Aged Stem Cells Reprogram Their Daily Rhythmic Functions to Adapt to Stress.

Normal homeostatic functions of adult stem cells have rhythmic daily oscillations that are believed to become arrhythmic during aging. Unexpectedly, we find that aged mice remain behaviorally circadian and that their epidermal and muscle stem cells retain a robustly rhythmic core circadian machinery. However, the oscillating transcriptome is extensively reprogrammed in aged stem cells, switching from genes involved in homeostasis to those involved in tissue-specific stresses, such as DNA damage or inefficient autophagy. Importantly, deletion of circadian clock components did not reproduce the hallmarks of this reprogramming, underscoring that rewiring, rather than arrhythmia, is associated with physiological aging. While age-associated rewiring of the oscillatory diurnal transcriptome is not recapitulated by a high-fat diet in young adult mice, it is significantly prevented by long-term caloric restriction in aged mice. Thus, stem cells rewire their diurnal timed functions to adapt to metabolic cues and to tissue-specific age-related traits.

Interactions between BRD4S, LOXL2, and MED1 drive cell cycle transcription in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) often develops resistance to single-agent treatment, which can be circumvented using targeted combinatorial approaches. Here, we demonstrate that the simultaneous inhibition of LOXL2 and BRD4 synergistically limits TNBC proliferation in vitro and in vivo. Mechanistically, LOXL2 interacts in the nucleus with the short isoform of BRD4 (BRD4S), MED1, and the cell cycle transcriptional regulator B-MyB. These interactions sustain the formation of BRD4 and MED1 nuclear transcriptional foci and control cell cycle progression at the gene expression level. The pharmacological co-inhibition of LOXL2 and BRD4 reduces BRD4 nuclear foci, BRD4-MED1 colocalization, and the transcription of cell cycle genes, thus suppressing TNBC cell proliferation. Targeting the interaction between BRD4S and LOXL2 could be a starting point for the development of new anticancer strategies for the treatment of TNBC.

Targeted and whole-genome sequencing reveal a north-south divide in P. falciparum drug resistance markers and genetic structure in Mozambique.

Mozambique is one of the four African countries which account for over half of all malaria deaths worldwide, yet little is known about the parasite genetic structure in that country. We performed P. falciparum amplicon and whole genome sequencing on 2251 malaria-infected blood samples collected in 2015 and 2018 in seven provinces of Mozambique to genotype antimalarial resistance markers and interrogate parasite population structure using genome-wide microhaplotyes. Here we show that the only resistance-associated markers observed at frequencies above 5% were pfmdr1-184F (59%), pfdhfr-51I/59 R/108 N (99%) and pfdhps-437G/540E (89%). The frequency of pfdhfr/pfdhps quintuple mutants associated with sulfadoxine-pyrimethamine resistance increased from 80% in 2015 to 89% in 2018 (p < 0.001), with a lower expected heterozygosity and higher relatedness of microhaplotypes surrounding pfdhps mutants than wild-type parasites suggestive of recent selection. pfdhfr/pfdhps quintuple mutants also increased from 72% in the north to 95% in the south (2018; p < 0.001). This resistance gradient was accompanied by a concentration of mutations at pfdhps-436 (17%) in the north, a south-to-north increase in the genetic complexity of P. falciparum infections (p = 0.001) and a microhaplotype signature of regional differentiation. The parasite population structure identified here offers insights to guide antimalarial interventions and epidemiological surveys.

Pathway analysis using random forests with bivariate node-split for survival outcomes.

MOTIVATION: There is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted. RESULTS: In this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival forests. The proposed method allows researchers to identify important pathways for predicting patient prognosis and time to disease progression, and discover important genes within those pathways. We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best. We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in dissecting pathways and for generating new biological hypothesis from microarray studies. AVAILABILITY: R package Pwayrfsurvival is available from URL: http://www.duke.edu/~hp44/pwayrfsurvival.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Screening of dementia genes by whole-exome sequencing in Spanish patients with early-onset dementia: likely pathogenic, uncertain significance and risk variants.

Early-onset Alzheimer's disease (EOAD) and frontotemporal dementia (FTD) have a high proportion of genetically determined cases. Next-generation sequencing technologies have triggered the discovery of new mutations and genetic variants in dementia-causal genes. We performed whole-exome sequencing and selective analysis of known genes causative of EOAD and FTD in a well-characterized Spanish cohort of 103 patients (60 EOAD, 43 FTD) to find genetic variants associated to patients' phenotype. In EOAD patients, a new likely pathogenic variant in PSEN1 gene (p.G378R) was found. In FTD patients, 2 likely pathogenic variants were found, one in MAPT gene (p.P397S) and one in VCP gene (p.R159H). In our series, 2% of early-onset dementia without criteria for clinical genetic testing according to current guidelines presented a likely pathogenic mutation. We have also detected 13 additional variants of uncertain significance in causal genes, as well as rare variants in risk genes for dementia (ABCA7, SORL1, SQSTM1, and TREM2). Next-generation technologies in neurodegenerative diseases constitute a powerful tool that significantly contributes to patients' diagnosis.

Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from ChIP-chip data.

BACKGROUND: ChIP-chip data are routinely used to identify transcription factor binding targets. However, the presence of false positives and false negatives in ChIP-chip data complicates and hinders analyses, especially when the binding targets for a specific transcription factor are compared across conditions or species. RESULTS: We propose an Expectation Maximization based approach to infer the underlying true counts of "positives" and "negatives" from the observed counts. Based on this approach, we study the effect of false positives and false negatives on inferences related to transcription regulation. CONCLUSION: Our results indicate that if there is a significant degree of association among the binding targets across conditions/species (log odds ratio > 4), moderate values of false positive and false negative rates (0.005 and 0.4 respectively) would not change our inference qualitatively (i.e. the presence or absence of conservation) based on the observed experimental data despite a significant change in the observed counts. However, if the underlying association is marginal, with odds ratios close to 1, moderate to large values of false positive and false negative rates (0.01 and 0.2 respectively) could mask the underlying association.

Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae.

MOTIVATION: Transcription factors regulate transcription in prokaryotes and eukaryotes by binding to specific DNA sequences in the regulatory regions of the genes. This regulation usually occurs in a coordinated manner involving multiple transcription factors. Genome-wide location data, also called ChIP-chip data, have enabled researchers to infer the binding sites for individual regulatory proteins. However, current methods to infer binding sites, such as simple thresholding based on p-values, are not optimal for a number of study objectives like combinatorial regulation, leading to potential loss of information. Hence, there is a need to develop more efficient statistical methods for analyzing such data. RESULTS: We propose to use log-linear models to study cooperative binding among transcription factors and have developed an Expectation-Maximization algorithm for statistical inferences. Our method is advantageous over simple thresholding methods both based on simulation and real data studies. We apply our method to infer the cooperative network of 204 regulators in Rich Medium and a subset of them in four different environmental conditions. Our results indicate that the cooperative network is condition specific; for a set of regulators, the network structure changes under different environmental conditions. AVAILABILITY: Our program is available at http://bioinformatics.med.yale.edu/TFcooperativity.

Loss of Dnmt3a and Dnmt3b does not affect epidermal homeostasis but promotes squamous transformation through PPAR-γ.

The DNA methyltransferase Dnmt3a suppresses tumorigenesis in models of leukemia and lung cancer. Conversely, deregulation of Dnmt3b is thought to generally promote tumorigenesis. However, the role of Dnmt3a and Dnmt3b in many types of cancer remains undefined. Here, we show that Dnmt3a and Dnmt3b are dispensable for homeostasis of the murine epidermis. However, loss of Dnmt3a-but not Dnmt3b-increases the number of carcinogen-induced squamous tumors, without affecting tumor progression. Only upon combined deletion of Dnmt3a and Dnmt3b, squamous carcinomas become more aggressive and metastatic. Mechanistically, Dnmt3a promotes the expression of epidermal differentiation genes by interacting with their enhancers and inhibits the expression of lipid metabolism genes, including PPAR-γ, by directly methylating their promoters. Importantly, inhibition of PPAR-γ partially prevents the increase in tumorigenesis upon deletion of Dnmt3a. Altogether, we demonstrate that Dnmt3a and Dnmt3b protect the epidermis from tumorigenesis and that squamous carcinomas are sensitive to inhibition of PPAR-γ.

Dnmt3a and Dnmt3b Associate with Enhancers to Regulate Human Epidermal Stem Cell Homeostasis.

The genome-wide localization and function of endogenous Dnmt3a and Dnmt3b in adult stem cells are unknown. Here, we show that in human epidermal stem cells, the two proteins bind in a histone H3K36me3-dependent manner to the most active enhancers and are required to produce their associated enhancer RNAs. Both proteins prefer super-enhancers associated to genes that either define the ectodermal lineage or establish the stem cell and differentiated states. However, Dnmt3a and Dnmt3b differ in their mechanisms of enhancer regulation: Dnmt3a associates with p63 to maintain high levels of DNA hydroxymethylation at the center of enhancers in a Tet2-dependent manner, whereas Dnmt3b promotes DNA methylation along the body of the enhancer. Depletion of either protein inactivates their target enhancers and profoundly affects epidermal stem cell function. Altogether, we reveal novel functions for Dnmt3a and Dnmt3b at enhancers that could contribute to their roles in disease and tumorigenesis.

Genomic analysis of the blood attributed to Louis XVI (1754-1793), king of France.

A pyrographically decorated gourd, dated to the French Revolution period, has been alleged to contain a handkerchief dipped into the blood of the French king Louis XVI (1754-1793) after his beheading but recent analyses of living males from two Bourbon branches cast doubts on its authenticity. We sequenced the complete genome of the DNA contained in the gourd at low coverage (~2.5×) with coding sequences enriched at a higher ~7.3× coverage. We found that the ancestry of the gourd's genome does not seem compatible with Louis XVI's known ancestry. From a functional perspective, we did not find an excess of alleles contributing to height despite being described as the tallest person in Court. In addition, the eye colour prediction supported brown eyes, while Louis XVI had blue eyes. This is the first draft genome generated from a person who lived in a recent historical period; however, our results suggest that this sample may not correspond to the alleged king.