Erythropoietin (EPO) is a hormone known to stimulate hematopoiesis. However, recent research suggests additional properties of EPO, such as protection against ischemia/reperfusion (I/R) injury in various tissues. We studied the effect of timing of EPO administration on cardioprotection during I/R in the heart. Male Sprague-Dawley rats were subjected to 45 minutes of coronary occlusion, followed by 24 hours of reperfusion. Animals were randomized to receive saline or single dose of EPO (5,000 IU/kg) either 2 hours before I/R, at the start of ischemia, or after the onset of reperfusion. The ratio of infarct area/area at risk (planimetry), left ventricular (LV) function (pressure development), and apoptosis (number of active caspase-3 positive cells) were determined after 24-hour reperfusion. Administration of EPO during different time points resulted in a 19 to 23% (P < 0.05) reduction in the infarct area/area at risk, which was accompanied by a trend toward better LV hemodynamic parameters. Apoptosis was significantly attenuated in groups treated with EPO at the start of ischemia (29% reduction) and after the onset of reperfusion (38%), and to a lesser extent (16%) in the group pre-treated with EPO. Thus, in vivo administration of EPO at different time points protects the myocardial structure and preserves cardiac function during I/R. Cardioprotective effect of EPO is associated with inhibition of apoptosis.
In chronic HF, treatment with ESPs is not associated with a higher mortality rate or more adverse events, whereas a beneficial effect on HF hospitalisation is seen. These outcomes are in contrast with studies in cancer and kidney disease, and support a large phase III morbidity and mortality trial of anaemia correction in patients with chronic HF.
Prokaryotes in natural environments respond rapidly to high concentrations of chemicals and physical stresses. Exposure to anthropogenic toxic substances-such as oil, chlorinated solvents, or antibiotics-favors the evolution of resistant phenotypes, some of which can use contaminants as an exclusive carbon source or as electron donors and acceptors. Microorganisms similarly adapt to extreme pH, metal, or osmotic stress. The metabolic plasticity of prokaryotes can thus be harnessed for bioremediation and can be exploited in a variety of ways, ranging from stimulated natural attenuation to bioaugmentation and from wastewater treatment to habitat restoration.
Bacteria that engage in long-standing associations with particular hosts are expected to evolve host-specific adaptations that limit their capacity to thrive in other environments. Consistent with this, many gut symbionts seem to have a limited host range, based on community profiling and phylogenomics. However, few studies have experimentally investigated host specialization of gut symbionts and the underlying mechanisms have largely remained elusive. Here, we studied host specialization of a dominant gut symbiont of social bees, Lactobacillus Firm5. We show that Firm5 strains isolated from honey bees and bumble bees separate into deep-branching host-specific phylogenetic lineages. Despite their divergent evolution, colonization experiments show that bumble bee strains are capable of colonizing the honey bee gut. However, they were less successful than honey bee strains, and competition with honey bee strains completely abolished their colonization. In contrast, honey bee strains of divergent phylogenetic lineages were able to coexist within individual bees. This suggests that both host selection and interbacterial competition play important roles in host specialization. Using comparative genomics of 27 Firm5 isolates, we found that the genomes of honey bee strains harbour more carbohydrate-related functions than bumble bee strains, possibly providing a competitive advantage in the honey bee gut. Remarkably, most of the genes encoding carbohydrate-related functions were not conserved among the honey bee strains, which suggests that honey bees can support a metabolically more diverse community of Firm5 strains than bumble bees. These findings advance our understanding of the genomic changes underlying host specialization.
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14 C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.
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