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Scientists and the public were alarmed at the first large viral variant of SARS‐CoV‐2 reported in December 2020. We have followed the time course of emerging viral mutants and variants during the SARS‐CoV‐2 pandemic in ten countries on four continents. We examined > 383,500 complete SARS‐CoV‐2 nucleotide sequences in GISAID (Global Initiative of Sharing All Influenza Data) with sampling dates extending until April 05, 2021. These sequences originated from ten different countries: United Kingdom, South Africa, Brazil, United States, India, Russia, France, Spain, Germany, and China. Among the 77 to 100 novel mutations, some previously reported mutations waned and some of them increased in prevalence over time. VUI2012/01 (B.1.1.7) and 501Y.V2 (B.1.351), the so‐called UK and South Africa variants, respectively, and two variants from Brazil, 484K.V2, now called P.1 and P.2, increased in prevalence. Despite lockdowns, worldwide active replication in genetically and socio‐economically diverse populations facilitated selection of new mutations. The data on mutant and variant SARS‐CoV‐2 strains provided here comprise a global resource for easy access to the myriad mutations and variants detected to date globally. Rapidly evolving new variant and mutant strains might give rise to escape variants, capable of limiting the efficacy of vaccines, therapies, and diagnostic tests. Synopsis This 2020/21 time course study shows the rapid rise of new SARS‐CoV‐2 mutants and variants across the entire genome during worldwide viral replication. In 10 countries, 40 to 65% of mutants were C to T transitions. Viral mutations will affect vaccination programs. We analyzed > 383,500 SARS‐CoV‐2 RNA sequences for the occurrence of mutations across the entire genome. The time course of mutations emerging between 01/2020 and 03/2021 was determined. We initially identified ~ 10 prevalent mutations. About 77 to 100 new mutations arose concomitant with the spread of Covid‐19 between March/April 2020 and January 2021, followed by the emergence of variants in December 2020. A study of the pathogenicity of viral mutations will help understand Covid‐19 outbreaks and symptoms. Monitoring mutant selection will aid Covid‐19 diagnosis, vaccine development and therapy. New mutants will compromise vaccine efficiency. Among the SARS‐CoV‐2 mutants, C to U transitions at a frequency between 40 to 65% were prevalent. Cellular cytosine deaminases, possibly of the APOBEC type, likely drive viral mutagenesis. Graphical Abstract This 2020/21 time course study shows the rapid rise of new SARS‐CoV‐2 mutants and variants across the entire genome during worldwide viral replication. In 10 countries, 40 to 65% of mutants were C to T transitions. Viral mutations will affect vaccination programs.

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes – hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

Human pluripotent stem cells (hPSCs) serve as a powerful model for studying human neuronal differentiation, yet the temporal control of this process remains poorly understood. This study compares two differentiation systems with distinct timing of differentiation: transcription factor (TF)‐induced forward programming and stepwise cellular differentiation by dual‐SMAD (DS) inhibition. The analyses reveal that divergent cellular trajectories drive distinct neurogenesis timing. Multi‐omic analysis identifies crucial gene regulatory networks (GRNs) that govern cell fate determination and timing control. Perturbation of these GRNs modulates the timing of neurogenesis and neuronal maturation. Specifically, OLIG family TFs, enriched in the TF‐induced system, promoted cell cycle exit via NOTCH signaling regulation; their ablation delays neurogenesis in this system. Additionally, NEUROD2 overexpression after neurogenesis accelerated in vitro neuronal maturation in both TF‐ and DS‐induced differentiating cells by enhanced activation of maturation gene modules. These findings elucidate transcriptional mechanisms governing differentiation timing and provide a framework for rationally designing timing‐controlled in vitro differentiation strategies. Single‐cell profiling of TF‐induced forward programming versus stepwise dual‐SMAD differentiation reveals that divergent trajectories set the pace of neurogenesis. OLIG TFs advance cell‐cycle exit via NOTCH modulation, while NEUROD2 later accelerates maturation. The study elucidates transcriptional mechanisms governing differentiation timing, providing a reference for rationally designing timing‐controlled in vitro differentiation strategies.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease affecting motor neurons (MNs) during late adulthood. Here, with the aim of identifying early changes underpinning ALS neurodegeneration, we analyzed the GABAergic/glycinergic inputs to E17.5 fetal MNs from SOD1 G93A (SOD) mice in parallel with chloride homeostasis. Our results show that IPSCs are less frequent in SOD animals in accordance with a reduction of synaptic VIAAT-positive terminals. SOD MNs exhibited an E GABAAR 10 mV more depolarized than in WT MNs associated with a KCC2 reduction. Interestingly, SOD GABAergic/glycinergic IPSCs and evoked GABA A R-currents exhibited a slower decay correlated to elevated [Cl - ] i . Computer simulations revealed that a slower relaxation of synaptic inhibitory events acts as compensatory mechanism to strengthen GABA/glycine inhibition when E GABAAR is more depolarized. How such mechanisms evolve during pathophysiological processes remain to be determined, but our data indicate that at least SOD1 familial ALS may be considered as a neurodevelopmental disease.

Phosphorylated proteins provide insight into tumor etiology and are used as diagnostic, prognostic, and therapeutic markers of complex diseases. However, pre-analytic variations, such as freezing delay after biopsy acquisition, often occur in real hospital settings and potentially lead to inaccurate results. The objective of this work is to develop statistical methodology to assess the stability of phosphorylated proteins under short-time cold ischemia. We consider a hierarchical model to determine if phosphorylation abundance of a protein at a particular phosphorylation site remains constant or not during cold ischemia. When phosphorylation levels vary across time, we estimate the direction of the changes in each protein based on the maximum overall posterior probability and on the pairwise posterior probabilities, respectively. We analyze a dataset of ovarian tumor tissues that suffered cold-ischemia shock before the proteomic profiling. Gajadhar et al. (2015) applied independent clusterings for each patient because of the high heterogeneity across patients, while our proposed model shares information allowing conclusions for the entire sample population. Using the proposed model, 15 out of 32 proteins show significant changes during 1-hour cold ischemia. Through simulation studies, we conclude that our proposed methodology has a higher accuracy for detecting changes compared to an order restricted inference method. Our approach provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease.

A new family of mixture models for the model-based clustering of longitudinal data is introduced. The covariance structures of eight members of this new family of models are given and the associated maximum likelihood estimates for the parameters are derived via expectation–maximization (EM) algorithms. The Bayesian information criterion is used for model selection and a convergence criterion based on the Aitken acceleration is used to determine the convergence of these EM algorithms. This new family of models is applied to yeast sporulation time course data, where the models give good clustering performance. Further constraints are then imposed on the decomposition to allow a deeper investigation of the correlation structure of the yeast data. These constraints greatly extend this new family of models, with the addition of many parsimonious models. Nous présentons une nouvelle famille de modèles de mélanges de regroupement, à l'aide de modèles, pour des données longitudinales. La structure de covariance de huit membres de cette nouvelle famille de modèles est donnée et les estimateurs du maximum de vraisemblance associés aux paramètres sont obtenus en utilisant les algorithmes espérance-maximisation (EM). Le critère d'information bayésien (BIC) est utilisé pour choisir le modèle et un critère de convergence basé sur l'accélération d'Aitken est utilisé pour déterminer la convergence de ces algorithmes EM. Cette nouvelle famille de modèles est appliquée sur les données de décours temporel de la sporulation de levures. Ces modèles sont performants pour faire les regroupements. Des contraintes additionnelles sont aussi imposées sur la décomposition afin d'examiner en plus de profondeur la structure de corrélation dans des données de levures. Ces contraintes généralisent grandement cette nouvelle famille de modèles avec l'ajout de modèles plus parcimonieux.

With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.

This application note describes an open-source web application software package for viewing and analysing time-course event sequences in the form of log files containing timestamps. Web pages allow the visualisation of time-course event sequences as time curves and the comparison of sequences against each other to visualise deviations between the timings of the sequences. A feature allows the analysis of the sequences by parsing selected sections with a support vector machine model that heuristically calculates a value for the likelihood of an error occurring based on the textual output in the log files. This allows quick analysis for errors in files with large numbers of log events. The software is written in ASP.NET with Visual Basic code-behind to allow it to be hosted on servers and integrated into web application frameworks.

Abstract Candida albicans is a commensal organism; however, it causes fatal diseases if the host immunity is compromised. The mortality rate is very high due to the lack of effective treatment, leading to ceaseless demand for novel pharmaceuticals. In this study, time-course proteomics of C. albicans during adaptation to fetal bovine serum (FBS) was described. Time-course proteomics is a promising way to understand the exact process of going adaptation in dynamically changing environments. Candida albicans was cultivated in yeast nitrogen base (YNB) ± FBS media, and we identified 1418 proteins in the endpoint samples incubated for 0 or 60 min by a LC-MS/MS system with a long monolithic silica capillary column. Next, we carried out time-course proteomics of the YNB + FBS samples to identify top-priority proteins for adaption to FBS. We identified 16 proteins as nascent/newly synthesized proteins, and they were recognized as candidates of important virulent factors. Gene ontology analysis revealed that transport-related proteins were enriched in the 16 proteins, indicating that C. albicans probably put priority in time on the acquisition of essential elements. Time-course proteomics of C. albicans revealed the order of priority to adapt to FBS. Depicting time-course dynamics will lead to profound understandings of virulence of C. albicans . This work uses state-of-the-art proteomics methodology (LC-MS/MS) to examine the growth of the important pathogen, Candida albicans , as it adapts to fetal serum. They identify several proteins that may be candidate virulence factors.

Extraradical mycelia from different ectomycorrhizal (ECM) roots coexist and interact under the forest floor. We investigated structural connections of conspecific mycelia and translocation of carbon and phosphorus between the same or different genets. Paired ECM Pinus thunbergii seedlings colonized by the same or different Pisolithus isolates were grown side by side in a rhizobox as their mycelia contacted each other. 14CO2 or 33P-phosphoric acid was fed to leaves or a spot on the mycelium in one of the paired seedlings. Time-course distributions of 14C and 33P were visualized using a digital autoradiographic technique with imaging plates. Hyphal connections were observed between mycelia of the same Pisolithus isolate near the contact site, but hyphae did not connect between different isolates. 14C and 33P were translocated between mycelia of the same isolate. In 33P-fed mycelia, accumulation of 33P from the feeding spot toward the host ECM roots was observed. No 14C and 33P translocation occurred between mycelia of different isolates. These results provide direct evidence that contact and hyphal connection between mycelia of the same ECM isolate can cause nutrient translocation. The ecological significance of contact between extraradical mycelia is discussed.