bluuebunny/crossref_metadata_2025_split huggingface dataset:
EcoFun-MAP: An Ecological Function Oriented Metagenomic Analysis Pipeline
Authors: Shi Zhou Jason , Xiao Naijia , Ning Daliang , Tian Renmao , Zhang Ping , Curtis Daniel , Nostrand Joy D. Van , Wu Liyou , Hazen Terry C. , Rocha Andrea M. , He Zhili , Arkin Adam P. , Firestone Mary K. , Zhou Jizhong 
Published: April 2022

AbstractAnnotating ecological functions of environmental metagenomes is challenging due to a lack of specialized reference databases and computational barriers. Here we present the Ecological Function oriented Metagenomic Analysis Pipeline (EcoFun-MAP) for efficient analysis of shotgun metagenomes in the context of ecological functions. We manually curated a reference database of EcoFun-MAP which is used for GeoChip design. This database included ∼1,500 functional gene families that were catalogued by important ecological functions, such as carbon, nitrogen, phosphorus, and sulfur cycling, metal homeostasis, stress responses, organic contaminant degradation, antibiotic resistance, microbial defense, electron transfer, virulence and plant growth promotion. EcoFun-MAP has five optional workflows from ultra-fast to ultra-conservative, fitting different research needs from functional gene exploration to stringent comparison. The pipeline is deployed on High Performance Computing (HPC) infrastructure with a highly accessible web-based interface. We showed that EcoFun-MAP is accurate and can process multi-million short reads in a minute. We applied EcoFun-MAP to analyze metagenomes from groundwater samples and revealed interesting insights of microbial functional traits in response to contaminations. EcoFun-MAP is available as a public web server at http://iegst1.rccc.ou.edu:8080/ecofunmap/.
Metformin disrupts Danio rerio metabolism at environmentally relevant concentrations: A generational study
Authors: Barros Susana , Ribeiro Marta , Coimbra Ana M. , Pinheiro Marlene , Morais Hugo , Alves Nélson , Montes Rosa , Rodil Rosario , Quintana José Benito , Santos Miguel. M. , Neuparth Teresa 
Published: April 2022

AbstractMetformin (MET) is an anti-diabetic pharmaceutical with a large-scale consumption, which is increasingly detected in surface waters. However, current knowledge on the generational effects of MET exposure in the metabolism of non-target organisms is limited. The present study aimed at investigating the effects of MET in the model freshwater teleost Danio rerio, following a generational exposure (from egg up to 9 months exposure) to environmentally relevant concentrations ranging from 361 ng/L to 13 000 ng/L. Biochemical markers were used to determine cholesterol and triglycerides levels, as well as mitochondrial complex I activity in males and females zebrafish liver. mRNA transcript changes were also assessed in the liver of both sexes by means of an exploratory RNA-seq analysis and expression levels of key genes involved in the energy metabolism and lipid homeostasis, i.e. acaca, acadm, cox5aa, idh3a, hmgcra, prkaa1, were determined using qRT-PCR analysis. The findings here reported revealed that MET was able to significantly disrupt critical biochemical and molecular processes involved in zebrafish metabolism, such as cholesterol and fatty acid biosynthesis, the mitochondrial electron transport chain and the tricarboxylic acid cycle, concomitantly to changes on the hepatosomatic index. Non-monotonic dose response curves were frequently detected in the gene expression profile, with higher effects observed for 361 ng/L and 2 166 ng/L concentrations. Collectively, the obtained results suggest that environmentally relevant concentrations of MET are able to severely disrupt D. rerio metabolism, with potential impacts at the ecological level, supporting the need to update the environmental quality standard (EQS) and predicted no-effect concentration (PNEC) for MET.Graphical abstractHighlightsDanio rerio was exposed to metformin for a full generation;MET affected COX I activity, as well as Chol and TGL content in zebrafish liver;MET altered mRNA levels of genes involved in energy metabolism and lipid content;Non-monotonic dose-response curves were frequently detected;Due to the results obtained, MET PNEC should be reviewed.
Chromenone derivatives as novel pharmacological chaperones for retinitis pigmentosa-linked rod opsin mutants
Authors: Ortega Joseph T. , McKee Andrew G. , Roushar Francis J. , Penn Wesley D. , Schlebach Jonathan P. , Jastrzebska Beata 
Published: April 2022

ABSTRACTThe correct expression of folded, functional rhodopsin (Rho) is critical for visual perception. However, this seven-transmembrane helical G protein-coupled receptor (GPCR) is prone to mutations with pathological consequences of retinal degeneration in retinitis pigmentosa (RP) due to Rho misfolding. Pharmacological chaperones that stabilize the inherited Rho variants by assisting their folding and membrane targeting could slow the progression of RP. In this study, we employed virtual screening of synthetic compounds with natural product scaffold in conjunction within vitroandin vivoevaluations to discover a novel chromenone-containing small molecule with favorable pharmacological properties that stabilizes rod opsin. This compound reversibly binds to unliganded bovine rod opsin with an EC50value comparable to the 9-cis-retinal chromophore analog and partially rescued membrane trafficking of multiple RP-related rod opsin variantsin vitro. Importantly, this novel ligand of rod opsin was effectivein vivoin murine models, protecting photoreceptors from deterioration caused either by bright light or genetic insult. Together, our current study suggests potential broad therapeutic implications of the new chromenone-containing non-retinoid small molecule against retinal diseases associated with photoreceptor degeneration.
A Systematic Evaluation of the Computational Tools for Ligand-receptor-based Cell-Cell Interaction Inference
Authors: Wang Saidi , Zheng Hansi , Choi James S. , Lee Jae K. , Li Xiaoman , Hu Haiyan 
Published: April 2022

AbstractCell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. Many molecules and signaling processes can mediate CCIs. One classical type of CCI mediator is the interaction between secreted ligands and cell surface receptors, i.e., ligand-receptor (LR) interaction. With the recent development of single-cell technologies, a large amount of single-cell RNA Sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used fifteen mouse scRNA-Seq samples that correspond to nearly 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.
The non-random assembly of functional motifs in plant-pollinator networks
Authors: Lanuza Jose B. , Allen-Perkins Alfonso , Bartomeus Ignasi 
Published: April 2022

AbstractEcological processes leave distinct structural imprints on the species interactions that shape the topology of mutualistic networks. Detecting those relationships is not trivial since they go beyond pairwise interactions, but may get blurred when considering full network descriptors. Recent work has shown that the network meso-scale can capture this important information. The meso-scale describes network subgraphs representing patterns of interactions between a small number of species (i.e., motifs) that constitute the building blocks of the whole network. Despite the possible implications of network motifs to better capture species interactions, they remain overlooked in natural plant-pollinator networks. By exploring 60 empirical plant-pollinator networks from 18 different studies with wide geographical coverage we show that some motifs are consistently under- or over-represented worldwide, suggesting that the building blocks of plant-pollinator networks are not random. Furthermore, we find that distinct motif positions describing species ecological roles (e.g., generalisation and number of indirect interactions) are occupied by different plant and floral visitor groups on both trophic levels. Bees appear less frequently in specialised motif positions with high number of indirect interactions, while the rest of floral visitor groups are infrequent in generalised motif positions with low number of indirect interactions. All plant groups tend to be over-represented on specialised motif positions, except tall plant species with separated sexes (e.g., woody dioecious or monoecious species), which are more frequent on motif positions with low number of indirect interactions. Interestingly, the realized combinations of different species groups within a motif can not be retrieved from their joint probability distributions, indicating that group combinations are not random either. Our result highlights the non-random structure of the meso-scale on plant-pollinators networks and the association of different plant and floral visitor groups with certain motifs that involve different ecological roles at a macro-ecological scale.
Discovery and Visualization of Age-dependent Patterns in the Diurnal Transcriptome of Drosophila
Authors: Sebastian Benjamin , Fey Rosalyn M. , Morar Patrick , Lasher Brittany , Chow Eileen S. , Geibultowicz Jadwiga M. , Hendrix David A. 
Published: April 2022

AbstractMany critical life processes are regulated by input from 24-hour external light/dark cycles, such as metabolism, cellular homeostasis, and detoxification. The circadian clock, which helps coordinate the response to these diurnal light/dark cycles, remains rhythmic across lifespan; however, rhythmic transcript expression is altered during normal aging. To better understand how aging impacts diurnal expression, we present an improved Fourier-based method for detecting and visualizing rhythmicity that is based on the relative power of the 24-hour period compared to other periods (RP24). We apply RP24 to transcript-level expression profiles from the heads of young (5-day) and old (55-day) Drosophila melanogaster, and reveal novel age-dependent rhythmicity changes that may be masked at the gene level. We show that core clock transcripts phase advance during aging, while most rhythmic transcripts phase delay. Transcripts rhythmic only in young flies tend to peak before lights on, while transcripts only rhythmic in old peak after lights on. We show that several pathways including glutathione metabolism, gain or lose coordinated rhythmic expression with age providing insight into possible mechanisms of age-onset neurodegeneration. Remarkably, we find that many pathways show very robust coordinated rhythms across lifespan, highlighting their putative roles in promoting neural health. We investigate statistically enriched transcription factor binding site motifs that may be involved in these rhythmicity changes.
Prioritizing Candidate eQTL Causal Genes in Arabidopsis using Random Forests
Authors: Hartanto Margi , Sami Asif Ahmed , de Ridder Dick , Nijveen Harm 
Published: April 2022

AbstractExpression quantitative trait locus (eQTL) mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of eQTL data has been generated for this model plant; however, only a few causal eQTL genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal eQTL genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype QTLs to be used for eQTLs by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize sixteen out of twenty-five potential eQTL causal genes within the 20% rank percentile. Several new features are important in prioritizing causal eQTL genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate eQTL causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.
Topological Data Analysis of Human Brain Networks Through Order Statistics
Authors: Das Soumya , Anand D. Vijay , Chung Moo K. 
Published: April 2022

AbstractUnderstanding the topological characteristics of the brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological features of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We conclude a statistically significant topological difference between the male and female brain networks.Author summaryWe fit a random graph model to the brain network and compute the expected persistent barcodes using order statistics. This novel approach significantly simplifies the computation of expected persistent barcodes, which otherwise requires complex theoretical constructs. Subsequently, the proposed statistical framework is used to discriminate if two groups of brain networks are topologically different. The method is applied in determining the sexual dimorphism in the shape of resting-state functional magnetic resonance images.
D27-LIKE1 carotenoid isomerase has a preference towards trans/cis and cis/cis conversions in Arabidopsis
Authors: Gulyás Zsolt , Moncsek Blanka , Áron Hamow Kamirán , Stráner Pál , Badics Eszter , Incze Norbert , Darkó Éva , Nagy Valéria , Perczel András , Kovács László , Soós Vilmos 
Published: April 2022

AbstractCarotenoids are colourful isoprenoids that contribute to a variety of physiological processes in plants. They also function as biosynthesis precursors of abscisic acid (ABA) and strigolactones (SLs). SL biosynthesis starts with the enzymatic conversion of all-trans-β-carotene to 9-cis-β-carotene by the DWARF27 (D27) isomerase. In Arabidopsis, D27 has two closely related paralogs, D27-LIKE1 and D27-LIKE2 which were predicted to be β-carotene-isomerases. Here we characterised D27-LIKE1 and identified some key aspects of its function. Arabidopsis d27-like1-1 mutant does not display any SL or karrikin-deficient traits, however, it exhibits a substantially higher 9-cis-violaxanthin content. In vitro feeding assays with recombinant D27-LIKE1 revealed that the protein exhibits affinity to all β-carotene isoforms but with an exclusive preference towards trans/cis conversions and the interconversion between 9-cis, 13-cis and 15-cis-β-carotene forms. Feeding experiments with zeaxanthin and violaxanthin isomers revealed that D27-LIKE1 accepts these xanthophylls as substrates. The remarkably higher 9-cis-violaxanthin content of the mutant is accompanied by a slightly higher ABA level. Finally, we presented evidence that D27-LIKE1 mRNA is phloem mobile and D27-LIKE1 is an ancient isomerase with long evolutionary history. In summary, we demonstrated that D27-LIKE1 is a carotenoid isomerase with multi-substrate specificity and has a characteristic preference towards the catalysation of cis/cis interconversion of carotenoids. Therefore, D27-LIKE1 is a potential regulator of carotenoid cis pools and eventually, SL and ABA biosynthesis pathways.
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy
Authors: Guo Binjie , Zheng Hanyu , Jiang Haohan , Li Xiaodan , Guan Naiyu , Zuo Yanming , Zhang Yicheng , Yang Hengfu , Wang Xuhua 
Published: April 2022

AbstractDue to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
Isometric Spiracular Scaling in Scarab Beetles: Implications for Diffusive and Advective Oxygen Transport
Authors: Wagner Julian M. , Klok C. Jaco , Duell Meghan E. , Socha John J. , Cao Guohua , Gong Hao , Harrison Jon F. 
Published: April 2022

AbstractThe scaling of respiratory structures has been hypothesized to be a major driving factor in the evolution of many aspects of animal physiology. Here we provide the first assessment of the scaling of the spiracles in insects using ten scarab beetle species differing 180x in mass, including some of the most massive extant insect species. Using X-ray microtomography, we measured the cross-sectional area and depth of all eight spiracles, enabling the calculation of their diffusive and advective capacities. Each of these metrics scaled with geometric isometry.Because diffusive capacities scale with lower slopes than metabolic rates, the largest beetles measured require 10-fold higher PO2gradients across the spiracles to sustain metabolism by diffusion compared to the smallest species. Large beetles can exchange sufficient oxygen for resting metabolism by diffusion across the spiracles, but not during flight. In contrast, spiracular advective capacities scale similarly or more steeply than metabolic rates, so spiracular advective capacities should match or exceed respiratory demands in the largest beetles. These data illustrate a general principle of gas exchange: scaling of respiratory transport structures with geometric isometry diminishes the potential for diffusive gas exchange but enhances advective capacities; combining such structural scaling with muscle-driven ventilation allows larger animals to achieve high metabolic rates when active.
A snapshot of statistical methods used in experimental immunoblotting: A scoping review
Authors: Gosselin Romain-Daniel 
Published: April 2022

AbstractAmong the many avenues considered to make life science more reproducible, the improvement of the quality and openness of statistical methods has taken centre stage. However, although disparities across research fields and techniques are not unknown, they remain largely unexplored. The present scoping review aims to provide a first overview of statistical methods used in biochemical research involving immunoblotting (also referred to as western blotting), a technique frequently used to quantify proteins of interest. A reverse chronological systematic sampling was implemented to explore the PubMed online browser and analyse 2932 experimental conditions (i.e., experimental groups) in quantitative immunoblots from 64 articles published at the end of 2021. The statistical test (actual study size n = 67) and software (actual study size n = 61) used for each article and the sample size for each experimental condition were documented. The results indicate an omnipresence of parametric tests, mostly one-way analysis of variance (ANOVA, 15/67) and Student’s t-test (13/67), but for many articles the statistical procedure was not clearly divulged (23/67). GraphPad Prism was the most commonly used statistical package (36/61), but many (14/61) articles did not reveal the package used. Finally, the sample size was clearly disclosed in only 1054/2932 conditions in which its median value was 3 (IQR = [3 - 6]). This study suggests that the transparency of reporting might be suboptimal in immunoblotting research and prompts more comprehensive systematic reviews in future.
Learning to Express Reward Prediction Error-like Dopaminergic Activity Requires Plastic Representations of Time
Authors: Cone Ian , Clopath Claudia , Shouval Harel Z. 
Published: April 2022

AbstractThe dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The normative motivation for TD theory is that the brain needs to learn about expected future rewards in order to learn how to maximize these rewards. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards and the actual rewards. What makes the TD learning theory so prominent is that the firing properties of dopaminergic neurons in the ventral tegmental area (VTA) appear similar to those of RPE model-neurons in TD learning. Biologically plausible implementations of TD learning assume a fixed temporal basis for each stimulus that might eventually predict a reward. Here we show on the basis of first principles that such a fixed temporal basis is implausible. We also show that certain predictions of TD learning are inconsistent with experimental data. We propose instead an alternative theoretical framework, coined FLEX (FlexiblyLearnedErrors in Expected Reward). In FLEX, feature specific representations of time are learned, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. As an indirect consequence, dopamine in FLEX resembles, but is not equivalent to RPE. In FLEX dopamine acts as an instructive signal which helps build temporal models of the environment. FLEX is a general theoretical framework that has many possible biophysical implementations. In order to show that FLEX is a feasible approach, we present a specific biophysically plausible model which implements the principles of FLEX. We show that this implementation can account for various reinforcement learning paradigms, and that its results and predictions are consistent with a preponderance of both existing and reanalyzed experimental data.