BK virus infection and outcome following kidney transplantation in childhood.

BK virus associated nephropathy (BKN) is an important cause of kidney allograft failure. In a cohort of paediatric kidney transplant recipients, we aimed to understand the incidence and clinical outcome associated with BKN, as well as identify risk factors for BKN and BK viraemia development. We retrospectively analysed all patients who received a kidney transplant and received follow up care in our centre between 2009-2019. Among 106 patients included in the study (mean follow up 4.5 years), 32/106 (30.2%) patients experienced BK viraemia. The incidence of BKN was 7/106 (6.6%). The median time of BK viraemia development post-transplant was 279.5 days compared to 90.0 days for BKN. Development of BKN was associated with younger age at transplantation (p = 0.013). Development of BK viraemia was associated with negative recipient serology for cytomegalovirus (CMV) at time of transplantation (p = 0.012) and a higher net level of immunosuppression (p = 0.039). There was no difference in graft function at latest follow up between those who experienced BKN and those without BKN. This study demonstrates that BK virus infection is associated with younger age at transplantation, CMV negative recipient serostatus and higher levels of immunosuppression. Judicious monitoring of BK viraemia in paediatric transplant recipients, coupled with timely clinical intervention can result in similar long-term outcomes for BKN patients compared to controls

Building a kinetic Monte Carlo model with a chosen accuracy.

The kinetic Monte Carlo (KMC) method is a popular modeling approach for reaching large materials length and time scales. The KMC dynamics is erroneous when atomic processes that are relevant to the dynamics are missing from the KMC model. Recently, we had developed for the first time an error measure for KMC in Bhute and Chatterjee [J. Chem. Phys. 138, 084103 (2013)]. The error measure, which is given in terms of the probability that a missing process will be selected in the correct dynamics, requires estimation of the missing rate. In this work, we present an improved procedure for estimating the missing rate. The estimate found using the new procedure is within an order of magnitude of the correct missing rate, unlike our previous approach where the estimate was larger by orders of magnitude. This enables one to find the error in the KMC model more accurately. In addition, we find the time for which the KMC model can be used before a maximum error in the dynamics has been reached

Advances in Applications of Metabolomics in Pluripotent Stem Cell Research.

Stem cells undergo extensive metabolic rewiring during reprogramming, proliferation and differentiation, and numerous studies have demonstrated a significant role of metabolism in controlling stem cell fates. Recent applications of metabolomics, the study of concentrations and fluxes of small molecules in cells, have advanced efforts to characterize and maturate stem cell fates, assess drug toxicity in stem cell tissue models, identify biomarkers, and study the effects of environment on metabolic pathways in stem cells and their progeny. Looking to the future, combining metabolomics with other -omics approaches will provide a deeper understanding of the complex regulatory mechanisms of stem cells

Accuracy of a Markov state model generated by searching for basin escape pathways.

Markov state models (MSMs) are employed extensively in literature with the kinetic Monte Carlo (KMC) method for studying state-to-state dynamics in a wide range of material systems. A MSM contains a list of atomic processes and their rate constants for different states of the system. In many situations, only few of the possible atomic processes are included in the MSM. The use of an incomplete MSM with the KMC method can lead to an error in the dynamics. In this work, we develop an error measure to assess the accuracy of a MSM generated using dynamical basin escape pathway searches. We show that the error associated with an incomplete MSM depends on the rate constants missing from the MSM. A procedure to estimate the missing rate constants is developed. We demonstrate our approach using some examples.A.C. acknowledges support from BRNS Young Scientist Award from Department of Atomic Energy (DAE-BRNS) No. 2011/36/43-BRNS/1975

The Poly (ADP-Ribose) Polymerase Inhibitor Veliparib and Radiation Cause Significant Cell Line Dependent Metabolic Changes in Breast Cancer Cells.

Breast tumors are characterized into subtypes based on their surface marker expression, which affects their prognosis and treatment. Poly (ADP-ribose) polymerase (PARP) inhibitors have shown promising results in clinical trials, both as single agents and in combination with other chemotherapeutics, in several subtypes of breast cancer patients. Here, we used NMR-based metabolomics to probe cell line-specific effects of the PARP inhibitor Veliparib and radiation on metabolism in three breast cancer cell lines. Our data reveal several cell line-independent metabolic changes upon PARP inhibition. Pathway enrichment and topology analysis identified that nitrogen metabolism, glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis and taurine and hypotaurine metabolism were enriched after PARP inhibition in all three breast cancer cell lines. Many metabolic changes due to radiation and PARP inhibition were cell line-dependent, highlighting the need to understand how these treatments affect cancer cell response via changes in metabolism. Finally, both PARP inhibition and radiation induced a similar metabolic responses in BRCA-mutant HCC1937 cells, but not in MCF7 and MDAMB231 cells, suggesting that radiation and PARP inhibition share similar interactions with metabolic pathways in BRCA mutant cells. Our study emphasizes the importance of differences in metabolic responses to cancer treatments in different subtypes of cancers

An off-lattice, self-learning kinetic Monte Carlo method using local environments.

We present a method called local environment kinetic Monte Carlo (LE-KMC) method for efficiently performing off-lattice, self-learning kinetic Monte Carlo (KMC) simulations of activated processes in material systems. Like other off-lattice KMC schemes, new atomic processes can be found on-the-fly in LE-KMC. However, a unique feature of LE-KMC is that as long as the assumption that all processes and rates depend only on the local environment is satisfied, LE-KMC provides a general algorithm for (i) unambiguously describing a process in terms of its local atomic environments, (ii) storing new processes and environments in a catalog for later use with standard KMC, and (iii) updating the system based on the local information once a process has been selected for a KMC move. Search, classification, storage and retrieval steps needed while employing local environments and processes in the LE-KMC method are discussed. The advantages and computational cost of LE-KMC are discussed. We assess the performance of the LE-KMC algorithm by considering test systems involving diffusion in a submonolayer Ag and Ag-Cu alloy films on Ag(001) surface.This work was supported by Indian Institute of Technology Kanpur Start-up grant IITK/CHE/2010010

Human pluripotent stem cell-derived epicardial progenitors can differentiate to endocardial-like endothelial cells.

During heart development, epicardial progenitors contribute various cardiac lineages including smooth muscle cells, cardiac fibroblasts, and endothelial cells. However, their specific contribution to the human endothelium has not yet been resolved, at least in part due to the inability to expand and maintain human primary or pluripotent stem cell (hPSC)-derived epicardial cells. Here we first generated CDH5-2A-eGFP knock-in hPSC lines and differentiated them into self-renewing WT1+ epicardial cells, which gave rise to endothelial cells upon VEGF treatment in vitro. In addition, we found that the percentage of endothelial cells correlated with WT1 expression in a WT1-2A-eGFP reporter line. The resulting endothelial cells displayed many endocardium-like endothelial cell properties, including high expression levels of endocardial-specific markers, nutrient transporters and well-organized tight junctions. These findings suggest that human epicardial progenitors may have the capacity to form endocardial endothelium during development and have implications for heart regeneration and cardiac tissue engineering

Chemically-defined albumin-free differentiation of human pluripotent stem cells to endothelial progenitor cells.

Human pluripotent stem cell (hPSC)-derived endothelial cells and their progenitors are important for vascular research and therapeutic revascularization. Here, we report a completely defined endothelial progenitor differentiation platform that uses a minimalistic medium consisting of Dulbecco's modified eagle medium and ascorbic acid, lacking of albumin and growth factors. Following hPSC treatment with a GSK-3β inhibitor and culture in this medium, this protocol generates more than 30% multipotent CD34+ CD31+ endothelial progenitors that can be purified to >95% CD34+ cells via magnetic activated cell sorting (MACS). These CD34+ progenitors are capable of differentiating into endothelial cells in serum-free inductive media. These hPSC-derived endothelial cells express key endothelial markers including CD31, VE-cadherin, and von Willebrand factor (vWF), exhibit endothelial-specific phenotypes and functions including tube formation and acetylated low-density lipoprotein (Ac-LDL) uptake. This fully defined platform should facilitate production of proliferative, xeno-free endothelial progenitor cells for both research and clinical applications

Metabolomics Identifies Metabolic Markers of Maturation in Human Pluripotent Stem Cell-Derived Cardiomyocytes.

Cardiovascular disease is a leading cause of death worldwide. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) hold immense clinical potential and recent studies have enabled generation of virtually pure hPSC-CMs with high efficiency in chemically defined and xeno-free conditions. Despite these advances, hPSC-CMs exhibit an immature phenotype and are arrhythmogenic in vivo, necessitating development of strategies to mature these cells. hPSC-CMs undergo significant metabolic alterations during differentiation and maturation. A detailed analysis of the metabolic changes accompanying maturation of hPSC-CMs may prove useful in identifying new strategies to expedite hPSC-CM maturation and also may provide biomarkers for testing or validating hPSC-CM maturation. In this study we identified global metabolic changes which take place during long-term culture and maturation of hPSC-CMs derived from three different hPSC lines. We have identified several metabolic pathways, including phospholipid metabolism and pantothenate and Coenzyme A metabolism, which showed significant enrichment upon maturation in addition to fatty acid oxidation and metabolism. We also identified increases in glycerophosphocholine and the glycerophosphocholine:phosphocholine ratio as potential metabolic biomarkers of maturation. These biomarkers were also affected in a similar manner during murine heart development in vivo. These results support that hPSC-CM maturation is associated with extensive metabolic changes in metabolic network utilization and understanding the roles of these metabolic changes has the potential to develop novel approaches to monitor and expedite hPSC-CM maturation