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Articles by D. R. Meldrum
Total Records ( 13 ) for D. R. Meldrum
  B. R Weil , A. M Abarbanell , J. L Herrmann , Y Wang and D. R. Meldrum
  Optimizing the function and proliferative capacity of stem cells is essential to maximize their therapeutic benefits. High glucose concentrations are known to have detrimental effects on many cell types. We hypothesized that human mesenchymal stem cells (hMSCs) cultured in high glucose-containing media would exhibit diminished proliferation and attenuated production of VEGF, hepatocyte growth factor (HGF), and FGF2 in response to treatment with TNF-, LPS, or hypoxia. hMSCs were plated in medium containing low (5.5 mM) and high (20 mM or 30 mM) glucose concentrations and treated with TNF-, LPS, or hypoxia. Supernatants were collected at 24 and 48 h and assayed via ELISA for VEGF, HGF, and FGF2. In addition, hMSCs were cultured on 96-well plates at the above glucose concentrations, and proliferation at 48 h was determined via bromo-2'-deoxy-uridine (BrdU) incorporation. At 24 and 48 h, TNF-, LPS, and hypoxia-treated hMSCs produced significantly higher VEGF, HGF, and FGF2 compared with control. Hypoxia-induced VEGF production by hMSCs was the most pronounced change over baseline. At both 24 and 48 h, glucose concentration did not affect production of VEGF, HGF, or FGF2 by untreated hMSCs and those treated with TNF-, LPS, or hypoxia. Proliferation of hMSCs as determined via BrdU incorporation was unaffected by glucose concentration of the media. Contrary to what has been observed with other cells, hMSCs may be resistant to the short-term effects of high glucose. Ongoing efforts to characterize and optimize ex vivo and in vivo conditions are critical if the therapeutic benefits of MSCs are to be maximized.
  W Torres Garcia , W Zhang , G. C Runger , R. H Johnson and D. R. Meldrum

Motivation: Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems.

Results: In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins.

  Y Wang , B. R Weil , J. L Herrmann , A. M Abarbanell , J Tan , T. A Markel , M. L Kelly and D. R. Meldrum

Human bone marrow mesenchymal stem cells (MSCs) are a potent source of growth factors, which are partly responsible for their beneficial paracrine effects. We reported previously that transforming growth factor- (TGF-), a putative mediator of wound healing and the injury response, increases the release of vascular endothelial growth factor (VEGF), augments tumor necrosis factor- (TNF-)-stimulated VEGF production, and activates mitogen-activated protein kinases and phosphatidylinositol 3-kinase (PI-3K) pathway in human MSCs. The experiments described in this report indicate that TGF- increases MSC-derived hepatocyte growth factor (HGF) production. TGF--stimulated HGF production was abolished by inhibition of MEK, p38, PI-3K, or by small interfering RNA (siRNA) targeting TNF receptor 2 (TNFR2), but was not attenuated by siRNA targeting TNF receptor 1 (TNFR1). Ablation of TNFR1 significantly increased basal and stimulated HGF. A potent synergy between TGF- and TNF- was noted in MSC HGF production. This synergistic effect was abolished by MEK, P38, PI-3K inhibition, or by ablation of both TNF receptors using siRNA. We conclude that 1) novel cross talk occurs between tumor necrosis factor receptor and TGF-/epidermal growth factor receptor in stimulating MSC HGF production; 2) this cross talk is mediated, at least partially, via activation of MEK, p38, and PI-3K; 3) TGF- stimulates MSCs to produce HGF by MEK, p38, PI-3K, and TNFR2-dependent mechanisms; and 4) TNFR1 acts to decrease basal TGF- and TNF--stimulated HGF.

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