Mouse models to investigate in situ cell fate decisions induced by p53.

Investigating how transcription factors control complex cellular processes requires tools that enable responses to be visualised at the single-cell level and their cell fate to be followed over time. For example, the tumour suppressor p53 (also called TP53 in humans and TRP53 in mice) can initiate diverse cellular responses by transcriptional activation of its target genes: Puma to induce apoptotic cell death and p21 to induce cell cycle arrest/cell senescence. However, it is not known how these processes are regulated and initiated in different cell types. Also, the context-dependent interaction partners and binding loci of p53 remain largely elusive. To be able to examine these questions, we here developed knock-in mice expressing triple-FLAG-tagged p53 to facilitate p53 pull-down and two p53 response reporter mice, knocking tdTomato and GFP into the Puma/Bbc3 and p21 gene loci, respectively. By crossing these reporter mice into a p53-deficient background, we show that the new reporters reliably inform on p53-dependent and p53-independent initiation of both apoptotic or cell cycle arrest/senescence programs, respectively, in vitro and in vivo.

MORC2 phosphorylation fine tunes its DNA compaction activity.

Variants in the poorly characterised oncoprotein, MORC2, a chromatin remodelling ATPase, lead to defects in epigenetic regulation and DNA damage response. The C-terminal domain (CTD) of MORC2, frequently phosphorylated in DNA damage, promotes cancer progression, but its role in chromatin remodelling remains unclear. Here, we report a molecular characterisation of full-length, phosphorylated MORC2, demonstrating its preference for binding open chromatin and functioning as a DNA sliding clamp. We identified a phosphate interacting motif within the CTD that dictates ATP hydrolysis rate and cooperative DNA binding. The DNA binding impacts several structural domains within the ATPase region. We provide the first visual proof that MORC2 induces chromatin remodelling through ATP hydrolysis-dependent DNA compaction, regulated by its phosphorylation state. These findings highlight phosphorylation of MORC2 CTD as a key modulator of chromatin remodelling, presenting it as a potential therapeutic target.

Cumulative All-Cause Mortality in Diverse Hispanic/Latino Adults : A Prospective, Multicenter Cohort Study.

BACKGROUND: All-cause mortality among diverse Hispanic/Latino groups in the United States and factors underlying mortality differences have not been examined prospectively. OBJECTIVE: To describe cumulative all-cause mortality (and factors underlying differences) by Hispanic/Latino background, before and during the COVID-19 pandemic. DESIGN: Prospective, multicenter cohort study. SETTING: Hispanic Community Health Study/Study of Latinos. PARTICIPANTS: 15 568 adults aged 18 to 74 years at baseline (2008 to 2011) of Central American, Cuban, Dominican, Mexican, Puerto Rican, South American, and other backgrounds from the Bronx, New York; Chicago, Illinois; Miami, Florida; and San Diego, California. MEASUREMENTS: Sociodemographic, acculturation-related, lifestyle, and clinical factors were assessed at baseline, and vital status was ascertained through December 2021 (969 deaths; 173 444 person-years of follow-up). Marginally adjusted cumulative all-cause mortality risks (11-year before the pandemic and 2-year during the pandemic) were examined using progressively adjusted Cox regression. RESULTS: Before the pandemic, 11-year cumulative mortality risks adjusted for age and sex were higher in the Puerto Rican and Cuban groups (6.3% [95% CI, 5.2% to 7.6%] and 5.7% [CI, 5.0% to 6.6%], respectively) and lowest in the South American group (2.4% [CI, 1.7% to 3.5%]). Differences were attenuated with adjustment for lifestyle and clinical factors. During the pandemic, 2-year cumulative mortality risks adjusted for age and sex ranged from 1.1% (CI, 0.6% to 2.0%; South American) to 2.0% (CI, 1.4% to 3.0%; Central American); CIs overlapped across groups. With adjustment for lifestyle factors, 2-year cumulative mortality risks were highest in persons of Central American and Mexican backgrounds and lowest among those of Puerto Rican and Cuban backgrounds. LIMITATION: Lack of data on race and baseline citizenship status; correlation between Hispanic/Latino background and site. CONCLUSION: Differences in prepandemic mortality risks across Hispanic/Latino groups were explained by lifestyle and clinical factors. Mortality patterns changed during the pandemic, with higher risks in persons of Central American and Mexican backgrounds than in those of Puerto Rican and Cuban backgrounds. PRIMARY FUNDING SOURCE: National Institutes of Health.

Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Comparisons of statistical methods for handling attrition in a follow-up visit with complex survey sampling.

Design-based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey sampling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non-response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design-based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design-based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing-completely-at-random or missing-at-random missing mechanism and when the missingness model is correctly specified or over-specified. We also illustrate the use of these methods using data from HCHS/SOL.

Efficient detection and classification of epigenomic changes under multiple conditions.

Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

Differential requirement for the Polycomb repressor complex 2 in dendritic cell and tissue-resident myeloid cell homeostasis.

Dendritic cells (DCs) and macrophages are at the forefront of immune responses, modifying their transcriptional programs in response to their tissue environment or immunological challenge. Posttranslational modifications of histones, such as histone H3 lysine-27 trimethylation (H3K27me3) by the Polycomb repressive complex 2 (PRC2), are tightly associated with epigenetic regulation of gene expression. To explore whether H3K27me3 is involved in either the establishment or function of the mononuclear phagocyte system, we selectively deleted core components of PRC2, either EZH2 or SUZ12, in CD11c-expressing myeloid cells. Unexpectedly, EZH2 deficiency neither prevented the deposition and maintenance of H3K27me3 in DCs nor hindered DC/macrophage homeostasis. In contrast, SUZ12 deficiency markedly impaired the capacity of DCs and macrophages to maintain H3K27me3. SUZ12 ablation induced a rapid loss of the alveolar macrophage and Langerhans cell networks under both steady state and inflammatory conditions because these cells could no longer proliferate to facilitate their self-renewal. Despite the reduced H3K27me3, DC development and function were unaffected by SUZ12 ablation, suggesting that PRC2-mediated gene repression was dispensable for DC homeostasis. Thus, the role of SUZ12 highlights the fundamentally different homeostatic mechanisms used by tissue-resident myeloid cells versus DCs.

On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos.

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.