Age-related loss of gene-to-gene transcriptional coordination among single cells

Gene co-expression networks reveal sex-biased differences in musculoskeletal ageing

Aging is a universal and progressive process involving the deterioration of physiological functions and the accumulation of cellular damage. Gene regulation programs influence how phenotypes respond to environmental and intrinsic changes during aging. Although several factors, including sex, are known to impact this process, the underlying mechanisms remain incompletely understood. Here, we investigate the functional organization patterns of skeletal muscle genes across different sexes and ages using gene co-expression networks (GCNs) to explore their influence on aging. We constructed GCNs for three different age groups for male and female samples, analyzed topological similarities and differences, inferred significant associated processes for each network, and constructed null models to provide statistically robust results. We found that each network is topologically and functionally distinct, with young women having the most associated processes, likely due to reproductive tasks. The functional organization and modularity of genes decline with age, starting from middle age, potentially leading to age-related deterioration. Women maintain better gene functional organization throughout life compared to men, especially in processes like macroautophagy and sarcomere organization. The study suggests that the loss of gene co-expression could be a universal aging marker. This research offers insights into how gene organization changes with age and sex, providing a complementary method to analyze aging.

Loss of coordination between basic cellular processes in human aging

Age-related loss of gene expression coordination has been reported for distinct cell types and may lead to impaired cellular function. Here we propose a method for quantifying age-related changes in transcriptional regulatory relationships between genes, based on a model learned from external data. We used this method to uncover age-related trends in gene-gene relationships across eight human tissues, which demonstrates that reduced co-expression may also result from coordinated transcriptional responses. Our analyses reveal similar numbers of strengthening and weakening gene-gene relationships with age, impacting both tissue-specific (for example, coagulation in blood) and ubiquitous biological functions. Regulatory relationships becoming weaker with age were established mostly between genes operating in distinct cellular processes. As opposed to that, regulatory relationships becoming stronger with age were established both within and between different cellular functions. Our work reveals that, although most transcriptional regulatory gene-gene relationships are maintained during aging, those with declining regulatory coupling result mostly from a loss of coordination between distinct cellular processes.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

Nature of epigenetic aging from a single-cell perspective

Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.

Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing tissues. These studies have revealed a gradual accumulation of mutations in normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, it is difficult to envision how the burden and stochastic nature of age-related somatic mutations identified so far can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be due to selective pressures acting on other phenotypes such as perhaps cancer. Recent studies in patients with higher somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing. Overall, with a few exceptions like cancer, recent DNA sequencing studies and inherited mutations do not support the idea that somatic mutations accumulating with age drive ageing phenotypes, and the phenotypic role, if any, of somatic mutations in ageing remains unclear.

Reduction in metabolic noise reveals rejuvenation following transient severe caloric restriction

Among land vertebrates, the laying hen stands out due to its great reproductive efficiency: producing an egg daily all year long. This production rate makes the laying hen a special model animal to study the general process of reproduction and aging. One unique aspect of hens is their ability to undergo reproductive plasticity and to rejuvenate their reproductive tract during molting, a standard industrial feed restriction protocol for transiently pausing reproduction, followed by improved laying efficiency almost to peak production. Here we use longitudinal metabolomics, immunology, and physiological assays to show that molting promotes reproduction, compresses morbidity, and restores youthfulness when applied to old hens. We identified circulating metabolic biomarkers that quantitatively predict the reproduction and age of individuals. Lastly, we introduce metabolic noise, a robust, unitless, and quantifiable measure for heterogeneity of the complete metabolome as a general marker that can indicate the rate of aging of a population. Indeed, metabolic noise increased with age in control hens, whereas molted hens exhibited reduced noise following molting, indicating systemic rejuvenation. Our results suggest that metabolic noise can be used as a quick and universal proxy for assessing successful aging treatments, accelerating the timeline for drug development.

Cellular age explains variation in age-related cell-to-cell transcriptome variability

Organs and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types, the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.

Genome-wide CRISPR activation screening in senescent cells reveals SOX5 as a driver and therapeutic target of rejuvenation

Our understanding of the molecular basis for cellular senescence remains incomplete, limiting the development of strategies to ameliorate age-related pathologies by preventing stem cell senescence. Here, we performed a genome-wide CRISPR activation (CRISPRa) screening using a human mesenchymal precursor cell (hMPC) model of the progeroid syndrome. We evaluated targets whose activation antagonizes cellular senescence, among which SOX5 outperformed as a top hit. Through decoding the epigenomic landscapes remodeled by overexpressing SOX5, we uncovered its role in resetting the transcription network for geroprotective genes, including HMGB2. Mechanistically, SOX5 binding elevated the enhancer activity of HMGB2 with increased levels of H3K27ac and H3K4me1, raising HMGB2 expression so as to promote rejuvenation. Furthermore, gene therapy with lentiviruses carrying SOX5 or HMGB2 rejuvenated cartilage and alleviated osteoarthritis in aged mice. Our study generated a comprehensive list of rejuvenators, pinpointing SOX5 as a potent driver for rejuvenation both in vitro and in vivo.

Progress in Discovering Transcriptional Noise in Aging

Increasing stochasticity is a key feature in the aging process. At the molecular level, in addition to genome instability, a well-recognized hallmark of aging, cell-to-cell variation in gene expression was first identified in mouse hearts. With the technological breakthrough in single-cell RNA sequencing, most studies performed in recent years have demonstrated a positive correlation between cell-to-cell variation and age in human pancreatic cells, as well as mouse lymphocytes, lung cells, and muscle stem cells during senescence in vitro. This phenomenon is known as the "transcriptional noise" of aging. In addition to the increasing evidence in experimental observations, progress also has been made to better define transcriptional noise. Traditionally, transcriptional noise is measured using simple statistical measurements, such as the coefficient of variation, Fano factor, and correlation coefficient. Recently, multiple novel methods have been proposed, e.g., global coordination level analysis, to define transcriptional noise based on network analysis of gene-to-gene coordination. However, remaining challenges include a limited number of wet-lab observations, technical noise in single-cell RNA sequencing, and the lack of a standard and/or optimal data analytical measurement of transcriptional noise. Here, we review the recent technological progress, current knowledge, and challenges to better understand transcriptional noise in aging.

Genome-wide RNA polymerase stalling shapes the transcriptome during aging

Gene expression profiling has identified numerous processes altered in aging, but how these changes arise is largely unknown. Here we combined nascent RNA sequencing and RNA polymerase II chromatin immunoprecipitation followed by sequencing to elucidate the underlying mechanisms triggering gene expression changes in wild-type aged mice. We found that in 2-year-old liver, 40% of elongating RNA polymerases are stalled, lowering productive transcription and skewing transcriptional output in a gene-length-dependent fashion. We demonstrate that this transcriptional stress is caused by endogenous DNA damage and explains the majority of gene expression changes in aging in most mainly postmitotic organs, specifically affecting aging hallmark pathways such as nutrient sensing, autophagy, proteostasis, energy metabolism, immune function and cellular stress resilience. Age-related transcriptional stress is evolutionary conserved from nematodes to humans. Thus, accumulation of stochastic endogenous DNA damage during aging deteriorates basal transcription, which establishes the age-related transcriptome and causes dysfunction of key aging hallmark pathways, disclosing how DNA damage functionally underlies major aspects of normal aging.

Nutrition and stem cell integrity in aging

Adult stem cells (SCs) represent the regenerative capacity of organisms throughout their lifespan. The maintenance of robust SC populations capable of renewing organs and physiological systems is one hallmark of healthy aging. The local environment of SCs, referred to as the niche, includes the nutritional milieu, which is essential to maintain the quantity and quality of SCs available for renewal and regeneration. There is increased recognition that SCs have unique metabolism and conditional nutrient needs compared to fully differentiated cells. However, the contribution of SC nutrition to overall human nutritional requirements is an understudied and underappreciated area of investigation. Nutrient needs vary across the lifespan and are modified by many factors including individual health, disease, physiological states including pregnancy, age, sex, and during recovery from injury. Although current nutrition guidance is generally derived for apparently healthy populations and to prevent nutritional deficiency diseases, there are increased efforts to establish nutrient-based and food-based recommendations based on reducing chronic disease. Understanding the dynamics of SC nutritional needs throughout the life span, including the role of nutrition in extending biological age by blunting biological systems decay, is fundamental to establishing food and nutrient guidance for chronic disease reduction and health maintenance. This review summarizes a 3-day symposium of the Marabou Foundation (www.marabousymposium.org) held to examine the metabolic properties and unique nutritional needs of adult SCs and their role in healthy aging and age-related chronic disease.

Somatic variation in normal tissues: friend or foe of cancer early detection?

BACKGROUND

Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection.

DESIGN

We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention.

RESULTS

The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clonal expansion are tissue-specific and inactivate tumor suppressor genes involved in chromatin modification and cell growth signaling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favoring clonal expansion also drive tumorigenesis.

CONCLUSIONS

Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.

Inference of age-associated transcription factor regulatory activity changes in single cells

Transcription factors (TFs) control cell identity and function. How their activity is altered during healthy aging is critical for an improved understanding of aging and disease risk, yet relatively little is known about such changes at cell-type resolution. Here we present and validate a TF activity estimation method for single cells from the hematopoietic system that is based on TF regulons, and apply it to a mouse single-cell RNA-sequencing atlas, to infer age-associated differentiation activity changes in the immune cells of different organs. This revealed an age-associated signature of macrophage dedifferentiation, which is shared across tissue types, and aggravated in tumor-associated macrophages. By extending the analysis to all major cell types, we reveal cell-type and tissue-type-independent age-associated alterations to regulatory factors controlling antigen processing, inflammation, collagen processing and circadian rhythm, that are implicated in age-related diseases. Finally, our study highlights the limitations of using TF expression to infer age-associated changes, underscoring the need to use regulatory activity inference methods.

Global coordination level in single-cell transcriptomic data

Genes are linked by underlying regulatory mechanisms and by jointly implementing biological functions, working in coordination to apply different tasks in the cells. Assessing the coordination level between genes from single-cell transcriptomic data, without a priori knowledge of the map of gene regulatory interactions, is a challenge. A ‘top-down’ approach has recently been developed to analyze single-cell transcriptomic data by evaluating the global coordination level between genes (called GCL). Here, we systematically analyze the performance of the GCL in typical scenarios of single-cell RNA sequencing (scRNA-seq) data. We show that an individual anomalous cell can have a disproportionate effect on the GCL calculated over a cohort of cells. In addition, we demonstrate how the GCL is affected by the presence of clusters, which are very common in scRNA-seq data. Finally, we analyze the effect of the sampling size of the Jackknife procedure on the GCL statistics. The manuscript is accompanied by a description of a custom-built Python package for calculating the GCL. These results provide practical guidelines for properly pre-processing and applying the GCL measure in transcriptional data.

Challenges of proving a causal role of somatic mutations in the aging process

Aging is accompanied by the progressive accumulation of permanent changes to the genomic sequence, termed somatic mutations. Small mutations, including single‐base substitutions and insertions/deletions, are key determinants of the malignant transformations leading to cancer, but their role as initiators of other age‐related phenotypes is controversial. Here, we present recent advances in the study of somatic mutagenesis in aging tissues and posit that the current uncertainty about its causal effects in the aging process is due to technological and methodological weaknesses. We highlight classical and novel experimental systems, including premature aging syndromes, that could be used to model the increase of somatic mutation burden and understand its functional role. It is important that studies are designed to take into account the biological context and peculiarities of each tissue and that the downstream impact of somatic mutation accumulation is measured by methods able to resolve subtle cellular changes.

A Mechanistic Theory of Development-Aging Continuity in Humans and Other Mammals

There is consensus among biogerontologists that aging occurs either as the result of a purposeful genome-based, evolved program or due to spontaneous, randomly occurring, maladaptive events. Neither concept has yet identified a specific mechanism to explain aging’s emergence and acceleration during mid-life and beyond. Presented herein is a novel, unifying mechanism with empirical evidence that describes how aging becomes continuous with development. It assumes that aging emerges from deterioration of a regulatory process that directs morphogenesis and morphostasis. The regulatory system consists of a genome-wide “backbone” within which its specific genes are differentially expressed by the local epigenetic landscapes of cells and tissues within which they reside, thereby explaining its holistic nature. Morphostasis evolved in humans to ensure the nurturing of dependent offspring during the first decade of young adulthood when peak parental vitality prevails in the absence of aging. The strict redundancy of each morphostasis regulatory cycle requires sensitive dependence upon initial conditions to avoid initiating deterministic chaos behavior. However, when natural selection declines as midlife approaches, persistent, progressive, and specific DNA damage and misrepair changes the initial conditions of the regulatory process, thereby compromising morphostasis regulatory redundancy, instigating chaos, initiating senescence, and accelerating aging thereafter.

Lack of evidence for increased transcriptional noise in aged tissues

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.

Adropin correlates with aging-related neuropathology in humans and improves cognitive function in aging mice

The neural functions of adropin, a secreted peptide highly expressed in the brain, have not been investigated. In humans, adropin is highly expressed in astrocytes and peaks during critical postnatal periods of brain development. Gene enrichment analysis of transcripts correlating with adropin expression suggests processes relevant to aging-related neurodegenerative diseases that vary with age and dementia state, possibly indicating survivor bias. In people aged <40 y and 'old-old' (>75 y) diagnosed with dementia, adropin correlates positively with genes involved in mitochondrial processes. In the 'old-old' without dementia adropin expression correlates positively with morphogenesis and synapse function. Potent neurotrophic responses in primary cultured neurons are consistent with adropin supporting the development and function of neural networks. Adropin expression in the 'old-old' also correlates positively with protein markers of tau-related neuropathologies and inflammation, particularly in those without dementia. How variation in brain adropin expression affects neurological aging was investigated using old (18-month) C57BL/6J mice. In mice adropin is expressed in neurons, oligodendrocyte progenitor cells, oligodendrocytes, and microglia and shows correlative relationships with groups of genes involved in neurodegeneration and cellular metabolism. Increasing adropin expression using transgenesis improved spatial learning and memory, novel object recognition, resilience to exposure to new environments, and reduced mRNA markers of inflammation in old mice. Treatment with synthetic adropin peptide also reversed age-related declines in cognitive functions and affected expression of genes involved in morphogenesis and cellular metabolism. Collectively, these results establish a link between adropin expression and neural energy metabolism and indicate a potential therapy against neurological aging.

From DNA damage to mutations: All roads lead to aging

Damage to the repository of genetic information in cells has plagued life since its very beginning 3-4 billion years ago. Initially, in the absence of an ozone layer, especially damage from solar UV radiation must have been frequent, with other sources, most notably endogenous sources related to cell metabolism, gaining in importance over time. To cope with this high frequency of damage to the increasingly long DNA molecules that came to encode the growing complexity of cellular functions in cells, DNA repair evolved as one of the earliest genetic traits. Then as now, errors during the repair of DNA damage generated mutations, which provide the substrate for evolution by natural selection. With the emergence of multicellular organisms also the soma became a target of DNA damage and mutations. In somatic cells selection against the adverse effects of DNA damage is greatly diminished, especially in postmitotic cells after the age of first reproduction. Based on an abundance of evidence, DNA damage is now considered as the single most important driver of the degenerative processes that collectively cause aging. Here I will first briefly review the evidence for DNA damage as a cause of aging since the beginning of life. Then, after discussing the possible direct adverse effects of DNA damage and its cellular responses, I will provide an overview of the considerable progress that has recently been made in analyzing a major consequence of DNA damage in humans and other complex organisms: somatic mutations and the resulting genome mosaicism. Recent advances in studying somatic mutagenesis and genome mosaicism in different human and animal tissues will be discussed with a focus on the possible mechanisms through which loss of DNA sequence integrity could cause age-related functional decline and disease.

Decoding and rejuvenating human ageing genomes: Lessons from mosaic chromosomal alterations

One of the most curious findings emerged from genome-wide studies over the last decade was that genetic mosaicism is a dominant feature of human ageing genomes. The clonal dominance of genetic mosaicism occurs preceding the physiological and physical ageing and associates with propensity for diseases including cancer, Alzheimer's disease, cardiovascular disease and diabetes. These findings are revolutionizing the ways biologists thinking about health and disease pathogenesis. Among all mosaic mutations in ageing genomes, mosaic chromosomal alterations (mCAs) have the most significant functional consequences because they can produce intercellular genomic variations simultaneously involving dozens to hundreds or even thousands genes, and therefore have most profound effects in human ageing and disease etiology. Here, we provide a comprehensive picture of the landscapes, causes, consequences and rejuvenation of mCAs at multiple scales, from cell to human population, by reviewing data from cytogenetic, genetic and genomic studies in cells, animal models (fly and mouse) and, more frequently, large-cohort populations. A detailed decoding of ageing genomes with a focus on mCAs may yield important insights into the genomic architecture of human ageing, accelerate the risk stratification of age-related diseases (particularly cancers) and development of novel targets and strategies for delaying or rejuvenating human (genome) ageing.

Age-related telomere attrition causes aberrant gene expression in sub-telomeric regions

Telomere attrition has been proposed as a biomarker and causal factor in aging. In addition to causing cellular senescence and apoptosis, telomere shortening has been found to affect gene expression in subtelomeric regions. Here, we analyzed the distribution of age-related differentially expressed genes from the GTEx RNA sequencing database of 54 tissue types from 979 human subjects and found significantly more upregulated than downregulated genes in subtelomeric regions as compared to the genome-wide average. Our data demonstrate spatial relationships between telomeres and gene expression in aging.

A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

Recent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

Metabolomic Changes Are Predictive of Aging in Laying Hens

Aging in vertebrates is an extremely complex process that is still poorly understood. One confining factor to studying vertebrate aging is the lack of appropriate models. The laying hen is a good model to study vertebrate aging, as it can be maintained under standard housing conditions, its breeds are genetically well defined and it exhibits significant aging phenotypes at around 18 months of age. Furthermore, laying hens are maintained in a challenging realistic environment and possess a fully functional immune system. Here we used, for the first time, metabolomic profiling of laying hens' blood for identifying biomarkers of aging. Random forest classifier was used to quantify the quality of the markers and found that the markers can predict the correct age group of individuals with 90% accuracy. Animals under time-restricted feeding, a condition known to increase health span, appeared younger under the markers, indicating that the aging biomarkers can also predict the effectiveness of environmental treatments. Additionally, we found that noise, defined as the ratio between the standard deviation and the mean, is an exceptionally robust and universal biomarker of aging, as metabolomic noise increases significantly with age in laying hens, humans, and mice. Our study suggests the laying hen as a useful model to study aging in vertebrates and establishes metabolomic noise as a novel, universal biomarker of aging.