Cactus: A user-friendly and reproducible ATAC-Seq and mRNA-Seq analysis pipeline for data preprocessing, differential analysis, and enrichment analysis.

The ever decreasing cost of Next-Generation Sequencing coupled with the emergence of efficient and reproducible analysis pipelines has rendered genomic methods more accessible. However, downstream analyses are basic or missing in most workflows, creating a significant barrier for non-bioinformaticians. To help close this gap, we developed Cactus, an end-to-end pipeline for analyzing ATAC-Seq and mRNA-Seq data, either separately or jointly. Its Nextflow-, container-, and virtual environment-based architecture ensures efficient and reproducible analyses. Cactus preprocesses raw reads, conducts differential analyses between conditions, and performs enrichment analyses in various databases, including DNA-binding motifs, ChIP-Seq binding sites, chromatin states, and ontologies. We demonstrate the utility of Cactus in a multi-modal and multi-species case study as well as by showcasing its unique capabilities as compared to other ATAC-Seq pipelines. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis.

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

Beyond Chronological Age: A Multidimensional Approach to Survival Prediction in Older Adults.

BACKGROUND: There is a growing interest in generating precise predictions of survival to improve the assessment of health and life-improving interventions. We aimed to (a) test if observable characteristics may provide a survival prediction independent of chronological age; (b) identify the most relevant predictors of survival; and (c) build a metric of multidimensional age. METHODS: Data from 3 095 individuals aged ≥60 from the Swedish National Study on Aging and Care in Kungsholmen. Eighty-three variables covering 5 domains (diseases, risk factors, sociodemographics, functional status, and blood tests) were tested in penalized Cox regressions to predict 18-year mortality. RESULTS: The best prediction of mortality at different follow-ups (area under the receiver operating characteristic curves [AUROCs] 0.878-0.909) was obtained when 15 variables from all 5 domains were tested simultaneously in a penalized Cox regression. Significant prediction improvements were observed when chronological age was included as a covariate for 15- but not for 5- and 10-year survival. When comparing individual domains, we find that a combination of functional characteristics (ie, gait speed, cognition) gave the most accurate prediction, with estimates similar to chronological age for 5- (AUROC 0.836) and 10-year (AUROC 0.830) survival. Finally, we built a multidimensional measure of age by regressing the predicted mortality risk on chronological age, which displayed a stronger correlation with time to death (R = -0.760) than chronological age (R = -0.660) and predicted mortality better than widely used geriatric indices. CONCLUSIONS: Combining easily accessible characteristics can help in building highly accurate survival models and multidimensional age metrics with potentially broad geriatric and biomedical applications.

Histone Purification Combined with High-Resolution Mass Spectrometry to Examine Histone Post-Translational Modifications and Histone Variants in Caenorhabditis elegans.

Histones are the major proteinaceous component of chromatin in eukaryotic cells and an important part of the epigenome, affecting most DNA-related events, including transcription, DNA replication, and chromosome segregation. The properties of histones are greatly influenced by their post-translational modifications (PTMs), over 200 of which are known today. Given this large number, researchers need sophisticated methods to study histone PTMs comprehensively. In particular, mass spectrometry (MS)-based approaches have gained popularity, allowing for the quantification of dozens of histone PTMs at once. Using these approaches, even the study of co-occurring PTMs and the discovery of novel PTMs become feasible. The success of MS-based approaches relies substantially on obtaining pure and well-preserved histones for analysis, which can be difficult depending on the source material. Caenorhabditis elegans has been a popular model organism to study the epigenome, but isolation of pure histones from these animals has been challenging. Here, we address this issue, presenting a method for efficient isolation of pure histone proteins from C. elegans at good yield. Further, we describe an MS pipeline optimized for accurate relative quantification of histone PTMs from C. elegans. We alkylate and tryptically digest the histones, analyze them by bottom-up MS, and then evaluate the resulting data by a C. elegans-adapted version of the software EpiProfile 2.0. Finally, we show the utility of this pipeline by determining differences in histone PTMs between C. elegans strains that age at different rates and thereby achieve very different lifespans. © 2020 The Authors. Basic Protocol 1: Large-scale growth and harvesting of synchronized C. elegans Basic Protocol 2: Nuclear preparation, histone extraction, and histone purification Basic Protocol 3: Bottom-up mass spectrometry analysis of histone PTMs and histone variants.

DAF-16/FOXO requires Protein Phosphatase 4 to initiate transcription of stress resistance and longevity promoting genes.

In C. elegans, the conserved transcription factor DAF-16/FOXO is a powerful aging regulator, relaying dire conditions into expression of stress resistance and longevity promoting genes. For some of these functions, including low insulin/IGF signaling (IIS), DAF-16 depends on the protein SMK-1/SMEK, but how SMK-1 exerts this role has remained unknown. We show that SMK-1 functions as part of a specific Protein Phosphatase 4 complex (PP4SMK-1). Loss of PP4SMK-1 hinders transcriptional initiation at several DAF-16-activated genes, predominantly by impairing RNA polymerase II recruitment to their promoters. Search for the relevant substrate of PP4SMK-1 by phosphoproteomics identified the conserved transcriptional regulator SPT-5/SUPT5H, whose knockdown phenocopies the loss of PP4SMK-1. Phosphoregulation of SPT-5 is known to control transcriptional events such as elongation and termination. Here we also show that transcription initiating events are influenced by the phosphorylation status of SPT-5, particularly at DAF-16 target genes where transcriptional initiation appears rate limiting, rendering PP4SMK-1 crucial for many of DAF-16's physiological roles.

Genomics of cellular proliferation in periodic environmental fluctuations.

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.