This study considers a time-varying coefficient additive hazards model with latent variables to examine potential observed and latent risk factors for survival of interest. The model consists of two ...parts: confirmatory factor analysis to measure each latent factor through multiple observable variables and a varying coefficient additive hazards model to examine the time-varying effects of the observed and latent risk factors on the hazard function. A hybrid estimation procedure that combines the expectation-maximum algorithm and corrected estimating equation method is developed to estimate the unknown parameters and nonparametric cumulative hazard functions. The consistency and asymptotic normality of the proposed estimators are established, and the pointwise confidence intervals and general confidence bands of the nonparametric functions are constructed accordingly. A satisfactory performance of the proposed method is demonstrated through simulation studies. An application to a study of chronic kidney disease for Chinese type 2 diabetes patients is presented.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has ...become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer’s disease neuroimaging initiative study further illustrates the utility of the methodology.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Tandem repetitive DNA is highly abundant in eukaryotic genomes and contributes to transcription control and genome stability. However, how the individual sequences within tandem repeats behave ...remains largely unknown. Here we develop a collection of fission yeast strains with a reporter gene inserted at different units in a tandem repeat array. We show that, contrary to what is usually assumed, transcriptional silencing and replication timing among the individual repeats differ significantly. RNAi-mediated H3K9 methylation is essential for the silencing position effect. A short hairpin RNA of ura4+ induces silencing in trans within the tandem array in a position-dependent manner. Importantly, the position effect depends on the condensin subunit, cut3+. Cut3 promotes the position effect via interaction with the RNA-induced transcriptional silencing (RITS) complex. This study reveals variations in silencing within tandem DNA repeats and provides mechanistic insights into how DNA repeats at the individual level are regulated.
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•Silencing between individual units in a tandem repeat array can vary significantly•ClrC complex and RNAi are essential for the position effect within the otr repeats•Cut3 regulates the position effect in otr repeats and CENP-A localization•Replication timing for individual otr repeats also differs significantly
How the individual sequences within tandem repeats behave remains poorly understood. He et al. show that the heterochromatin silencing between individual units within a peri-centromeric tandem array can vary significantly. They further show that condensin and RNAi components are important for the distinct silencing states in individual repeats.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Centromeric histone CENP-A, a variant of canonical histone H3, plays a central role in proper chromosome segregation. Loading of CENP-A at centromeres is cell cycle-regulated: parental CENP-A is ...deposited at centromeres during S phase, whereas newly synthesized CENP-A is deposited during later stages of the cell cycle. The mechanisms involved in deposition of CENP-A at centromeres during S phase remain poorly understood. In fission yeast, loading of CENP-A during S phase is regulated by the GATA-type factor, Ams2. Here we show that the Dos1/2-Cdc20 complex, previously characterized as a silencing complex essential for inheritance of H3K9 methylation during S phase, is also required for localization of CENP-A ᶜⁿᵖ¹ at centromeres at this stage. Disruption of Dos1 (also known as Raf1/Clr8/Cmc1), Dos2 (also known as Raf2/Clr7/Cmc2), or Cdc20, a DNA polymerase epsilon subunit, results in dissociation of CENP-A from centromeres and mislocalization of the protein to noncentromeric sites. All three mutants display spindle disorganization and mitotic defects. Inactivation of Dos1 or Cdc20 also results in accumulation of noncoding RNA transcripts from centromeric cores, a feature common to mutants affecting kinetochore integrity. We further find that Dos1 physically associates with Ams2 and is required for the association of Ams2 with centromeric cores during S phase. Finally, we show that Dos2 associates with centromeric cores during S phase and that its recruitment to centromeric cores depends on Cdc20. This study identifies a physical link between DNA replication and CENP-A assembly machinery and provides mechanistic insight into how CENP-A is faithfully inherited during S phase.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
CENP-A is a centromere-specific histone 3 variant essential for centromere specification. CENP-A partially replaces canonical histone H3 at the centromeres. How the particular CENP-A/H3 ratio at ...centromeres is precisely maintained is unknown. It also remains unclear how CENP-A is excluded from non-centromeric chromatin. Here, we identify Ccp1, an uncharacterized NAP family protein in fission yeast that antagonizes CENP-A loading at both centromeric and non-centromeric regions. Like the CENP-A loading factor HJURP, Ccp1 interacts with CENP-A and is recruited to centromeres at the end of mitosis in a Mis16-dependent manner. These data indicate that factors with opposing CENP-A loading activities are recruited to centromeres. Furthermore, Ccp1 also cooperates with H2A.Z to evict CENP-A assembled in euchromatin. Structural analyses indicate that Ccp1 forms a homodimer that is required for its anti-CENP-A loading activity. Our study establishes mechanisms for maintenance of CENP-A homeostasis at centromeres and the prevention of ectopic assembly of centromeres.
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•Ccp1 antagonizes CENP-A loading at both centromeric and non-centromeric regions•Ccp1 is recruited to centromeres at the end of mitosis in a Mis16-dependent manner•Ccp1 cooperates with H2A.Z to evict CENP-A assembled in euchromatin•Ccp1 forms a homodimer that is required for its anti-CENP-A loading activity
Dong et al. identify Ccp1, a NAP family protein, antagonizing the loading of CENP-A at both centromeric and non-centromeric regions. This study provides insights into both how the balance of CENP-A and histone H3 levels is achieved at centromeres and how non-centromeric regions are protected from mistakenly assembling CENP-A.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Splicing factors have recently been shown to be involved in heterochromatin formation, but their role in controlling heterochromatin structure and function remains poorly understood. In this study, ...we identified a fission yeast homologue of human splicing factor RBM10, which has been linked to TARP syndrome. Overexpression of Rbm10 in fission yeast leads to strong global intron retention. Rbm10 also interacts with splicing factors in a pattern resembling that of human RBM10, suggesting that the function of Rbm10 as a splicing regulator is conserved. Surprisingly, our deep-sequencing data showed that deletion of Rbm10 caused only minor effect on genome-wide gene expression and splicing. However, the mutant displays severe heterochromatin defects. Further analyses indicated that the heterochromatin defects in the mutant did not result from mis-splicing of heterochromatin factors. Our proteomic data revealed that Rbm10 associates with the histone deacetylase Clr6 complex and chromatin remodelers known to be important for heterochromatin silencing. Deletion of Rbm10 results in significant reduction of Clr6 in heterochromatin. Our work together with previous findings further suggests that different splicing subunits may play distinct roles in heterochromatin regulation.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Genetic information stored in DNA is accurately copied and transferred to subsequent generations through DNA replication. This process is accomplished through the concerted actions of highly ...conserved DNA replication components. Epigenetic information stored in the form of histone modifications and DNA methylation, constitutes a second layer of regulatory information important for many cellular processes, such as gene expression regulation, chromatin organization, and genome stability. During DNA replication, epigenetic information must also be faithfully transmitted to subsequent generations. How this monumental task is achieved remains poorly understood. In this review, we will discuss recent advances on the role of DNA replication components in the inheritance of epigenetic marks, with a particular focus on epigenetic regulation in fission yeast. Based on these findings, we propose that specific DNA replication components function as key regulators in the replication of epigenetic information across the genome.
The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which ...must be characterized by multiple observed indicators from various perspectives. Moreover, the predictor effects may vary with time in practice. Accommodating such latent variables and identifying temporal covariate effects are frequently of primary interest. This study proposes a generalized structural equation model to investigate the temporal effects of observed and latent risk factors on the hazards of interest. The proposed model comprises a confirmatory factor analysis model as the measurement equation and a varying-coefficient PH model with observed and latent predictors as the structural equation. A hybrid procedure that combines the expectation-maximization (EM) algorithm and the corrected estimating equation approach is developed to estimate unknown parameters and coefficient functions. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to a health survey study reveals insights into risk factors for elders' life expectancy.
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BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the ...cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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