The spatiotemporal dynamics of microglia across the human lifespan

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A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan

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MetaGSCA: A tool for meta-analysis of gene set differential coexpression

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package “MetaGSCA”. It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.     

A biologically inspired measure for coexpression analysis

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus. As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure, called BioSim, which assumes values between -1 and +1 corresponding to negative and positive dependency and 0 for independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it accounts for the features of expression similarity, expression deviation and also the relative dependence. It is demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim measure.     

Determinants of time allocation across the lifespan

This paper lays the groundwork for a theory of time allocation across the life course, based on the idea that strength and skill vary as a function of age, and that return rates for different activities vary as a function of the combination of strength and skills involved in performing those tasks. We apply the model to traditional human subsistence patterns. The model predicts that young children engage most heavily in low-strength/low-skill activities, middle-aged adults in high-strength/high-skill activities, and older adults in low-strength/high-skill activities. Tests among Machiguenga and Piro forager-horticulturalists of southeastern Peru show that males and females focus on low-strength/low-skill tasks early in life (domestic tasks and several forms of fishing), switch to higher-strength/higher-skill activities in their twenties and thirties (hunting, fishing, and gardening for males; fishing and gardening for females), and shift focus to high-skill activities late in life (manufacture/repair, food processing). Michael Gurven is an assistant professor of anthropology at the University of California-Santa Barbara. He received his Ph.D. from the University of New Mexico in 2000. He has conducted fieldwork in Paraguay and Bolivia with Ache and Tsimane forager-horticulturalists. His research interests include intragroup cooperation and problems of collective action, and the application of life history theory to explaining human longevity, cognitive development, delayed maturation, and sociality. Since 2002, Gurven and Kaplan have co-directed the Tsimane Health and Life History Initiative, a five-year project to develop theory and test implications of different models of human life history evolution. Hillard Kaplan is a professor of anthropology at University of New Mexico. He received his Ph.D. from the University of Utah in 1983. He has conducted fieldwork in Paraguay, Brazil, Botswana, and Bolivia. His research interests include evolutionary perspectives on life course development and senescence, and brain evolution. He has launched theoretical and empirical investigations into each of these areas, uniting evolutionary and economic approaches. He has applied human capital theory toward explaining human life history evolution, and the proximate physiological and psychological mechanisms governing fertility and parental investment in both traditional, high-fertility, subsistence economies and modern, low-fertility, industrial societies.     

A brain atlas of synapse protein lifetime across the mouse lifespan

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Word learning and lexical development across the lifespan

Word learning is one of the core components of language acquisition. In this article, we provide an overview of the theme issue on word learning, describing some of the ways in which research in the area has progressed and diverged. In recent years, word learning has become central in a wider range of research areas, and is important to research on adult, as well as child and infant language. We introduce 10 papers that cover the recent developments from a wide range of perspectives, focusing on developmental research, the influence of reading skills, neuroimaging and the relationship between word learning and general models of memory.     

Prediction of human disease genes by human-mouse conserved coexpression analysis

Background

Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.

Methodology/Principal Findings

We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

Conclusion

Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.     

Sex differences in interest in infants across the lifespan

This study investigated sex differences in interest in infants among children, adolescents, young adults, and older individuals. Interest in infants was assessed with responses to images depicting animal and human infants versus adults, and with verbal responses to questionnaires. Clear sex differences, irrespective of age, emerged in all visual and verbal tests, with females being more interested in infants than males. Male interest in infants remained fairly stable across the four age groups, whereas female interest in infants was highest in childhood and adolescence and declined thereafter, particularly for the responses to visual stimuli. The observed developmental changes in female interest in infants are consistent with the hypothesis that they represent a biological adaptation for parenting. This study was supported by NIH grants R01-MH57249, R01-MH62577, and K02-MH63097. Dario Maestripieri, Ph.D., is an assistant professor of Human Development and Evolutionary Biology at the University of Chicago. He has broad research interests in behavior, development, and evolution, and conducted research on primate parenting and development at the University of Cambridge and at the Yerkes Regional Primate Research Center before moving to Chicago. Suzanne Pelka is a graduate student in Human Development at the University of Chicago.     

Born this way: Hippocampal neurogenesis across the lifespan

The capability of the mammalian brain to generate new neurons through the lifespan has gained much attention for the promise of new therapeutic possibilities especially for the aging brain. One of the brain regions that maintains a neurogenesis‐permissive environment is the dentate gyrus of the hippocampus. Here, new neurons are generated from a pool of multipotent neural progenitor cells to become fully functional neurons that are integrated into the brain circuitry. A growing body of evidence points to the fact that neurogenesis in the adult hippocampus is necessary for certain memory processes, and in mood regulation, while alterations in hippocampal neurogenesis have been associated with a myriad of neurological and psychiatric disorders. More recently, evidence has come to light that new neurons may differ in their vulnerability to environmental and disease‐related influences depending on the time during the life course at which they are exposed. Thus, it has been the topic of intense research in recent years. In this review, we will discuss the complex process and associated functional relevance of hippocampal neurogenesis during the embryonic/postnatal period and in adulthood. We consider the implications of hippocampal neurogenesis during the developmentally critical periods of adolescence and older age. We will further consider the literature surrounding hippocampal neurogenesis and its functional role during these critical periods with a view to providing insight into the potential of harnessing neurogenesis for health and therapeutic benefit.     

An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments

A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a moderately large number of pairs. To address this, an empirical Bayesian approach for identifying DC gene pairs is developed. The approach provides a false discovery rate controlled list of significant DC gene pairs without sacrificing power. It is applicable within a single study as well as across multiple studies. Computations are greatly facilitated by a modification to the expectation-maximization algorithm and a procedural heuristic. Simulations suggest that the proposed approach outperforms existing methods in far less computational time; and case study results suggest that the approach will likely prove to be a useful complement to current DE methods in high-throughput genomic studies.     

Non-coding RNA networks underlying cognitive disorders across the lifespan

Non-coding RNAs (ncRNAs) and their associated regulatory networks are increasingly being implicated in mediating a complex repertoire of neurobiological functions. Cognitive and behavioral processes are proving to be no exception. In this review, we discuss the emergence of many novel, diverse and rapidly expanding classes and subclasses of short and long ncRNAs. We briefly review the life cycles and molecular functions of these ncRNAs. We also examine how ncRNA circuitry mediates brain development, plasticity, stress responses and aging, and highlight its potential roles in the pathophysiology of cognitive disorders, including neural developmental and age-associated neurodegenerative diseases, as well as those that manifest throughout the lifespan.     

PDGF-responsive progenitors persist in the subventricular zone across the lifespan

The SVZ (subventricular zone) contains neural stem cells and progenitors of various potentialities. Although initially parsed into A, B, and C cells, this germinal zone is comprised of a significantly more diverse population of cells. Here, we characterized a subset of postnatal PRPs (PDGF-AA-responsive precursors) that express functional PDGFα and β receptors from birth to adulthood. When grown in PDGF-AA, dissociated neonatal rat SVZ cells divided to produce non-adherent clusters of progeny. Unlike the self-renewing EGF/FGF-2-responsive precursors that produce neurospheres, these PRPs failed to self-renew after three passages; therefore, we refer to the colonies they produce as spheroids. Upon differentiation these spheroids could produce neurons, type 1 astrocytes and oligodendrocytes. When maintained in medium supplemented with BMP-4 they also produced type 2 astrocytes. Using lineage tracing methods, it became evident that there were multiple types of PRPs, including a subset that could produce neurons, oligodendrocytes, and type 1 and type 2 astrocytes; thus some of these PRPs represent a unique population of precursors that are quatropotential. Spheroids also could be generated from the newborn neocortex and they had the same potentiality as those from the SVZ. By contrast, the adult neocortex produced less than 20% of the numbers of spheroids than the adult SVZ and spheroids from the adult neocortex only differentiated into glial cells. Interestingly, SVZ spheroid producing capacity diminished only slightly from birth to adulthood. Altogether these data demonstrate that there are PRPs that persist in the SVZ that includes a unique population of quatropotential PRPs.     

The lipidomic correlates of epigenetic aging across the adult lifespan: A population-based study

Lipid signaling is involved in longevity regulation, but which specific lipid molecular species affect human biological aging remains largely unknown. We investigated the relation between complex lipids and DNA methylation-based metrics of biological aging among 4181 participants (mean age 55.1 years (range 30.0–95.0)) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. The absolute concentration of 14 lipid classes, covering 964 molecular species and 267 fatty acid composites, was measured by Metabolon Complex Lipid Panel. DNA methylation-based metrics of biological aging (AgeAccelPheno and AgeAccelGrim) were calculated based on published algorithms. Epigenome-wide association analyses (EWAS) of biological aging-associated lipids and pathway analysis were performed to gain biological insights into the mechanisms underlying the effects of lipidomics on biological aging. We found that higher levels of molecular species belonging to neutral lipids, phosphatidylethanolamines, phosphatidylinositols, and dihydroceramides were associated with faster biological aging, whereas higher levels of lysophosphatidylcholine, hexosylceramide, and lactosylceramide species were associated with slower biological aging. Ceramide, phosphatidylcholine, and lysophosphatidylethanolamine species with odd-numbered fatty acid tail lengths were associated with slower biological aging, whereas those with even-numbered chain lengths were associated with faster biological aging. EWAS combined with functional pathway analysis revealed several complex lipids associated with biological aging as important regulators of known longevity and aging-related pathways.