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Cancer researchers have traditionally used the mouse and the rat as staple model organisms. These animals are very short-lived, reproduce rapidly and are highly prone to cancer. They have been very useful for modelling some human cancer types and testing experimental treatments; however, these cancer-prone species offer little for understanding the mechanisms of cancer resistance. Recent technological advances have expanded bestiary research to non-standard model organisms that possess unique traits of very high value to humans, such as cancer resistance and longevity. In recent years, several discoveries have been made in non-standard mammalian species, providing new insights on the natural mechanisms of cancer resistance. These include mechanisms of cancer resistance in the naked mole rat, blind mole rat and elephant. In each of these species, evolution took a different path, leading to novel mechanisms. Many other long-lived mammalian species display cancer resistance, including whales, grey squirrels, microbats, cows and horses. Understanding the molecular mechanisms of cancer resistance in all these species is important and timely, as, ultimately, these mechanisms could be harnessed for the development of human cancer therapies.

The germline mutation rate has been extensively studied and has been found to vary greatly between species, but much less is known about the somatic mutation rate in multicellular organisms, which remains very difficult to determine. Here, we present data on somatic mutation rates in mice and humans, obtained by sequencing single cells and clones derived from primary fibroblasts, which allows us to make the first direct comparison with germline mutation rates in these two species. The results indicate that the somatic mutation rate is almost two orders of magnitude higher than the germline mutation rate and that both mutation rates are significantly higher in mice than in humans. Our findings demonstrate both the privileged status of germline genome integrity and species-specific differences in genome maintenance. Germline mutation rates are known to vary between species but somatic mutation rates are less well understood. Here the authors compare mice and humans, observing that somatic mutation rates were nearly two orders of magnitude higher in both species, with both mutation rates significantly higher in mice.

Accumulation of mutations in somatic cells has been implicated as a cause of aging since the 1950s. However, attempts to establish a causal relationship between somatic mutations and aging have been constrained by the lack of methods to directly identify mutational events in primary human tissues. Here we provide genome-wide mutation frequencies and spectra of human B lymphocytes from healthy individuals across the entire human lifespan using a highly accurate single-cell whole-genome sequencing method. We found that the number of somatic mutations increases from <500 per cell in newborns to >3,000 per cell in centenarians. We discovered mutational hotspot regions, some of which, as expected, were located at Ig genes associated with somatic hypermutation (SHM). B cell–specific mutation signatures associated with development, aging, or SHM were found. The SHM signature strongly correlated with the signature found in human B cell tumors, indicating that potential cancer-causing events are already present even in B cells of healthy individuals. We also identified multiple mutations in sequence features relevant to cellular function (i.e., transcribed genes and gene regulatory regions). Such mutations increased significantly during aging, but only at approximately one-half the rate of the genome average, indicating selection against mutations that impact B cell function. This full characterization of the landscape of somatic mutations in human B lymphocytes indicates that spontaneous somatic mutations accumulating with age can be deleterious and may contribute to both the increased risk for leukemia and the functional decline of B lymphocytes in the elderly.

Single-cell multiple displacement amplification (SCMDA) and a tool for single-nucleotide-variant calling (SCcaller) dramatically decrease artifacts in genome-wide variant calling from single cells. Mutation analysis in single-cell genomes is prone to artifacts associated with cell lysis and whole-genome amplification. Here we addressed these issues by developing single-cell multiple displacement amplification (SCMDA) and a general-purpose single-cell-variant caller, SCcaller ( https://github.com/biosinodx/SCcaller/ ). By comparing SCMDA-amplified single cells with unamplified clones from the same population, we validated the procedure as a firm foundation for standardized somatic-mutation analysis in single-cell genomics.

Key Points Comparative biology provides a powerful tool for understanding mechanisms of longevity and cancer resistance. The rodent clade is particularly suitable for the comparative study of ageing, as it contains species that differ nearly 10-fold in longevity and >1,000-fold in body mass. Replicative senescence and repression of telomerase activity evolve in species with body mass greater than ~10 kg to counteract increased cancer risk that is conferred by larger numbers of cells. Small species with lifespans greater than ~10 years evolve additional telomere-independent tumour suppressor mechanisms. There is evidence that long-lived species have more efficient genome maintenance mechanisms. Different cancer-resistant species evolve distinct anticancer mechanisms. Cancer resistance is mediated by high-molecular-mass hyaluronan in the naked mole rat and depends on the interferon-mediated elimination of precancerous cells in the blind mole rat. Recent advances in whole-genome sequencing open new avenues for identifying genes and pathways that are responsible for longevity and cancer resistance in exceptionally long-lived animals. Among rodent species, there is a wide diversity in lifespans and cancer susceptibilities, which makes comparative studies of rodents an attractive strategy for identifying molecular mechanisms that underlie ageing and cancer. This Review describes the various biological insights provided by comparative rodent genomics, including those from whole-genome sequencing of long-lived and highly cancer-resistant species. Such progress has potential implications for understanding and modulating human disease. Mammals have evolved a remarkable diversity of ageing rates. Within the single order of Rodentia, maximum lifespans range from 4 years in mice to 32 years in naked mole rats. Cancer rates also differ substantially between cancer-prone mice and almost cancer-proof naked mole rats and blind mole rats. Recent progress in rodent comparative biology, together with the emergence of whole-genome sequence information, has opened opportunities for the discovery of genetic factors that control longevity and cancer susceptibility.

Background Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation. Results We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks. Conclusions We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

Summary The workshop entitled 'Interventions to Slow Aging in Humans: Are We Ready?' was held in Erice, Italy, on October 8-13, 2013, to bring together leading experts in the biology and genetics of aging and obtain a consensus related to the discovery and development of safe interventions to slow aging and increase healthy lifespan in humans. There was consensus that there is sufficient evidence that aging interventions will delay and prevent disease onset for many chronic conditions of adult and old age. Essential pathways have been identified, and behavioral, dietary, and pharmacologic approaches have emerged. Although many gene targets and drugs were discussed and there was not complete consensus about all interventions, the participants selected a subset of the most promising strategies that could be tested in humans for their effects on healthspan. These were: (i) dietary interventions mimicking chronic dietary restriction (periodic fasting mimicking diets, protein restriction, etc.); (ii) drugs that inhibit the growth hormone/IGF-I axis; (iii) drugs that inhibit the mTOR-S6K pathway; or (iv) drugs that activate AMPK or specific sirtuins. These choices were based in part on consistent evidence for the pro-longevity effects and ability of these interventions to prevent or delay multiple age-related diseases and improve healthspan in simple model organisms and rodents and their potential to be safe and effective in extending human healthspan. The authors of this manuscript were speakers and discussants invited to the workshop. The following summary highlights the major points addressed and the conclusions of the meeting.

Summary Aging is the single largest risk factor for chronic disease. Studies in model organisms have identified conserved pathways that modulate aging rate and the onset and progression of multiple age-related diseases, suggesting that common pathways of aging may influence age-related diseases in humans as well. To determine whether there is genetic evidence supporting the notion of common pathways underlying age-related diseases, we analyzed the genes and pathways found to be associated with five major categories of age-related disease using a total of 410 genomewide association studies (GWAS). While only a small number of genes are shared among all five disease categories, those found in at least three of the five major age-related disease categories are highly enriched for apoliprotein metabolism genes. We found that a more substantial number of gene ontology (GO) terms are shared among the 5 age-related disease categories and shared GO terms include canonical aging pathways identified in model organisms, such as nutrient-sensing signaling, translation, proteostasis, stress responses, and genome maintenance. Taking advantage of the vast amount of genetic data from the GWAS, our findings provide the first direct evidence that conserved pathways of aging simultaneously influence multiple age-related diseases in humans as has been demonstrated in model organisms.

Age-related accumulation of ploidy changes is associated with decreased expression of genes controlling chromosome segregation and cohesin functions. To determine the consequences of whole chromosome instability ( W-CIN ) we down-regulated the spindle assembly checkpoint component BUB1 and the mitotic cohesin SMC1A, and used four-color-interphase-FISH coupled with BrdU incorporation and analyses of senescence features to reveal the fate of W-CIN cells. We observed significant correlations between levels of not-diploid cells and senescence-associated features ( SAFs ). W-CIN induced DNA double strand breaks and elevated oxidative stress, but caused low apoptosis. SAFs of W-CIN cells were remarkably similar to those induced by replicative senescence but occurred in only 13 days versus 4 months. Cultures enriched with not-diploid cells acquired a senescence-associated secretory phenotype (SASP) characterized by IL1B, CXCL8, CCL2, TNF, CCL27 and other pro-inflammatory factors including a novel SASP component CLEC11A. These findings suggest that W-CIN triggers premature senescence, presumably to prevent the propagation of cells with an abnormal DNA content. Cells deviating from diploidy have the ability to communicate with their microenvironment by secretion of an array of signaling factors. Our results suggest that aneuploid cells that accumulate during aging in some mammalian tissues potentially contribute to age-related pathologies and inflammation through SASP secretion.

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. Pathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for patient stratification.