bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of large-language model versus human performance for genetics questions
Authors: Duong Dat , Solomon Benjamin D. 
Published: January 2023

AbstractLarge-language models like ChatGPT have recently received a great deal of attention. To assess ChatGPT in the field of genetics, we compared its performance to human respondents in answering genetics questions (involving 13,636 responses) that had been posted on social media platforms starting in 2021. Overall, ChatGPT did not perform significantly differently than human respondents, but did significantly better on memorization-type questions versus critical thinking questions, frequently provided different answers when asked questions multiple times, and provided plausible explanations for both correct and incorrect answers.
Vitamin interdependencies predicted by metagenomics-informed network analyses validated in microbial community microcosms
Authors: Hessler Tomas , Huddy Robert J. , Sachdeva Rohan , Lei Shufei , Harrison Susan T.L. , Diamond Spencer , Banfield Jillian F. 
Published: January 2023

AbstractMicrobial communities are fundamental components of most ecosystems but little is known about the interactions that structure them. Metagenomic data have revolutionized our understanding of complex consortia, yet predicted interactions are rarely explored experimentally. We developed an organism abundance correlation network to investigate factors that control community organization in soil-derived laboratory microbial consortia grown under dozens of conditions. The network was overlaid with metagenomic information about functional capacities to generate testable hypotheses. We developed a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs and predicted that aVariovoraxspecies is a hub because it is a highly important source of thiamine. Quantification of thiamine during the growth ofVariovoraxin minimal media conditions showed extraordinarily high levels of production of thiamine, up to 100 mg/L. This over-production of thiamine explains why the auxotrophs correlated withVariovoraxare reliant on this organism for thiamine, despite the presence of other predicted thiamine producers in the community. A few of these thiamine auxotrophs are predicted to produce pantothenate that we show thatVariovoraxrequires to grow, implying that a subset of vitamin-dependent interactions are mutualistic. We found thatCryptococcusproduces the B-vitamin pantothenate, and co-cultures led to a 90-130-fold fitness increase for bothCryptococcusandVariovorax. This study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.
Early HIV-1 Gag Assembly on Lipid Membrane with vRNA
Authors: Zhou Anne X.-Z. , Hammond John A. , Sheng Kai , Millar David P. , Williamson James R. 
Published: January 2023

AbstractMass photometry (MP) was used to investigate the assembly of myristoylated full-length HIV-1 Gag (myr-Gag) and vRNA 5’ UTR fragment in a supported lipid bilayer (SLB) model system. The MP trajectories demonstrated that Gag trimerization on the membrane is a key step of early Gag assembly in the presence of vRNA. Growth of myr-Gag oligomers requires vRNA, occuring by addition of 1 or 2 monomers at a time from solution. These data support a model where formation of the Gag hexamers characteristic of the immature capsid lattice occurs by a gradual edge expansion, following a trimeric nucleation event. These dynamic single molecule data involving protein, RNA, and lipid components together, provide novel and fundamental insights into the initiation of virus capsid assembly.
Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions
Authors: Braxton Alicia M , Kiemen Ashley L , Grahn Mia P , Forjaz André , Babu Jaanvi Mahesh , Zheng Lily , Jiang Liping , Cheng Haixia , Song Qianqian , Reichel Rebecca , Graham Sarah , Damanakis Alexander I , Fischer Catherine G , Mou Stephanie , Metz Cameron , Granger Julie , Liu Xiao-Ding , Bachmann Niklas , Almagro-Pérez Cristina , Jiang Ann Chenyu , Yoo Jeonghyun , Kim Bridgette , Du Scott , Foster Eli , Hsu Jocelyn Y , Rivera Paula Andreu , Chu Linda C , Liu Fengze , Niknafs Noushin , Fishman Elliot K , Yuille Alan , Roberts Nicholas J , Thompson Elizabeth D , Scharpf Robert B , Cornish Toby C , Jiao Yuchen , Karchin Rachel , Hruban Ralph H , Wu Pei-Hsun , Wirtz Denis , Wood Laura D 
Published: January 2023

ABSTRACTPancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogeneKRAS, but no gene other thanKRASwas altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multipleKRASmutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.
Cross-species transcriptomic analysis identifies mitochondrial dysregulation as a functional consequence of the schizophrenia-associated 3q29 deletion
Authors: Purcell Ryan H. , Sefik Esra , Werner Erica , King Alexia T. , Mosley Trenell J. , Merritt-Garza Megan E. , Chopra Pankaj , McEachin Zachary T. , Karne Sridhar , Raj Nisha , Vaglio Brandon J. , Sullivan Dylan , Firestein Bonnie L. , Tilahun Kedamawit , Robinette Maxine I. , Warren Stephen T. , Wen Zhexing , Faundez Victor , Sloan Steven A. , Bassell Gary J. , Mulle Jennifer G. 
Published: January 2023

AbstractRecent advances in the genetics of schizophrenia (SCZ) have identified rare variants that confer high disease risk, including a 1.6 Mb deletion at chromosome 3q29 with a staggeringly large effect size (O.R. > 40). Understanding the impact of the 3q29 deletion (3q29Del) on the developing CNS may therefore lead to insights about the pathobiology of schizophrenia. To gain clues about the molecular and cellular perturbations caused by the 3q29 deletion, we interrogated transcriptomic effects in two experimental model systems with complementary advantages: isogenic human forebrain cortical organoids and isocortex from the 3q29Del mouse model. We first created isogenic lines by engineering the full 3q29Del into an induced pluripotent stem cell line from a neurotypical individual. We profiled transcriptomes from isogenic cortical organoids that were aged for 2 months and 12 months, as well as day p7 perinatal mouse isocortex, all at single cell resolution. Differential expression analysis by genotype in each cell-type cluster revealed that more than half of the differentially expressed genes identified in mouse cortex were also differentially expressed in human cortical organoids, and strong correlations were observed in mouse-human differential gene expression across most major cell-types. We systematically filtered differentially expressed genes to identify changes occurring in both model systems. Pathway analysis on this filtered gene set implicated dysregulation of mitochondrial function and energy metabolism, although the direction of the effect was dependent on developmental timepoint. Transcriptomic changes were validated at the protein level by analysis of oxidative phosphorylation protein complexes in mouse brain tissue. Assays of mitochondrial function in human heterologous cells further confirmed robust mitochondrial dysregulation in 3q29Del cells, and these effects are partially recapitulated by ablation of the 3q29Del genePAK2. Taken together these data indicate that metabolic disruption is associated with 3q29Del and is conserved across species. These results converge with data from other rare SCZ-associated variants as well as idiopathic schizophrenia, suggesting that mitochondrial dysfunction may be a significant but overlooked contributing factor to the development of psychotic disorders. This cross-species scRNA-seq analysis of the SCZ-associated 3q29 deletion reveals that this copy number variant may produce early and persistent changes in cellular metabolism that are relevant to human neurodevelopment.
Heterogeneity in quiescent Müller glia in the uninjured zebrafish retina drive differential responses following photoreceptor ablation
Authors: Krylov Aaron J. , Yu Shuguang , Newton Axel , He Jie , Jusuf Patricia R. 
Published: January 2023

AbstractLoss of neurons in the neural retina is a leading cause of vision loss. While humans do not possess the capacity for retinal regeneration, zebrafish can achieve this through activation of resident Müller glia. Remarkably, despite the presence of Müller glia in humans and other mammalian vertebrates, these cells lack an intrinsic ability to contribute to regeneration. Upon activation, zebrafish Müller glia can adopt a stem cell-like state, undergo proliferation and generate new neurons. However, the underlying molecular mechanisms of this activation subsequent retinal regeneration remains unclear. To address this, we performed single-cell RNA sequencing (scRNA-seq) and report remarkable heterogeneity in gene expression within quiescent Müller glia across distinct dorsal, central and ventral retina pools of such cells. Next, we utilised a genetically driven, chemically inducible nitroreductase approach to study Müller glia activation following selective ablation of three distinct photoreceptor subtypes: long wavelength sensitive cones, short wavelength sensitive cones, and rods. There, our data revealed that a region-specific bias in activation of Müller glia exists in the zebrafish retina, and this is independent of the distribution of the ablated cell type across retinal regions. Notably, gene ontology analysis revealed that injury-responsive dorsal and central Müller glia express genes related to dorsal/ventral pattern formation, growth factor activity, and regulation of developmental process. Through scRNA-seq analysis, we identify a shared genetic program underlying initial Müller glia activation and cell cycle entry, followed by differences that drive the fate of regenerating neurons. We observed an initial expression of AP-1 and injury-responsive transcription factors, followed by genes involved in Notch signalling, ribosome biogenesis and gliogenesis, and finally expression of cell cycle, chromatin remodeling and microtubule-associated genes. Taken together, our findings document the regional specificity of gene expression within quiescent Müller glia and demonstrate unique Müller glia activation and regeneration features following neural ablation. These findings will improve our understanding of the molecular pathways relevant to neural regeneration in the retina.
Elucidating Fibroblast Growth Factor-induced kinome dynamics using targeted mass spectrometry and dynamic modeling
Authors: Veth Tim S. , Francavilla Chiara , Heck Albert J.R. , Altelaar Maarten 
Published: January 2023

SummaryFibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition (EMT). The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals tight regulation of the RAF kinase family.
Different time scales of common-cause evidence shape multisensory integration, recalibration and motor adaptation
Authors: Debats Nienke B , Heuer Herbert , Kayser Christoph 
Published: January 2023

AbstractPerception engages the processes of integration, recalibration and sometimes motor adaptation to deal with discrepant multisensory stimuli. These processes supposedly deal with sensory discrepancies on different time scales, with integration reducing immediate ones and recalibration and motor adaptation reflecting the cumulative influence of their recent history. Importantly, whether discrepant signals are bound during perception is guided by the brains’ inference of whether they originate from a common cause. When combined, these two notions lead to the hypothesis that the different time scales on which integration and recalibration (or motor adaptation) operate are associated with different time scales of evidence of a common cause underlying two signals. We tested this prediction in a well‐established visuo‐motor paradigm, in which human participants performed visually guided hand movements. The kinematic correlation between hand and cursor movements indicates their common origin, allowing us to manipulate the common‐cause evidence by this correlation between visual and proprioceptive signals. Specifically, we dissociated hand and cursor signals during individual movements while preserving their correlation across movement endpoints. Following our hypothesis, this manipulation reduced integration compared to a condition in which visual and proprioceptive signals were perfectly correlated. In contrast, recalibration and motor adaption were not affected. This supports the notion that multisensory integration and recalibration are guided by common‐cause evidence but deal with sensory discrepancies on different time scales: while integration is prompted by local common‐cause evidence and reduces immediate discrepancies instantaneously, recalibration and motor adaptation are prompted by global common‐cause evidence and reduce persistent discrepancies.
Long-term herbivore removal experiments reveal different impacts of geese and reindeer on vegetation and ecosystem CO2-fluxes in high-Arctic tundra
Authors: Bon Matteo Petit , Hansen Brage B. , Loonen Maarten J. J. E. , Petraglia Alessandro , Bråthen Kari Anne , Böhner Hanna , Layton-Matthews Kate , Beard Karen H. , Moullec Mathilde Le , Jónsdóttir Ingibjörg S. , van der Wal René 
Published: January 2023

AbstractGiven the current and anticipated rates of global change, with associated shifts in herbivore population densities, understanding the role of different herbivores in shaping ecosystem structure and processes is critical for predicting ecosystem responses. Here, we examined the controls exerted by migratory geese and resident, non-migratory ungulates, two dominating yet functionally contrasting herbivores, on the rapidly warming Arctic tundra.We collected vegetation and ecosystem carbon flux data at peak plant growing season in the two longest running herbivore removal experiments in high-Arctic Svalbard. Herbivore exclosures had been set up independently in a wet habitat utilised by barnacle geese (Branta leucopsis) in summer and in mesic-to-dry habitats utilised by wild reindeer (Rangifer tarandus platyrhynchus) year-round.Excluding geese produced vegetation state transitions from heavily grazed, moss-dominated (4 g m-2dry weight of live aboveground vascular plants) to ungrazed, graminoid-dominated (60 g m-2; after 4-yr exclusion) and then horsetail-dominated (150 g m-2; after 15-yr exclusion) tundra. This caused large increases in vegetation carbon and nitrogen pools, dead biomass and moss-layer depth. Modifications in nitrogen concentrations and carbon-to-nitrogen ratios of vegetation and soil suggested overall slower nutrient cycling rates in the short-term absence of geese. Long-term goose removal quadrupled the net ecosystem carbon sequestration by increasing gross ecosystem photosynthesis more than ecosystem respiration.Excluding reindeer for 21 years also produced detectable, but weaker, increases in live and dead biomass, vegetation carbon and nitrogen pools, moss-layer depth and ecosystem respiration. Yet, reindeer removal did not alter the chemistry of either vegetation or soil, nor net ecosystem carbon sequestration.Our findings suggest that, though both herbivores were key drivers of ecosystem structure and processes, localised effects of geese, highly concentrated in space and time, are larger than those exerted by more widely dispersed reindeer. We illustrate that the impacts of herbivory across the tundra landscape are contingent on the habitat utilised for foraging, its sensitivity, the exerted grazing pressure, and herbivore characteristics. Our results underscore the conspicuous heterogeneity in how Arctic herbivores control ecosystem functioning, with important implications under current and future global change.
Nesting flight statistics for wind turbine planning: a MoveApps workflow
Authors: Kölzsch Andrea , Gal Johannes 
Published: January 2023

AbstractAs green, renewable energy is increasing by the installation of more and more wind turbines, the assessment of their impact on protected species has to be improved by more automatized, data-driven risk analyses.We have developed as set of two workflows to extract simple parameters for collision risk models from GPS tracks of sensitive bird species during nesting. The workflows have been integrated into the free MoveApps platform and are available there. The analysis code of all components of the workflows is openly available on GitHub, and improvement and adaption to other, similar requirements is encouraged.With three example data sets of white storks (WS), red kites (RK) and marsh harriers (MH), we illustrate how the workflows are used. The first workflow identifies nesting sites and time of nesting from the GPS tracks, the second workflow calculates flight speeds, flight duration, flight height and distance from the nest. Estimated flight speeds show low within species variability, with averages of 4.1 m/s (MH), 6.9 m/s (RK) and 10.9 m/s (WS). Extracted times of nesting are widely spread through the season and flight height and distance to the nest when in flight show large differences between individuals and years. Similar to the central evaluation distances around the nest required by national legislation, the 50% in-flight usage thresholds are 700 m (MH), 1100 m (RK) and 1400 m (WS). Flight height during nesting is rather low, on average 16 m (MH), 75 m (RK) and 193 m (WS) above the ground.These values can help to estimate collision risk with wind turbines for large birds in Central Europe during their nesting period. Finally, the developed MoveApps workflows (an possible adaptions) can be used to extract required parameters from tracking studies of any other vulnerable species or populations in an unbiased, automated manner to improve wind turbine placement in relation to nesting sites.
Protein aggregation in wound fluid confines bacterial lipopolysaccharide and reduces inflammation
Authors: Petrlova Jitka , Hartman Erik , Petruk Ganna , Hwee Lim Jeremy Chun , Adav Sunil Shankar , Kjellström Sven , Puthia Manoj , Schmidtchen Artur 
Published: January 2023

AbstractBacterial lipopolysaccharide (LPS) induces the rapid formation of protein aggregates in human wound fluid. We aimed to define such LPS-induced aggregates and the functional consequences of protein aggregation using a combination of mass spectrometry analyses, biochemical imaging, and experimental animal models. We show that such wound-fluid aggregates contain a multitude of protein classes, including sequences from coagulation factors, annexins, histones, antimicrobial proteins/peptides, and apolipoproteins. Proteins and peptides with a high aggregation propensity were identified, and selected components were verified biochemically by western blot analysis. Staining by thioflavin T and the Amytracker probe demonstrated the presence of amyloid-like aggregates formed after exposure to LPS in vitro in human wound fluid and in vivo in porcine wound models. Using NF-κB-reporter mice and IVIS bioimaging, we show that such wound-fluid LPS aggregates induce a significant reduction in local inflammation compared with LPS in plasma. The results show that protein/peptide aggregation is a mechanism for confining LPS and reducing inflammation and further underscore the connection between host defense and amyloidogenesis.
DeepERA: deep learning enables comprehensive identification of drug-target interactions via embedding of heterogeneous data
Authors: Li Le , Wierbowski Shayne D. , Yu Haiyuan 
Published: January 2023

AbstractDrug-target interaction prediction is a crucial step in drug development, discovery, and repurposing. Due to the tremendous combinatorial search space of all drug-protein pairs, machine learning algorithms have been utilized to facilitate the identification of novel drug-target interactions. Deep learning, known as a powerful learning system, has recently shown superior performance to traditional machine learning in many biological and biomedical areas. In this paper, we proposed an end-to-end deep learning model, DeepERA, to identify drug-target interactions based on heterogeneous data. This model assembles three independent feature embedding modules (intrinsic embedding, relational embedding, and annotation embedding) which each represent different attributes of the dataset and jointly contribute to the comprehensive predictions. This is the first work that, to our knowledge, applied deep learning models to learn each intrinsic features, relational features, and annotation features and combine them to predict drug-protein interactions. Our results showed that DeepERA outperformed other deep learning approaches proposed recently. The studies of individual embedding modules explained the dominance of DeepERA and confirmed the effects of the “guilt by associations” assumption on the performance of the prediction model. Using our DeepERA framework, we identified 45,603 novel drug-protein interactions for the whole human proteome, including 356 drug-protein interactions for the human proteins targeted by SARS-CoV-2 viral proteins. We also performed computational docking for the selected interactions and conducted a two-way statistical test to “normalize” the docking scores of different proteins/drugs to support our predictions.