bluuebunny/crossref_metadata_2025_split huggingface dataset:
Netrin-1 regulates the balance of glutamatergic connectivity in the adult ventral tegmental area
Authors: Cline Marcella M , Juarez Barbara , Hunker Avery C. , Regiarto Ernesto G. , Hariadi Bryan , Soden Marta E. , Zweifel Larry S. 
Published: May 2022

ABSTRACTThe axonal guidance cue netrin-1 serves a critical role in neural circuit development by promoting growth cone motility, axonal branching, and synaptogenesis. Within the adult mouse brain, expression of the gene encoding netrin-1 (Ntn1) is highly enriched in the ventral midbrain where it is expressed in both GABAergic and dopaminergic neurons, but its function in these cell types in the adult system remains largely unknown. To address this, we performed viral-mediated, cell-type specific CRISPR-Cas9 mutagenesis of Ntn1 in the ventral tegmental area (VTA) of adult mice. Ntn1 loss-of-function in either cell type resulted in a significant reduction in excitatory postsynaptic connectivity. In dopamine neurons, reduced excitatory tone had a minimal phenotypic behavioral outcome; however, reduced glutamatergic tone on VTA GABA neurons induced behaviors associated with a hyperdopaminergic phenotype. Loss of Ntn1 function in both cell types simultaneously largely rescued the phenotype observed in the GABA-only mutagenesis. These findings demonstrate an important role for netrin-1 in maintaining excitatory connectivity in the adult midbrain and that a balance in this connectivity within two of the major cell types of the VTA is critical for the proper functioning of the mesolimbic system.
Epitope-Evaluator: an interactive web application to study predicted T-cell epitopes
Authors: Soto Luis Fernando , Requena David , Fuxman Bass Juan Ignacio 
Published: May 2022

AbstractMultiple immunoinformatic tools have been developed to predict T-cell epitopes from protein amino acid sequences for different major histocompatibility complex (MHC) alleles. These prediction tools output hundreds of potential peptide candidates which require further processing; however, these tools are either not graphical or not friendly for non-programming users. We present Epitope-Evaluator, a web tool developed in the Shiny/R framework to interactively analyze predicted T-cell epitopes. This includes providing the distribution of epitopes across a selected set of MHC alleles, the promiscuity and conservation of epitopes, and their density and location within antigens. Epitope-Evaluator requires as input the fasta file of protein sequences and the output prediction file coming out from any predictor. By choosing different cutoffs and parameters, users can produce several interactive plots and tables that can be downloaded as JPG and text files, respectively. Epitope-Evaluator removes the programming barrier and provides intuitive tools, allowing a straightforward interpretation and graphical representations that facilitate the selection of candidate epitopes for experimental evaluation.Author SummaryWith the advent of the COVID-19 pandemic as well as past pandemics and epidemics, scientists have focused on immunological studies to develop better vaccines as well as understand immune responses. Many of the questions are centered on studying T-cell epitopes, and peptide sequences that can be presented to immune cells to elicit responses against pathogens. Although current software can produce hundreds of predictions, they are generally not user-friendly nor graphical. In order to remove the existing programming barrier, we developed a Web tool to allow scientists to analyze and filter T-cell epitopes in an easy, intuitive, interactive, and versatile way. We have included two biological cases identifying new biological insights and showing the importance of having this type of toolset, especially for nonprogrammer researchers in the immunology field.
TSC2 regulates tumor susceptibility to TRAIL-mediated T cell killing by orchestrating mTOR signaling
Authors: Lin Chun-Pu , Traets Joleen J.H. , Vredevoogd David W. , Visser Nils L. , Peeper Daniel S. 
Published: May 2022

AbstractResistance to cancer immunotherapy continues to impair common clinical benefit. Here, we uncover an important role for Tuberous Sclerosis Complex 2 (TSC2) in determining tumor susceptibility to cytotoxic T lymphocytes (CTL) killing, bothin vitroandin vivo.TSC2-depleted tumor cells showed disrupted mTOR regulation upon CTL attack, which was associated with enhanced cell death. Tumor cells adapted to CTL attack by shifting their mTOR signaling balance toward increased mTORC2 activity to circumvent apoptosis and necroptosis. TSC2 critically protected tumor cells as its ablation strongly augmented tumor cell sensitivity to CTL attack. Mechanistically, TSC2 inactivation caused elevation of TRAIL receptors expression, cooperating with mTORC1-S6 signaling to induce tumor cell death. Clinically, we found a negative correlation betweenTSC2expression and TRAIL signaling in TCGA patient cohorts. Moreover, a lower TSC2 immune response signature was observed in melanomas from patients responding to immune checkpoint blockades. Our study uncovers a pivotal role for TSC2 in the cancer immune response by governing crosstalk between TSC2-mTOR and TRAIL signaling, aiding future therapeutic exploration of this pathway in immuno-oncology.
The endothelial-specific LINC00607 mediates endothelial angiogenic function
Authors: Boos Frederike , Oo James A. , Warwick Timothy , Günther Stefan , Ponce Judit Izquierdo , Buchmann Giulia , Li Tianfu , Seredinski Sandra , Haydar Shaza , Kashefiolasl Sepide , Baker Andrew H. , Boon Reinier A. , Schulz Marcel H. , Miller Francis J. , Brandes Ralf P. , Leisegang Matthias S. 
Published: May 2022

AbstractLong non-coding RNAs (lncRNAs) can act as regulatory RNAs which, by altering the expression of target genes, impact on the cellular phenotype and cardiovascular disease development. Endothelial lncRNAs and their vascular functions are largely undefined. Deep RNA-Seq and FANTOM5 CAGE analysis revealed the lncRNA LINC00607 to be highly enriched in human endothelial cells. LINC00607 was induced in response to hypoxia, arteriosclerosis regression in non-human primates and also in response to propranolol used to induce regression of human arteriovenous malformations. siRNA knockdown or CRISPR/Cas9 knockout of LINC00607 attenuated VEGF-A-induced angiogenic sprouting. LINC00607 knockout in endothelial cells also integrated less into newly formed vascular networks in an in vivo assay in SCID mice. Overexpression of LINC00607 in CRISPR knockout cells restored normal endothelial function. RNA- and ATAC-Seq after LINC00607 knockout revealed changes in the transcription of endothelial gene sets linked to the endothelial phenotype and in chromatin accessibility around ERG-binding sites. Mechanistically, LINC00607 interacted with the SWI/SNF chromatin remodeling protein BRG1. CRISPR/Cas9-mediated knockout of BRG1 in HUVEC followed by CUT&RUN revealed that BRG1 is required to secure a stable chromatin state, mainly on ERG-binding sites. In conclusion, LINC00607 is an endothelial-enriched lncRNA that maintains ERG target gene transcription by interacting with the chromatin remodeler BRG1.
Efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs
Authors: Moen Marcus T. , Johnston Iain G. 
Published: May 2022

AbstractThe progression of cancer and other diseases, the evolution of organismal features in biology, and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (for example, genetic changes, symptoms, or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use. Here we introduce HyperHMM, an adapted Baum-Welch (expectation maximisation) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to existing approaches. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways.
Ubiquitous bacterial polyketides induce cross-kingdom microbial interactions
Authors: Krespach Mario K. C. , Stroe Maria C. , Netzker Tina , Rosin Maira , Zehner Lukas M. , Komor Anna J. , Beilmann Johanna M. , Krüger Thomas , Kniemeyer Olaf , Schroeckh Volker , Hertweck Christian , Brakhage Axel A. 
Published: May 2022

AbstractAlthough the interaction of prokaryotic and eukaryotic microorganisms is critical for the functioning of ecosystems, knowledge of the processes driving microbial interactions within communities is in its infancy. We previously reported that the soil bacteriumStreptomyces iranensisspecifically triggers the production of natural products in the fungusAspergillus nidulans.Here, we discovered that arginine-derived polyketides serve as the bacterial signals for this induction. Arginine-derived polyketide-producing bacteria occur world wide. These producer bacteria and the fungi that decode and respond to this signal can be co-isolated from the same soil sample. Arginine-derived polyketides impact surrounding microorganisms both directly as well as indirectly, by inducing the production of natural products in fungi that further influence the composition of microbial consortia.One-Sentence SummaryUbiquitous bacterial polyketides are universal components of the chemical network for microbial communication
Deep Reinforcement Learning for Optimal Experimental Design in Biology
Authors: Treloar Neythen J. , Braniff Nathan , Ingalls Brian , Barnes Chris P. 
Published: May 2022

AbstractThe field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.1Author summaryBiological systems are often complex and typically exhibit non-linear behaviour, making accurate model parametrisation difficult. Optimal experimental design tools help address this problem by identifying experiments that are predicted to provide maximally accurate parameter estimates. In this work we use reinforcement learning, an artificial intelligence method, to determine such experiments. Our simulation studies show that this approach allows uncertainty in model parameterisation to be directly incorporated into the search for optimal experiments, opening a practical avenue for training an experimental controller without confident knowledge of the system’s parameter values. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a fundamental role to play in the future of optimal experimental design.
A realistic benchmark for the identification of differentially abundant taxa in (confounded) human microbiome studies
Authors: Wirbel Jakob , Essex Morgan , Forslund Sofia Kirke , Zeller Georg 
Published: May 2022

AbstractBackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance (DA) testing is lacking, and it remains unexplored how these cope with confounding. Previous DA benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsHere we develop a simulation framework which implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole-metagenome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies to a much greater extent than in previous benchmarks. With extensively parametrized simulations we benchmark the performance of eighteen DA methods and further evaluate the best ones on confounded simulations. Only linear models,limma, fastANCOM, and the Wilcoxon test properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find thatpost hocadjustment can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsFor microbiome association studies tight error control is critical. The unsatisfactory performance of many DA methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.
Trapped pore waters in the open proton channel HV1
Authors: Boytsov Danila , Brescia Stefania , Chaves Gustavo , Koefler Sabina , Hannesschlaeger Christof , Siligan Christine , Gössweiner-Mohr Nikolaus , Musset Boris , Pohl Peter 
Published: May 2022

AbstractThe voltage-gated proton channel, HV1, is crucial for innate immune responses. According to alternative hypotheses, protons either hop on top of an uninterrupted water wire or bypass titratable amino acids, interrupting the water wire halfway across the membrane. To distinguish between both hypotheses, we estimate the water mobility for the putative case of an uninterrupted wire. The predicted single-channel water permeability 3×10−12cm3s−1 reflects the permeability-governing number of hydrogen bonds between water molecules in single-file configuration and pore residues. However, the measured unitary water permeability does not confirm the prediction, i.e., it is negligible. Osmotic deflation of reconstituted lipid vesicles reveals trapped water inside the HV1 wild-type channel and D174A mutant open at 0 mV. The conductance of 1400 H+ s−1 per wild-type channel agrees with the calculated diffusion limit for a ~2 Å capture radius for protons. Removal of a charged amino acid (D174) at the pore mouth decreases H+ conductance, conceivably by reducing the capture radius. At least one intervening amino acid contributes to H+ conductance while blocking water flow.
Application of rapid invisible frequency tagging for brain computer interfaces
Authors: Brickwedde Marion , Bezsudnova Yulia , Kowalczyk Anna , Jensen Ole , Zhigalov Alexander 
Published: May 2022

AbstractBackgroundBrain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded in real-time by analysing the power of neuronal responses at the flicker frequency.New methodWe implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440 Hz. We flickered the luminance of visual objects at 56 and 60 Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control a real-time BCI PONG game.ResultsOur results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60%. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response.Comparison with existing methodsIn contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates.ConclusionUsing rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.
Peptide Avidity for TCR on CD4 Effectors Determines the Extent of Memory Generation
Authors: Jones Michael C. , Castonguay Catherine , Nanaware Padma P. , Weaver Grant C. , Stadinski Brian , Kugler-Umana Olivia A. , Huseby Eric , Stern Lawrence J. , Devarajan Priyadharshini , Swain Susan L. 
Published: May 2022

AbstractThe impact of initial peptide antigen affinity for TCR in driving memory fate has been studied previously, however its contributions when effectors contract to memory is unclear. To become memory, effector CD4 T cells must recognize antigen at 5-8 days post-infection, at what we call the “effector checkpoint.” We examined whether peptide affinity for the TCR of effectors impacts the extent of memory and degree of protection against rechallenge. We made an influenza A virus (IAV) nucleoprotein (NP)-specific TCR transgenic strain, FluNP, and generated NP-peptide variants that bind FluNP TCR with a broad range of avidity. To vary avidityin vivo, we primed naïve donor FluNP in IAV-infected hosts, purified 6d FluNP effectors and co-transferred them with peptide-pulsed APC into uninfected second hosts. Higher affinity peptides yielded higher numbers of FluNP memory cells in the spleen and most dramatically in the lung and dLN, and drove better protection against lethal influenza infection. The major impact of avidity was on memory cell number, not cytokine production, and was already apparent within several days of transfer. We previously showed that autocrine IL-2 production during the effector checkpoint prevented default effector apoptosis and supported memory formation. Here, peptide avidity determined the level of IL-2 produced by effectors. IL-2Rα expression by APC drove more memory cell formation, suggesting that transpresentation of IL-2 by APC at this checkpoint enhanced CD4 memory generation. Secondary memory generation was also avidity-dependent. We propose this pathway selects CD4 effectors of highest affinity to progress to memory.
PeSTo: parameter-free geometric deep learning for accurate prediction of protein interacting interfaces
Authors: Krapp Lucien F. , Abriata Luciano A. , Rodriguez Fabio Cortés , Peraro Matteo Dal 
Published: May 2022

AbstractPredicting the interactions that a protein can establish with other molecules from its structure remains a major challenge. As shown by recent applications to tertiary structure prediction and opposite to current mainstream methods for interaction interface prediction, low-level, geometry-based, physicochemical-agnostic representations of structures have several advantages over methods that require pre-calculation of surfaces, charges, hydrophobicity, and other kinds of parameterizations. Here we introduce a new geometric transformer that acts directly on protein atoms labelled with nothing more than element names. The resulting model outperforms the state of the art for the prediction of protein-protein interaction interfaces and distinguishes interfaces with nucleic acids, lipids, small molecules and ions with high confidence. The low computational cost of this method (available online at https://pesto.epfl.ch/) enables processing high volumes of structural data, such as molecular dynamics trajectories allowing the discovery of interfaces that remain inconspicuous in static experimentally solved structures.
No branch left behind: tracking terrestrial biodiversity from a phylogenetic completeness perspective
Authors: Pinto-Ledezma Jesús N. , Díaz Sandra , Halpern Benjamin S. , Khoury Colin , Cavender-Bares Jeannine 
Published: May 2022

AbstractBiodiversity, as we see it today, ultimately is the outcome of millions of years of evolution; however, biodiversity in its multiple dimensions is changing rapidly due to increasing human domination of Earth. Here, we present the “phylogenetic completeness” (PC) a concept and methodology that intends to safeguard Earth’s evolutionary heritage by maintaining all branches of the tree of life. We performed a global evaluation of the PC approach using data from five major terrestrial clades and compared the results to an approach in which species are conserved or lost randomly. We demonstrate that under PC, for a given number of species extinctions, it is possible to maximize the protection of evolutionary innovations in every clade. The PC approach is flexible and can be used to conduct a phylogenetic audit of biodiversity under different conservation scenarios. The PC approach complements existing conservation efforts and is linked to the post-2020 Convention of Biodiversity targets.
Chronic clinical signs of upper respiratory tract disease shape gut and respiratory microbiomes in cohabitating domestic felines
Authors: Arnold Holly K. , Hanselmann Rhea , Duke Sarah M. , Sharpton Thomas J. , Beechler Brianna R. 
Published: May 2022

AbstractFeline upper respiratory tract disease (FURTD), often caused by infections etiologies, is a multifactorial syndrome affecting feline populations worldwide. Because of its highly transmissible nature, infectious FURTD is most prevalent anywhere cats are housed in groups such as animal shelters, and is associated with negative consequences such as decreasing adoption rates, intensifying care costs, and increasing euthanasia rates. Understanding the etiology and pathophysiology of FURTD is thus essential to best mitigate the negative consequences of this disease. Clinical signs of FURTD include acute respiratory disease, with a small fraction of cats developing chronic sequelae. It is thought that nasal mucosal microbiome changes play an active role in the development of acute clinical signs, but it remains unknown if the microbiome may play a role in the development and progression of chronic clinical disease. To address the knowledge gap surrounding how microbiomes link to chronic FURTD, we asked if microbial community structure of upper respiratory and gut microbiomes differed between cats with chronic FURTD signs and clinically normal cats. We selected 8 households with at least one cat exhibiting chronic clinical FURTD, and simultaneously collected samples from cohabitating clinically normal cats. Microbial community structure was assessed via 16S rDNA sequencing of both gut and nasal microbiome communities. Using a previously described ecophylogenetic method, we identified 37 and 27 microbial lineages within gut and nasal microbiomes respectively that significantly associated with presence of active FURTD clinical signs in cats with a history of chronic signs. Overall, we find that nasal and gut microbial communities may contribute to the development of chronic clinical course, but more research is needed to confirm our observations.