METHODS: We evaluated the expression patterns of 11 candidate miRNAs using quantitative real-time PCR in whole blood (n = 10) and muscle biopsy samples (n = 9) of DM1 patients, and compared them to those of normal control samples (whole blood, n = 10; muscle, n = 9).
RESULTS: In DM1 whole blood, miRNA-133a, -29b, and -33a were significantly upregulated, whereas miRNA-1, -133a, and -29c were significantly downregulated in the skeletal muscles compared to controls.
CONCLUSIONS: Our findings align to those reported in other studies and point towards pathways that potentially contribute toward pathogenesis in DM1. However, the currently available data is not sufficient for these miRNAs to be made DM1-specific biomarkers because they seem to be common to many muscle pathologies. Hence, they lack specificity, but reinforce the need for further exploration of DM1 biomarkers.
RESULTS: We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer's disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.
CONCLUSION: Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson's disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.
RESULTS: Thirty Hy-Line Gray and thirty Lohmann Pink laying hens were used in this study to determine the impact of cecal microbial structure on odor production of laying hens. The hens were managed under the same husbandry and dietary regimes. Results of in vivo experiments showed a lower hydrogen sulfide (H2S) production from Hy-Line hens and a lower concentration of soluble sulfide (S2-) but a higher concentration of butyrate in the cecal content of the Hy-Line hens compared to Lohmann Pink hens (P 0.05). Significant microbial structural differences existed between the two breed groups. The relative abundance of some butyrate producers (including Butyricicoccus, Butyricimonas and Roseburia) and sulfate-reducing bacteria (including Mailhella and Lawsonia) were found to be significantly correlated with odor production and were shown to be different in the 16S rRNA and PCR data between two breed groups. Furthermore, some bacterial metabolism pathways associated with energy extraction and carbohydrate utilization (oxidative phosphorylation, pyruvate metabolism, energy metabolism, two component system and secretion system) were overrepresented in the Hy-Line hens, while several amino acid metabolism-associated pathways (amino acid related enzymes, arginine and proline metabolism, and alanine-aspartate and glutamate metabolism) were more prevalent in the Lohmann hens.
CONCLUSION: The results of this study suggest that genotype of laying hens influence cecal microbiota, which in turn modulates their odor production. Our study provides references for breeding and enteric manipulation for defined microbiota to reduce odor gas emission.