bluuebunny/crossref_metadata_2025_split huggingface dataset:
Guidelines for Performing Ribosome Profiling in Plants Including Structural Analysis of rRNA Fragments
Authors: Ting Michael K. Y. , Gao Yang , Barahimipour Rouhollah , Ghandour Rabea , Liu Jinghan , Martinez-Seidel Federico , Smirnova Julia , Gotsmann Vincent Leon , Fischer Axel , Haydon Michael J. , Willmund Felix , Zoschke Reimo 
Published: November 2023

AbstractRibosome profiling (or Ribo-seq) is a technique that provides genome-wide information on the translational landscape (translatome). Across different plant studies, variable methodological setups have been described which raises questions about the general comparability of data that were generated from diverging methodologies. Furthermore, a common problem when performing Ribo-seq are abundant rRNA fragments that are wastefully incorporated into the libraries and dramatically reduce sequencing depth. To remove these rRNA contaminants, it is common to perform preliminary trials to identify these fragments because they are thought to vary depending on nuclease treatment, tissue source, and plant species. Here, we compile valuable insights gathered over years of generating Ribo-seq datasets from different species and experimental setups. We highlight which technical steps are important for maintaining cross experiment comparability and describe a highly efficient approach for rRNA removal. Furthermore, we provide evidence that many rRNA fragments are structurally preserved over diverse nuclease regimes, as well as across plant species. Using a recently published cryo-electron microscopy (cryo-EM) structure of the tobacco 80S ribosome, we show that the most abundant rRNA fragments are spatially derived from the solvent-exposed surface of the ribosome. The guidelines presented here shall aid newcomers in establishing ribosome profiling in new plant species and provide insights that will help in customizing the methodology for individual research goals.
Progression of herpesvirus infection remodels mitochondrial organization and metabolism
Authors: Leclerc Simon , Gupta Alka , Ruokolainen Visa , Chen Jian-Hua , Kunnas Kari , Ekman Axel A. , Niskanen Henri , Belevich Ilya , Vihinen Helena , Turkki Paula , Perez-Berna Ana J. , Kapishnikov Sergey , Mäntylä Elina , Harkiolaki Maria , Dufour Eric , Hytönen Vesa , Pereiro Eva , McEnroe Tony , Fahy Kenneth , Kaikkonen Minna U. , Jokitalo Eija , Larabell Carolyn A. , Weinhardt Venera , Mattola Salla , Aho Vesa , Vihinen-Ranta Maija 
Published: November 2023

AbstractViruses target mitochondria to promote their replication, and infection-induced stress during the progression of infection leads to the regulation of antiviral defenses and mitochondrial metabolism which are opposed by counteracting viral factors. The precise structural and functional changes that underlie how mitochondria react to the infection remain largely unclear. Here we show extensive transcriptional remodeling of protein-encoding host genes involved in the respiratory chain, apoptosis, and structural organization of mitochondria as herpes simplex virus type 1 lytic infection proceeds from early to late stages of infection. High-resolution microscopy and interaction analyses unveiled infection-induced emergence of rough, thin, and elongated mitochondria relocalized at the perinuclear area, a significant increase in the number and clustering of ER-mitochondria contact sites, and thickening and shortening of mitochondrial cristae. Finally, metabolic analyses demonstrated that reactivation of ATP production is accompanied by increased mitochondrial Ca2+content and proton leakage as the infection proceeds. Overall, the significant structural and functional changes in the mitochondria triggered by the viral invasion are tightly connected to the progression of the virus infection.
The meditative brain: State and trait changes in harmonic complexity for long-term mindfulness meditators
Authors: Atasoy Selen , Escrichs Anira , Stark Eloise , Terry Kendra G. M. , Camara Estela , Sanjuan Ana , Chandaria Shamil , Deco Gustavo , Kringelbach Morten L. 
Published: November 2023

ABSTRACTMeditation is an ancient practice that is shown to yield benefits for cognition, emotion regulation and human flourishing. In the last two decades, there has been a surge of interest in extracting the neural correlates of meditation, in particular of mindfulness meditation. Yet, these efforts have been mostly limited to the analysis of certain regions or networks of interest and a clear understanding of meditation-induced changes in the whole-brain dynamics has been lacking. Here, we investigate meditation-induced changes in brain dynamics using a novel connectome-specific harmonic decomposition method. Specifically, utilising the connectome harmonics as brain states - elementary building blocks of complex brain dynamics - we study the immediate (state) and long-term (trait) effects of mindfulness meditation in terms of the energy, power and complexity of the repertoire of these harmonic brain states. Our results reveal increased power, energy and complexity of the connectome harmonic repertoire and demonstrate that meditation alters brain dynamics in a frequency selective manner. Remarkably, the frequency-specific alterations observed in meditation are reversed in resting state in group-wise comparison revealing for the first time the long-term (trait) changes induced by meditation. These findings also provide evidence for the entropic brain hypothesis in meditation and provide a novel understanding of state and trait changes in brain dynamics induced by mindfulness meditation revealing the unique connectome harmonic signatures of the meditative brain.
The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study protocol and rationale
Authors: Jansen Michelle G. , Zwiers Marcel P. , Marques Jose P. , Chan Kwok-Shing , Amelink Jitse S. , Altgassen Mareike , Oosterman Joukje M. , Norris David G. 
Published: November 2023

AbstractTo understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n= 120), we recorded sleep-wake rhythms with an actigraphy device for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
New sensitive tools to characterize meta-metabolome response to short- and long-term cobalt exposure in dynamic river biofilm communities
Authors: Colas Simon , Marie Benjamin , Morin Soizic , Milhe-Poutingon Mathieu , Foucault Pierre , Chalvin Siann , Gelber Clémentine , Baldoni-Andrey Patrick , Gurieff Nicholas , Fortin Claude , Faucheur Séverine Le 
Published: November 2023

AbstractUntargeted metabolomics is a non-a priorianalysis of biomolecules that characterizes the metabolome variations induced by short- and long-term exposures to stressors. Even if the metabolite annotation remains lacunar due to database gaps, the global metabolomic fingerprint allows for trend analyses of dose-response curves for hundreds of cellular metabolites. The combination of untargeted metabolomic features and benchmark-dose (BMD) calculations then makes it possible to determine concentration range inducing defense responses (CRIDeR) and concentration range inducing damage responses (CRIDaR). To develop this approach in a context of time-dependent microbial community changes, mature river biofilms were exposed for 1 month to four cobalt (Co) concentrations (background concentration, 1 x 10-7, 5 x 10-7and 1 x 10-6M) in an open system of artificial streams. The meta-metabolomic response of biofilms was compared against a multitude of biological parameters (including bioaccumulation, biomass, chlorophyllacontent, composition and structure of prokaryotic and eukaryotic communities) monitored at set exposure times (from 1 hour to 28 days). Cobalt exposure induced extremely rapid responses of the meta-metabolome, with time range inducing defense responses (TRIDeR) of around ten seconds, and time range inducing damage responses (TRIDaR) of several hours. Even in biofilms whose structure had been altered by Co bioaccumulation (reduced biomass, chlorophyllacontents and changes in the composition and diversity of prokaryotic and eukaryotic communities), CRIDeRs with similar initiation thresholds (1.41 ± 0.77 x 10-10M Co2+added in the exposure medium) were set up at the meta-metabolome level at every time point. In contrast, the CRIDaR initiation thresholds increased by 10 times in long-term Co exposed biofilms. The present study demonstrates that defense and damage responses of biofilm meta-metabolome exposed to Co are rapidly and sustainably impacted, even within tolerant and resistant microbial communities.Graphical abstractHighlightsProkaryotic community structures were impacted after 1 h of exposure to CoBiofilm meta-metabolome was impacted after 36 s of exposure to CoBiofilm meta-metabolome response was faster than changes in biofilm communitiesShort- and long-term exposed biofilms have similar CRIDeR initiation thresholdsLong-term exposed biofilms have higher CRIDaR initiation thresholds
Generation and characterization of a novel mouse model of Becker Muscular Dystrophy with a deletion of exons 52 to 55
Authors: Perillat Lucie O. M. , Wong Tatianna W. Y. , Maino Eleonora , Ahmed Abdalla , Scott Ori , Hyatt Elzbieta , Delgado-Olguin Paul , Ivakine Evgueni A. , Cohn Ronald D. 
Published: November 2023

AbstractBecker Muscular Dystrophy (BMD) is a rare X-linked recessive neuromuscular disorder caused by in-frame deletions in theDMDgene that result in the production of a truncated, yet functional, dystrophin protein. BMD is often considered a milder form of Duchenne Muscular Dystrophy, in which mutations typically result in the disruption of the reading frame and the malfunction or loss of dystrophin. The consequences of BMD-causing in-frame deletions on the organism are more difficult to predict, especially in regard to long-term prognosis. Here, we employed CRISPR-Cas9 technology to generate a newDmddel52-55 mouse model by deleting exons 52-55, resulting in a typical BMD-like in-frame deletion. To delineate the long-term effects of this deletion, we studied these mice over 52 weeks. Our results suggest that a truncated dystrophin is sufficient to maintain wildtype-like muscle and heart functions in young mice. However, the truncated protein appears insufficient to maintain normal muscle homeostasis and protect against exercise-induced damage at 52 weeks. To further delineate the effects of the exons 52-55 in-frame deletion, we performed RNA-Seq pre– and post-exercise and identified several differentially expressed pathways that could explain the abnormal muscle phenotype observed at 52 weeks in the BMD model.Summary StatementWe generated and characterized the long-term effects of a Becker Muscular Dystrophy-like in-frame deletion of exon 52 to 55 in mice.
An enterococcal phage protein broadly inhibits type IV restriction enzymes involved in antiphage defense
Authors: Bullen Nathan P. , Johnson Cydney N. , Andersen Shelby E. , Arya Garima , Marotta Sonia R. , Lee Yan-Jiun , Weigele Peter R. , Whitney John C. , Duerkop Breck A. 
Published: November 2023

ABSTRACTThe prevalence of multidrug resistant (MDR) bacterial infections continues to rise as the development of antibiotics needed to combat these infections remains stagnant. MDR enterococci are a major contributor to this crisis. A potential therapeutic approach for combating MDR enterococci is bacteriophage (phage) therapy, which uses lytic viruses to infect and kill pathogenic bacteria. While phages that lyse some strains of MDR enterococci have been identified, other strains display high levels of resistance and the mechanisms underlying this resistance are poorly defined. Here, we use a CRISPR interference (CRISPRi) screen to identify a genetic locus found on a mobilizable plasmid fromEnterococcus faecalisinvolved in phage resistance. This locus encodes a putative serine recombinase followed by a Type IV restriction enzyme (TIV-RE) that we show restricts the replication of phage phi47 inE. faecalis. We further find that phi47 evolves to overcome restriction by acquiring a missense mutation in a TIV-RE inhibitor protein. We show that this inhibitor, termed type IV restriction inhibiting factor A (tifA), binds and inactivates diverse TIV-REs. Overall, our findings advance our understanding of phage defense in drug-resistantE. faecalisand provide mechanistic insight into how phages evolve to overcome antiphage defense systems.
Hierarchical annotation of eQTLs enables identification of genes with cell-type divergent regulation
Authors: Przytycki Pawel F. , Pollard Katherine S. 
Published: November 2023

AbstractWhile context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell-type specific eQTLs are complicated by the nested nature of cell types. We present a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single cell chromatin accessibility data and no clustering of cells into discrete cell types. Using our model, we annotated bulk-derived eQTLs from the developing brain with high specificity to levels of a cell-type hierarchy. The increased annotation power provided by the hierarchical model allowed for sensitive detection of genes with multiple distinct non-coding elements regulating their expression in different cell types, which we validated in single-cell multiome data and reporter assays. Overall, we find that incorporating the hierarchical organization of cell types provides a powerful way to account for the relationships between cell types in complex tissues.
Drug Response Prediction and Biomarker Discovery Using Multi-Modal Deep Learning
Authors: Taj Farzan , Stein Lincoln D. 
Published: November 2023

AbstractA major challenge in cancer care is that patients with similar demographics, tumor types, and medical histories can respond quite differently to the same drug regimens. This difference is largely explained by genetic and other molecular variabilities among the patients and their cancers. Efforts in the pharmacogenomics field are underway to understand better the relationship between the genome of the patient’s healthy and tumor cells and their response to therapy. To advance this goal, research groups and consortia have undertaken large-scale systematic screening of panels of drugs across multiple cancer cell lines that have been molecularly profiled by genomics, proteomics, and similar techniques. These large data drug screening sets have been applied to the problem of drug response prediction (DRP), the challenge of predicting the response of a previously untested drug/cell-line combination. Although deep learning algorithms outperform traditional methods, there are still many challenges in DRP that ultimately result in these models’ low generalizability and hampers their clinical application. In this paper, we describe a novel algorithm that addresses the major shortcomings of current DRP methods by combining multiple cell line characterization data, addressing drug response data skewness, and improving chemical compound representation. The result is an open-source, Python-based, command-line program available athttps://github.com/LincolnSteinLab/MMDRP.
PanScreen: A Comprehensive Approach to Off-Target Liability Assessment
Authors: Sellner Manuel S. , Lill Markus A. , Smieško Martin 
Published: November 2023

AbstractDrug development projects are getting increasingly more expensive while their success rate is stagnating. Safety issues attributed to off-target binding represent a major reason for the failure of new drugs. Besides desired on-target binding, small molecules may interact with off-targets, triggering adverse effects. Therefore, the development of novel methods for early recognition of such issues that are resource-efficient and cost-effective becomes vital. Here, we introduce PanScreen, an online platform for the automated assessment of off-target liabilities. PanScreen combines structure-based modeling techniques with state-of-the-art deep learning methods to not only predict accurate binding affinities but also give insight into potential modes of action. We show that the predictions are approaching experimental accuracy found in public datasets and that the same technology can also be used for other research areas, such as drug repurposing. Such fast and inexpensive methods allow researchers to test not only drug candidates, but all small molecules that might come into contact with a human organism for potential safety concerns very early in the development process. PanScreen is publicly available atwww.panscreen.ch.
Mixed Waste Contamination Selects for a Mobile Genetic Element Population Enriched in Multiple Heavy Metal Resistance Genes
Authors: Goff Jennifer L. , Lui Lauren M. , Nielsen Torben N. , Poole Farris L. , Smith Heidi J. , Walker Kathleen F. , Hazen Terry C. , Fields Matthew W. , Arkin Adam P. , Adams Michael W. W. 
Published: November 2023

ABSTRACTMobile genetic elements (MGEs) like plasmids, viruses, and transposable elements can provide fitness benefits to their hosts for survival in the presence of environmental stressors. Heavy metal resistance genes (HMRGs) are frequently observed on MGEs, suggesting that MGEs may be an important driver of adaptive evolution in environments contaminated with heavy metals. Here, we report the meta-mobilome of the heavy metal contaminated regions of the Oak Ridge Reservation (ORR) subsurface. This meta-mobilome was compared to one derived from samples collected from unimpacted regions of the ORR subsurface. We assembled 1,615 unique circularized DNA elements that we propose to be MGEs. The circular elements from the highly contaminated subsurface were enriched in HMRG clusters relative to those from the nearby unimpacted regions. Additionally, we found that these HMRGs were associated with Gamma and Betaproteobacteria hosts in the contaminated subsurface and potentially facilitate the persistence and dominance of these taxa in this region. Finally, the HMRGs were associated with conjugative elements, suggesting their potential for future lateral transfer. We demonstrate how our understanding of MGE ecology, evolution, and function can be enhanced through the genomic context provided by completed MGE assemblies.
Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling
Authors: Fontana Jason , Sparkman-Yager David , Faulkner Ian , Cardiff Ryan , Kiattisewee Cholpisit , Walls Aria , Primo Tommy G. , Kinnunen Patrick C. , Martin Hector Garcia , Zalatan Jesse G. , Carothers James M. 
Published: November 2023

ABSTRACTEngineering bacterial metabolism to efficiently produce chemicals and materials from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important metabolic engineering tools for programming multi-gene expression regulation. However, poor predictability of guide RNA folding can disrupt enzyme balance through unreliable expression control. We devised a set of computational parameters that can describe guide RNA folding, and we expect them to be broadly applicable across CRISPR-Cas9 systems. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) inE. coliwith a kinetic parameter describing folding rate into the active structure. This parameter also enables forward design of new scRNAs, with no observed failures in our screen. We use CRISPRa target sequences from this set to design a system of three synthetic promoters that can orthogonally activate and tune expression of chosen outputs over a >35-fold dynamic range. Independent activation tuning allows experimental exploration of a three-dimensional expression design spaceviaa 64-member combinatorial triple-scRNA library. We apply these CRISPRa programs to two biosynthetic pathways, demonstrating production of valuable pteridine and human milk oligosaccharide products inE. coli. Profiling these design spaces indicated expression combinations producing up to 2.3-fold higher titer than that produced by maximal expression. Mapping production can also identify bottlenecks as targets for pathway redesign, improving titer of the oligosaccharide lacto-N-tetraose by 6-fold. Aided by computational scRNA efficacy prediction, the combinatorial CRISPRa strategy enables effective optimization of multi-step metabolic pathways. More broadly, the guide RNA design rules uncovered here may enable the routine design of effective multi-guide programs for a wide range of model- and data-driven applications of CRISPR gene regulation in bacterial hosts.
The similarities in microbial and chemical patterns of fermentation in two open environments were promoted by using 150-year-old Nukadoko as starters
Authors: Yamaguchi Marin , Imai Kota , Chen Dominique , Seong Young ah , Jo Kazuhiro , Ito Kohei 
Published: November 2023

AbstractNukadoko, a fermented rice bran employed in traditional Japanese pickling, uses lactic acid bacteria to ferment vegetables. Here, we report the microbial and chemical data of a mixture of matured 150-year-old nukadoko and commercially available rice bran placed in two open environments over 29 days. Across the two environments,Loigolactobacilluswas identified as the dominant microbial genera in the later stages of fermentation in nukadoko. The period of increase in the relative abundance ofLoigolactobacilluscorrelated with a decrease in pH and Oxidation-Reduction Potential (ORP) values. While the two environments showed a difference in the rate of change in microbial diversity, they shared the common process through whichLoigolactobacillusoutcompeted adventitious bacteria in nukadoko, as indicated by the alpha and beta diversity index. Thus, the similarities in microbial and chemical data across two open environments during fermentation using starters indicate that starters contribute to the stability of fermentation in open environments.