bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient microbial glucose-fructose conversion at ambient temperature through adaptive laboratory evolution and application for D-allulose production
Authors: Lehnert Alexander , Gentile Rocco , Tahiraj Claudia , Wirtz Astrid , Baumgart Meike , Polen Tino , Gohlke Holger , Bott Michael 
Published: December 2024

AbstractGlucose and fructose, the major monosaccharides in nature, are metabolically interconverted in phosphorylated form by glucose 6-phosphate isomerase. An enzyme specifically evolved for interconversion of the non-phosphorylated forms has not been discovered yet. Xylose isomerase catalyzes the conversion as a side reaction, but with very low activity at ambient temperatures. Here, we developed a microbial catalyst enabling the efficient interconversion of glucose and fructose at 30°C. Using adaptive laboratory evolution (ALE) of a selection strain dependent on xylose isomerase activity for growth, we obtained enzyme variants with up to ten-fold improved catalytic efficiency. Fast growth additionally required a mutation in the transporter IolT1, boosting sugar uptake and increasing the intracellular substrate concentration. During engineering of a strain unable to grow on glucose, we identified mutations in the sucrose transporter PtsS, presumably converting it into a transporter for glucose and fructose. Molecular dynamics simulations of the transporter and xylose isomerase variants suggested mechanistic consequences of the mutations. Our novel strains enabled the microbial conversion of glucose to the low-calorie sweetener D-allulose with a yield of 15%.Graphical abstract
High-dimensional mediation analysis to elucidate the role of metabolites in the association between PFAS exposure and reduced SARS-CoV-2 IgG in pregnancy
Authors: Guan Haibin , Chen Jia , Kaur Kirtan , Amreen Bushra , Lesseur Corina , Dolios Georgia , Andra Syam S. , Narasimhan Srinivasan , Pulivarthi Divya , Midya Vishal , De Witte Lotje D. , Bergink Veerle , Rommel Anna-Sophie , Petrick Lauren M. 
Published: December 2024

AbstractObjectiveWe previously found that per- and polyfluoroalkyl substances (PFAS) mixture exposure is inversely associated with SARS-CoV-2 IgG (IgG) antibody levels in pregnant individuals. Here, we aim to identify metabolites mediating this relationship to elucidate the underlying biological pathways.MethodsWe included 59 pregnant participants from a US-based pregnancy cohort. Untargeted metabolomic profiling was performed using Liquid Chromatography-High Resolution Sass spectrometry (LC-HRMS), and weighted Quantile Sum (WQS) regression was applied to assess the PFAS and metabolites mixture effects on IgG. Metabolite indices positively or negatively associated with IgG levels were constructed separately and their mediation effects were examined independently and jointly.ResultsThe PFAS-index was negatively associated with IgG levels (beta=-0.273, p=0.002), with PFHpS and PFHxS as major contributors. Two metabolite-indices were constructed, one positively (beta=1.260, p<0.001) and one negatively (beta=-0.997, p<0.001) associated with IgG. Key contributors for these indices included protoporphyrin, 5-hydroxytryptophan, n-acetylproline, and tyrosine. Analysis of single mediator showed that 48.9% (95%CI: 21.9%,125.0%) and 50.1% (95% CI: 8.1%, 90.1%) of the PFAS index-IgG total effect were mediated by the negative and positive metabolite-indices, respectively. Joint analysis of the metabolite-indices indicated a cumulative mediation effect of 73.6% (95%CI: 44.9%, 116.4%). Enriched pathways associated with these metabolites indices were phenylalanine, tyrosine, and tryptophan biosynthesis and arginine metabolism.ConclusionsWe observed significant mediation effects of plasma metabolites on the PFAS-IgG relationship, suggesting that PFAS disrupts the balance of plasma metabolites that contributes to reduced plasma IgG production.
Cell cycle-regulated transcriptional pausing ofDrosophilareplication-dependent histone genes
Authors: Kemp James P. , Geisler Mark S. , Hoover Mia , Cho Chun-Yi , O’Farrell Patrick H. , Marzluff William F. , Duronio Robert J. 
Published: December 2024

AbstractCoordinated expression of replication-dependent (RD) histones genes occurs within the Histone Locus Body (HLB) during S phase, but the molecular steps in transcription that are cell cycle regulated are unknown. We report thatDrosophilaRNA Pol II promotes HLB formation and is enriched in the HLB outside of S phase, including G1-arrested cells that do not transcribe RD histone genes. In contrast, the transcription elongation factor Spt6 is enriched in HLBs only during S phase. Proliferating cells in the wing and eye primordium express full-length histone mRNAs during S phase but express only short nascent transcripts in cells in G1 or G2 consistent with these transcripts being paused and then terminated. Full-length transcripts are produced when Cyclin E/Cdk2 is activated as cells enter S phase. Thus, activation of transcription elongation by Cyclin E/Cdk2 and not recruitment of RNA pol II to the HLB is the critical step that links histone gene expression to cell cycle progression inDrosophila.
Clinical and Biological Stratification in 121,560 Antidepressant Prescription Trajectories using Unsupervised Modelling and Clustering
Authors: Herrero-Zazo Maria , Fitzgerald Tomas , Banasik Karina , Louloudis Ioannis , Vassos Evangelos , Colón-Ruiz Critóbal , Segura-Bedmar Isabel , Kessing Lars V. , Ostrowski Sisse R. , Pedersen Ole B. , Schork Andrew J. , Sørensen Erik , Ullum Henrik , Werge Thomas M. , Bruun Mie T. , Christoffersen Lea AN. , Didriksen Maria , Erikstrup Christian , Aagaard Bitten , Mikkelsen Christina ,   DBDS Genomic Consortium, Lewis Cathryn M. , Brunak Søren , Birney Ewan 
Published: December 2024

AbstractMajor depressive disorder is a complex condition with diverse presentations and polygenic underpinnings. Leveraging large biobanks linked to primary care prescription data, we developed a data-driven approach based on antidepressant prescription trajectories for patient stratification and novel phenotype identification. We extracted quantitative prescription trajectories for 56,951 UK Biobank (UKB) and 64,609 Danish National Biobank (CHB+DBDS) individuals. Using Hidden Markov Models and K-means clustering, we identified five and six patient clusters, respectively. Multinomial logistic regression and non-parametric association tests, using clinical information, enabled patient group characterization. We consistently identified three common patient groups across cohorts: first, a majority group of individuals with mild to moderate depression; second, those with severe mental illness (i.e., a group with a higher likelihood of psychiatric diagnoses, such as bipolar depression, with odds ratios: ORUKB= 1.87 [95% CI = 1.48, 2.35], p = 2.7e-6; ORCHB+DBDS= 1.69 [95% CI = 1.41, 2.02], p = 2.3e-7); and third, patients with less severe forms of depression or receiving treatment for conditions other than depression (i.e., a group with a lower likelihood of depression diagnosis: ORUKB= 0.80 [95% CI = 0.74, 0.85], p = 3e-10; ORCHB+DBDS= 0.77 [95% CI = 0.73, 0.82], p < 1e-10). Genome-wide association studies (GWAS) revealed 14 significant loci, includingUSP4andBCHEon chromosome 3, as well as a locus associated with the drug metabolising enzymeCYP2D6. These findings, and the reproducibility across cohorts, demonstrate the power of unsupervised phenotyping from primary care prescriptions for patient stratification and pharmacogenetics research.
Population comparisons reveal more social interactions are correlated with larger brains in the Lake Tanganyikan cichlidNeolamprologus brevis
Authors: Ma Bin , Dunster Boyd , Lein Etienne , Zhu Boshan , Li Weiwei , Goverts Zoë , Jordan Alex 
Published: December 2024

AbstractThe Social Brain Hypothesis (SBH) proposes that complex social environments drive the evolution of larger brains and specific neuroanatomical adaptations. This relationship can be difficult to study in the wild, because species that differ in social organization may also diverge in morphology, ecology, phylogeny, and other life history parameters. Here we use two populations of the shell-dwelling cichlidNeolamprologus breviswith contrasting social environments to test whether increased social complexity is associated with larger brain sizes or specific regional adaptations. Behavioral observations revealed similarly low feeding rates in both populations, but significantly more frequent social interaction frequencies in the population one of the populations. This population had larger total brain volumes relative to body size, with a disproportionately larger telencephalon and a smaller hypothalamus, suggesting region-specific adaptations to social demands. By integrating behavioral quantification and neuroanatomical analysis, our study highlights the importance of sociality as a driver of brain evolution and demonstrates the utility of cichlid fish as a model for testing the SBH in non-mammalian systems. These findings provide empirical support for the SBH and underscore the value of combining behavioral and morphological data in evolutionary neuroethology.
Small molecule activation of the tumor suppressor kinase LKB1
Authors: Kretschmer Lin Song , Mitchell Dominique C. , Liu Jin , Wang LeeAnn , Wang Zheng , He Changliang , Ding Luo , Adler Marc , Turnham Rigney E. , Kellett Timothy , Keith Aidan , Hwang Yeonjoo C. , Chan Gary K. L. , Li Weicheng , Ramamoorthi Roopa , Lively Sarah , Drakas Robert A. , Ramani Vijay , Qing Tingting , Verba Kliment A. , Ostrem Jonathan M. L. , Beresis Richard T. , Gordan John D. 
Published: December 2024

AbstractThe ability to identify and target oncogenic signals has transformed clinical oncology. Drug development for targeted therapies has historically focused on the inhibition of oncogenic kinases and GTPases. However, many cancer patients do not benefit from targeted approaches because their tumors lack targetable mutations. Therapeutic augmentation of tumor suppressive signaling could be a viable alternative but poses challenges. Specifically, designing compounds capable of stimulating kinase activity is more structurally challenging than inhibitor design, and most kinases lack targetable allosteric pockets. Inactivation of the liver kinase B1 (LKB1) tumor suppressor kinase is associated with poor prognosis and therapeutic resistance. Thus, augmented LKB1 function could be beneficial for cancer patients whose tumors retain intact copies of the gene. LKB1 signals as part of an obligate trimer including the scaffolding protein Mouse protein-25 (MO25) and the pseudokinase (PSK) STE20-related kinase adapter protein (STRAD). As STRAD binds but does not metabolize ATP, it provides the opportunity for a novel activation strategy. We have developed STRAD-binding compounds capable of activating LKB1 and demonstrate the therapeutic benefits of LKB1 activation in a target-dependent manner within cancer cell lines.
Improving whole biodiversity monitoring and discovery with environmental DNA metagenomics
Authors: Curto Manuel , Veríssimo Ana , Riccioni Giulia , Santos Carlos D. , Ribeiro Filipe , Jentoft Sissel , Alves Maria Judite , Gante Hugo F. 
Published: December 2024

AbstractEnvironmental DNA (eDNA) metagenomics sequences all DNA molecules present in environmental samples and has the potential of identifying virtually any organism from which they are derived. However, due to unacceptable levels of false positives and negatives, this approach is underexplored as a tool for biodiversity monitoring across the tree of life, particularly for non-microscopic eukaryotes. We present SEQIDIST, a framework that combines multilocus BLAST matches against several reference databases followed by analysis of sequence identity distribution patterns to disentangle false positives while revealing new biodiversity and increasing the accuracy of metagenomic approaches. We tested SEQIDIST on an eDNA metagenomic dataset from a riverine site and compare the results to those obtained with an eDNA metabarcoding approach for benchmarking purposes. We start by characterizing the biological community (∼ 2000 taxa) across the tree of life at low taxonomic levels and show that eDNA metagenomics has a higher sensitivity than eDNA metabarcoding in discovering new diversity. We show that limited representation of whole genome sequences in reference databases can lead to false positives. For non-microscopic eukaryotes, eDNA metagenomic data often consist of a few sparse, anonymous sequences scattered across the genome, making metagenome assembly methods unfeasible. Finally, we infer eDNA source and residency time using read length distributions as a measure of decay status. The higher accuracy of SEQIDIST opens the discussion of the archival potential of eDNA metagenomics and its implementation in biodiversity monitoring actions at large planetary and temporal scales.
Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation
Authors: Mialland Adrien , Fukunaga Shuzo , Katsuki Riku , Dong Yunfei , Yamaguchi Hideki , Saito Yutaka 
Published: December 2024

AbstractNavigating the protein fitness landscape is critical for understanding sequence-function relationships and improving variant effect prediction. However, the limited availability of experimentally measured functional data poses a significant bottleneck. To address this, we present a novel data augmentation strategy called fitness translocation, which leverages fitness landscapes from related proteins to enhance the performance of variant effect predictors on a target protein. Using embeddings from protein language models and by translocating the features within the sequence space, we transfer the fitness information from homologous protein datasets to a target protein to augment its dataset. Our approach was evaluated across diverse protein species, including IGPS orthologs, GFP orthologs, and SARS-CoV-2 spike proteins strains for cell entry and ACE2 binding. The results demonstrate consistent and substantial improvements in predictive performances, particularly for datasets with limited training data. Furthermore, we introduce a systematic selection framework for identifying the most beneficial protein datasets for augmentation and optimizing predictive gains. This study highlights the potential of related protein fitness translocation to advance protein engineering and variant effect prediction. The implementation of the method is available athttps://github.com/adrienmialland/ProtFitTrans.
Diversity and productivity of a natural grassland decline with the number of global change factors
Authors: Wang Jianyong , Liu Yingxia , Oduor Ayub M.O. , Kleunen Mark van , Liu Yanjie 
Published: December 2024

AbstractGrasslands are highly diverse ecosystems providing important ecosystem services, but they currently face a variety of anthropogenic stressors simultaneously. Quantifying grassland responses to global change factors (GCFs) is crucial for developing effective strategies to mitigate the negative impacts of global change on grassland communities and to promote their resilience in the face of future environmental challenges. We conducted a field experiment in the Songnen grassland, northeastern China, to test the combined effects of 0, 1, 2, 4, 6, and 8 GCFs, including fungicide, herbicide, insecticide, antibiotic stress, heavy metal pollution, light pollution, microplastic pollution, nitrogen deposition, tillage disturbance, and increased precipitation. We found that within one year, the increasing number of GCFs negatively impacts on both the productivity and diversity of grassland communities. In comparison to exposure to a single GCF, exposure to 8 GCFs led to a reduction in productivity and species richness by 42.8% and 42.9%, respectively. Furthermore, these negative effects seem to be linked to the reduction of dominant species and the concurrent increase in neonative species. The results of hierarchical diversity-interaction modeling suggested that the adverse impacts of an increasing number of GCFs on community productivity and diversity are attributable to both the specific identities of GCFs involved and their unique pairwise interactions. The results suggest that grasslands may quickly loose stability and degrade more rapidly in response to multiple co-occurring GCFs. Greater efforts should be made to conserve the functions and services of grassland ecosystems by reducing the impacts of human activities.
The proteasome activator PA200 regulates tumor cell invasion
Authors: Yazgili Ayse Seda , Živković Dušan , Dafun Angelique Sanchez , Giotopoulou Georgia G. , Behrend Sabine , Koops Frauke , Zemke Linda , Stacher-Priehse Elvira , Goldmann Torsten , Reiling Norbert , Stathopoulos Georgios T. , Marcoux Julien , Bousquet Marie-Pierre , Meiners Silke 
Published: December 2024

ABSTRACTThe proteasome is a well-identified therapeutic target for cancer treatment but the efficacy of proteasome inhibitors for solid tumors is severely hampered by cytotoxic side effects due to broad proteasome inhibition in non-tumor tissues. We and others observed significant upregulation of proteasome activator 200 (PA200) specifically in non-small cell lung cancer (NSCLC) tissues correlating with poor prognosis. To understand the functional role of PA200, we genetically depleted PA200 in two different NSCLC cell lines and characterized its functional effectsin vitroandin vivo.Of note, we observed a striking disparity in proteasome regulation, cellular growth, and transcriptome regulation upon deletion of PA200 in the two lung cancer cell lines. A common response to PA200 depletion, however, was the concerted impairment of tumor cell migration and invasionin vitroand diminished flank tumor growthin vivo. These data strongly suggest that the function of PA200 in tumor cells depends on the cellular context but has a conserved effect on tumor cell migration. Furthermore, using anin vitrodrug screening system with purified 20S and recombinantly expressed PA200, we were able to identify a PA200 inhibitor. This compound inhibits the activity of PA200-proteasome complexes but only marginally interferes with the catalytic activity of the 20S proteasome core. The drug showed inhibitory activity in cell extracts and reduced the invasive capacity of A549 lung cancer cells. Taken together, our data uncover PA200 as a potential target for lung cancer therapy and identify Prest2-M17 as a potential PA200 inhibitor.
Exploring Genetic Interactions with Rare Variants Reveals Gene Networks Susceptible to Complex Diseases
Authors: Jiang Hui , Tang Bin , Li Kun , Zhang Liubing , Liang Junhao , Tang Clara Sze-Man , Tam Paul Kwong-Hang , Wang Binbin , Song Youqiang , Wang Qiang , Li Mulin Jun , Huang Hailiang , Li Miaoxin 
Published: December 2024

AbstractGenetic interactions play a crucial role in understanding the susceptibility and etiology of complex human diseases. However, existing methods for analyzing gene-gene interactions are often limited to common variants and may not capture the effects of rare variants. In this study, we propose a novel method called GGI-RUNNER that is specifically designed for analyzing gene-gene interactions of rare variants. GGI-RUNNER evaluates the enrichment of rare variant interaction burden in patients relative to baselines in the general population. The baselines are estimated for pairwise genes by a recursive truncated negative-binomial regression model on multiple genomic features available from public data. Intensive simulations demonstrate that GGI-RUNNER exhibits substantially higher statistical power than alternative epistasis tests and maintains reasonable type I error rates even in stratified populations. Applied to real data, GGI-RUNNER identified significant rare variant interactions associated with Alzheimer’s disease, Hirschsprung’s disease, Ulcerative colitis and Crohn’s disease. Furthermore, we found that these identified genes for each trait can form interconnected networks, which may provide valuable insights into the underlying molecular mechanisms.
mitoXplorer 3.0, a web tool for exploring mitochondrial dynamics in single-cell RNA-seq data
Authors: Haering Margaux , del Bondio Andrea , Puccio Helene , Habermann Bianca H. 
Published: December 2024

ABSTRACTMitochondria are important eukaryotic organelles, best known for their function in ATP production and in cellular metabolism and signalling. It is widely accepted that their structure, composition and function differ across cell types. However, little is known about mitochondrial variability within the same cell type. To truly understand mitochondrial function and dynamics, we need to study individual cell types, as well as mitochondrial variability on a single-cell level.Based on our mitoXplorer 2.0 web tool, we introduce mitoXplorer 3.0 with new features adapted for analysing single-cell sequencing data, focusing only on mitochondria. We provide a formatting script, scXplorer to generate mitoXplorer 3.0 compatible files for upload. This script creates pseudo-bulk transcriptomes of cell types from scRNA-seq data for differential expression analysis and subsequent mitochondria-centric analysis with mitoXplorer classical interfaces. It also creates a single-cell expression matrix only containing mitochondria-associated genes (mito-genes), which can be analysed for cell-to-cell variability with novel, interactive interfaces created for mitoXplorer 3.0: these new interfaces help to identify sub-clusters of cell types based only on mito-genes and offer in-depth mitochondria-centric analysis of subpopulations. We demonstrate the usability and predictive power of mitoXplorer 3.0 using single-cell transcriptome data from a single-cell study of Spinocerebellar Ataxia Type 1. We identified several mito-processes and mito-genes that are majorly affected in SCA1 Purkinje cells and which might contribute to our understanding of mitochondrial decline and subsequent Purkinje cell loss in this disease.MitoXplorer 3.0 is freely available athttps://mitoxplorer3.ibdm.univ-amu.fr.
What makes human cortical pyramidal neurons functionally complex
Authors: Aizenbud Ido , Yoeli Daniela , Beniaguev David , de Kock Christiaan PJ , London Michael , Segev Idan 
Published: December 2024

AbstractHumans exhibit unique cognitive abilities within the animal kingdom, but the neural mechanisms driving these advanced capabilities remain poorly understood. Human cortical neurons differ from those of other species, such as rodents, in both their morphological and physiological characteristics. Could the distinct properties of human cortical neurons help explain the superior cognitive capabilities of humans? Understanding this relationship requires a metric to quantify how neuronal properties contribute to the functional complexity of single neurons, yet no such standardized measure currently exists. Here, we propose the Functional Complexity Index (FCI), a generalized, deep learning-based framework to assess the input-output complexity of neurons. By comparing the FCI of cortical pyramidal neurons from different layers in rats and humans, we identified key morpho-electrical factors that underlie functional complexity. Human cortical pyramidal neurons were found to be significantly more functionally complex than their rat counterparts, primarily due to differences in dendritic membrane area and branching pattern, as well as density and nonlinearity of NMDA-mediated synaptic receptors. These findings reveal the structural-biophysical basis for the enhanced functional properties of human neurons.