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• Research Paper Volume 13, Issue 21 pp 23981-24016

  Restoration of aged hematopoietic cells by their young counterparts through instructive microvesicles release

  Relevance score: 7.458925

  Steven J. Greco, Seda Ayer, Khadidiatou Guiro, Garima Sinha, Robert J. Donnelly, Markos H. El-Far, Lauren S. Sherman, Yannick Kenfack, Sri Harika Pamarthi, Marina Gergues, Oleta A. Sandiford, Michael J. Schonning, Jean-Pierre Etchegaray, Pranela Rameshwar

  Keywords: age, hematopoiesis, bone marrow, miRNA, microvesicles

  Published in Aging on November 11, 2021

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  This study addresses the potential to reverse age-associated morbidity by establishing methods to restore the aged hematopoietic system. Parabiotic animal models indicated that young secretome could restore aged tissues, leading us to establish a heterochronic transwell system with aged mobilized peripheral blood (MPB), co-cultured with young MPB or umbilical cord blood (UCB) cells. Functional studies and omics approaches indicate that the miRNA cargo of microvesicles (MVs) restores the aged hematopoietic system. The in vitro findings were validated in immune deficient (NSG) mice carrying an aged hematopoietic system, improving aged hallmarks such as increased lymphoid:myeloid ratio, decreased inflammation and cellular senescence. Elevated MYC and E2F pathways, and decreased p53 were key to hematopoietic restoration. These processes require four restorative miRs that target the genes for transcription/differentiation, namely PAX and phosphatase PPMIF. These miRs when introduced in aged cells were sufficient to restore the aged hematopoietic system in NSG mice. The aged MPBs were the drivers of their own restoration, as evidenced by the changes from distinct baseline miR profiles in MPBs and UCB to comparable expressions after exposure to aged MPBs. Restorative natural killer cells eliminated dormant breast cancer cells in vivo, indicating the broad relevance of this cellular paradigm - preventing and reversing age-associated disorders such as clearance of early malignancies and enhanced responses to vaccine and infection.

• Research Paper Volume 8, Issue 10 pp 2407-2413

  Height indicates hematopoietic capacity in elderly Japanese men

  Relevance score: 8.526444

  Yuji Shimizu, Shimpei Sato, Jun Koyamatsu, Hirotomo Yamanashi, Mako Nagayoshi, Koichiro Kadota, Takahiro Maeda

  Keywords: reticulocyte, hematopoiesis, height, elderly men

  Published in Aging on October 4, 2016

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  Previously, we reported that height is an indicator of the capacity of vascular repair in elderly men, especially hypertensive men. On the other hand, hemoglobin could act as a possible biochemical index of hypertension-induced vascular damage. However, no studies have clarified the correlation between height and hematopoietic activity. We conducted a cross-sectional study of 249 men aged 65-69 undergoing a general health check-up. Reticulocyte was used to evaluate hematopoietic activity. Because hemoglobin concentration should influence hematopoietic activity, analyses stratified by hemoglobin level were performed. Independent of known cardiovascular risk factors and other hematological parameters (white blood cell count), a significant positive correlation was seen between height and reticulocytes for total subjects and subjects with a high hemoglobin concentration (≥14.5 g/dL), but not in subjects with a low hemoglobin concentration (<14.5 g/dL). The standardized parameter estimates (β) were β=0.18, p=0.003 for total subjects, β=0.28, p=0.001 for subjects with a high hemoglobin concentration, and β=0.03, p=0.717 for subjects with low hemoglobin. Independently, height is significantly positively correlated with reticulocyte in elderly Japanese men, particularly in men with a high hemoglobin concentration. These results indicate that subjects with a short stature might have lower hematopoietic capacity than those with a high stature.

  

  Simple linear regression analysis of reticulocytes and height among (a) total subjects, (b) subjects with high hemoglobin and (c) subjects with low hemoglobin.

  
  
  

• Research Paper Volume 6, Issue 12 pp 1033-1048

  Stochastic modeling indicates that aging and somatic evolution in the hematopoietic system are driven by non-cell-autonomous processes

  Relevance score: 6.169853

  Andrii I. Rozhok, Jennifer L. Salstrom, James DeGregori

  Keywords: hematopoiesis, carcinogenesis, leukemogenesis somatic evolution, fitness, driver mutation, microenvironment, adaptation

  Published in Aging on December 17, 2014

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  Age-dependent tissue decline and increased cancer incidence are widely accepted to be rate-limited by the accumulation of somatic mutations over time. Current models of carcinogenesis are dominated by the assumption that oncogenic mutations have defined advantageous fitness effects on recipient stem and progenitor cells, promoting and rate-limiting somatic evolution. However, this assumption is markedly discrepant with evolutionary theory, whereby fitness is a dynamic property of a phenotype imposed upon and widely modulated by environment. We computationally modeled dynamic microenvironment-dependent fitness alterations in hematopoietic stem cells (HSC) within the Sprengel-Liebig system known to govern evolution at the population level. Our model for the first time integrates real data on age-dependent dynamics of HSC division rates, pool size, and accumulation of genetic changes and demonstrates that somatic evolution is not rate-limited by the occurrence of mutations, but instead results from aged microenvironment-driven alterations in the selective/fitness value of previously accumulated genetic changes. Our results are also consistent with evolutionary models of aging and thus oppose both somatic mutation-centric paradigms of carcinogenesis and tissue functional decline. In total, we demonstrate that aging directly promotes HSC fitness decline and somatic evolution via non-cell-autonomous mechanisms.

  

  Lower panels, a wide DFE leads to a large fitness differential among cells in the pool. Mutations affecting phenotype are known to have mostly negative fitness effects accounting for the large negative tail in the wide DFE. Cells harboring negative mutations will be eliminated by cell competition effects. These cells are likely to be cells that have undergone a greater number of divisions and thereby possess more mutations. Consequently, mutations accumulate more slowly in the population. Upper panels: less frequent non-neutral mutations (a narrow DFE) generate less fitness differential in the pool, and thus the frequency of survival of mutated cells is dominated by drift (chance) rather than selection; the average number of mutations per cell will accumulate faster, at a rate that more closely reflects mutation occurrence and thus cell division frequency.

  
  
  

  

  1 – initial cells (a total of 300); 2-5 – initial cell parameters; 6-15 – cell fate decisions during model cycle updated in “weekly” increments throughout the simulated human 85 year lifespan; 16 – cell leaves the pool. Timing of cells division (8), net fitness change per division (12), and fitness-based competition for niche space (14) are defined in stochastic trials based on distributions of average division timing, mutation DFE, and fitness-dependent stochastic competition scheme, respectively.

  
  
  

  

  (A) Dynamics of the pool size; the dynamics were inferred based on postnatal and adult HSC pool size estimates in [49, 72] (B) Average cell division frequencies; modeled based on estimates of HSC division rates at different ages in [20, 21, 49].

  
  
  

  

  (A) Mutation accumulation in the Tier 3 genome of AML (see inset for blowup of AMKL in children). (B) DNA methylation accumulation at neutral CpG islands of the human genome with age (from Horvath, 2013). (C-D) Simulated mutation accumulation in Tier 3 under mutation DFE variance σ= 0.000005 (C) and σ= 0.0005 (D) and different mutation rate fold increases over lifetime. (E) Simulated mutation accumulation in Tier 3 under stable mutation rate over lifetime and different mutation DFE variance. (F) The range (shaded) of mutation DFE variance (Y axis) and mutation fold rate increase over lifetime (X axis) that replicate WGS-derived slope of mutation accumulation in AML genomes and DNA methylation accumulation in the hematopoietic tissue within 95% confidence interval.

  
  
  

  

  (A) Shaded regions represent the plausible range of mutation DFE variance and rate increase over lifetime (for derivation see Fig. 4) under different proportions of mutations in the positive tail of the DFE. (B) Plots of shape match landscapes within the studied ranges of mutation DFE variance (σ; Y-axis) and mutation rate increase (X-axis). Colored landscapes represent age-dependent rates of somatic evolution as shown in the panel C. The proportion of mutation DFE variance in the positive tail is indicated in white text. Plausible ranges of mutation parameters from panel A are compared to mutation parameters that replicate exponentially increasing rates of somatic evolution that shape-match the leukemia incidence curve. (C) Color scale for panel B; colors represent the goodness of shape match between age-dependent leukemia incidence (green line) and simulated clonal expansions (blue line depicts the share of the most successful clone at any given time). (D) Age-dependent average cell fitness decline in the simulated pool for the indicated values of mutation DFE (σ).

  
  
  

  

  OVERLAP (left panels): % area of the plausible range of mutation parameters that allows age-dependent exponential clonal expansions under different stringencies (a minimum of 0.7, 0.8, or 0.9 shape match) of the expansions' match to the reference leukemia curve. ENV DFE – DFE imposed by microenvironment (explained in the text). EXPANSIONS (right panels): average magnitude of clonal expansions under different parameters of mutation and microenvironmental DFEs, measured as the % of pool occupied by the most successful clone at the end of the simulated life. (A) Comparison between mutation-alone (upper panels) and mutations + microenvironment (mutations + ENV) models (lower panels) under a stable adult HSC pool size of 11,000 cells; mutation DFE in the positive tail in all mutation + ENV conditions was set to 0%. (B) Same as A under different adult pool sizes and a slower cell cycling speed estimates (slow cycle); 11k->25k – the adult pool size increases over lifetime from 11,000 to 25,000 cells; mutation DFE in the positive tail in ALL conditions was set to 1% and ENV DFE in the positive tail was set to 0%. Numeric data is presented in Table S1.

  
  
  

  

  (A) An illustration of the Shelford's Law of Tolerance: species survival decreases with a change in an environmental factor from the optimum towards its extrema within the species' tolerance range; selection drives adaptation of a species towards best survival optima (occupied by phenotype A in panel A). (B) Body fitness decline in humans and mammals is delayed until the post-reproductive period when selection for high fitness of the body is relaxed; at the animal population level microenvironment integrity is not supported by selection as age advances into the post-reproductive period and cells do not evolve to optimally perform in the altered microenvironment. (C) An example of cell fitness determination within a hypothetical bifactorial tissue microenvironment. The normal cell phenotype evolves for optimal performance at the animal population level, and thus the probability of somatic mutations that enhance cell performance is reduced. The evolved (“normal”) and mutant cell phenotypes have different degrees of adaptation to microenvironmental factors A and B (solid lines connecting to the right “adaptation” axis); following the Sprengel-Liebig Law of the Minimum, fitness of both phenotypes is limited by the factor each phenotype is least adapted to (dashed lines connecting to the left “fitness” axis). (D) An altered microenvironment of aging post-reproductive tissues (factor A and B intensities have changed). Selection at the animal population level is relaxed and neither of the cell phenotypes have evolved to an aged microenvironment (both are out of optima), but the fitness of the mutant phenotype may become higher in the altered microenvironment. (E) A phenotypically homogenous population of cells will decline in fitness in a degraded microenvironment, revealing the microenvironment's uniform component that affects fitness. (F) Phenotypic diversity creates fitness differential in the cell pool. In a degraded microenvironment relative fitness of cells may change and initially disadvantageous mutant phenotypes may gain in fitness relative to others (red cell in panel F) and vice versa (green cell), revealing the microenvironment's stochastic component affecting fitness independently of the initial fitness distribution.

  
  
  

  

  (A) Shaded regions represent the plausible range of mutation DFE variance and rate increase over lifetime (for derivation see Fig. 4) under different proportions of mutations in the positive tail of the ENV DFE (B) Plots of shape match landscapes within the studied ranges of mutation DFE variance (σ; Y-axis) and mutation rate increase (X-axis). Colored landscapes represent age-dependent rates of somatic evolution as shown in the panel C. The proportion of ENV DFE variance in the positive tail is indicated in white text. Plausible ranges of mutation parameters from panel A are compared to mutation parameters that replicate exponentially increasing rates of somatic evolution that shape-match leukemia incidence curve. (C) Color scale for panel B; colors represent the goodness of shape match between age-dependent leukemia incidence (green line) and simulated clonal expansions (blue line depicts the share of the most successful clone at any given time). (D) Age-dependent average cell fitness decline in the simulated pool for the indicated values of mutation DFE (σ).

  
  
  

• Research Paper Volume 4, Issue 9 pp 606-619

  Aging induced decline in T-lymphopoiesis is primarily dependent on status of progenitor niches in the bone marrow and thymus

  Relevance score: 5.4536967

  Liguang Sun, Robert Brown, Shande Chen, Qichuan Zhuge, Dong-Ming Su

  Keywords: Hematopoiesis, bone marrow, T-lineage cells, niche cells, microenvironment, thymic stromal cells, aging, competitive repopulation

  Published in Aging on October 8, 2012

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  Age-related decline in the generation of T cells is associated with two primary lymphoid organs, the bone marrow (BM) and thymus. Both organs contain lympho-hematopoietic progenitor/stem cells (LPCs) and non-hematopoietic stromal/niche cells. Murine model showed this decline is not due to reduced quantities of LPCs, nor autonomous defects in LPCs, but rather defects in their niche cells. However, this viewpoint is challenged by the fact that aged BM progenitors have a myeloid skew. By grafting young wild-type (WT) BM progenitors into aged IL-7R^−/− hosts, which possess WT-equivalent niches although LPCs are defect, we demonstrated that these young BM progenitors also exhibited a myeloid skew. We, further, demonstrated that aged BM progenitors, recruited by a grafted fetal thymus in the in vivo microenvironment, were able to compete with their young counterparts, although the in vitro manipulated old BM cells were not able to do so in conventional BM transplantation. Both LPCs and their niche cells inevitably get old with increasing organismal age, but aging in niche cells occurred much earlier than in LPCs by an observation in thymic T-lymphopoiesis. Therefore, the aging induced decline in competence to generate T cells is primarily dependent on status of the progenitor niche cells in the BM and thymus.

  

  (A) Schematic workflow of the BM-niche age influence assay. (B) A representative flow cytometry analysis shows donor congenic marker gates (left panels), and profiles of T-lineage (Thy1.2⁺) vs. myeloid-lineage (Mac1⁺) cells derived from old (18 months old) and young (2 months old) IL7R^−/− host BM niches after culture on OP9-DL1 stromal cell monolayer (right panels). These cells were originally from the same pool of young WT BM progenitors. (C) A summary of panel B in % myeloid-lineage cells (left) and % T-lineage cells (right) derived from old (striped bar) and young (grey bar) IL7R^−/− host BM niches. Data show Mean ± SEM in all bar graphs, n = IL7R^−/− host mouse number. (D) A representative culture result of Panel A shows absolute cell numbers (Mean ± SD) of myeloid cells vs. T-lineage cells derived from old or young IL7R^−/− host BM niche-modulated young WT donor BM progenitors (~2000 sorted LSK cells loaded per well) after 14 days in culture on OP9-DL1 stromal cell monolayer (n = IL7R^−/− host animal #).

  
  
  

  

  (A) Thymocyte number in dGUO-treated (top panel) or intact (bottom panel) grafted fetal thymic lobes, at various weeks after transplanted under the kidney capsules of young (~2 months) and old (20 - 22 months) WT mice. The images shown are representative results of grafted thymic lobe size in the hosts? kidney capsules. Each data point (triangle or square) represents 2-3 host mice. (B) A representative result shows differentiation profiles (CD44 vs. CD25 at one-week time point, and CD4 vs. CD8 at all other longer time points) of thymocytes from grafted fetal thymic lobes under the young and old kidney capsules.

  
  
  

  

  (A) Results of competitive repopulation of grafted fetal thymus by “young and old” or “old and young” natural thymus-seeding cells from the first and second hosts. Left panel shows % T-lineage thymocytes (gated on DP, CD4⁺ and CD8⁺ SPs) in the grafted thymus naturally seeded by young (~2 month old, circles) and old (22 months old, triangles) BM progenitors in the different seeding orders (initial seeding: filled circles or triangles; subsequent/second seeding: open circles or triangles). Each triangle or circle represents one animal. An unpaired Student's t-test shows p > 0.05 (no significant). The table in the A panel shows absolute cell numbers per grafted lobe (each host mouse was grafted with 2-3 fetal thymic lobes, n = animal number). (B) A representative result of differentiated CD4⁺ and CD8⁺ T cells from the grafted fetal thymic lobes (bottom panels). The thymocytes are derived from first and second seeded young- and old-BM progenitors, identified by CD45.1 and CD45.2 congenic markers (top panels).

  
  
  

  

  (A) Schematic workflow of the comprehensive competitive culture assay showing the recruitment of old and young natural thymus-seeding cells in vivo to the in vitro competitive co-culture on OP9-DL1 monolayer stromal cells. (B) A representative result of % T-lineage cells derived from old (CD45.2⁺) and young (CD45.1⁺) thymus-seeding LPCs after competitive co-culture on OP9-DL1 stromal cell monolayer. (C) A summary of % T-lineage cells derived from old (CD45.2⁺) and young (CD45.1⁺) thymus-seeding LPCs after competitive co-culture on OP9-DL1 stromal cells. (D) A representative flow cytometry dot-plot shows CD4 vs. CD8 profile of T-lineage cells from the grafted fetal thymic lobes 7 days after KCT (left panels); purification of DN cells after negative-selection with beads (middle panels); and CD4 vs. CD8 profile of T-lineage cells after competitive co-culture on OP9-DL1 stromal cells (right panels). (E) A summary of % CD4⁺CD8⁺ (DP) cells derived from old (CD45.2⁺) and young (CD45.1⁺) thymus-seeding LPCs after competitive co-culture on OP9-DL1 stromal cells. Data in C and E panels show mean ± SEM, n = competitive co-culture wells; total host animal number is 5 young and 5 old WT mice. The experiment was conducted 5 times (i.e. 5 FTOC, 5 sorts, and 5 cultures).

  
  
  

  

  (A) A schematic diagram of workflow. (B) Left panel shows the gross appearance of young IL7R^−/− mouse thymus size from a representative experiment, 5 weeks after infusion with PBS, or equal numbers of 2-, 8-, 12-, 18-, or 22-month-old WT BM cells. Right panel shows a summary of total thymocyte number in young IL7R^−/− host mouse thymus, derived from donor WT mouse BM cells of different ages. (C) Left panel shows the gross appearance of 1- (left column) and 12-month-old (right column) IL7R^−/− mouse thymus size from a representative experiment, 5 weeks after transplantation with equal numbers of ~2-month-old WT BM cells (top row) or PBS (bottom row). Right panel shows a summary of total thymocyte number derived from young donor WT mouse BM cells in IL7R^−/− host niches of different ages. (D) Left panel shows the linear regression of thymocyte number derived from donor WT BM cells of different ages in young IL7R^−/− host niches (blue line, Exp-A) and from young donor WT BM cells in IL7R^−/− host niches of different ages (red line, Exp-B). Test for equal slopes for the blue (slope −3.72 ~ −1.28) and red (slope −7.23 ~ −3.97) gives a (2-sided) p-value of 0.016 (significantly different). Right panel shows donor-derived thymocyte numbers from 11-to-13-month-old donor WT BM cells in young IL7R^−/− host niches (left bar) and young donor WT BM cells in 11-to-13-month-old IL7R^−/- host niches (right bar). Data show mean ± SEM in all bar graphs, n = IL7R^−/− host animal number, each triangle and square in C represents one animal. Experiments were repeated over 5 times.

  
  
  

• Editorial pp undefined-undefined

  Exploring clonal hematopoiesis and its impact on aging, cancer, and patient care

  Relevance score: 8.583199

  Julieta Elena Rodriguez, Jean Baptiste Micol, Capucine Baldini

  Keywords: clonal hematopoiesis, aging, solid tumors

  Published in Aging on Invalid Date