bluuebunny/crossref_metadata_2025_split huggingface dataset:
HGFAC is a ChREBP Regulated Hepatokine that Enhances Glucose and Lipid Homeostasis
Authors: Sargsyan Ashot , Doridot Ludivine , Hannou Sarah A. , Tong Wenxin , Srinivasan Harini , Ivison Rachael , Monn Ruby , Kou Henry H. , Haldeman Jonathan M. , Arlotto Michelle , White Phillip J. , Grimsrud Paul A. , Astapova Inna , Tsai Linus , Herman Mark A. 
Published: July 2021

AbstractCarbohydrate Responsive Element-Binding Protein (ChREBP) is a carbohydrate sensing transcription factor that regulates both adaptive and maladaptive genomic responses in coordination of systemic fuel homeostasis. Genetic variants in the ChREBP locus associate with diverse metabolic traits in humans, including circulating lipids. To identify novel ChREBP-regulated hepatokines that contribute to its systemic metabolic effects, we integrated ChREBP ChIP-seq analysis in mouse liver with human genetic and genomic data for lipid traits and identified Hepatocyte Growth Factor Activator (HGFAC) as a promising ChREBP-regulated candidate in mice and humans. HGFAC is a protease that activates the pleiotropic hormone Hepatocyte Growth Factor (HGF). We demonstrate that HGFAC KO mice have phenotypes concordant with putative loss-of-function variants in human HGFAC. Moreover, in gain- and loss-of-function genetic mouse models, we demonstrate that HGFAC enhances lipid and glucose homeostasis, in part, through actions to activate hepatic PPARγ activity. Together, our studies show that ChREBP mediates an adaptive response to overnutrition via activation of an HGFAC-HGF-PPARγ signaling axis in the liver to preserve glucose and lipid homeostasis.
Calcium-binding protein S100A6 interaction with VEGF receptors integrates signaling and trafficking pathways
Authors: Bao Leyuan , Fearnley Gareth W. , Lin Chi-Chuan , Odell Adam F. , Redondo Ana C. , Kinsella Gemma K. , Findlay John B. C. , Ladbury John E. , Harrison Michael A. , Ponnambalam Sreenivasan 
Published: July 2021

AbstractThe mammalian endothelium which lines all blood vessels responds to soluble factors which control vascular development and sprouting. Endothelial cells bind to vascular endothelial growth factor A via two different receptor tyrosine kinases (VEGFR1, VEGFR2) which regulate such cellular responses. The integration of VEGFR signal transduction and membrane trafficking is not well understood. Here, we used a yeast-based membrane protein screen to identify VEGFR-interacting factor(s) which modulate endothelial cell function. By screening a human endothelial cDNA library, we identified a calcium-binding protein, S100A6, which can interact with either VEGFR. We found that S100A6 binds in a calcium-dependent manner to either VEGFR1 or VEGFR2. S100A6 binding was mapped to the VEGFR2 tyrosine kinase domain. Depletion of S100A6 impacts on VEGF-A-regulated signaling through the canonical mitogen-activated protein kinase (MAPK) pathway. Furthermore, S100A6 depletion caused contrasting effects on biosynthetic VEGFR delivery to the plasma membrane. Co-distribution of S100A6 and VEGFRs on tubular profiles suggest the presence of transport carriers that facilitate VEGFR trafficking. We propose a mechanism whereby S100A6 acts as a calcium-regulated switch which facilitates biosynthetic VEGFR trafficking from the TGN-to-plasma membrane. VEGFR-S100A6 interactions thus enable integration of signaling and trafficking pathways in controlling the endothelial response to VEGF-A.
ADAD2 regulates heterochromatin in meiotic and post-meiotic male germ cells via translation of MDC1
Authors: Chukrallah Lauren G. , Badrinath Aditi , Vittor Gabrielle G. , Snyder Elizabeth M. 
Published: July 2021

AbstractMale germ cells establish a unique heterochromatin domain, the XY-body, early in meiosis. How this domain is maintained through the end of meiosis and into post-meiotic germ cell differentiation is poorly understood. ADAD2 is a late meiotic male germ cell specific RNA binding protein, loss of which leads to post-meiotic germ cell defects. Analysis of ribosome association in Adad2 mutants revealed defective translation of Mdc1, a key regulator of XY-body formation, late in meiosis. As a result, Adad2 mutants show normal establishment but failed maintenance of the XY-body. XY-body defects are concurrent with abnormal autosomal heterochromatin and ultimately lead to severely perturbed post-meiotic germ cell heterochromatin and cell death. These findings highlight the requirement of ADAD2 for Mdc1 translation, the role of MDC1 in maintaining meiotic male germ cell heterochromatin, and the importance of late meiotic heterochromatin for normal post-meiotic germ cell differentiation.SummaryChukrallah et al. demonstrate ADAD2 is required for normal meiotic heterochromatin in male germ cells and loss leads to post-meiotic cell death defining ADAD2 as a heterochromatin maintenance factor.
An Oscillatory Mechanism for Multi-level Storage in Short-term Memory
Authors: Champion Kathleen P. , Gozel Olivia , Lankow Benjamin S. , Ermentrout G. Bard , Goldman Mark S. 
Published: July 2021

AbstractOscillatory activity is commonly observed during the maintenance of information in short-term memory, but its role remains unclear. Non-oscillatory models of short-term memory storage are able to encode stimulus identity through their spatial patterns of activity, but are typically limited to either an all-or-none representation of stimulus amplitude or exhibit a biologically implausible exact-tuning condition. Here, we demonstrate a simple phase-locking mechanism by which oscillatory input enables a circuit to generate persistent or sequential activity patterns that encode information not only in their location but also in their discretely graded amplitudes.SignificanceA core observation in many memory systems and tasks is the presence of oscillations during memory maintenance. Here, we demonstrate a mechanism for the accumulation and storage of information in short-term memory in which oscillatory activity enables a solution to long-standing challenges in modeling the persistent neural activity underlying working memory. These challenges include the ability to encode information with low firing rates, multi-level storage of stimulus amplitude without extreme fine tuning, and multi-level storage of information in sequential activity. Altogether, this work proposes a new class of models for the storage of information in working memory, a new potential role for brain oscillations, and a novel dynamical mechanism for multi-stability.
AnchorWave: sensitive alignment of genomes with high diversity, structural polymorphism and whole-genome duplication variation
Authors: Song Baoxing , Marco-Sola Santiago , Moreto Miquel , Johnson Lynn , Buckler Edward S. , Stitzer Michelle C. 
Published: July 2021

AbstractMillions of species are currently being sequenced and their genomes are being compared. Many of them have more complex genomes than model systems and raised novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication or polyploidy levels. Here we introduce AnchorWave, which performs whole-genome duplication informed collinear anchor identification between genomes and performs base-pair resolution global alignments for collinear blocks using the wavefront algorithm and a 2-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multi-kilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs between two maize lines. By contrast, other genome alignment tools showed almost zero power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome than the closest competitive approach, when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor binding sites (TFBSs) at a rate of 1.05-74.85 fold higher than other tools, while with significantly lower false positive alignments. AnchorWave shows obvious improvement when applied to genomes with dispersed repeats, active transposable elements, high sequence diversity and whole-genome duplication variation.Significance statementOne fundamental analysis needed to interpret genome assemblies is genome alignment. Yet, accurately aligning regulatory and transposon regions outside of genes remains challenging. We introduce AnchorWave, which implements a genome duplication informed longest path algorithm to identify collinear regions and performs base-pair resolved, end-to-end alignment for collinear blocks using an efficient 2-piece affine gap cost strategy. AnchorWave improves alignment of partially synthetic and real genomes under a number of scenarios: genomes with high similarity, large genomes with high TE activity, genomes with many inversions, and alignments between species with deeper evolutionary divergence and different whole-genome duplication histories. Potential use cases for the method include genome comparison for evolutionary analysis of non-genic sequences and population genetics of taxa with complex genomes.
Investigating molecular crowding during cell division in budding yeast with FRET
Authors: Lecinski Sarah , Shepherd Jack W , Frame Lewis , Hayton Imogen , MacDonald Chris , Leake Mark C 
Published: July 2021

AbstractCell division, aging, and stress recovery triggers spatial reorganization of cellular components in the cytoplasm, including membrane bound organelles, with molecular changes in their compositions and structures. However, it is not clear how these events are coordinated and how they integrate with regulation of molecular crowding. We use the budding yeastSaccharomyces cerevisiaeas a model system to study these questions using recent progress in optical fluorescence microscopy and crowding sensing probe technology. We used a Förster Resonance Energy Transfer (FRET) based sensor, illuminated by confocal microscopy for high throughput analyses and Slimfield microscopy for single-molecule resolution, to quantify molecular crowding. We determine crowding in response to cellular growth of both mother and daughter cells, in addition to osmotic stress, and reveal hot spots of crowding across the bud neck in the burgeoning daughter cell. This crowding might be rationalized by the packing of inherited material, like the vacuole, from mother cells. We discuss recent advances in understanding the role of crowding in cellular regulation and key current challenges and conclude by presenting our recent advances in optimizing FRET-based measurements of crowding whilst simultaneously imaging a third color, which can be used as a marker that labels organelle membranes. Our approaches can be combined with synchronised cell populations to increase experimental throughput and correlate molecular crowding information with different stages in the cell cycle.
SMGen: A generator of synthetic models of biochemical reaction networks
Authors: Riva Simone G. , Cazzaniga Paolo , Nobile Marco S. , Spolaor Simone , Rundo Leonardo , Besozzi Daniela , Tangherloni Andrea 
Published: July 2021

AbstractSeveral software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of Computational Systems Biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by both features (e.g., system connectivity, reaction discreteness) and non trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component, which generally results in a computationally demanding task. To avoid any burden in the execution time, SMGen exploits a Main-Worker paradigm to speed up the overall process. SMGen is also provided with a user-friendly Graphical User Interface that allows the user to easily set up all the parameters required to generate a set of synthetic models with any user-defined number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric Reaction-based Models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct high numbers of errors during the creation process of the RBMs, a circumstance that increases the overall running time. Still, SMGen can create synthetic models with 512 species and reactions in less than 7 seconds. The open-source code of SMGen is available on GitLab:https://gitlab.com/sgr34/smgen.
Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning
Authors: Anwar Haroon , Caby Simon , Dura-Bernal Salvador , D’Onofrio David , Hasegan Daniel , Deible Matt , Grunblatt Sara , Chadderdon George L , Kerr Cliff C , Lakatos Peter , Lytton William W , Hazan Hananel , Neymotin Samuel A 
Published: July 2021

AbstractRecent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance.Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time.Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward.Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.
A sequence-based global map of regulatory activity for deciphering human genetics
Authors: Chen Kathleen M. , Wong Aaron K. , Troyanskaya Olga G. , Zhou Jian 
Published: July 2021

AbstractSequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.
Non-coding circular RNAs repertoire and expression profile during Brassica rapa pollen development
Authors: Babaei Saeid , Singh Mohan B. , Bhalla Prem L 
Published: July 2021

AbstractCircular RNAs (circRNAs) are covalently closed long non-coding RNA (lncRNA) molecules generated by the back-splicing of exons from linear precursor mRNAs. Though linear lncRNAs have been shown to play important regulatory roles in diverse biological and developmental processes, little is known about the role of their circular counterparts. In this study, we have performed high-throughput RNA sequencing to delineate the expression profile and potential function of circRNAs during the five stages of pollen development in Brassica rapa. A total of 1180 circRNAs were detected in pollen development, of which 367 showed stage-specific expression patterns. Functional enrichment and metabolic pathway analysis showed that the parent genes of circRNAs were mainly involved in pollen-related molecular and biological processes such as mitotic and meiosis cell division, DNA processes, protein synthesis, protein modification, and polysaccharide biosynthesis. Moreover, by predicting the circRNA-miRNA network from our differentially expressed circRNAs, we found 88 circRNAs with potential miRNA binding sites suggesting their role in post-transcriptional regulation of the genes. Finally, we confirmed the back-splicing sites of 9 randomly selected circRNAS using divergent primers and Sanger sequencing. Our study presents the first systematic analysis of circular RNAs during pollen development and forms the basis of future studies for unlocking complex gene regulatory networks underpinning reproduction in flowering plants.
Direct and indirect interactions promote complexes of the lipoprotein LbcA, the CtpA protease and its substrates, and other cell wall proteins in Pseudomonas aeruginosa
Authors: Chakraborty Dolonchapa , Darwin Andrew J. 
Published: July 2021

ABSTRACTThe Pseudomonas aeruginosa lipoprotein LbcA was discovered because it copurified with and promoted the activity of CtpA, a carboxyl-terminal processing protease (CTP) required for type III secretion system function, and for virulence in a mouse model of acute pneumonia. In this study we explored the role of LbcA by determining its effect on the proteome and its participation in protein complexes. lbcA and ctpA null mutations had strikingly similar effects on the proteome, suggesting that facilitating CtpA might be the most impactful role of LbcA in the bacterial cell. Independent complexes containing LbcA and CtpA, or LbcA and substrate, were isolated from P. aeruginosa cells, indicating that LbcA facilitates proteolysis by recruiting the protease and its substrates independently. An unbiased examination of proteins that copurified with LbcA revealed an enrichment for proteins associated with the cell wall. One of these copurification partners was found to be a new CtpA substrate, and the first substrate that is not a peptidoglycan hydrolase. Many of the other LbcA copurification partners are known or predicted peptidoglycan hydrolases. However, some of these LbcA copurification partners were not cleaved by CtpA, and an in vitro assay revealed that while CtpA and all of its substrates bound to LbcA directly, these non-substrates did not. Subsequent experiments suggested that the non substrates might co-purify with LbcA by participating in multi-enzyme complexes containing LbcA-binding CtpA substrates.IMPORTANCECarboxyl-terminal processing proteases (CTPs) are widely conserved and associated with the virulence of several bacteria, including CtpA in Pseudomonas aeruginosa. CtpA copurifies with the uncharacterized lipoprotein, LbcA. This study shows that the most impactful role of LbcA might be to promote CtpA-dependent proteolysis, and that it achieves this as a scaffold for CtpA and its substrates. It also reveals that LbcA copurification partners are enriched for cell wall-associated proteins, one of which is a novel CtpA substrate. Some of the other LbcA copurification partners are not cleaved by CtpA, but might copurify with LbcA because they participate in multi-enzyme complexes containing CtpA substrates. These findings are important, given the links between CTPs, their associated proteins, peptidoglycan remodeling, and virulence.
Locked down and drinking? Are more people self-identifying as having mental health difficulties alongside their drinking via an online platform?
Authors: Calcan Angela , Holmes Mark 
Published: July 2021

Purpose
The purpose of this paper is to offer a viewpoint on the impact that the COVID-19 pandemic has had on people’s engagement with an online alcohol service in the UK. This paper examines whether self-reported mental health concerns increased during the pandemic compared to pre-pandemic times.

Design/methodology/approach
A comparison was made between service data captured in one contract area – West Sussex, England – pre-pandemic (April 2019 to March 2020) and during pandemic (April 2020 to March 2021).

Findings
Self-reported mental health treatment needs increased during the pandemic period (71.2% of presentations compared to 50% pre-pandemic) via the online coaching service. Male referrals increased by 74% during the pandemic period compared to the previous year. Female referrals decreased by 4% in the same time period. Feelings of shame and guilt as well as loss of a confidential space to engage in online interventions were common concerns reported by service users.

Research limitations/implications
Research limitations include the relatively small sample size, the sample comprised of self-referred treatment seeking clients, and there was no control group. All data collected is self-report therefore subjective and not necessarily meeting diagnostic criteria.

Practical implications
Of note was the impact of the pandemic on women and their reduced access to the online service during the pandemic. Commissioners and services must adapt their service design and delivery alongside the new “normal” way of living and working. Routine screening of mental health and alcohol use are recommended.

Originality/value
This paper offers insight from an established online/digital service and the impact of the pandemic on people’s engagement with the service.