Vitamin K antagonists predispose to calciphylaxis in patients with end-stage renal disease.

BACKGROUND/AIMS: Calciphylaxis is associated with a poor prognosis in dialysis patients, and its pathogenesis remains incompletely understood. Although the use of vitamin K antagonists (VKA) has been implicated, previous reports are conflicting. We aimed to determine if vitamin K antagonists conferred an increased risk of calciphylaxis in patients on dialysis. METHODS: We performed a single-centre, retrospective cohort study of 2,234 patients receiving dialysis, and compared the characteristics of those with and without calciphylaxis. RESULTS: We identified 5 cases of calciphylaxis (all female) between January 2009 and December 2013. Overall, 142 patients (6.4%) were treated with VKA during the study period. Calciphylaxis was more common in the VKA group (4 of 142 patients, OR = 61, 95% CI 6.7-546, p = 0.0001). VKA was withdrawn in all cases and treatment instituted with sodium thiosulphate, cinacalcet and supportive measures. All patients recovered, although there was one sudden cerebrovascular death during follow-up. CONCLUSION: Treatment with VKA predisposes to the development of calciphylaxis.TFH is funded by the Cambridge BRC and NIHR.This is the accepted manuscript for a paper published in Nephron Clinical Practice, Vol. 129, No. 3, 2015, DOI:10.1159/00037144

Toward assessing farm-based anaerobic digestate public health risks : comparative investigation with slurry, effect of pasteurization treatments, and use of miniature bioreactors as proxies for pathogen spiking trials

Manure and slurry may contain a range of bacterial, viral, and parasitic pathogens and land application of these organic fertilizers typically occurs without prior treatment. In-situ treatment through farm-based anaerobic digestion (AD) of such organic fertilizers co-digested with food-production wastes is multi-beneficial due to energy recovery, increased farm incomes and noxious gas reduction. Before risk assessment can be carried out at field scale an investigation of the fate of relevant target pathogens during the actual AD process must be undertaken, requiring the development of practical test systems for evaluation of pathogen survival. The present study examines miniature (50 mL) and laboratory (10 L) scale AD systems. Treatments included slurry co-digested with fats, oils, and grease (FOG) under typical operating and pasteurization conditions used in farm-based AD, in batch-fed miniature and laboratory mesophilic (37°C) continuously stirred tank reactors. Biogas production, pH, chemical oxygen demand, volatile solids, and ammonia concentration were measured throughout the trial, as were fecal indicator bacteria (FIB) i.e., total coliforms, Escherichia coli, and Enterococcus species. The miniature and laboratory bioreactors performed similarly in terms of physicochemical parameters and FIB die-off. In the absence of pasteurization, after 28 days, enterococci numbers were below the <1,000 cfu g−1 threshold required for land application, while E. coli was no longer detectable in the digestate. For comparison, FIB survival in slurry was examined and after 60 days of storage, none of the FIB tested was <1,000 cfu g−1, suggesting that slurry would not be considered safe for land application if FIB thresholds required for AD digestate were to be applied. Taken together we demonstrate that (i) miniature-scale bioreactors are valid proxies of farm-based AD to carry out targeted pathogen survival studies and (ii) in situ AD treatment of slurry prior to land application reduces the level of FIB, independently of pasteurization, which in turn might be indicative of a decreased potential pathogen load to the environment and associated public health risks

Sensitive droplet digital PCR method for detection of TERT promoter mutations in cell free DNA from patients with metastatic melanoma

Background: Currently mainly BRAF mutant circulating tumor DNA (ctDNA) is utilized to monitor patients with melanoma. TERT promoter mutations are common in various cancers and found in up to 70% of melanomas, including half of BRAF wildtype cases. Therefore, a sensitive method for detection of TERT promoter mutations would increase the number of patients that could be monitored through ctDNA analysis. Methods: A droplet digital PCR (ddPCR) assay was designed for the concurrent detection of chr5:1,295,228 C \u3e T and chr5:1,295,250 C \u3e T TERT promoter mutations. The assay was validated using 39 melanoma cell lines and 22 matched plasma and tumor samples. In addition, plasma samples from 56 metastatic melanoma patients and 56 healthy controls were tested for TERT promoter mutations. Results: The established ddPCR assay detected TERT promoter mutations with a lower limit of detection (LOD) of 0.17%. Total concordance was demonstrated between ddPCR and Sanger sequencing in all cell lines except one, which carried a second mutation within the probe binding-site. Concordance between matched plasma and tumor tissue was 68% (15/22), with a sensitivity of 53% (95% CI, 27%- 79%) and a specificity of 100% (95% CI, 59%-100%). A significantly longer PFS (p=0.028) was evident in ctDNA negative patients. Importantly, our TERT promoter mutations ddPCR assay allowed detection of ctDNA in 11 BRAF wild-type cases. Conclusions: The TERT promoter mutation ddPCR assay offers a sensitive test for molecular analysis of melanoma tumors and ctDNA, with the potential to be applied to other cancers

Drought soil legacy overrides maternal effects on plant growth

Maternal effects (i.e. trans-generational plasticity) and soil legacies generated by drought and plant diversity can affect plant performance and alter nutrient cycling and plant community dynamics. However, the relative importance and combined effects of these factors on plant growth dynamics remain poorly understood. We used soil and seeds from an existing plant diversity and drought manipulation field experiment in temperate grassland to test maternal, soil drought and diversity legacy effects, and their interactions, on offspring plant performance of two grassland species (Alopecurus pratensis and Holcus lanatus) under contrasting glasshouse conditions. Our results showed that drought soil legacy effects eclipsed maternal effects on plant biomass. Drought soil legacy effects were attributed to changes in both abiotic (i.e. nutrient availability) and biotic soil properties (i.e. microbial carbon and enzyme activity), as well as plant root and shoot atom 15N excess. Further, plant tissue nutrient concentrations and soil microbial C:N responses to drought legacies varied between the two plant species and soils from high and low plant diversity treatments. However, these diversity effects did not affect plant root or shoot biomass. These findings demonstrate that while maternal effects resulting from drought occur in grasslands, their impacts on plant performance are likely minor relative to drought legacy effects on soil abiotic and biotic properties. This suggests that soil drought legacy effects could become increasingly important drivers of plant community dynamics and ecosystem functioning as extreme weather events become more frequent and intense with climate change. A plain language summary is available for this article.</p

ObStruct: A method to objectively analyse factors driving population structure using Bayesian ancestry profiles

Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin) correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses. © 2014 Gayevskiy et al

Near-Infrared and Optical Observations of Type Ic SN 2021krf: Luminous Late-time Emission and Dust Formation

We present near-infrared (NIR) and optical observations of the Type Ic supernova (SN Ic) SN 2021krf obtained between days 13 and 259 at several ground-based telescopes. The NIR spectrum at day 68 exhibits a rising $K$-band continuum flux density longward of $\sim$ 2.0 $\mu$m, and a late-time optical spectrum at day 259 shows strong [O I] 6300 and 6364 \r{A} emission-line asymmetry, both indicating the presence of dust, likely formed in the SN ejecta. We estimate a carbon-grain dust mass of $\sim$ 2 $\times$ 10$^{-5}$ M$_{\odot}$ and a dust temperature of $\sim$ 900 - 1200 K associated with this rising continuum and suggest the dust has formed in SN ejecta. Utilizing the one-dimensional multigroup radiation hydrodynamics code STELLA, we present two degenerate progenitor solutions for SN 2021krf, characterized by C-O star masses of 3.93 and 5.74 M$_{\odot}$, but with the same best-fit $^{56}$Ni mass of 0.11 M$_{\odot}$ for early times (0-70 days). At late times (70-300 days), optical light curves of SN 2021krf decline substantially more slowly than that expected from $^{56}$Co radioactive decay. Lack of H and He lines in the late-time SN spectrum suggests the absence of significant interaction of the ejecta with the circumstellar medium. We reproduce the entire bolometric light curve with a combination of radioactive decay and an additional powering source in the form of a central engine of a millisecond pulsar with a magnetic field smaller than that of a typical magnetar.Comment: Accepted for publication in ApJ, 27 pages, 21 figures, 6 tables. Previous arXiv submission (arXiv:2211.00205) replaced after acceptanc

riboSeed:leveraging prokaryotic genomic architecture to assemble across ribosomal regions

The vast majority of bacterial genome sequencing has been performed using Illumina short reads. Because of the inherent difficulty of resolving repeated regions with short reads alone, only similar to 10% of sequencing projects have resulted in a closed genome. The most common repeated regions are those coding for ribosomal operons (rDNAs), which occur in a bacterial genome between 1 and 15 times, and are typically used as sequence markers to classify and identify bacteria. Here, we exploit the genomic context in which rDNAs occur across taxa to improve assembly of these regions relative to de novo sequencing by using the conserved nature of rDNAs across taxa and the uniqueness of their flanking regions within a genome. We describe a method to construct targeted pseudocontigs generated by iteratively assembling reads that map to a reference genome's rDNAs. These pseudocontigs are then used to more accurately assemble the newly sequenced chromosome. We show that this method, implemented as riboSeed, correctly bridges across adjacent contigs in bacterial genome assembly and, when used in conjunction with other genome polishing tools, can assist in closure of a genome

The influence of tumor size and environment on gene expression in commonly used human tumor lines

BACKGROUND: The expression profiles of solid tumor models in rodents have been only minimally studied despite their extensive use to develop anticancer agents. We have applied RNA expression profiling using Affymetrix U95A GeneChips to address fundamental biological questions about human tumor lines. METHODS: To determine whether gene expression changed significantly as a tumor increased in size, we analyzed samples from two human colon carcinoma lines (Colo205 and HCT-116) at three different sizes (200 mg, 500 mg and 1000 mg). To investigate whether gene expression was influenced by the strain of mouse, tumor samples isolated from C.B-17 SCID and Nu/Nu mice were also compared. Finally, the gene expression differences between tissue culture and in vivo samples were investigated by comparing profiles from lines grown in both environments. RESULTS: Multidimensional scaling and analysis of variance demonstrated that the tumor lines were dramatically different from each other and that gene expression remained constant as the tumors increased in size. Statistical analysis revealed that 63 genes were differentially expressed due to the strain of mouse the tumor was grown in but the function of the encoded proteins did not link to any distinct biological pathways. Hierarchical clustering of tissue culture and xenograft samples demonstrated that for each individual tumor line, the in vivo and in vitro profiles were more similar to each other than any other profile. We identified 36 genes with a pattern of high expression in xenograft samples that encoded proteins involved in extracellular matrix, cell surface receptors and transcription factors. An additional 17 genes were identified with a pattern of high expression in tissue culture samples and encoded proteins involved in cell division, cell cycle and RNA production. CONCLUSIONS: The environment a tumor line is grown in can have a significant effect on gene expression but tumor size has little or no effect for subcutaneously grown solid tumors. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments. This could be used as a quality control tool for preclinical oncology studies

A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RARECOVER. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RARECOVER to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RARECOVER analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.National Science Foundation (U.S.) (grant (IIS-0810905)National Institutes of Health (U.S.) (U19 AG023122-05)National Institutes of Health (U.S.) (R01 MH078151-03)Louis & Harold Price FoundationNational Institutes of Health (U.S.) (N01 MH22005)National Institutes of Health (U.S.) (U01-DA024417-01)National Institutes of Health (U.S.) (P50 MH081755-01)National Institutes of Health (U.S.) (R01 AG030474-02)National Institutes of Health (U.S.) (N01 MH022005)National Institutes of Health (U.S.) (R01 HL089655-02)National Institutes of Health (U.S.) (R01 MH080134-03)National Institutes of Health (U.S.) (U54 CA143906-01)National Institutes of Health (U.S.) (UL1 RR025774-03)Scripps Genomic Medicine ProgramNational Human Genome Research Institute (U.S.) (Grant Number T32 HG002295

Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT

Background: Although rare in the general population, highly penetrant germline mutations in CDKN2A are responsible for 5%-40% of melanoma cases reported in melanoma-prone families. We sought to determine whether MELPREDICT was generalizable to a global series of families with melanoma and whether performance improvements can be achieved. Methods: In total, 2116 familial melanoma cases were ascertained by the international GenoMEL Consortium. We recapitulated the MELPREDICT model within our data (GenoMELPREDICT) to assess performance improvements by adding phenotypic risk factors and history of pancreatic cancer. We report areas under the curve (AUC) with 95% confidence intervals (CIs) along with net reclassification indices (NRIs) as performance metrics. Results: MELPREDICT performed well (AUC 0.752, 95% CI 0.730-0.775), and GenoMELPREDICT performance was similar (AUC 0.748, 95% CI 0.726-0.771). Adding a reported history of pancreatic cancer yielded discriminatory improvement (P &lt; .0001) in GenoMELPREDICT (AUC 0.772, 95% CI 0.750-0.793, NRI 0.40). Including phenotypic risk factors did not improve performance. Conclusion: The MELPREDICT model functioned well in a global data set of familial melanoma cases. Adding pancreatic cancer history improved model prediction. GenoMELPREDICT is a simple tool for predicting CDKN2A mutational status among melanoma patients from melanoma-prone families and can aid in directing these patients to receive genetic testing or cancer risk counseling