Platelets Boost Recruitment of CD133+ Bone Marrow Stem Cells to Endothelium and the Rodent Liver-The Role of P-Selectin/PSGL-1 Interactions

Lehwald N, Duhme C, Pinchuk I, et al. Platelets Boost Recruitment of CD133+ Bone Marrow Stem Cells to Endothelium and the Rodent Liver-The Role of P-Selectin/PSGL-1 Interactions. International journal of molecular sciences. 2020;21(17): 6431.We previously demonstrated that clinical administration of mobilized CD133+ bone marrow stem cells (BMSC) accelerates hepatic regeneration. Here, we investigated the potential of platelets to modulate CD133+BMSC homing to hepatic endothelial cells and sequestration to warm ischemic livers. Modulatory effects of platelets on the adhesion of CD133+BMSC to human and mouse liver-sinusoidal- and micro- endothelial cells (EC) respectively were evaluated in in vitro co-culture systems. CD133+BMSC adhesion to all types of EC were increased in the presence of platelets under shear stress. This platelet effect was mostly diminished by antagonization of P-selectin and its ligand P-Selectin-Glyco-Ligand-1 (PSGL-1). Inhibition of PECAM-1 as well as SDF-1 receptor CXCR4 had no such effect. In a model of the isolated reperfused rat liver subsequent to warm ischemia, the co-infusion of platelets augmented CD133+BMSC homing to the injured liver with heightened transmigration towards the extra sinusoidal space when compared to perfusion conditions without platelets. Extravascular co-localization of CD133+BMSC with hepatocytes was confirmed by confocal microscopy. We demonstrated an enhancing effect of platelets on CD133+BMSC homing to and transmigrating along hepatic EC putatively depending on PSGL-1 and P-selectin. Our insights suggest a new mechanism of platelets to augment stem cell dependent hepatic repair

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here

Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values

Evidence-Based Annotation of Gene Function in Shewanella oneidensis MR-1 Using Genome-Wide Fitness Profiling across 121 Conditions

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes

Controversies in the Use of MIGS

Abstract Minimally invasive glaucoma surgery (MIGS) has fulfilled an unmet need in the management of glaucoma. This chapter highlights some controversial issues regarding the use of MIGS in clinical practice, including (1) whether there is sufficient evidence to advocate combining MIGS with cataract surgery over cataract surgery alone, (2) the merits and drawbacks of different approaches to trabecular bypass and canal-based MIGS procedures, (3) the effect of MIGS on endothelial cell loss, (4) suprachoroidal MIGS devices and whether there is still a role for these procedures, and (5) a comparison between subconjunctival MIGS and trabeculectomy. Several questions are still left unanswered and hopefully, further research and more clinical experience with these new technologies will help improve surgical outcomes for patients