, lacking Rad53 causes a more severe defect than lacking Mec1, implying that Rad53 has activities beyond checkpoint signaling. Consistent with this suggestion, the kinase-deficient mutant rad53K227A lacks checkpoint function but retains growth-associated activity and an additional rad53 mutation was found that is checkpoint proficient but supports cell growth poorly (Gunjan and Verreault, 2003; Hoch et al., 2013; Holzen and Sclafani, 2010; Pellicioli et al., 1999). Rad53 is central to the transcriptional response to DNA damage. It is well established that the Dun1 protein kinase acts downstream of Rad53 to phosphorylate and inactivate the transcriptional repressor Rfx1/Crt1 and thereby upregulate target genes (Huang et al., 1998), including RNR2, RNR3, and RNR4, encoding subunits of RNR. However, the induced expression of RNR1, which encodes the major isoform of the RNR large subunit, is not controlled by the Rfx1 repressor, but by Ixr1 binding to the RNR1 promoter upon genotoxic stress (Tsaponina et al., 2011). This Ixr1-dependent regulation of RNR1 is independent of Dun1 but requires Rad53. Another Rad53-dependent, Dun1-independent regulation of RNR1 involves dissociation of the Nrm1 repressor from MBF following Nrm1 phosphorylation by Rad53 (Travesa et al., 2012). Thus, the Rad53-dependent transcription control is complex, but not yet fully understood. In addition to upregulating the dNTP pools, defects in cells lacking Rad53 can be suppressed by manipulating factors functioning in transcription regulation, cell wall maintenance, proteolysis, and cell cycle control (Desany et al., 1998; Manfrini et al., 2012). Moreover, Rad53 kinase targets and interaction partners found in biochemical and proteomic studies suggest that the kinase is pleiotropic (Gunjan and Verreault, 2003; Jaehnig et al., 2013; Lao et al., 2018; Smolka et al., 2007; Smolka et al., 2006). In this study, while investigating the response of yeast cells to replication stresses caused by depletion of dNTPs, we found that Rad53 localized to more than 20% of gene promoters in the S. cerevisiae genome, suggesting a multifaceted role in coordinating stress responses. Furthermore, we provide evidence that in the absence of the DRC checkpoint pathway, untimely activation of replication from late origins can negatively affect transcription activity of nearby genes. Results Rad53 is recruited to genomic loci other than replication origins in proliferating yeast cells Previous studies focusing on specific genomic features, mainly subsets of DNA replication origins, have detected Rad53 binding to replication origins and replication forks (Can et al., 2019; Dohrmann and Sclafani, 2006). However, given the evidence that Rad53 has functions beyond the control of DNA replication stress, it is possible that Rad53 also functions at additional genomic loci. We therefore investigated the genome-wide distribution of Rad53 by chromatin immunoprecipitation and deep sequencing (ChIP-seq) in proliferating yeast cells (Behrouzi et al., 2016). Since the sml1 null mutation (sml1Δ) allows cells to bypass the requirement for Rad53, or its upstream kinase Mec1, for growth, we also performed ChIP-seq in the sml1Δ mutant and the rad53 null (rad53Δ sml1Δ) as controls for antibody specificity (Figure 1). Figure 1 Download asset Open asset Rad53 is recruited to genomic loci other than replication origins in proliferating yeast cells. (a) Coverage tracks of Rad53 ChIP-seq signals in WT, sml1Δ, rad53Δsml1Δ, and mec1Δ sml1Δ for chromosome III. Asynchronous yeast cultures were processed for ChIP-seq analysis for distribution of Rad53 at genomic loci. The results from two independent experiments are presented. Experiment 1 compares only sml1Δ, rad53Δsml1Δ. (b) Heatmaps and average signals of Rad53 across 2-kb intervals centered on transcription start sites (TSSs) for proliferating WT, sml1Δ, rad53Δsml1Δ, and mec1Δ sml1Δ cells. (c) Heatmaps of ChIP-seq signals across 30-kb intervals centered on all origins annotated in OriDB database (Siow et al., 2012). Visual inspection of the ChIP-seq peaks from normalized coverage tracks suggested that Rad53 bound to sites throughout the genome (Figure 1a). Rad53 binding to the same sites also occurred in the sml1Δ mutant and the mec1Δ sml1Δ mutant, but was absent in the rad53Δ sml1Δ mutant. Computational analysis was performed to generate heatmaps of Rad53 ChIP-seq signal across 2-kb intervals centered on transcription start sites (TSSs) (Figure 1b). The average Rad53 ChIP-seq signal on top of the heatmap show that Rad53 was most concentrated within 500-bp upstream of TSSs in WT, sml1Δ, and mec1Δ sml1Δ mutants. A smaller fraction of genes also have signal within the gene bodies. No Rad53 signal beyond background was detected at TSSs in the rad53Δ sml1Δ cells, demonstrating antibody specificity. Analysis by calculating empirical p values for the ChIP-seq signals 500-bp upstream of TSS found that, out of 6604 genes, 1464, 1983, and 1293 in WT, sml1Δ, and mec1Δ sml1Δ, respectively, exhibited significant Rad53 signal at this region. This represents ~20% or greater of all gene promoters. Heatmap analysis was also performed on Rad53 ChIP-seq signal across 30-kb intervals centered on all origins annotated in the OriDB database (Siow et al., 2012). The average signal across the whole region was higher in WT, sml1Δ, and mec1Δ sml1Δ than in rad53Δ sml1Δ after normalization (Figure 1c). However, the signal was not concentrated at the origins. Since these ChIP-seq datasets are from asynchronous cells, this finding is consistent with the idea that recruitment of Rad53 to replication origins is cell cycle regulated rather than constant binding to origins. Binding of Rad53 to upstream TSS changes with cell cycle stages To gain insight into the dynamics of Rad53 recruitment to genomic loci such as promoters and replication origins, Rad53 ChIP-seq was analyzed in samples from synchronized cell cultures. Cells were arrested in G1 using α-factor and then released into media containing hydroxyurea (HU) to induce replication stress caused by limiting dNTPs. Three stages of synchronous cell cultures were collected and referred to as G1 (for cells arrested in G1), HU45 and HU90 (for cells released from G1 into HU for 45 and 90 min, respectively). These cell samples were then processed for ChIP-seq analysis. Heatmaps of the Rad53 signals at 2-kb intervals centered on all TSSs show a trend of increasing Rad53 binding as cells progress from G1-phase into HU45 or HU90 (Figure 2a), concomitant with increased levels of Rad53 protein in cells treated with HU (Figure 2—figure supplement 1). The increase in Rad53 parallels entry into S-phase, as measured by Orc6 phosphorylation (Figure 2—figure supplement 1a). Similar to the data from asynchronous cell samples, Rad53 signals were most concentrated upstream of TSSs (Figure 2a, left panel). The ChIP-seq signals using antibodies against γ-H2A, which like Rad53 is a target of the sensor kinase Mec1, were not enriched upstream of TSSs (Figure 2a, right panel). In fact, the signals for γ-H2A were lower immediately upstream of TSSs than the surrounding regions, consistent with promoters being histone-free regions. Figure 2 with 4 supplements see all Download asset Open asset Rad53 is recruited to transcription start site (TSS) and the binding changes with the cell cycle stage. Cells were synchronized in G1 phase and released into YPD containing 0.2M hydroxyurea (HU) for 45 and 90min (HU45 and HU90, respectively). (a) Heatmaps and average signals of Rad53 and γ-H2A ChIP-seq signals across 2-kb intervals centered on TSSs for WT cells at stages of G1, HU45, and HU90. (b) Distribution of aggregated peak numbers around TSS using merged Rad53 ChIP-seq peaks from all three stages (G1, HU45, and HU90). Pie charts showing the distribution of Rad53 ChIP-seq peaks in relation to gene features. (c) Rad53 ChIP-seq profiles near RNR1, PCL1, and TOS6 genes in proliferating WT, sml1Δ, rad53Δ sml1Δ, and mec1Δ sml1Δ cells, and WT cells at stages of G1, HU45, and HU90. (d) Lorenz curves for ChIP-seq read counts of Swi6 and Rad53 mapped to gene promoters showing inequality for promoter binding. Further analysis showed that >85% of the Rad53 ChIP-seq peaks overlap with gene promoters (defined as 500-bp upstream and 50-bp downstream of TSS) at all stages investigated. The distribution of aggregated peak numbers around TSSs showed highest count numbers immediately upstream of the TSS (Figure 2b). By visual inspection of normalized ChIP-seq coverage tracks, we noticed that signals at promoters of genes such as RNR1, PCL1, and TOS6 varied depending on the cell cycle stage (Figure 2c, bottom three tracks for WT at stages of G1, HU45, and HU90) while signals at neighboring gene promoters remained largely constant. Thus, the recruitment of Rad53 to a subset of gene promoters is regulated. Because RNR1, PCL1, and TOS6 are known targets of the cell cycle regulator SBF, a sequence-specific transcription factor composed of subunits Swi4 and Swi6, we compared Rad53 ChIP-seq data and a previous ChIP-seq dataset of Swi6 (Park et al., 2013). As indicators for the gene specificity of protein binding, the Gini indices were computed from Lorenz curves of ChIP-seq data for Swi6 and two of our Rad53 replicates (Figure 2d), being 0.763, 0.2918, and 0.2982, respectively. Rad53 has a coverage for many promoters while Swi6 shows substantially high coverage only for a limited number of promoters. Thus, it is likely Rad53 would effect a wider range of genes than the Swi6 regulatory network. Previous studies have found that under certain conditions, regions of the genome are promiscuously present in ChIP-seq studies independent of the antibody used (Park et al., 2013; Teytelman et al., 2013). These regions were termed hyper-ChIPable regions and were found enriched for sequences in and around gene bodies of highly expressed genes. We therefore examined whether these regions were promiscuously present under our conditions. Analysis of ChIP-seq data for Rad53 and γ-H2A with or without the hyper-ChIPable regions observed by Teytelman et al. and Park et al. did not alter the pattern of Rad53 binding to TSSs (Figure 2—figure supplement 2a–d). When we specifically examined the pattern of Rad53 ChIP-seq signals around TSSs of the 296 genes associated with those hyper-ChIPable sequences, we observed not only localization to TSSs, but also enrichment to the gene bodies as previously reported (Park et al., 2013; Teytelman et al., 2013). This pattern is distinct from the pattern of promoter localization (compare Figure 2—figure supplement 2d, e). Furthermore, KEGG analysis of the genes enriched in the studies by Teytelman et al. and Park et al. showed predominantly genes encoding snoRNAs and tRNAs, genes we did not find in the promoter binding for Rad53 (Figure 2—figure supplement 2f). Finally, we did not observe any enrichment at gene promoters when the RAD53 gene was deleted from the strain (Figure 1a, b) or when anti-γ-H2A antibodies were used (Figure 2a). Thus, we suggest that the Rad53 binding observed here is not the same as the promiscuous, non-specific enrichment of hyper-ChIPable regions reported previously. Moreover, Rad53 binding to a subset of promoters is transcription factor dependent (see below). Rad53 is recruited to sites of DNA synthesis independent of checkpoint signaling Since previous studies reported localization of Rad53 to replication origins (Can et al., 2019; Dohrmann and Sclafani, 2006), we also performed heatmap analysis of ChIP-seq signal around replication origins for Rad53, γ-H2A, and Cdc45, a component of active helicase complex and hence the marker for active replication forks (Figure 2—figure supplement 3). The 30-kb window for the heatmap around origins was chosen based on our prior knowledge that the extent of DNA synthesis in WT cells under similar growth conditions at HU90 was about 10 kb (Sheu et al., 2014). In WT cells, the Rad53 signal was present at regions associated with early firing origins but not with late firing origins that are inactive due to inhibition by the HU-induced checkpoint. However, Rad53 signal was also present at late origins in the kinase-deficient rad53K227A and mrc1Δ mutants, both of which allow activation of late origins as a result of the checkpoint defect. Thus, Rad53 was recruited only to replication forks associated with activated origins and the pattern was similar to that of Cdc45. Surprisingly, Rad53 binding to replication forks did not require Mrc1 or its own kinase activity, suggesting checkpoint-independent recruitment of Rad53 to sites of DNA synthesis. Interestingly, γ-H2A was observed at genomic regions surrounding the very late origins in G1-phase in both WT and mutants (Figure 2—figure supplement 3b). It is possible that these γ-H2A signals reflect a low level of ssDNA gaps at these late-replicating regions that were tolerated and carried over from the previous cell cycle, similar to unrepaired post-replication gaps resulting from low level of UV irradiation found in S. pombe G2-phase (Callegari and Kelly, 2006). Identification of genes with differential binding of Rad53 at promoters Rad53 promoter binding was temporally dynamic in a subset of genes, suggesting regulation by cell cycle progression or DNA replication stress. To identify genes with differential or dynamic binding of Rad53 at their promoters (DB genes), we applied residual analysis. The read count difference was investigated for the promoter regions (500-bp window upstream of TSSs) of all genes for Rad53 ChIP-seq. The comparison was done between stages G1 and HU45 of WT samples from two independent experiments (termed CP and TF, Figure 3a). Since each dataset had two biological replicates from stages G1 and HU45, we extracted the top 1000 genes that displayed dynamic binding from the aggregated read coverage and called these genes the top DB genes (Figure 3a). Among the 1000 top DB genes from each set of comparison, 435 genes were identified in both sets (435 top DB overlap). Figure 3 Download asset Open asset Identification of genes with Rad53-binding changes at the promoters. (a) Scatter plots compare the signals in G1 (x-axis) and HU45 (y-axis) at 500-bp intervals upstream of transcription start sites (TSSs) for all genes in WT datasets from two independent experiments, TF and CP. Orange dots indicated the 1000 genes with highest binding changes (top 1000 DB) and satisfying the filter of minimal signal of −0.075 (Maximal = 1). The Venn diagram to the right illustrates the identification of 435 genes that are found in both experiments (435 top DB overlap). (b) Binding changes (y-axis: residuals from analysis of experiment TF) for 435 top DB overlap. (c) Examples of coverage tracks for selected genes show Rad53 signal changes at the indicated gene promoters from G1 to HU45 and HU90. Overall, during the G1- to S-phase transition (HU45), there are more genes with increased Rad53 promoter binding than those with decreased binding: within the list of 435 genes, 337 show increased Rad53 binding at their promoters, while 98 show decreased binding (Figure 3b). These genes include those involved in cell cycle progression (e.g., genes encoding cyclins and regulators of DNA replication) and cell growth (e.g., cell wall maintenance and mating response). Figure 3c shows Rad53 dynamic binding, either up or down, at the promoters of representative genes as cells transitioned from G1 phase to HU45 and HU90 time points. The relationship between Rad53 promoter binding and gene expression To gain insight into the relationship between Rad53 promoter recruitment and gene expression, RNA-seq analysis was performed under the same growth conditions used for the ChIP-seq experiments. RNA-seq replicates of datasets from four yeast strains (WT, rad9Δ, rad53K227A, and mrc1Δ), each with three stages (G1, HU45, and HU90) were analyzed using rank data analysis (Figure 4a). In the hierarchical clustering, cell cycle stage contributes more to similarities in expression than the genotype. In particular, the expression profiles in G1 were very similar among all strains. In HU45, however, two subgroups clustered by genotype were evident: rad9Δ clustered with WT, consistent with Rad9 having no role in the DRC checkpoint branch, while rad53K227A and mrc1Δ clustered together, consistent with Rad53 and Mrc1 functioning together in the DRC response to HU stress. Figure 4 with 1 supplement see all Download asset Open asset Gene expression changes in WT and checkpoint mutants under stress and the tendency of higher Rad53 binding at promoter of genes with significant differential expression. WT, rad9Δ, rad53K227A, and mrc1Δ cells were synchronized in G1-phase and released into YPD containing 0.2M hydroxyurea (HU). Cells at stages of G1, HU45, and HU90 were collected and processed for RNA-seq analysis. (a) Rank data analysis of RNA-seq samples. (b) Bar graph summarizing on the x-axis the number of genes that show statistically significant differential expression (DEGs). The types of pairwise comparison are indicated to the right. Blue bars, downregulated DEGs. Orange bars, upregulated DEGs. (c) Average Rad53 ChIP-seq signal across 2-kb intervals centered on at transcription start site (TSS) for statistically significant DEGs (red) and the insignificant (cyan). Left panel shows the result of DEGs from comparison of G1 and HU45 datasets of WT (DEGs: WT(G1 → HU45)) and right panel shows DEGs from comparison of G1 and HU90 datasets (DEGs: WT(G1 → HU90)). Pairwise comparisons of G1 to HU45 samples showed that, in both WT and rad9Δ cells, ~2300 genes exhibited significant expression changes (differentially expressed genes, DEGs; Figure 4b). The number of DEGs increased to ~3000 when comparing G1 to HU90. Moreover, in both rad53K227A and mrc1Δ mutants, ~2500 DEGs were detected from G1 to HU45, which increased to >3400 in G1 to HU90. The response to cell cycle stage was largely equally distributed between up- and downregulation. Comparison of WT to rad53K227A in the HU45 and HU90 conditions found 517 and 2234 DEGs, respectively, while comparison of WT to mrc1Δ revealed 370 and 1166 DEGs in HU45 and HU90. A WT and rad9Δ comparison at all stages showed only five DEGs, including the deleted RAD9 gene and its marker cassette HIS3. Thus, Rad9 did not contribute much to gene expression changes under HU stress. on ChIP-seq the average Rad53-binding upstream of TSSs was higher in the significant DEGs than in the when comparing G1 to HU45 or to HU90 of the WT datasets (Figure and Figure supplement 1a). These results suggested that Rad53 may a role in the control of gene expression at a subset of expressed genes. Similar were also when comparing samples in the checkpoint mutants (Figure supplement demonstrating that the functions of Rad53 at gene promoters were not checkpoint we performed gene analysis based on in the et al., analysis of significant DEGs in WT(G1 → 10 (Figure Rad53 binding at promoter regions occurred in most (Figure with enriched functions including cell cycle regulation, mating proteolysis, and (Figure the 435 top DB overlap gene (Figure genes were also detected as DEGs in the WT(G1 → The between binding changes and expression changes was significant (Figure left p of Rad53-binding changes against gene expression changes of these revealed a between Rad53-binding and gene expression with a = (Figure left panel). Among this out of genes with decreased Rad53 signal were downregulated in genes with increased Rad53 signals into both up- and and respectively). Further of the by revealed that genes in 1 and exhibited the most significant between Rad53-binding and gene expression changes (Figure = and = respectively). Thus, specific subsets of DEGs in the from G1 → HU exhibited between a in gene expression and Rad53 promoter binding. Figure Download asset Open asset between differential binding of Rad53 at promoter and differential gene expression (a) for significant DEGs in WT(G1 → for Gene level of differential expression in (b) Heatmaps of Rad53 ChIP-seq signal across 2-kb intervals centered on transcription start site (TSS) by the in within each are in to the differential expression level (c) Scatter plots of Rad53-binding changes at the promoter against expression changes for the significant DEGs in the 435 top DB overlap and subgroups in and is indicated on top of each number of genes in the (d) of for between binding changes and expression changes for in not Examples of genes in the are the mutants of gene expression near promiscuously active late origins We the localization of Rad53 in a subset of from the HU45 comparison (Figure and the HU45 comparison (Figure supplement 1a). In these we noticed a in which downregulated genes to have a Rad53 signal not only upstream of the but a signal within gene bodies (Figure and Figure supplement 1b). This pattern was in the mrc1Δ mutant in HU45 and in HU90. The gene localization was also found more transiently in HU45 from rad53K227A cells. This signal pattern was not as in the WT HU45 and HU90 samples. Since Rad53 is also recruited to active origins and with the replication we that these gene signals in the checkpoint mutants were caused by the activation of origins that are inactive in WT, between DNA replication and gene The of the Rad53 localization at gene bodies in this of genes in the rad53K227A mutant is also consistent with the signal pattern at origins (Figure 2—figure supplement Thus, we investigated the relationship between these genes and their replication origins. Figure with 1 supplement see all Download asset Open asset expressed genes are in the mrc1Δ mutant. DEGs from HU45 comparison were (a) for significant DEGs. for Gene level of differential expression in (b) Heatmaps of Rad53 ChIP-seq signal across 2-kb intervals centered on transcription start site (TSS) by the in ChIP-seq signal in WT, rad53K227A, and mrc1Δ mutant cells at stages G1, HU45, and HU90 are (c) of relation for DEGs between each TSS and its is indicated in as well as and and are (d) charts number (top and of and upregulated DEGs as by TSS to within of TSS no origins within of and The from the TSS of each gene to the replication origins, the of gene transcription to the or and the late or see of the types in Materials and was and in the same as in the heatmaps of in Figure (Figure Overall, most of the downregulated genes in 1 of this were very to active origins kb between and and Interestingly, the pattern of for to TSS largely the of the Rad53 signal within the gene bodies (Figure This pattern was not found in the WT To the relationship between replication origins and genes, we the of up- and downregulated DEGs from the HU45 comparison in of the of their relation to the origins (Figure In the we this to the range of between TSS and origins (Figure left this genes kb or more from the active origins were distributed between up- and of gene expression. However, for those genes that are to an active the downregulated genes those genes less than 1 kb from active origins, more than were

Introduction: Pre-operative endocrine therapy is an evolving area with the advantage of gaining information on tumor response early the treatment course. There is limited data on how pre-operative endocrine therapy affects tumor gene expression. Our goal was to characterize changes in gene expression associated with pre-operative letrozole therapy among postmenopausal women with estrogen-receptor positive (ER+) HER2/neu-negative (HER2-) breast cancer. Approximately 75% of ER+ breast cancers are androgen receptor (AR)+, and there is emerging evidence that the balance between ER and AR signaling is a critical determinant of tumor growth. This study examines changes in overall gene expression, as well as gene expression related to ER and AR signaling. Methods: Our study population included 33 post-menopausal females with mean age of 65 years and ER+/HER2- breast cancer who underwent surgical resection of the tumor after receiving neoadjuvant letrozole (median days of treatment = 23 (IQR 21-30 d)). Patients had pT3 and pN2a disease or less, and grade 1 (66.6%) or grade 2 (33.3%) disease. Most patients had ER > 90% by IHC (91% of population). Gene expression was assessed on the initial diagnostic biopsy and the operative specimen. Samples were sequenced on an Illumina NextSeq P4 flowcell. Alignment was performed against the iGenomes hg38 reference genome using STAR v-2.7.10b, with quality checks using FastQC and RSeQC. We used STAR2’s –quantMode flag with Gencode v38 gene definitions to perform gene expression quantification and generate counts, followed by edgeR to perform the differential expression analysis in R. A significance threshold of log2FC >= 1 or log2FC <= -1 at 5% FDR was used to define the genes of interest. Results: 14971 genes were analyzed, of which 347 genes were differentially expressed following letrozole therapy. 203 genes were significantly downregulated, such as SERPINA6 (log2FC -4.2), CYP2A6 (log2FC -3.9), IGSF1 (log2FC -2.7), PDZK1 (log2FC -2.7), TMC3 (log2FC -2.6). The five most upregulated genes include FOSB (log2FC 5.2), FOS (log2FC 4.3), DUSP1 (log2FC 3.7), RGS1 (log2FC 3.6), and ATF3 (log2FC 3.1). 41 genes related to ER/AR signaling were significantly differentially expressed following letrozole treatment, of which the majority were related to AR signaling (37 genes). Most genes related to AR signaling were significantly downregulated (92%), such as PDZK1 (log2FC -2.7), TROAP (log2FC -2.1), NOY1R (log2FC -2.1), PGR (log2FC -1.9), and ESPL1 (log2FC -1.9). Discussion: This study revealed modulation of gene expression after short-term pre-operative letrozole therapy. We are currently examining pathway level changes associated with pre-operative letrozole treatment to better understand how aromatase inhibitors impact AR signaling. Additionally, our team is investigating whether our observed changes in gene expression are associated with quantitative AR receptor expression by IHC and other outcome measures, such as changes in the proliferation marker Ki67. Further studies should aim to understand how aromatase inhibitors impact AR signaling and the potential impact on the efficacy of endocrine treatment and overall patient outcomes. Citation Format: Simone Dekker, Pritha Chanana, Matthew Fitzgibbon, Jennifer M. Specht, Hannah M. Linden, Meghan R. Flanagan. Gene expression changes following pre-operative letrozole therapy among postmenopausal women with estrogen-receptor positive HER2/neu-negative invasive breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-01-21.

Expression profiling: Opportunities and pitfalls and impact on the study and management of allergic diseases

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Characterizing the ontologies of genes directly regulated by a transcription factor (TF), can help to elucidate the TF’s biological role. Previously, we developed a widely used method, BETA, to integrate TF ChIP-seq peaks with differential gene expression (DGE) data to infer direct target genes. Here, we provide Cistrome-GO, a website implementation of this method with enhanced features to conduct ontology analyses of gene regulation by TFs in human and mouse. Cistrome-GO has two working modes: solo mode for ChIP-seq peak analysis; and ensemble mode, which integrates ChIP-seq peaks with DGE data. Cistrome-GO is freely available at http://go.cistrome.org/.

Transcription changes in cells taken from bronchoalveolar fluid of COVID-19 patients indicate severe disruption of coagulation and fibrinolytic pathways in the lung and suggest similar processes in other organs.

Interferon tau (IFNT), a type I IFN similar to alpha IFNs (IFNA), is the pregnancy recognition signal produced by the ruminant conceptus. To elucidate specific effects of bovine IFNT and of other conceptus-derived factors, endometrial gene expression changes during early pregnancy were compared to gene expression changes after intrauterine application of human IFNA2. In experiment 1, endometrial tissue samples were obtained on Day (D) 12, D15, and D18 postmating from nonpregnant or pregnant heifers. In experiment 2, heifers were treated from D14 to D16 of the estrous cycle with an intrauterine device releasing IFNA2 or, as controls, placebo lipid extrudates or PBS only. Endometrial biopsies were performed after flushing the uterus. All samples from both experiments were analyzed with an Affymetrix Bovine Genome Array. Experiment 1 revealed differential gene expression between pregnant and nonpregnant endometria on D15 and D18. In experiment 2, IFNA2 treatment resulted in differential gene expression in the bovine endometrium. Comparison of the data sets from both studies identified genes that were differentially expressed in response to IFNA2 but not in response to pregnancy on D15 or D18. In addition, genes were found that were differentially expressed during pregnancy but not after IFNA2 treatment. In experiment 3, spatiotemporal alterations in expression of selected genes were determined in uteri from nonpregnant and early pregnant heifers using in situ hybridization. The overall findings of this study suggest differential effects of bovine IFNT compared to human IFNA2 and that some pregnancy-specific changes in the endometrium are elicited by conceptus-derived factors other than IFNT.

Postmortem and neuroimaging studies show low levels of cortical muscarinic M1 receptors (CHRM1) in patients with schizophrenia which is significant because CHRM signalling has been shown to change levels of gene expression and cortical gene expression is altered in schizophrenia. We decided to identify CHRM1-mediated changes in cortical gene expression by measuring levels of RNA in the cortex of the Chrm1−/− mouse (n = 10), where there would be no signalling by that receptor, and in wild type mouse (n = 10) using the Affymetrix Mouse Exon 1.0 ST Array. We detected RNA for 15,501 annotated genes and noncoding RNA of which 1,467 RNAs were higher and 229 RNAs lower in the cortex of the Chrm1−/− mouse. Pathways and proteins affected by the changes in cortical gene expression in the Chrm1−/− are linked to the molecular pathology of schizophrenia. Our human cortical gene expression data showed 47 genes had altered expression in Chrm1−/− mouse and the frontal pole from patients with schizophrenia with the change in expression of 44 genes being in opposite directions. In addition, genes with altered levels of expression in the Chrm1−/− mouse have been shown to affect amyloid precursor protein processing which is associated with the pathophysiology of Alzheimer’s disease, and 69 genes with altered expression in the Chrm1−/− mouse are risk genes associated with human cognitive ability. Our findings argue CHRM1-mediated changes in gene expression are relevant to the pathophysiologies of schizophrenia and Alzheimer’s disease and the maintenance of cognitive ability in humans.

Global Gene Expression Changes Induced in the Human Placenta during Labor

BackgroundMicroarray studies using in vitro cultures of synchronized, blood-stage Plasmodium falciparum malaria parasites have revealed a ‘just-in-time’ cascade of gene expression with some indication that these transcriptional patterns remain stable even in the presence of external stressors. However, direct analysis of transcription in P. falciparum blood-stage parasites obtained from the blood of infected patients suggests that parasite gene expression may be modulated by factors present in the in vivo environment of the host. The aim of this study was to examine changes in gene expression of the rodent malaria parasite, Plasmodium yoelii 17X, while varying the in vivo setting of replication.MethodsUsing P. yoelii 17X parasites replicating in vivo, differential gene expression in parasites isolated from individual mice, from independent infections, during ascending, peak and descending parasitaemia and in the presence and absence of host antibody responses was examined using P. yoelii DNA microarrays. A genome-wide analysis to identify coordinated changes in groups of genes associated with specific biological pathways was a primary focus, although an analysis of the expression patterns of two multi-gene families in P. yoelii, the yir and pyst-a families, was also completed.ResultsAcross experimental conditions, transcription was surprisingly stable with little evidence for distinct transcriptional states or for consistent changes in specific pathways. Differential gene expression was greatest when comparing differences due to parasite load and/or host cell availability. However, the number of differentially expressed genes was generally low. Of genes that were differentially expressed, many involved biologically diverse pathways. There was little to no differential expression of members of the yir and pyst-a multigene families that encode polymorphic proteins associated with the membrane of infected erythrocytes. However, a relatively large number of these genes were expressed during blood-stage infection regardless of experimental condition.ConclusionsTaken together, these results indicate that 1) P. yoelii gene expression remains stable in the presence of a changing host environment, and 2) concurrent expression of a large number of the polymorphic yir and pyst-a genes, rather than differential expression in response to specific host factors, may in itself limit the effectiveness of host immune responses.

Differential network analysis is an important tool to investigate the rewiring of gene interactions under different conditions. Several computational methods have been developed to estimate differential networks from gene expression data, but most of them do not consider that gene network rewiring may be driven by the differential expression of individual genes. New differential network analysis methods that simultaneously take account of the changes in gene interactions and changes in expression levels are needed. : In this article, we propose a differential network analysis method that considers the differential expression of individual genes when identifying differential edges. First, two hypothesis test statistics are used to quantify changes in partial correlations between gene pairs and changes in expression levels for individual genes. Then, an optimization framework is proposed to combine the two test statistics so that the resulting differential network has a hierarchical property, where a differential edge can be considered only if at least one of the two involved genes is differentially expressed. Simulation results indicate that our method outperforms current state-of-the-art methods. We apply our method to identify the differential networks between the luminal A and basal-like subtypes of breast cancer and those between acute myeloid leukemia and normal samples. Hub nodes in the differential networks estimated by our method, including both differentially and nondifferentially expressed genes, have important biological functions. All the datasets underlying this article are publicly available. Processed data and source code can be accessed through the Github repository at https://github.com/Zhangxf-ccnu/chNet. Supplementary data are available at Bioinformatics online.

Porcine alveolar macrophages (PAMs) can resist infection caused by Mycoplasma hyopneumoniae (Mhp) through phagocytosis. However, it is unknown what gene expression changes occur in PAM after Mhp stimulation. Therefore, the differential gene expression (DGE) profiling technique was employed to analyze differentially expressed genes in PAMs infected with Mhp strain 232. Eighty-six and 889 differentially expressed (DE) genes were identified in PAMs at 12 hours post-infection (hpi) and 24 hpi. Using Gene Ontology (GO) analysis, DE genes were involved in 54 (12 hpi) and 128 (24 hpi) GO enrichment items. Using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, DE genes were involved in 101 (12 hpi) and 250 (24 hpi) KEGG enrichment items. Using Ingenuity Pathway Analysis (IPA), DE genes were connected, forming 25 internally interacting subnetworks. STRING analysis revealed 131 proteins encoded by DE genes involved in network interactions. Five novel genes were closely related to clinical symptoms and pathological changes of Mycoplasma hyopneumoniae. This is the first study to investigate PAM transcriptional responses to Mhp infection using the DGE profiling technique.

Chapter 9 - Changes in cortical gene expression in major depressive disorders: More evidence implicating inflammatory-related pathways in disease etiology

1025 - A Quantitative Perspective of Transcription in Pluripotent and Differentiated Cells