Precise Regulation of Gene Expression Dynamics Favors Complex Promoter Architectures

Promoters process signals through recruitment of transcription factors and RNA polymerase, and dynamic changes in promoter activity constitute a major noise source in gene expression. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics. Here, we employ prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. These promoters combine the action of a general regulatory factor with that of specific transcription factors, a common motif of many eukaryotic promoters. By comprehensively analyzing stationary and dynamic promoter properties, this model-based approach enables us to pinpoint the structural characteristics underlying the observed behavior. Functional tradeoffs impose constraints on the promoter architecture of ribosomal protein genes. We find that a stable scaffold in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. This configuration also exhibits superior shut-off properties, and it can serve as a tunable switch in living cells. Model validation with independent experimental data suggests that the models are sufficiently realistic. When combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. Many of these findings hold for a broad range of model parameters and likely apply to other eukaryotic promoters of similar structure

The evolving epidemic of breast cancer in sub‐Saharan

Breast cancer (BC) is the leading cause of cancer in sub-Saharan Africa (SSA) with rapidly increasing incidence rates reported in Uganda and Zimbabwe. However, the magnitude of these rising trends in pre- and post-menopausal women is unknown in most African countries. We used data from the African Cancer Registry Network on incident breast cancers in women from 11 population-based cancer registries in 10 countries representing each of the four SSA regions. We explored incidence changes among women before and after age 50 by calendar period and, where possible, generational effects in this unique sub-Saharan African cohort. Temporal trends revealed increasing incidence rates in all registries during the study period, except in Nairobi where rates stabilized during 2010-2014 after rapidly increasing from 2003-2010 (APC = 8.5 95%, CI:3.0-14.2). The cumulative risk varied between and within regions, with the highest risks observed in Nairobi-Kenya, Mauritius and the Seychelles. There were similar or more rapidly increasing incidence rates in women aged 50+ compared to women <50 years in all registries except The Gambia. Birth cohort analyses revealed increases in the incidence rates in successive generations of women aged 45 and over in Harare-Zimbabwe and Kampala-Uganda. In conclusion, the incidence of BC is increasing rapidly in many parts of Africa; however, the magnitude of these changes differs. These results highlight the need for urgent actions across the cancer continuum from in-depth risk factor studies to provision of adequate therapy as well as the necessity of supporting the maintenance of good quality population-based cancer registration in Africa

SCAI acts as a suppressor of cancer cell invasion through the transcriptional control of β₁-integrin

Gene expression reprogramming governs cellular processes such as proliferation, differentiation and cell migration through the complex and tightly regulated control of transcriptional cofactors that exist in multiprotein complexes. Here we describe SCAI (suppressor of cancer cell invasion), a novel and highly conserved protein that regulates invasive cell migration through three-dimensional matrices. SCAI acts on the RhoA-Dia1 signal transduction pathway and localizes in the nucleus, where it binds and inhibits the myocardin-related transcription factor MAL by forming a ternary complex with serum response factor (SRF). Genomewide expression analysis surprisingly reveals that one of the strongest upregulated genes after suppression of SCAI is beta(1)-integrin. Decreased levels of SCAI are tightly correlated with increased invasive cell migration, and SCAI is downregulated in several human tumours. Functional analysis of the beta(1)-integrin gene strongly argues that SCAI is a novel transcriptional cofactor that controls gene expression downstream of Dia1 to dictate changes in cell invasive behaviour

Evaluating the PCPT risk calculator in ten international biopsy cohorts: results from the Prostate Biopsy Collaborative Group

OBJECTIVES: To evaluate the discrimination, calibration and net benefit performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) across five European Randomized study of Screening for Prostate Cancer (ERSPC), 1 United Kingdom, 1 Austrian and 3 US biopsy cohorts. METHODS: PCPTRC risks were calculated for 25,733 biopsies using prostate-specific antigen (PSA), digital rectal examination, family history and history of prior biopsy, and single imputation for missing covariates. Predictions were evaluated using the areas underneath the receiver operating characteristic curves (AUC), discrimination slopes, chi-square tests of goodness of fit, and net benefit decision curves. RESULTS: AUCs of the PCPTRC ranged from a low of 56% in the ERSPC Goeteborg Rounds 2-6 cohort to a high of 72% in the ERSPC Goeteborg Round 1 cohort, and were statistically significantly higher than that of PSA in 6 out of the 10 cohorts. The PCPTRC was well-calibrated in the SABOR, Tyrol and Durham cohorts. There was limited to no net benefit to using the PCPTRC for biopsy referral compared to biopsying all or no men in all five ERSPC cohorts and benefit within a limited range of risk thresholds in all other cohorts. CONCLUSIONS: External validation of the PCPTRC across ten cohorts revealed varying degree of success highly dependent on the cohort, most likely due to different criteria for and work-up before biopsy. Future validation studies of new calculators for prostate cancer should acknowledge the potential impact of the specific cohort studied when reporting successful versus failed validation