Unveiling the components of the signaling pathway between endosomes and the phagocytic uptake machinery in Dictyostelium discoideum

The soil amoebae Dictyostelium discoideum take up particles from their environment in order to obtain nutrition. The particle transits through the cell within a phagosome that fuses with organelles of different molecular compositions, undergoing a gradual degradation by different sets of hydrolytic enzymes. Griffiths’ concept of “phagosome individuality” predicts signaling from phagosomes into the cytoplasm, which might regulate many aspects of cell physiology. The finding that Dictyostelium cells depleted of the lysozyme AlyA or over-expressing the esterase Gp70 exhibit increased uptake of food particles, led to the postulation of a signaling cascade between endocytic compartments and the cytoskeletal uptake machinery at the plasma membrane. Assuming that Gp70 acts downstream of AlyA, gene-expression profiling of both mutants revealed different and overlapping sets of misregulated genes that might participate in this signaling cascade. Based on these results, we analyzed the effects of the artificial misregulation of six candidate genes by over-expression or negative genetic interference, in order to reconstruct at least part of the signaling pathway. SSB420 and SSL793 were chosen as candidates for the first signaling step, as they were up-regulated in AlyA-null cells and remained unaltered in the Gp70 over-expressing cells. The over-expression of SSB420 enhanced phagocytosis and raised the expression levels of Gp70, supporting its involvement in the signaling pathway between AlyA and Gp70 as a positive regulator of phagocytosis. However, this was not the case of cells over-expressing SSL793, as this mutation had no effects on phagocytosis. For the signaling downstream of Gp70, we studied four commonly misregulated genes in AlyA-depleted and Gp70 over-expressing cells. The expression levels of SLB350, SSB389 and TipD were lower in both mutants and therefore these were assumed as possible candidates for the negative regulation of phagocytosis. Cells depleted of SLB350 exhibited an increased phagocytic activity and no effect on Gp70 expression, proving its participation in the signaling pathway downstream of Gp70. Unlike SLB350, the disruption of the genes coding for SSB389 and TipD had no effects on particle uptake, excluding them from the pathway. The fourth candidate was Yipf1, the only gene that was commonly up-regulated in both mutants. Yet, the artificial over-expression of this protein had no effects on phagocytosis, so this candidate is also not included in the signaling pathway. Furthermore, localizing the products of the candidate genes within the cell helped unveiling several cellular organelles that receive signals from the phagosome and transduce them towards the uptake machinery.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.