Gene Control Latchman 16.pdf
Sharolyn Uriegas
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The aim of the present survey is to compile an updated list of p53 target genes from individual gene analyses and high-throughput studies that will serve as a resource, and to evaluate the regulation of these genes based on the frequency of their identification in independent studies. Results from a recent meta-analysis of 20 genome-wide p53 gene expression profiles, and 15 p53 binding profiles, document that many p53 target genes are regulated across cell types as well as treatments.18 Moreover, a comparison of binding studies shows that functional p53 binding is independent of cell type and treatment.19 In the present survey, a p53 target gene is defined as a protein-coding gene that is differentially regulated following p53 activation or inactivation, and that is bound by p53 near the gene locus.
Survey of 3509 target genes derived from 16 high-throughput data sets. (a) The number of potential p53 targets is compared with the number of data sets that commonly identify them. (b) The number of genes is displayed that is identified by an increasing number of data sets as being directly activated by p53. (c) The number of genes is displayed that is identified by an increasing number of data sets as being directly repressed by p53.
When p53 target genes are grouped into those that are activated by or repressed by p53, it is evident that the majority of data sets exclusively identified target genes that are activated by p53. In contrast, target genes that are repressed by p53 were not commonly identified (Figures 2b and c). This finding is in agreement with the current model that describes p53 solely as a transcriptional activator, and not as repressor.53
Activation of p53 is induced by cell stress including DNA damage, oncogene activation, ribosomal stress or hypoxia.4 DNA damage, for example, initiates a series of p53 pulses that ultimately lead to target gene activation.57 The p53 transcription factor uses two transactivation domains to drive gene expression58 and the transactivation of target genes requires cooperative interaction between the p53 molecules at DNA REs.23, 59 Target genes were reported to be activated by p53 with varying kinetics through stimulus- and promoter-specific recruitment of transcription initiation components and polymerase II.60, 61, 62, 63 Genome-wide data, however, do not support promoter-specific activities of p53, but instead suggest unsophisticated p53 binding.19
Numerous mechanisms have been proposed for mediating gene downregulation in response to p53 activation69, 70, 71, 72 (Figure 3). In 1993, p53 was first reported to bind to coactivators, including the TATA-box binding protein,73, 74 the CCAAT-box binding factor (NF-Y)75 and specificity protein 1 (Sp1) that binds to GC-boxes,76 and to interfere with their transactivator function. While many additional coactivators are believed to be blocked by p53, NF-Y77 and Sp178, 79 are the coactivators most commonly linked to p53-dependent gene downregulation through a mechanism of p53 interference. Note, however, that interference of p53 with coactivators is not supported by results of genome-wide analyses:53 phylogenetically conserved TATA-boxes, CCAAT-boxes and GC-boxes are not enriched among genes that are downregulated in response to p53 activation.
Table 2 shows the contradictions and limited reproducibility found in the literature on p53-dependently repressed genes. Individual gene studies and genome-wide analyses report potential targets that are directly repressed by p53, and that are likely to be false positives (Supplementary Table S1 and Figure 2c). Reproducibility issues, however, are not limited to reports on directly repressed p53 target genes: of 242 protein-coding genes that are reportedly directly activated by p53 (Supplementary Table S1), only 150 (62.0%) have been identified in at least one out of 16 genome-wide data sets (Supplementary Table S2). These 16 genome-wide data sets cover a broad range of cell types and treatments, and recent findings indicate that p53 binds target genes independent of cell type and treatment.19 However, 92 of the genes that are reportedly directly activated p53 targets are not supported by any of the 16 genome-wide data sets, including BNIP3L,130 ESR1,131 FDFT1, FDPS, LDLR,132 PARK2,133 POMC,134 SHBG,135 Toll-like receptors 2, 4, 5, 8 and 10136 and ULK1 and ULK2137 (Supplementary Table S2). The reason behind this lack of reproducibility is unclear, but it points to a need for caution in interpreting research findings that have not been reproduced by independent approaches and by a number of investigators. It is well known that research findings can have limited reproducibility,138 and while some of these false findings are caused by chance, many others may be the consequences of prevailing biases.138 The survey of 319 studies on individual genes together shows that p53 target gene research still relies on the error-prone ChIP-epPCR methodology, which may promote false findings (Figure 1g). Notably, the ChIP technique in general can produce false findings. Transcription factors undergo fast turnover at non-functional binding sites that can be fixated during ChIP protocol, thereby leading to false-positive hits,139 and ChIP signals vary in general relative to formaldehyde crosslinking time.140, 141 In addition, sometimes polyclonal antibody batches are used that do not contain the same antibody. To predict functional sites that lead to target gene activation, recent approaches now rely on ranking p53 binding sites based on multiple genome-wide data sets.18, 19, 56
Here, p53 target genes are ranked by the number of data sets that report them as potential p53 target genes. The data sets include 16 genome-wide data sets and one literature-based data set, as described above (Supplementary Table S2). To be considered as high-confidence p53 target gene, a protein-coding gene was required to be identified as a p53-activated target in at least three of the 17 data sets, which ensures identification by at least two independent approaches. These criteria were met by 343 genes (Supplementary Table S3). Such an integrative approach identifies target genes that may have been missed in some data sets but have been identified in several others, and displays genes that are identified only in a small number of data sets and have a higher likelihood of being false positives.
To identify biological processes that are enriched among direct p53 target genes, a gene ontology (GO) term enrichment analysis was performed of the 343 genes that were considered as high-confidence p53 targets. As expected, GO terms associated with cell cycle arrest, apoptosis and metabolism, processes that are central to the p53 response and tumor suppression, are highly enriched for these target genes (Supplementary Table S4). Taken together, high-confidence p53 target genes function in multiple processes that include, but are not limited to, cell cycle arrest, DNA repair, apoptosis, metabolism, autophagy, translation control and feedback mechanisms (Figure 4).
p53 directly activates target genes that mediate various functions. Proteins encoded by p53 target genes function in multiple processes that include, but are not limited to, cell cycle arrest, DNA repair, apoptosis, metabolism, autophagy, translation control and feedback mechanisms.
Protein biosynthesis and mRNA translation are both influenced by p53. When cells undergo stress and p53 becomes active, mRNA translation and protein biosynthesis is repressed, to inhibit cell growth. Induction of p53 leads to downregulation of rRNA genes212, 213 and of genes that are required for import and export of ribosomal proteins from the nucleus.214 In addition, p53 uses two direct target genes, SESN1 and SESN2, to block mTOR and to repress mRNA translation.211, 215
We analyzed whole exomes from the BioMe BioBank and UK Biobank, and whole genomes from a cohort of 67 European patients diagnosed with both IBD and PD to examine the effects of LRRK2 missense variants on IBD, PD and their co-occurrence (IBD-PD). We performed optimized sequence kernel association test (SKAT-O) and network-based heterogeneity clustering (NHC) analyses using high-impact rare variants in the IBD-PD cohort to identify novel candidate genes, which we further prioritized by biological relatedness approaches. We conducted phenome-wide association studies (PheWAS) employing BioMe BioBank and UK Biobank whole exomes to estimate the genetic relevance of the 14 prioritized genes to IBD-PD.
Our study confirms and uncovers new LRRK2 associations in IBD-PD. The identification of novel inflammation and autophagy-related genes supports and expands previous findings related to IBD-PD pathogenesis, and underscores the significance of therapeutic interventions for reducing systemic inflammation.
The most well-established gene implicated in the IBD-PD pleiotropy is leucine-rich repeat kinase 2 (LRRK2). Polymorphisms in LRRK2 have been shown to be associated with both PD and CD, suggesting the impact of impaired autophagy in the pathogenesis of both conditions [5]. Variants that result in increased activity of LRRK2 have been shown to be associated with an elevated risk of developing both PD and CD, whilst a haplotype with a deactivating LRRK2 mutation, R1398H, has been found to be associated with protection against CD [5] and PD [10,11,12]. However, despite genetic pleiotropy for some of the LRRK2 variants (i.e., G2019S, N2081D, N551K, and R1398H) [5], each of these conditions is associated with specific LRRK2 variants. For example, G2019S is the major genetic risk for PD [13], whereas N2081D is considered a risk for CD [5, 14]. Moreover, other strong genetic predictors of PD, such as R1441G/C/H, Y1699C, R1628P, G2385R, and I2020T, have been shown to be associated exclusively with PD [15], whereas M2397T was not linked to PD [16]. Therefore, it is not immediately clear whether any of these or other LRRK2 variants may lead to IBD-PD comorbidity. Other than LRRK2, several other pleiotropic loci, including MAPT, HLA, MHC, ATP6V0A1, and NOD2, have been identified to be associated with PD, CD, UC, and other autoimmune disorders [4, 7, 8]. Previous studies that have examined the genetic pleiotropy between IBD and PD have primarily estimated the genetic correlation between these two conditions by means of genome-wide association study (GWAS) data from separate analyses of IBD and PD [7, 8]. However, conducting a joint analysis of individuals affected by both IBD and PD would provide important insights into the underlying mechanisms shared by these two conditions.
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