Low Intensity Vibrations Augment Mesenchymal Stem Cell Proliferation and Differentiation Capacity During \u3ci\u3ein vitro\u3c/i\u3e Expansion

A primary component of exercise, mechanical signals, when applied in the form of low intensity vibration (LIV), increases mesenchymal stem cell (MSC) osteogenesis and proliferation. While it is generally accepted that exercise effectively combats the deleterious effects of aging in the musculoskeletal system, how long-term exercise affects stem cell aging, which is typified by reduced proliferative and differentiative capacity, is not well explored. As a first step in understanding the effect of long-term application of mechanical signals on stem cell function, we investigated the effect of LIV during in vitro expansion of MSCs. Primary MSCs were subjected to either a control or to a twice-daily LIV regimen for up to sixty cell passages (P60) under in vitro cell expansion conditions. LIV effects were assessed at both early passage (EP) and late passage (LP). At the end of the experiment, P60 cultures exposed to LIV maintained a 28% increase of cell doubling and a 39% reduction in senescence-associated β-galactosidase activity (p \u3c 0.01) but no changes in telomere lengths and p16INK4a levels were observed. Prolonged culture-associated decreases in osteogenic and adipogenic capacity were partially protected by LIV in both EP and LP groups (p \u3c 0.05). Mass spectroscopy of late passage MSC indicated a synergistic decrease of actin and microtubule cytoskeleton-associated proteins in both control and LIV groups while LIV induced a recovery of proteins associated with oxidative reductase activity. In summary, our findings show that the application of long-term mechanical challenge (+LIV) during in vitro expansion of MSCs for sixty passages significantly alters MSC proliferation, differentiation and structure. This suggests LIV as a potential tool to investigate the role of physical activity during aging

Unifying Community Detection Across Scales from Genomes to Landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity

Ecologically Guided Searches for New Riboswitch Functions

Riboswitches are structured RNA elements that regulate gene expression by directly binding ligands. Bioinformatics approaches have greatly accelerated the discovery of candidate riboswitches by searching for conserved RNA structures. However, discovering the ligand that binds to a conserved element remains challenging. Traditional approach search for clues to the binding partners of riboswitches by analyzing the function of downstream genes. This approach has proven highly successful, but poses challenges in poorly annotated genomes, such as from metagenomic samples, and may miss important environmentally delivered ligands. Here we propose to use ecological interactions to search for riboswitch functions. For our model system we will use herbivores which feed on sagebrush, highly defended by toxic chemicals known as plant secondary metabolites (PSM). We hypothesize that the microbial communities from these gut environments will contain riboswitches responding to the plant chemicals. Our collaborative research will produce shotgun metagenomics data from the fecal samples collected from animals ingesting sagebrush, bioinformatics searches for candidate riboswitch variants in the samples, and biochemical and biophysical validation of riboswitches using plant extracted chemicals. If successful, the research will produce new antibiotic candidates and targets, and will advance our understanding of how microbial communities contribute to the plant-herbivore arms race that drives biodiversity across the planet

Low Intensity Vibrations Augment Mesenchymal Stem Cell Proliferation and Differentiation Capacity during in vitro Expansion

Abstract A primary component of exercise, mechanical signals, when applied in the form of low intensity vibration (LIV), increases mesenchymal stem cell (MSC) osteogenesis and proliferation. While it is generally accepted that exercise effectively combats the deleterious effects of aging in the musculoskeletal system, how long-term exercise affects stem cell aging, which is typified by reduced proliferative and differentiative capacity, is not well explored. As a first step in understanding the effect of long-term application of mechanical signals on stem cell function, we investigated the effect of LIV during in vitro expansion of MSCs. Primary MSCs were subjected to either a control or to a twice-daily LIV regimen for up to sixty cell passages (P60) under in vitro cell expansion conditions. LIV effects were assessed at both early passage (EP) and late passage (LP). At the end of the experiment, P60 cultures exposed to LIV maintained a 28% increase of cell doubling and a 39% reduction in senescence-associated β-galactosidase activity (p < 0.01) but no changes in telomere lengths and p16INK4a levels were observed. Prolonged culture-associated decreases in osteogenic and adipogenic capacity were partially protected by LIV in both EP and LP groups (p < 0.05). Mass spectroscopy of late passage MSC indicated a synergistic decrease of actin and microtubule cytoskeleton-associated proteins in both control and LIV groups while LIV induced a recovery of proteins associated with oxidative reductase activity. In summary, our findings show that the application of long-term mechanical challenge (+LIV) during in vitro expansion of MSCs for sixty passages significantly alters MSC proliferation, differentiation and structure. This suggests LIV as a potential tool to investigate the role of physical activity during aging

Primers to Highly Conserved Elements Optimized for qPCR-Based Telomere Length Measurement in Vertebrates

Telomere length dynamics are an established biomarker of health and ageing in animals. The study of telomeres in numerous species has been facilitated by methods to measure telomere length by real-time quantitative PCR (qPCR). In this method, telomere length is determined by quantifying the amount of telomeric DNA repeats in a sample and normalizing this to the total amount of genomic DNA. This normalization requires the development of genomic reference primers suitable for qPCR, which remains challenging in nonmodel organism with genomes that have not been sequenced. Here we report reference primers that can be used in qPCR to measure telomere lengths in any vertebrate species. We designed primer pairs to amplify genetic elements that are highly conserved between evolutionarily distant taxa and tested them in species that span the vertebrate tree of life. We report five primer pairs that meet the specificity and reproducibility standards of qPCR. In addition, we demonstrate an approach to choose the best primers for a given species by testing the primers on multiple individuals within a species and then applying an established computational tool. These reference primers can facilitate qPCR-based telomere length measurements in any vertebrate species of ecological or economic interest

Unifying community detection across scales from genomes to landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity