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Time-resolved crystallography enabled the visualization of protein molecular motion during reaction. While light is commonly used to initiate reactions in time-resolved crystallography, only a small number of proteins can in fact be activated by light. However, many biological reactions can be triggered by the interaction of proteins with ligands. The sample delivery method presented here uses a mix-and-extrude approach based on 3D printed microchannels in conjunction with a micronozzle to study the dynamics of samples in viscous media that can be triggered by diffusive mixing. The device design allows for mixing of ligands and protein crystals in a time window of 2 to 20 seconds. The device characterization using a model system (fluorescence quenching of iq-mEmerald proteins by copper ions) demonstrated that ligand and protein crystals, each within the lipidic cubic phase, can be mixed efficiently. The potential use of this approach for time-resolved membrane protein crystallography to support in the development of new drugs is also discussed.Synopsis3D printed mixing-HVE devices address time-resolved membrane protein crystallography challenges via compact dual-flow LCP injection.Abstract Figure

Hereditary Leiomyomatosis and renal cell cancer (HLRCC) is a cancer syndrome caused by inactivating germline mutations in fumarate hydratase (FH) and subsequent accumulation of fumarate. Fumarate accumulation leads to the activation of an anti-oxidant response via nuclear translocation of the transcription factor NRF2. The activation of the anti-oxidant response is key for cellular survival in FH-deficient cells, yet the extent to which chromatin remodelling shapes the anti-oxidant response is currently unknown. Here, we explored the global effects of FH loss on the chromatin landscape to identify transcription factor networks involved in the highly remodelled chromatin landscape of FH-deficient cells. We identify FOXA2 as a key transcription factor which directly regulates anti-oxidant response genes and subsequent metabolic rewiring. Moreover, we also find that FOXA2 regulates anti-oxidant genes independent of the canonical anti-oxidant regulator NRF2. The identification of FOXA2 as an anti-oxidant regulator provides new insights into the molecular mechanisms behind cell responses to fumarate accumulation, and potentially provides new avenues for therapeutic intervention for HLRCC.

Fumarate Hydratase (FH) is a mitochondrial enzyme that catalyses the reversible hydration of fumarate to malate in the TCA cycle. Germline mutations of FH lead to HLRCC, a cancer syndrome characterised by a highly aggressive form of renal cancer(1). Although HLRCC tumours metastasise rapidly, FH-deficient mice develop premalignant cysts in the kidneys, rather than carcinomas (2). How Fh1-deficient cells overcome these tumour suppressive events during transformation is unknown. Here, we perform a genome-wide CRISPR/Cas9 screen to identify genes that, when ablated, enhance the proliferation of Fh1-deficient cells. We found that the depletion of HIRA enhances proliferation and invasion of Fh1-deficient cells in vitro and in vivo. Mechanistically, Hira loss enables the activation of MYC and its target genes, increasing nucleotide metabolism specifically in Fh1-deficient cells, independent of its histone chaperone activity. These results are instrumental for understanding mechanisms of tumorigenesis in HLRCC and the development of targeted treatments for patients.

Transfer RNAs acquire a large plethora of chemical modifications. Among those, modifications of the anticodon loop play important roles in translational fidelity and tRNA stability. Four human wobble U containing tRNAs obtain 5-methoxycarbonylmethyluridine (mcm5U34) or 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U34), which play a role in decoding. This mark involves a cascade of enzymatic activities. The last step is mediated by Alkylation Repair Homolog 8 (ALKBH8). In this study, we performed a transcriptome-wide analysis of the repertoire of ALKBH8 RNA targets. Using a combination of HITS-CLIP-seq and RIP-seq analyses, we uncover ALKBH8-bound RNAs. It targets an additional wobble U-containing tRNA tRNALys(UUU). More interestingly, the spectrum of bound RNAs includes other types of non-coding RNAs, such as C/D box snoRNAs, 7SK RNA or some miRNAs, respectively.

Deregulated Fbxo7 expression is associated with many pathologies, including anaemia, male sterility, cancer, and Parkinson’s disease, demonstrating its critical role in a variety of cell types. Although Fbxo7 is an F-box protein that recruits substrates for SCF-type E3 ubiquitin ligases, it also promotes the formation of cyclin D/Cdk6/p27 complexes in an E3-ligase independent fashion. We discovered PFKP, the major gatekeeper of glycolysis, in a screen for Fbxo7 substrates. PFKP has been previously shown to be a critical substrate of Cdk6 for the viability of T-ALL cells. We investigated the molecular relationships between Fbxo7, Cdk6 and PFKP, and the functional effect Fbxo7 has on T cell metabolism, viability, and activation. Fbxo7 promotes Cdk6-independent ubiquitination and Cdk6-dependent phosphorylation of PFKP. Importantly Fbxo7-deficient cells have reduced Cdk6 activity, and haematopoietic and lymphocytic cell lines show a significant dependency on Fbxo7. Compared to WT cells, CD4+ T cells with reduced Fbxo7 expression show increased glycolysis, despite lower cell viability and activation levels. Metabolomic studies of activated CD4+ T cells confirm increased glycolytic flux in Fbxo7-deficient cells, as well as altered nucleotide biosynthesis and arginine metabolism. We show Fbxo7 expression is glucose-responsive at the mRNA and protein level, and we propose Fbxo7 inhibits PFKP and glycolysis via its activation of Cdk6.

SUMMARYMetabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage.HighlightsBranched-chain amino acids catabolism is reprogrammed in ccRCC tumorsBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCCAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginineASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levelsSilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.Required MetadataCurrent code version

Continuation of experience-dependent neural activity during offline periods of sleep and wakefulness is a critical element of memory consolidation. Using functional magnetic resonance imaging (fMRI), offline consolidation effects have been evidenced probing behavioral and neurophysiological changes during memory retrieval, i.e. in the context of task practice. Resting state fMRI (rfMRI) further allows investigating the offline evolution of recently learned information, without confounding task-related effects. In the present study, we used rfMRI to investigate offline changes in functional connectivity (FC) and Amplitude of Low Frequency Fluctuation (ALFF) associated with learning and relearning in a spatial navigation task, following an episode of post-training wake or sleep, respectively. Resting state activity was measured immediately (i) before and (ii) after topographical learning in a virtual town, (iii) 4 days later after regular sleep (RS) vs. sleep deprivation (SD) on the first post-learning night, and (iv) after topographical re-learning in an extended town encompassing the initial map. Task (navigation)-based fMRI activity was also recorded on Day 1 and Day 4 during target retrieval. Our results highlight the continuation of navigation-related activity in the subsequent resting state, as evidenced by changes in FC and ALFF in task-related neural networks. Behavioural performance was not modulated by post-training SD vs RS. However, in line with prior reports, post-training SD was associated with increased FC between navigation-related brain structures when faced to the task of learning a novel but related environment in the extended version of the city at Day 4. These results suggest the use of compensatory resources to link novel information with SD-related less efficiently consolidated memory traces.

Study ObjectivesSleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease. Our goal was to assess whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset.MethodsWe computed whole-genome Polygenic Risk Scores (PRS) for AD and extensively phenotyped sleep under different sleep conditions, including baseline sleep, recovery sleep following sleep deprivation and extended sleep opportunity, in a carefully selected homogenous sample of healthy 363 young men (22.1 y ± 2.7) devoid of sleep and cognitive disorders.ResultsAD PRS was associated with more slow wave energy, i.e. the cumulated power in the 0.5-4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss, and potentially with large slow wave sleep rebound following sleep deprivation. Furthermore, higher AD PRS was correlated with higher habitual daytime sleepiness.ConclusionsThese results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.Statement of SignificanceWe show that the genetic liability for developing for Alzheimer’s disease (AD), as grasped over the entire genome using polygenic risk scores (PRS), is associated with sleep intensity and daytime sleepiness in healthy individuals devoid of sleep disorders and aged < 30 y, i.e. 30 to 60 years before typical onset of AD cognitive symptoms. Sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative. The findings reinforce the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.

Age-related cognitive decline arises from alterations in brain structure as well as in sleep-wake regulation. Here, we investigated whether preserved wake-dependent regulation of cortical function could represent a positive factor for cognitive fitness in aging. We quantified cortical excitability dynamics during prolonged wakefulness as a sensitive marker of age-related alteration in sleep-wake regulation in 60 healthy older individuals (50–69 y; 42 women). Brain structural integrity was assessed with amyloid-beta- and tau-PET, and with MRI. Participants’ cognition was investigated using an extensive neuropsychological task battery. We show that individuals with preserved wake-dependent cortical excitability dynamics exhibit better cognitive performance, particularly in the executive domain which is essential to successful cognitive aging. Critically, this association remained significant after accounting for brain structural integrity measures. Preserved dynamics of basic brain function during wakefulness could therefore be essential to cognitive fitness in aging, independently from age-related brain structural modifications that can ultimately lead to dementia., Van Egroo, Narbutas, Chylinski et al. report cortical excitability dynamics during prolonged wakefulness as a sensitive marker of age-related alterations in sleep-wake regulation in older individuals. They show that preserved wake-dependent cortical excitability dynamics are associated with better cognitive performance.

Study ObjectivesNew challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the inter- and intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined.MethodsA first dataset (DS1, 4 recordings) scored by 6 experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to investigate intra-expert variability. Percentage agreements and Conger’s kappa were derived from epoch-by-epoch comparisons on pairwise, consensus and majority scorings.ResultsOn DS1 the number of epochs of agreement decreased when the number of expert increased, in both majority and consensus scoring, where agreement ranged from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0.81 to 0.79. Agreement between expert consensus and autoscoring was 93%. On DS2 intra-expert variability was evidenced by the kappa systematic decrease between autoscoring and each single expert between datasets (0.75 to 0.70).ConclusionsVisual scoring induces inter- and intra-expert variability, which is difficult to address especially in big data studies. When proven to be reliable and if perfectly reproducible, autoscoring methods can cope with intra-scorer variability making them a sensible option when dealing with large datasets.Statement of SignificanceWe confirmed and extended previous findings highlighting the intra- and inter-expert variability in visual sleep scoring. On large datasets those variability issues cannot be completely addressed by neither practical nor statistical solutions such as group training, majority or consensus scoring.When an automated scoring method can be proven to be as reasonably imperfect as visual scoring but perfectly reproducible, it can serve as a reliable scoring reference for sleep studies.