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Summary Clearing senescent cells extends healthspan in mice. Using a hypothesis-driven bioinformatics-based approach, we recently identified pro-survival pathways in human senescent cells that contribute to their resistance to apoptosis. This led to identification of dasatinib (D) and quercetin (Q) as senolytics, agents that target some of these pathways and induce apoptosis preferentially in senescent cells. Among other pro-survival regulators identified was Bcl-xl. Here, we tested whether the Bcl-2 family inhibitors, navitoclax (N) and TW-37 (T), are senolytic. Like D and Q, N is senolytic in some, but not all types of senescent cells: N reduced viability of senescent human umbilical vein epithelial cells (HUVECs), IMR90 human lung fibroblasts, and murine embryonic fibroblasts (MEFs), but not human primary preadipocytes, consistent with our previous finding that Bcl-xl siRNA is senolytic in HUVECs, but not preadipocytes. In contrast, T had little senolytic activity. N targets Bcl-2, Bcl-xl, and Bcl-w, while T targets Bcl-2, Bcl-xl, and Mcl-1. The combination of Bcl-2, Bcl-xl, and Bcl-w siRNAs was senolytic in HUVECs and IMR90 cells, while combination of Bcl-2, Bcl-xl, and Mcl-1 siRNAs was not. Susceptibility to N correlated with patterns of Bcl-2 family member proteins in different types of human senescent cells, as has been found in predicting response of cancers to N. Thus, N is senolytic and acts in a potentially predictable cell type-restricted manner. The hypothesis-driven, bioinformatics-based approach we used to discover that dasatinib (D) and quercetin (Q) are senolytic can be extended to increase the repertoire of senolytic drugs, including additional cell type-specific senolytic agents.

Summary Recent studies demonstrate that aging exacerbates hypertension-induced cognitive decline, but the specific age-related mechanisms remain elusive. Cerebral microhemorrhages (CMHs) are associated with rupture of small intracerebral vessels and are thought to progressively impair neuronal function. To determine whether aging exacerbates hypertension-induced CMHs young (3 months) and aged (24 months) mice were treated with angiotensin II plus L-NAME. We found that the same level of hypertension leads to significantly earlier onset and increased incidence of CMHs in aged mice than in young mice, as shown by neurological examination, gait analysis, and histological assessment of CMHs in serial brain sections. Hypertension-induced cerebrovascular oxidative stress and redox-sensitive activation of matrix metalloproteinases (MMPs) were increased in aging. Treatment of aged mice with resveratrol significantly attenuated hypertension-induced oxidative stress, inhibited vascular MMP activation, significantly delayed the onset, and reduced the incidence of CMHs. Collectively, aging promotes CMHs in mice likely by exacerbating hypertension-induced oxidative stress and MMP activation. Therapeutic strategies that reduce microvascular oxidative stress and MMP activation may be useful for the prevention of CMHs, protecting neurocognitive function in high-risk elderly patients.

Background NCBI’s Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. Results Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. Conclusion Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.

Background Even though only 1.5% of the human genome is translated into proteins, recent reports indicate that most of it is transcribed into non-coding RNAs (ncRNAs), which are becoming the subject of increased scientific interest. We hypothesized that examining how different classes of ncRNAs co-localized with annotated epigenomic elements could help understand the functions, regulatory mechanisms, and relationships among ncRNA families. Results We examined 15 different ncRNA classes for statistically significant genomic co-localizations with cell type-specific chromatin segmentation states, transcription factor binding sites (TFBSs), and histone modification marks using GenomeRunner ( http://www.genomerunner.org ). P-values were obtained using a Chi-square test and corrected for multiple testing using the Benjamini-Hochberg procedure. We clustered and visualized the ncRNA classes by the strength of their statistical enrichments and depletions. We found piwi-interacting RNAs (piRNAs) to be depleted in regions containing activating histone modification marks, such as H3K4 mono-, di- and trimethylation, H3K27 acetylation, as well as certain TFBSs. piRNAs were further depleted in active promoters, weak transcription, and transcription elongation regions, and enriched in repressed and heterochromatic regions. Conversely, transfer RNAs (tRNAs) were depleted in heterochromatin regions and strongly enriched in regions containing activating H3K4 di- and trimethylation marks, H2az histone variant, and a variety of TFBSs. Interestingly, regions containing CTCF insulator protein binding sites were associated with tRNAs. tRNAs were also enriched in the active, weak and poised promoters and, surprisingly, in regions with repetitive/copy number variations. Conclusions Searching for statistically significant associations between ncRNA classes and epigenomic elements permits detection of potential functional and/or regulatory relationships among ncRNA classes, and suggests cell type-specific biological roles of ncRNAs.

Background The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance. Results We propose a novel framework for comparing microbial shifts between conditions. Using data from one of the largest human metagenome projects to date, the American Gut Project (AGP), we obtain differential abundance vectors for microbes using experimental condition information provided with the AGP metadata, such as patient age, dietary habits, or health status. We show it can be used to identify similar and opposing shifts in microbial species, and infer putative interactions between microbes. Our results show that groups of shifts with similar effects on microbiome can be identified and that similar dietary interventions display similar microbial abundance shifts. Conclusions Without comparison to prior data, it is difficult for experimentalists to know if their observed changes in species abundance have been observed by others, both in their conditions and in others they would never consider comparable. Yet, this can be a very important contextual factor in interpreting the significance of a shift. We’ve proposed and tested an algorithmic solution to this problem, which also allows for comparing the metagenomic signature shifts between conditions in the existing body of data.

Ageing is the main risk factor for the development of cardiovascular diseases. A central mechanism by which ageing promotes vascular pathologies is compromising endothelial health. The age-related attenuation of endothelium-dependent dilator responses (endothelial dysfunction) associated with impairment of angiogenic processes and the subsequent pathological remodelling of the microcirculation contribute to compromised tissue perfusion and exacerbate functional decline in older individuals. This Review focuses on cellular, molecular, and functional changes that occur in the endothelium during ageing. We explore the links between oxidative and nitrative stress and the conserved molecular pathways affecting endothelial dysfunction and impaired angiogenesis during ageing. We also speculate on how these pathological processes could be therapeutically targeted. An improved understanding of endothelial biology in older patients is crucial for all cardiologists because maintenance of a competently functioning endothelium is critical for adequate tissue perfusion and long-term cardiac health.

Background Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments. Methods We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data. We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two. We have developed a simple tool to remove low complexity regions, RepeatSoaker, available at https://github.com/mdozmorov/RepeatSoaker , and tested its effect on the alignment statistics and the results of the enrichment analyses. Results Both adapter trimming and duplicate removal moderately improved the strength of biological signals in RNA-seq and ChIP-seq data. Aggressive filtering of reads overlapping with low complexity regions, as defined by RepeatMasker, further improved the strength of biological signals, and the correlation between RNA-seq and microarray gene expression data. Conclusions Adapter trimming and duplicates removal, coupled with filtering out reads overlapping low complexity regions, is shown to increase the quality and reliability of detecting biological signals in RNA-seq and ChIP-seq data.

Epigenetic alterations are a hallmark of aging and age‐related diseases. Computational models using DNA methylation data can create “epigenetic clocks” which are proposed to reflect “biological” aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology. To do this, we examined over 450,000 methylation sites from 9,699 samples. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under‐represented in canonical regions of epigenetic regulation. There was only a weak association between “accelerated” epigenetic aging and disease. We also compare tissue‐specific and pan‐tissue clock performance. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate “epigenetic aging” in unavailable tissues (e.g., brain). Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti‐aging interventions. Purpose‐built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. However, if purpose‐built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context. Epigenetic clocks show promise as a biomarker of aging. Our study identifies components of epigenetic clocks critical to understanding their uses and where improvements are still needed. We identified a parabolic trajectory of methylation aging and the extent to which epigenetic clocks are robust to removing the most age‐predictive loci. These data shape our understanding of epigenetic age acceleration and what epigenetic clocks can tell us about the biology of aging.

Background Global meta-analysis (GMA) of microarray data to identify genes with highly similar co-expression profiles is emerging as an accurate method to predict gene function and phenotype, even in the absence of published data on the gene(s) being analyzed. With a third of human genes still uncharacterized, this approach is a promising way to direct experiments and rapidly understand the biological roles of genes. To predict function for genes of interest, GMA relies on a guilt-by-association approach to identify sets of genes with known functions that are consistently co-expressed with it across different experimental conditions, suggesting coordinated regulation for a specific biological purpose. Our goal here is to define how sample, dataset size and ranking parameters affect prediction performance. Results 13,000 human 1-color microarrays were downloaded from GEO for GMA analysis. Prediction performance was benchmarked by calculating the distance within the Gene Ontology (GO) tree between predicted function and annotated function for sets of 100 randomly selected genes. We find the number of new predicted functions rises as more datasets are added, but begins to saturate at a sample size of approximately 2,000 experiments. For the gene set used to predict function, we find precision to be higher with smaller set sizes, yet with correspondingly poor recall and, as set size is increased, recall and F-measure also tend to increase but at the cost of precision. Conclusions Of the 20,813 genes expressed in 50 or more experiments, at least one predicted GO category was found for 72.5% of them. Of the 5,720 genes without GO annotation, 4,189 had at least one predicted ontology using top 40 co-expressed genes for prediction analysis. For the remaining 1,531 genes without GO predictions or annotations, ~17% (257 genes) had sufficient co-expression data yet no statistically significantly overrepresented ontologies, suggesting their regulation may be more complex.

Understanding molecular mechanisms involved in vascular aging is essential to develop novel interventional strategies for treatment and prevention of age-related vascular pathologies. Recent studies provide critical evidence that vascular aging is characterized by NAD+ depletion. Importantly, in aged mice, restoration of cellular NAD+ levels by treatment with the NAD+ booster nicotinamide mononucleotide (NMN) exerts significant vasoprotective effects, improving endothelium-dependent vasodilation, attenuating oxidative stress, and rescuing age-related changes in gene expression. Strong experimental evidence shows that dysregulation of microRNAs (miRNAs) has a role in vascular aging. The present study was designed to test the hypothesis that age-related NAD+ depletion is causally linked to dysregulation of vascular miRNA expression. A corollary hypothesis is that functional vascular rejuvenation in NMN-treated aged mice is also associated with restoration of a youthful vascular miRNA expression profile. To test these hypotheses, aged (24-month-old) mice were treated with NMN for 2 weeks and miRNA signatures in the aortas were compared to those in aortas obtained from untreated young and aged control mice. We found that protective effects of NMN treatment on vascular function are associated with anti-aging changes in the miRNA expression profile in the aged mouse aorta. The predicted regulatory effects of NMN-induced differentially expressed miRNAs in aged vessels include anti-atherogenic effects and epigenetic rejuvenation. Future studies will uncover the mechanistic role of miRNA gene expression regulatory networks in the anti-aging effects of NAD+ booster treatments and determine the links between miRNAs regulated by NMN and sirtuin activators and miRNAs known to act in the conserved pathways of aging and major aging-related vascular diseases.