bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unified knowledge-driven network inference from omics data
Authors: Rodriguez-Mier Pablo , Garrido-Rodriguez Martin , Gabor Attila , Saez-Rodriguez Julio 
Published: October 2024

AbstractAnalysing omics data requires computational methods to effectively handle its complexity and to derive meaningful hypotheses about molecular mechanisms. While data-driven statistical and machine learning methods can identify patterns from omics data across multiple samples, they typically require a large number of samples and they often lack interpretability and alignment with existing biological knowledge. In contrast, knowledge-based network methods integrate molecular data with prior knowledge to provide results that are biologically interpretable, but they lack both a unified mathematical framework, leading to ad-hoc solutions specific to particular data types or prior knowledge, limiting their generalisability, and a common modelling interface for programmatic manipulation, restricting method extensions. Furthermore, existing methods generally cannot perform joint network inference across multiple samples or conditions, which restricts their capacity to capture shared mechanisms, making these methods more sensitive to noise and prone to overfitting. To address these limitations, we introduce CORNETO (Constrained Optimisation for the Recovery of NETworks from Omics), a unified framework for knowledge-driven network inference. CORNETO redefines the joint inference task as a constrained optimisation problem with a penalty that induces structured sparsity, allowing for simultaneous network inference across multiple samples. The framework is highly flexible and supports a wide variety of prior knowledge networks—undirected, directed and signed graphs, as well as hypergraphs—enabling the generalisation and improvement of many network inference methods, despite their seemingly different assumptions. We demonstrate its utility by presenting novel extensions of methods for signalling, metabolism and protein-protein interactions. We show how these new methods improve the performance of traditional techniques on a diverse set of biological tasks using simulated and real data. CORNETO is available as an open-source Python package (github.com/saezlab/corneto), facilitating researchers in extending, reusing, and harmonising methods for network inference.
Multivariate Trait Evolution: Models for the Evolution of the Quantitative Genetic G-Matrix on Phylogenies
Authors: Blomberg Simone P. , Muniz Michelle , Bui Mai Ngoc , Janke Cooper 
Published: October 2024

AbstractGenetic covariance matrices (G-matrices) are a key focus for research and predictions from quantitative genetic evolutionary models of multiple traits. There is a consensus among quantitative geneticists that the G-matrix can evolve through “deep” time. Yet, quantitative genetic models for the evolution of the G-matrix are conspicuously lacking. In contrast, the field of macroevolution has several stochastic models for univariate traits evolving on phylogenies. However, despite much research into multivariate phylogenetic comparative methods, analytical models of how multivariate trait matrices might evolve on phylogenies have not been considered. Here we show how three analytical models for the evolution of matrices and multivariate traits on phylogenies, based on Lie group theory, Riemannian geometry and stochastic differential (diffusion) equations, can be combined to unify quantitative genetics and macroevolutionary theory in a coherent mathematical framework. The models provide a basis for understanding how G-matrices might evolve on phylogenies, and we show how to fit models to dataviasimulation using Approximate Bayesian Computation. Such models can be used to generate and test hypotheses about the evolution of genetic variances and covariances, together with the evolution of the traits themselves, and how these might vary across a phylogeny. This unification of macroevolutionary theory and quantitative genetics is an important advance in the study of phenotypes, allowing for the construction of a synthetic quantitative theory of the evolution of species and multivariate traits over “deep” time.Lay SummaryWe unite Quantitative Genetics, the major mathematical theory of multivariate quantitative trait microevolution, with the mathematical theory of multivariate macroevolution. To do this, we allow the key component of quantitative genetic theory, the matrix of additive genetic variances and covariances (the G-matrix) to evolve along evolutionary trees. This is an advance because the G-matrix is assumed to be constant in quantitative genetics, but it has been recognised that it evolves on macroevolutionary timescales (in “deep time”). Uniting Quantitative Genetics with macroevolutionary theory allows for a more complete mathematical description of Darwin’s theory of evolution, and allows for further testing of evolutionary hypotheses.
Introgression of the sunflowerHaHB4gene in modern wheat: An advancement in resilience to deal with climate change
Authors: Ayala Francisco , Curin Facundo , Diaz-Zorita Martín , Murguía José , Montero Bulacio Enrique , Portapila Margarita , Otegui María Elena , Chan Raquel Lía , González Fernanda Gabriela 
Published: October 2024

AbstractBreeding for improved tolerance to water deficit is critical to mitigate climate change impact on wheat yield. In 2020, Argentina approved the first wheat transformed with the sunflowerHaHB4gene (INDØØ412-7), which increased yield by 16% compared to the non-transformed parental Cadenza under drought conditions. Previous studies may have overestimatedHaHB4benefits, as Cadenza is a long cycle for the Pampas region. Quantifying the yield advantage ofHaHB4in modern, well-adapted cultivars and elucidating the physiological processes involved, is crucial for identifying optimal environments for this technology. In a broad network of experiments (one greenhouse and 29 field environments), anHaHB4-introgressed line of Algarrobo was compared with the conventional cultivar. Under water deficit during the reproductive phase, the yield advantage ofHaHB4was 15% in the greenhouse and 13% in the field.HaHB4improved the relative yield by 0.06 to 0.08 % per mm of water deficit. The enhanced water use and water use efficiency conferred byHaHB4, allowed maintaining growth and yield under water deficit.HaHB4showed the highest benefit with moderate heat stress (∑Tmax > 30 °C ∼40-60 °Cd). In areas prone to drought combined with heat stress,HaHB4would enhance yield stability by improving water-limited yield.HighlightsHaHB4-introgressed wheat enhanced water-limited yield by maintaining the crop growth under moderate to severe water deficit combined with mild heat stress through increasing water use and water use efficiency.
Disentangling the CHAOS of intrinsic disorder in human proteins
Authors: Vries Ida de , Bak Jitske , Salmoral Daniel Álvarez , Xie Ren , Borza Razvan , Konijnenberg Maria , Perrakis Anastassis 
Published: October 2024

AbstractMost proteins consist of both folded domains and Intrinsically Disordered Regions (IDRs). However, the widespread occurrence of intrinsic disorder in human proteins, along with its characteristics, is often overlooked by the broader communities of structural and molecular biologists. Building on the MobiDB database of intrinsically disorder in proteins, here we develop a comprehensive dataset (Comprehensive analysis ofHuman proteinsAnd their disOrderedSegments (CHAOS)). We implement empirical internally consistent definitions of what constitutes a disordered region, annotate general characteristics such as cellular location, essentiality, and post-translational modifications, and cross-reference to structure predictions from AlphaFold. Most proteins contain at least one disordered region, predominantly located at the protein termini. IDRs are less hydrophobic and are enriched in post-translational modifications compared to non-IDRs. Additionally, we discovered that proteins residing in different cellular locations possess distinct disorder profiles. Finally, the predicted AlphaFold models of proteins in CHAOS suggest that while protein disorder may be intrinsic, it does not have to be extrinsic. Hereby we enhance the visibility and understanding of intrinsic disorder in human proteins.Key messages83% of human proteins contain one or more intrinsically disordered regions.Intrinsically disordered regions are predominantly located at protein termini.The amount and location of disordered regions s differs throughout cellular compartments.AlphaFold predicts secondary structure elements within intrinsically disordered regions and fully disordered proteins.
Multi-character assessment of caryopsis in diverse common wheat germplasm, including high protein breeds
Authors: Kosina Romuald , Tomaszewska Paulina 
Published: October 2024

ABSTRACTIn 15 varieties and hybrids of common wheat, including high-protein, varieties such as Atlas 66, Nap Hal, and Lancota, the microstructural characters of the caryopsis were analysed. Among the wheats, a high variability in the correlation matrices of characters was observed. A variable relationship between the specific gravity of the caryopsis and the size of the endosperm cavity was demonstrated. Some principal components (I, II, IV and V) contained the majority of the information regarding the weight, quality, vitreousness, and aleurone layer of the caryopsis, respectively. In the ordination space of nonmetric multidimensional scaling (nmMDS), two groups of wheat were distinguished: high-protein and low-protein subaleurone layers. Endosperm morphotypes were characterised by the proportion of two types of starch tissue, cylindric and isodiametric. In the nmMDS ordination space, wheat was described by the frequency distributions of six endosperm cavity morphotypes, with more (leptokurtosis) or less (platykurtosis) cumulated variation. In the case of endosperm developmental anomalies, the intrusive growth of cell walls, particularly in the aleurone layer, was identified as playing a significant role in the formation of endosperm mosaic architecture and domains. Varietal developmental specificity was discovered in the development of acellular starch domains within the endosperm tissue and starch grain storage in the cavity (as observed in cv. Purdue).
Formation of polyphasic RNP granules by intrinsically disordered Qβ coat proteins and hairpin-containing RNA
Authors: Granik Naor , Goldberg Sarah , Amit Roee 
Published: October 2024

AbstractRNA-protein (RNP) granules are fundamental components in mammalian cells where they perform multiple crucial functions. Many RNP granules form via phase separation driven by protein-protein, protein-RNA, and RNA-RNA interactions. Notably, associated proteins frequently contain intrinsically disordered regions (IDRs) which can associate with multiple partners. Previously we have shown that synthetic RNA molecules containing multiple hairpin coat-protein binding sites can phase separate, forming granules capable of selectively incorporating proteins inside. Here, we expand this platform by introducing a phage coat protein with a known IDR which facilitates protein-protein interactions. We show that the coat protein phase-separates on its ownin vivo, and that introduction of hairpin-containing RNA molecules can lead to dissolvement of the protein granules. We further demonstrate via multiple assays that RNA valency, determined by the number of hairpins present on the RNA, leads to distinctly different phase behaviors, effectively forming a polyphasic programmable RNP granule. Moreover, by incorporating the gene for a blue fluorescent protein into the RNA, we demonstrate a phase-dependent boost to protein titer. These insights not only shed light on the behavior of natural granules, but also hold profound implications for the biotechnology field, offering a blueprint for engineering cellular compartments with tailored functionalities.
Identification of a key gain-of-function residue for effector binding byin vitroshuffling of barleyMla NLRgenes
Authors: Zhang Xiaoxiao , Gao Jialing , Molloy Lucy M. , Crean Lauren M. , Williams Simon J. , Rathjen John P. 
Published: October 2024

AbstractNatural plant populations maintain high resistance (R) gene diversities that provide effective pathogen resistance; however, such diversity has been reduced significantly by the genetic bottlenecks associated with plant domestication and breeding. Agricultural crops typically contain limitedRgene diversity so resistance is often short-lived as pathogens evolve rapidly to evade recognition. Themildew resistance locus A(Mla)Rgene family of barley and wheat represents a rich source of natural genetic variation that is ideal for mining disease resistance specificities.Mla Rgenes encode immune receptor proteins of the nucleotide-binding leucine-rich repeat (NLR) class that recognise unrelated plant pathogens by binding secreted virulence proteins termed effectors. The barley NLRs MLA13 and MLA7 confer resistance to different strains of the barley powdery mildew pathogen through direct interaction with the pathogen effectors AVRA13and AVRA7respectively. Using DNA shuffling, we generated a variant library by recombining theMla7andMla13genesin vitro. The variant library was cloned into yeast generating ∼4,000 independent clones and was screened for interaction with AVRA13and AVRA7using a yeast-two-hybrid (Y2H) assay. This yielded a number of clones that encode NLR proteins that interacted with AVRA13. Sequence analysis showed that the interacting MLA proteins can be clustered into three groups, all of which contain a critical residue from MLA13. While MLA13 and MLA7 differ by 30 residues across the LRR domain, the replacement of leucine to serine at this position in MLA7 facilitated interaction with AVRA13in yeast and AVRA13-dependent immune signallingin planta. We have established a pipeline that evolves MLAs to recognise distinct pathogen effectors without the requirement for protein structural knowledge and the use of rationale design. We suggest these findings represent a step towards evolving novel recognition capabilities rapidlyin vitro.
LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data
Authors: Deng Tao , Huang Mengqian , Xu Kaichen , Lu Yan , Xu Yucheng , Chen Siyu , Xie Nina , Wu Hao , Sun Xiaobo 
Published: October 2024

AbstractIdentifying co-expressed genes across tissue domains and cell types is essential for revealing co-functional genes involved in biological or pathological processes. While both single-cell RNA-sequencing (scRNA-seq) and spatially-resolved transcriptomic (SRT) data offer insights into gene co-expression patterns, current methods typically utilize either data type alone, potentially diluting the co-functionality signals within co-expressed gene groups. To bridge this gap, we introduce LEGEND, a novel computational method that integrates scRNA-seq and SRT data for identifying groups of co-expressed genes at both cell type and tissue domain levels. LEGEND employs an innovative hierarchical clustering algorithm designed to maximize intra-cluster redundancy and inter-cluster complementarity, effectively capturing more nuanced patterns of gene co-expression and spatial coherence. Enrichment and cofunction analyses further showcase the biological relevance of these gene clusters, and their utilities in exploring context-specific novel gene functions. Notably, LEGEND can reveal shifts in gene-gene interactions under different conditions, furnishing insights for disease-associated gene crosstalk. Moreover, LEGEND can be utilized to enhance the annotation accuracy of both spatial spots in SRT and single-cells in scRNA-seq, and pioneers in identifying genes with designated spatial expression patterns. LEGEND is available athttps://github.com/ToryDeng/LEGEND.
Cloudgene 3: Transforming Nextflow Pipelines into Powerful Web Services
Authors: Forer Lukas , Schönherr Sebastian 
Published: October 2024

AbstractSummaryNextflow has emerged as a flexible and scalable workflow system for building computational pipelines in life sciences. Simultaneously, Software-as-a-Service platforms enable researchers to concentrate on data analysis rather than on setting up pipelines locally. However, converting existing pipelines into web services can be challenging due to complexities such as handling large datasets, web development and overall security. Cloudgene 3 addresses these challenges by providing a platform that transforms Nextflow pipelines into scalable web services in just a few steps. Making pipelines available as public or private services enhances collaboration among diverse teams and improves accessibility, eliminating the need for extensive technical expertise.Availability and ImplementationCloudgene 3 is free available athttps://www.cloudgene.ioContactlukas.forer@i-med.ac.at
Chitinase-1 inhibition reverses metabolic dysregulation and restores homeostasis in MASH animal models
Authors: Drzewicka Katarzyna , Głuchowska Katarzyna , Mlącki Michał , Hofman Bartłomiej , Tuszynska Irina , Ryan Tristram , Piwowar Katarzyna , Wilczyński Bartosz , Dymkowska Dorota , Dymek Barbara , Rejczak Tomasz , Lisiecki Kamil , Gołębiowski Adam , Jagielski Adam , Muchowicz Angelika , Ryan Dylan , Zabłocki Krzysztof , O’Neill Luke A.J. , Zasłona Zbigniew 
Published: October 2024

AbstractOATD-01 is a chitinase-1 (CHIT1) inhibitor, reducing inflammation and fibrosis in animal models where chronic inflammation leads to tissue remodeling. CHIT1, predominantly secreted by macrophages, is overexpressed in metabolic-dysfunction-associated steatohepatitis (MASH). In this study, we demonstrated the efficacy of OATD-01 in two murine and a rat model of MASH. RNA-Seq analysis revealed that OATD-01 reversed MASH-dysregulated genes. Apart from the attenuation of inflammation and fibrosis, OATD-01 regulated metabolic processes such as lipid metabolism and glycolysis. We demonstrated that both genetic and pharmacological inactivation of CHIT1 resulted in inhibition of glycolysis and glucose uptake in primary macrophages. As a consequence, we observed increased ATP, lower citrate and increased acetate levels, resulting in a reduced IL-1β secretion. These results revealed the key role for CHIT1 in regulating metabolism. OATD-01 is a macrophage modulator that can directly restore metabolic balance and consequently inhibit inflammation and fibrosis, supporting its use for MASH treatment.
Modulation of Host Cell Membrane Biophysics Dynamics by Neospora caninum: A Study Using LAURDAN Fluorescence with Hyperspectral Imaging and Phasor Analysis
Authors: Díaz Marcela , Robello Carlos , Cabrera Andrés , Malacrida Leonel 
Published: October 2024

AbstractNeospora caninumis known to manipulate host cell organelles and recruit lipids for its survival. However, the impact of this lipid redistribution on host cell membranes remains poorly understood. This study used LAURDAN fluorescence, hyperspectral imaging, and phasor plot analysis to investigate howN. caninummodifies membrane order in Vero cells. The results revealed a significant decrease in host cell plasma and internal membrane order upon infection, suggesting that cholesterol is redistributed from the host plasma membrane to the parasitophorous vacuoles. To mimic cholesterol depletion, uninfected cells were treated with methyl-β-cyclodextrin (MBCD), which increased membrane fluidity. Conversely, replenishing infected cells with cholesterol-loaded MBCD restored membrane fluidity to levels lower than control cells, indicating cholesterol enrichment. These findings provide novel insights into howN. caninummodulates host cell membrane dynamics through lipid manipulation, potentially aiding its intracellular survival.
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location and histology using GCN
Authors: Xue Shuailin , Zhu Fangfang , Chen Jinyu , Min Wenwen 
Published: October 2024

AbstractSpatial transcriptomics technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer (ViT) and Graph Convolutional Network (GCN) that integrates histological images, spot-based spatial transcriptomics data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately, aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations.Key PointsscstGCN combines multi-modal information including histology image, spot-based spatial transcriptomics (ST) data, and physical spatial location through deep learning methods to achieve single-cell resolution of spot-based ST data without requiring single-cell references.scstGCN employs GCN to capture complex relationships between neighboring cells, facilitating the integration of multimodal feature information based on single-cell level, and then accurately infers single-cell resolution spatial gene expression.scstGCN can infer single-cell resolution gene expression across the entire tissue region. Through transfer learning, gene expression in three-dimensional tissues can be characterized efficiently. Furthermore, it demonstrates outstanding performance in spatial patterns enhancement, functional enrichment analysis, and annotate tissues at the high-resolution.
Secreted phospholipase PLA2G12A-driven lysophospholipid signaling via lipolytic modification of extracellular vesicles facilitates pathogenic Th17 differentiation
Authors: Mochizuki Chika , Taketomi Yoshitaka , Irie Atsushi , Kano Kuniyuki , Nagasaki Yuki , Miki Yoshimi , Ono Takashi , Nishito Yasumasa , Nakajima Takahiro , Tomabechi Yuri , Hanada Kazuharu , Shirouzu Mikako , Watanabe Takashi , Hata Kosuke , Izumi Yoshihiro , Bamba Takeshi , Chun Jerold , Kudo Kai , Kotani Ai , Endo Yusuke , Aoki Junken , Murakami Makoto 
Published: October 2024

ABSTRACTLipogenesis-driven metabolic flux is crucial for differentiation of pathogenic Th17 cells. Although our previous CRISPR-based screening identified PLA2G12A as a key player in this process, it has remained obscure how this secreted phospholipase A2isoform controls Th17 differentiation. Here we show that global, T cell-specific, or fibroblast-specific deletion of PLA2G12A prevents Th17 differentiation and associated diseases including psoriasis and arthritis. PLA2G12A acts on Th17-derived extracellular vesicles (EVs) to produce lysophospholipids including the RORγt activator 1-oleoyl-lysophosphatidylethanolamine. These lysophospholipids are further converted by autotaxin to lysophosphatidic acid (LPA), which assists Th17 differentiation mainly via LPA2receptor. Moreover, PLA2G12A promotes the secretion and uptake of EVs by Th17 cells and alters their cargo contents. Defective Th17 differentiation by PLA2G12A deficiency is rescued by supplementation with PLA2G12A-modified EVs. Importantly, a PLA2G12A-blocking antibody prevents Th17 differentiation and ameliorates psoriasis and arthritis models. Thus, targeting the PLA2G12A-EV-lysophospholipid axis may be useful for treatment of Th17-related diseases.