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Biology subjects

Podshivalova, K.

Publications and source records attributed to Podshivalova, K..

3 recordsLinked to original sources

Imageomics defines granular morphological changes of human skin with age and reveals a rejuvenating effect of xenografting

Rejuvenating aging human skin is a major therapeutic goal, but objective, quantitative measures of intrinsic aging are limited. We performed a cross-sectional histological study of UV-protected buttock and abdominal skin in adults spanning multiple decades of life to identify features that reliably track age. Epidermal thickness measured between rete ridges was unchanged, but rete ridge size declined linearly with age: ridges became shorter and thinner in both sites, though rete ridge number decreased only in the abdomen. Consistent with these structural changes, proliferative cells (Ki67+) per ridge and expression of integrin {beta}4 (ITGB4), a putative stem-cell marker, were reduced in aged skin. We combined these biomarkers into a predictive model that estimated skin age more accurately than any single marker. To test whether the model detects longitudinal change, we analyzed aged abdominal skin before and after xenografting onto young or aged mice, a procedure previously reported to rejuvenate human skin in young but not aged recipient mice. Both individual biomarkers and the imaging model indicated rejuvenation regardless of host age; however, notably, engraftment efficiency was lower in aged hosts, with surviving grafts showing younger histological phenotypes. These results provide quantitative criteria for assessing intrinsic skin aging and suggest that the process of engraftment itself is sufficient to induce rejuvenation-like changes.

pathology↗

The complete cell atlas of an aging multicellular organism

Here we describe a single-cell atlas of aging for the nematode Caenorhabditis elegans. This unique resource describes the expression across adulthood of over 20,000 genes among 211 groups of cells that correspond to virtually every cell type in this organism. Our findings suggest that C. elegans aging is not random and stochastic in nature, but rather characterized by coordinated changes in functionally related metabolic and stress-response genes in a highly cell-type specific fashion. Aging signatures of different cell types are largely different from one another, downregulation of energy metabolism being the only nearly universal change. Some biological pathways, such as genes associated with translation, DNA repair and the ER unfolded protein response, exhibited strong (in some cases opposite) changes in subsets of cell types, but many more were limited to a single cell type. Similarly, the rates at which cells aged, measured as genome-wide expression changes, differed between cell types; some of these differences were tested and validated in vivo by measuring age-dependent changes in mitochondrial morphology. In some, but not all, cell types, aging was characterized by an increase in cell-to-cell variance. Finally, we identified a set of transcription factors whose activities changed coordinately across many cell types with age. This set was strongly enriched for stress-resistance TFs known to influence the rate of aging. We tested other members of this set, and discovered that some, such as GEI-3, likely also regulate the rate of aging. Our dataset can be accessed and queried at c.elegans.aging.atlas.research.calicolabs.com/.

genomics↗

A resource for analyzing C. elegans’ gene expression data using transcriptional gene modules and module-weighted annotations

Identification of gene co-expression patterns (gene modules) is widely used for grouping functionally-related genes during transcriptomic data analysis. An organism-wide atlas of high quality fundamental gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method of independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially-expressed genes fail to do so. Using this resource, users can easily identify active modules in their gene expression data and access detailed descriptions of each module. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g. GO term). Analysis of \"module-weighted annotations\" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. Interactive access to the resource is provided at http://genemodules.org/.

bioinformatics↗