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Cryo‐SEM and large volume FIB‐SEM of Arabidopsis cotyledons: Degradation of lipid bodies, biogenesis of glyoxysomes and reorganisation of organelles during germination
No full textUntil recently, the lack of three-dimensional visualisation of whole cells at the electron microscopic (EM) level has led to a significant gap in our understanding of the interaction of cellular organelles and their interconnection. This is particularly true with regard to the role of the endoplasmic reticulum (ER). In this study, we perform three-dimensional reconstructions of serial FIB/SEM stacks and anaglyphs derived from volume rendering, cryo-scanning electron microscopy (cryo-SEM) and state-of-the-art electron microscopy immobilisation and imaging techniques. The results show that glyoxysomes are formed de novo in large numbers and in characteristic clusters on the ER upon germination in mesophyll cells of Arabidopsis cotyledons. The degradation of lipid bodies during germination occurs not only via the ER, which enlarges by taking up polar lipids resulting from enzymatic degradation by lipases, but also via glyoxysomes, which engulf lipid bodies. Dictyosomal (Golgi-derived) vesicles, which fuse with glyoxysomes or their precursors, also appear to be involved in the differentiation of glyoxysomes from segments of the ER. The formation of the central vacuole is the result of the fusion of protein storage vacuoles (protein bodies), which become complex three-dimensional structures during germination. Our observations also suggest that the vacuole plays a role in the degradation of glyoxysomes. The evidence provided in three dimensions shows that the endoplasmic reticulum plays a central role in the biogenesis and degradation of lipid bodies, the ontogeny of glyoxysomes and the development of plastids in the mesophyll cells of Arabidopsis cotyledons
Individual bioenergetic capacity as a potential source of resilience to Alzheimer’s disease
Full text linkChimeric cofactors enable methyltransferase-catalyzed prenylation
Full text linkThe bigger picture
Enzymes can perform alkylation reactions of their target substrate with high selectivity under mild conditions. Suitable transferases can be found in different enzyme classes and rely on different cofactor scaffolds depending on whether they transfer one-carbon or non-methyl groups. S-Adenosyl-l-methionine (AdoMet or SAM) is characteristic of methyltransferases (C1), whereas prenyltransferases (C5) rely on dimethylallyl diphosphate (DMAPP). We engineered a chimeric AdoMet/DMAPP cofactor and found that it is efficiently converted by various methyltransferases acting on different targets at C, N, and O atoms. The chimeric cofactor is highly reactive, and prenylation is preferred over the methylation in direct competition with the natural AdoMet.
Our work unlocks hundreds of methyltransferases as biocatalysts for regioselective prenylation and will prove useful in making pseudo-natural products and new-to-nature bioconjugates. We also show that the concept can be extended to C10 and C15 units as well as clickable groups. The finding that the AdoMet scaffold can be used for efficient prenyl transfer by wild-type methyltransferases shows that there is no inherent chemical or enzymatic reason that C1 and C5 transfer is catalyzed by different enzyme classes. This relaxes the paradigm of one-carbon- versus non-methyl-group-transferring enzyme classes for biotransformations and therefore opens new doors in biocatalytic alkylation.
Highlights
• Chimeric cofactors make methyltransferases act as prenyltransferases
• The tested methyltransferases favor prenylation over natural methylation
• This strategy can transfer prenyl, geranyl, farnesyl, and clickable groups
Summary
Enzymatic alkylation is known for its selectivity and specificity. Transferases are found in enzymes that transfer one-carbon groups and in those that transfer non-methyl groups. Both classes catalyze the attack of a nucleophilic substrate but use different cofactors. S-Adenosyl-l-methionine (AdoMet or SAM) is characteristic of methyltransferases (MTases), whereas prenyltransferases (PTases) rely on dimethylallyl diphosphate (DMAPP). It is unclear whether this preference originates from inherent chemical or enzymatic requirements. We find that DNA, RNA, and small-molecule MTases acting on C, N, and O atoms function as PTases when offered a chimeric AdoMet-DMAPP cofactor (AdoPrenyl). This cofactor is highly reactive, necessitating its enzymatic in situ formation and leading to preferential MTase-catalyzed prenylation. The DNA-MTase M.TaqI efficiently transfers geranyl (C10) and farnesyl (C15) moieties as well. Our work shows that the AdoMet scaffold can function as an efficient prenyl donor. Because there are hundreds of MTases, this route to regio- and sequence-selective prenylation is versatile for forming pseudo-natural products and new-to-nature bioconjugates
SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration
Full text linkAlgorithm configuration deals with the automatic optimization of an algorithm's parameters to maximize its performance on a distribution of problem instances, such as Boolean satisfiability or the traveling salesperson problem. While significant progress has been made in developing optimizers for algorithm configuration - so-called algorithm configurators - their evaluation remains computationally expensive and often relies on real-world scenarios with hard-to-control characteristics. This makes it challenging to analyze their strengths and weaknesses systematically. To address this, we introduce SynthACticBench, a synthetic benchmark specifically designed to isolate and investigate key properties of algorithm configuration problems. Our benchmark distinguishes between properties related to the configuration space and those associated with the objective function. We define a configurator's ability to handle a particular property as its capability -for example, the capability to manage hierarchical configuration spaces. Using SynthACticBench, we evaluate two state-of-the-art algorithm configurators, SMAC and irace, examining their complementary capabilities and analyzing their performances across diverse benchmark functions. By providing a controlled, scalable, and capability-based evaluation environment, SynthACticBench facilitates a more targeted analysis of algorithm configurators, helping to advance research in the field. The benchmark is available at: https://github.com/annaelisalappe/SynthACticBench/
Bayesian StairwayPlot for Inferring Single Population Demographic Histories From Site Frequency Spectra
Full text linkThe StairwayPlot approach provides an elegant, flexible and powerful method to estimate complex demographic histories of single populations from site frequency spectrum data. It uses expected coalescent times to compute the expected site frequency spectrum within a multinomial likelihood function. Population sizes are allowed to vary freely between coalescent events but are constant within each interval. Here, we implement the StairwayPlot approach in the Bayesian software package RevBayes. We use approaches developed for Bayesian Skyline Plots, which include independent and identically distributed (i.i.d.) population sizes, Gaussian Markov random fields and Horseshoe Markov random fields as prior distributions on population sizes. Furthermore, we implement a recently developed approach for computing the leave-one-out cross-validation probability for efficient model selection. We compare inference from our Bayesian implementation to the original Maximum Likelihood implementation, StairwayPlot2. Our results show that our Bayesian implementation in RevBayes performs comparable to StairwayPlot2 in terms of parameter accuracy, which is expected given that both use the same underlying likelihood function. From our set of prior models, the Gaussian Markov random field prior performed best for smoothly varying demographic histories, while the Horseshoe Markov random field performs best for abruptly changing demographic histories. We conclude the study by exploring several choices often faced in empirical studies, including the estimate of the total sequence length, the assumed mutation rate, as well as biases through mis-calling ancestral alleles. We show using our empirical example that as few as 10 diploid individuals are sufficient to infer complex demographic histories, but at least 500 k single nucleotide polymorphisms (SNPs) are required
Secondary sclerosing cholangitis in patients suffering cardiogenic shock
Full text linkAims
Cardiogenic shock (CS) patients suffer from severe organ hypoperfusion, yet the incidence of secondary sclerosing cholangitis in critically ill patients (SSC-CIP) in CS is poorly described. Given the limited evidence and severity of this syndrome, we aimed to further investigate SSC-CIP in the context of CS.
Methods and results
24 251 total CS patients admitted between 1 January 2010 and 31 December 2023 were retrospectively screened for the diagnosis of SSC-CIP across nine German tertiary care centers. Following identification of confirmed SSC-CIP diagnosis, baseline characteristics, laboratory values, SSC-CIP-specific imaging, diagnostics, and outcomes were obtained for analysis. 35 CS patients with a diagnosis of SSC-CIP were identified, representing a prevalence of 0.14% [95% confidence interval (CI) 0.10, 0.19]. Patients were predominantly male (77.1%) with a median age of 58 years (interquartile range [IQR] 52.5, 68.0). Acute myocardial infarction (42.9%) was the most common aetiology of CS, followed by cardiac arrhythmias (20.0%). Endoscopic retrograde cholangiopancreatography (ERCP) was performed in 77.1% of cases after a median of 33 days following CS onset [IQR 24, 65], showing typical biliary casts (60.0%), intraductal filling defects (28.6%), and bile duct obliteration (20.0%). Cast removal and stent placement was performed in nearly half of ERCP procedures (45.7%). Magnetic resonance cholangiopancreatography (MRCP) was performed in 22.9% of cases and showed intraductal dilation (11.4%), lumen narrowing (17.1%), or strictures (14.3%). Median intensive care unit and hospital length of stay was 43 days [IQR 33, 66] and 58 days [IQR 33, 88], respectively. In-hospital mortality was 57.1%. One-year (65.7%) and 3-year (71.4%) mortality remained high. Two patients underwent liver transplantation after a median of 113 days [IQR 105, 122] and were alive at 3-year follow-up.
Conclusions
In this multicentre retrospective analysis in a high-risk CS cohort, SSC-CIP was a rare yet serious complication of intensive care unit stay with high in-hospital mortality. Treatment options are limited, and liver transplantation remains the only viable long-term treatment option
Conformal Prediction without Nonconformity Scores
Full text linkConformal prediction (CP) is an uncertainty quantification framework that allows for constructing statistically valid prediction sets. Key to the construction of these sets is the notion of a nonconformity function, which assigns a real-valued score to individual data points: only those (hypothetical) data points contribute to a prediction set that sufficiently conform to the data. The point of departure of this work is the observation that CP predictions are invariant against (strictly) monotone transformations of the nonconformity function. In other words, it is only the ordering of the scores that matters, not their quantitative values. Consequently, instead of scoring individual data points, a conformal predictor only needs to be able to compare pairs of data points, deciding which of them is the more conforming one. This suggests an interesting connection between CP and preference learning, in particular learning-to-rank methods, and makes CP amenable to training data in the form of (qualitative) preferences. Elaborating on this connection, we propose methods for preference-based CP and show their usefulness in real-world classification tasks