Postmitotic cell longevity–associated genes: a transcriptional signature of postmitotic maintenance in neural tissues
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Abstract Background Microarray expression profiling has been widely used to identify differentially expressed genes in complex cellular systems. However, while such methods can be used to directly infer intracellular regulation within homogeneous cell populations, interpretation of in vivo gene expression data derived from complex organs composed of multiple cell types is more problematic. Specifically, observed changes in gene expression may be due either to changes in gene regulation within a given cell type or to changes in the relative abundance of expressing cell types. Consequently, bona fide changes in intrinsic gene regulation may be either mimicked or masked by changes in the relative proportion of different cell types. To date, few analytical approaches have addressed this problem. Results We have chosen to apply a computational method for deconvoluting gene expression profiles derived from intact tissues by using reference expression data for purified populations of the constituent cell types of the mammary gland. These data were used to estimate changes in the relative proportions of different cell types during murine mammary gland development and Ras-induced mammary tumorigenesis. These computational estimates of changing compartment sizes were then used to enrich lists of differentially expressed genes for transcripts that change as a function of intrinsic intracellular regulation rather than shifts in the relative abundance of expressing cell types. Using this approach, we have demonstrated that adjusting mammary gene expression profiles for changes in three principal compartments – epithelium, white adipose tissue, and brown adipose tissue – is sufficient both to reduce false-positive changes in gene expression due solely to changes in compartment sizes and to reduce false-negative changes by unmasking genuine alterations in gene expression that were otherwise obscured by changes in compartment sizes. Conclusion By adjusting gene expression values for changes in the sizes of cell type-specific compartments, this computational deconvolution method has the potential to increase both the sensitivity and specificity of differential gene expression experiments performed on complex tissues. Given the necessity for understanding complex biological processes such as development and carcinogenesis within the context of intact tissues, this approach offers substantial utility and should be broadly applicable to identifying gene expression changes in tissues composed of multiple cell types.
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Limited evidence on healthy longevity was provided in the world, and no studies investigated the fractions of healthy longevity attributed to modifiable factors.
Healthy aging
Centenarian
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The effects of temperature and nutrition on the adult longevity of Microplitis tuberculifer(Hymenoptera:Braconidae) were examined in laboratory.The results indicated that the adult longevity had significantly difference under different temperature conditions,which shortened with increasing of temperature.Water supply had no significant influence on adult longevity.Honey water can effectively extend adult longevity.The adult longevity was the longest(24.6±3.0 days for female and 13.8±2.5 days for male)at 18℃ with 20% honey water supply,which was the shortest(1.4±0.1 days for female and 13.8±2.5 days for male)at 30℃ without food supply.The longevity of female was longer than that of male under the same conditions.
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Abstract Delta-9-tetrahydrocannabinol (THC) is known to modulate immune response in peripheral blood cells. The mechanisms of THC’s effects on gene expression in human immune cells remains poorly understood. Combining a within-subject design with single cell transcriptome mapping, we report that THC acutely alters gene expression in 15,973 blood cells. We identified 294 transcriptome-wide significant genes among eight cell types including 69 common genes and 225 cell-type-specific genes affected by THC administration, including those genes involving in immune response, cytokine production, cell proliferation and apoptosis. We revealed distinct transcriptomic sub-clusters affected by THC in major immune cell types where THC perturbed cell-type-specific intracellular gene expression correlations. Gene set enrichment analysis further supports the findings of THC’s common and cell-type-specific effects on immune response and cell toxicity. This comprehensive single-cell transcriptomic profiling provides important insights into THC’s acute effects on immune function that may have important medical implications.
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Abstract Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.
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Abstract The study of gene expression (i.e., the study of the transcriptome) in different cells and tissues allows us to understand the molecular mechanisms of their differentiation, development and functioning. In this article, we describe some studies of gene-expression profiling for the purposes of understanding developmental (age-related) changes in the brain using different technologies (e.g., DNA-Microarray) and the new and increasingly popular RNA-Seq. We focus on advancements in studies of gene expression in the human brain, which have provided data on the structure and age-related variability of the transcriptome in the brain. We present data on RNA-Seq of the transcriptome in three distinct areas of the neocortex from different ages: mature and elderly individuals. We report that most age-related transcriptional changes affect cellular signaling systems, and, as a result, the transmission of nerve impulses. In general, the results demonstrate the high potential of RNA-Seq for the study of distinctive features of gene expression among cortical areas and the changes in expression through normal and atypical development of the central nervous system.
Neocortex
RNA-Seq
Expression (computer science)
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Abstract Motivation Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single <gene, function, cell type> for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. Results We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets <gene, function, cell type> by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. Availability The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open source software. Implementation GSMCA is implemented in R.
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This chapter contains sections titled: Summary: Sanitas Summum Bonus Introduction Orthogonal Pathways for Longevity in Mammals The Value of Different Model Systems Tools to Dissect Conserved Orthogonal Longevity Mechanisms Common Antiaging Mechanisms and Longevity Pathways Insights on Pure Human Mechanisms of Longevity Come from Centenarians The Most Promising Approach to Increase Longevity: Sirtuins, SREBP, and Resveratrol CR/DR (Without Malnutrition) is Key to Gain Health and Longevity The Real Prototype for Longevity, Vitality, and Fertility: Queen Honeybee Can we Learn from Queen Honeybee's Longevity? Yes, we can Ad Meliorem – Conclusion and Perspective for Longevity in Humans References
Queen (butterfly)
Vitality
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Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
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