Toxokinetik und -dynamik ausgewahlter individueller chlorierter Biphenyle

SchluBbericht zum Forschungsvorhaben 03F0551

Excess mortality in US Veterans during the COVID-19 pandemic: an individual-level cohort study.

BACKGROUND: Most analyses of excess mortality during the COVID-19 pandemic have employed aggregate data. Individual-level data from the largest integrated healthcare system in the US may enhance understanding of excess mortality. METHODS: We performed an observational cohort study following patients receiving care from the Department of Veterans Affairs (VA) between 1 March 2018 and 28 February 2022. We estimated excess mortality on an absolute scale (i.e. excess mortality rates, number of excess deaths) and a relative scale by measuring the hazard ratio (HR) for mortality comparing pandemic and pre-pandemic periods, overall and within demographic and clinical subgroups. Comorbidity burden and frailty were measured using the Charlson Comorbidity Index and Veterans Aging Cohort Study Index, respectively. RESULTS: Of 5 905 747 patients, the median age was 65.8 years and 91% were men. Overall, the excess mortality rate was 10.0 deaths/1000 person-years (PY), with a total of 103 164 excess deaths and pandemic HR of 1.25 (95% CI 1.25-1.26). Excess mortality rates were highest among the most frail patients (52.0/1000 PY) and those with the highest comorbidity burden (16.3/1000 PY). However, the largest relative mortality increases were observed among the least frail (HR 1.31, 95% CI 1.30-1.32) and those with the lowest comorbidity burden (HR 1.44, 95% CI 1.43-1.46). CONCLUSIONS: Individual-level data offered crucial clinical and operational insights into US excess mortality patterns during the COVID-19 pandemic. Notable differences emerged among clinical risk groups, emphasizing the need for reporting excess mortality in both absolute and relative terms to inform resource allocation in future outbreaks

The Complex Genetic Architecture of the Metabolome

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes

Fitness effects associated with the major flowering time gene FRIGIDA in Arabidopsis thaliana in the field

To date, the effect of natural selection on candidate genes underlying complex traits has rarely been studied experimentally, especially under ecologically realistic conditions. Here we report that the effect of selection on the flowering time gene FRIGIDA (FRI) reverses depending on the season of germination and allelic variation at the interacting gene FLOWERING LOCUS C (FLC). In field studies of 136 European accessions of Arabidopsis thaliana, accessions with putatively functional FRI alleles had higher winter survival in one FLC background in a fall‐germinating cohort, but accessions with deletion null FRI alleles had greater seed production in the other FLC background in a spring‐germinating cohort. Consistent with FRI’s role in flowering, selection analyses suggest that the difference in winter survival can be attributed to time to bolting. However, in the spring cohort, the fitness difference was associated with rosette size. Our analyses also reveal that controlling for population structure with estimates of inferred ancestry and a geographical restriction was essential for detecting fitness associations. Overall, our results suggest that the combined effects of seasonally varying selection and epistasis could explain the maintenance of variation at FRI and, more generally, may be important in the evolution of genes underlying complex traits

A retrospective chart review of drug treatment patterns and clinical outcomes among patients with metastatic or recurrent soft tissue sarcoma refractory to one or more prior chemotherapy treatments

Background: Limited clinical data on real-world practice patterns are available for patients with metastatic/relapsed soft tissue sarcomas (STS). The primary objective of this study was to evaluate treatment patterns in patients with metastatic/relapsed STS following failure of prior chemotherapy by examining data collected from 2000 to 2011 from a major tertiary academic cancer center in the United States. Methods: Medical records, including community-based referral records, from a tertiary cancer center for adult patients with metastatic/relapsed STS with confirmed disease progression who commenced second-line treatment between January 1, 2000 and February 4, 2011, and with at least 3 months of follow-up data following second-line treatment initiation, were retrospectively reviewed. Overall survival, time to progression, and clinician-reported tumor response were collected. Results: A total of 99 patients (leiomyosarcoma, n = 48; synovial cell sarcoma, n = 7; liposarcoma, n = 5; or other histological subtypes, n = 39) received an average of four lines of treatment (maximum of 10). No consistent or dominant regimens were used in each treatment line beyond the second line. Median second-line treatment duration was 4.1 months (95% confidence interval, 3.0–5.0). Overall, 72 of 99 patients (73%) discontinued second-line treatment due to progressive disease. Median progression-free survival from initiation of second-line treatment varied across regimens from 2.0 to 6.6 months (overall median, 5.4 months). Conclusions: Wide variations in treatment were evident, with no single standard of care for patients with metastatic/relapsed STS. Most patients discontinued second-line treatment due to progressive disease, often receiving additional systemic therapy with other drugs. These data suggest a high unmet need for more efficacious treatment options and improved data collection to guide practice among patients with relapsed/refractory STS