bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays
Authors: Buccino Alessio Paolo , Damart Tanguy , Bartram Julian , Mandge Darshan , Xue Xiaohan , Zbili Mickael , Gänswein Tobias , Jaquier Aurélien , Emmenegger Vishalini , Markram Henry , Hierlemann Andreas , Van Geit Werner 
Published: August 2022

AbstractIn computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments.In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution.In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures.The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated.Author SummaryMulticompartment models are one of the most biophysically detailed representations of single neurons. The vast majority of these models are built using experimental data from somatic recordings. However, neurons are much more than just their soma and one needs recordings from distal neurites to build an accurate model. In this article, we combine the patch-clamp technique with extracellular high-density microelectrode arrays (HD-MEAs) to compensate this shortcoming. In fact, HD-MEAs readouts allow one to record the neuronal signal in the entire axonal arbor. We show that the proposed multi-modal strategy is superior to the use of patch clamp alone using an existing model as ground-truth. Finally, we show an application of this strategy on experimental data from cultured neurons.
Facilitating CG simulations with MAD: the MArtini Database Server
Authors: Hilpert Cécile , Beranger Louis , Souza Paulo C.T. , Vainikka Petteri A. , Nieto Vincent , Marrink Siewert J. , Monticelli Luca , Launay Guillaume 
Published: August 2022

AbstractThe MArtini Database (MAD - www.mad.ibcp.fr) is a web server designed for the sharing structures and topologies of molecules parameterized with the Martini coarse-grained (CG) force field. MAD can also convert atomistic structures into CG structures and prepare complex systems (including proteins, lipids etc.) for molecular dynamics (MD) simulations at the CG level. It is dedicated to the generation of input files for Martini 3, the most recent version of this popular CG force field. Specifically, the MAD server currently includes tools to submit or retrieve CG models of a wide range of molecules (lipids, carbohydrates, nanoparticles, etc.), transform atomistic protein structures into CG structures and topologies, with fine control on the process and assemble biomolecules into large systems and deliver all files necessary to start simulations in the GROMACS MD engine.
Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics
Authors: Gueto-Tettay Carlos , Tang Di , Happonen Lotta , Heusel Moritz , Khakzad Hamed , Malmström Johan , Malmström Lars 
Published: August 2022

AbstractGenerating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systematically varying the composition and size of the training set. We assessed the generated models’ performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set’s size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEM models extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEM models’ proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.
Does the paradigm of genotype-environment associations need to be re-assessed? The paradox of adaptive phenotypic clines with non-clinal patterns in causal alleles
Authors: Lotterhos Katie E 
Published: August 2022

AbstractMultivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study elucidates the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to a multivariate environment. A novel set of simulations was created that all evolved similar phenotypic clines, but with varying proportions of causal alleles with clines. Phenotypic clines evolved mostly without clines in the causal allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Although univariate and multivariate GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios, individual multivariate traits could be accurately predicted from genotype and environmental data without any knowledge of the genetic architecture. This research challenges the utility of GEAs for understanding the genetic basis of adaptation to the environment, and instead suggests that multivariate trait predictions are a more fruitful approach for genomic forecasting and assisted gene flow efforts.
FAIR Data Station for Lightweight Metadata Management & Validation of Omics Studies
Authors: Nijsse Bart , Schaap Peter J. , Koehorst Jasper J. 
Published: August 2022

AbstractBackgroundThe Life sciences is an interdisciplinary field of research and one of the the biggest suppliers of scientific data. Reusing and connecting this data can uncover hidden insights and lead to new concepts, provided there is machine-actionable metadata available about the scientific experiments conducted with a degree of completeness that reflect the FAIR guiding principles. While stakeholders have embraced the FAIR principles, in practice there are a limited number of easy to adopt practical implementations available that fulfil the needs of data producers.FindingsWe developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It uses the ISA metadata framework and metadata standards to capture experimental metadata. The FAIR Data Station metadata registration workflow consists of three main modules. Based on the minimal information checklist(s) selected by the user, a web-based “form generation module” creates a standardized metadata template Excel workbook which is used as a familiar environment for offline sample metadata registration. A web-based “validation module” checks the format of the metadata recorded in the workbook. The “resource module” subsequently exports the validated set of recorded metadata into an RDF data file, enabling (cross-project) meta data searches.ConclusionsTurning FAIR into reality requires the availability of easy to adopt data FAIRification workflows that provide immediate beneficial incentives to the individual researcher. As such the FAIR Data Station provides in addition to the means to correctly FAIRify sequence data, the means to build searchable databases of (local) projects and can assists in ENA metadata submission of sequence data. The FAIR Data Station is available at http://fairbydesign.nl.
Interactions of curcumin’s degradation products with the Aβ42 dimer: A computational study
Authors: Dehabadi Maryam Haji , Caflisch Amedeo , Ilie Ioana M. , Firouzi Rohoullah 
Published: August 2022

ABSTRACTAmyloid-β (Aβ) dimers are the smallest toxic species along the amyloid aggregation pathway and among the most-populated oligomeric accumulations present in the brain affected by Alzheimer’s disease (AD). A proposed therapeutic strategy to avoid the aggregation of Aβ into higher order structures is to develop molecules that inhibit the early stages of aggregation, i.e. dimerization. Under physiological conditions the Aβ dimer is highly dynamic and does not attain a single well defined structure but is rather characterized by an ensemble of conformations. In a recent work, a highly heterogeneous library of conformers of the Aβ dimer was generated by an efficient sampling method with constraints based on ion mobility mass spectrometry data. Here, we make use of the Aβ dimer library to study the interaction with two curcumin degradation products, ferulic aldehyde and vanillin, by molecular dynamics (MD) simulations. Ensemble docking and MD simulations are used to provide atomistic detail of the interactions between the curcumin degradation products and the Aβ dimer. The simulations show that the aromatic residues of Aβ, and in particular 19FF20 interact with ferulic aldehyde and vanillin through π−π stacking. The binding of these small molecules induces significant changes on the 16KLVFF20 region.
TMAO Destabilizes RNA Secondary Structure via Direct Hydrogen Bond Interactions
Authors: Cho Samuel S. , Green Adam T. , Hyeon Changbong , Thirumalai D. 
Published: August 2022

AbstractTrimethylamine N-oxide (TMAO) is an osmolyte that accumulates in cells in response to osmotic stress. TMAO stabilizes proteins by the entropic stabilization mechanism, which pictures TMAO as a nano-crowder that predominantly destabilizes the unfolded state. However, the mechanism of action of TMAO on RNA is much less understood. Here, we use all atom molecular dynamics simulations to investigate how TMAO interacts with a 12-nt RNA hairpin with a high melting temperature, and an 8-nt RNA hairpin, which has a relatively fluid native basin in the absence of TMAO. The use of the two hairpins with different free energy of stabilization allows us to probe the origin of the destabilization effect of TMAO on RNA molecules without the possibility of forming tertiary interactions. We generated multiple trajectories using allatom molecular dynamics (MD) simulations in explicit water by employing AMBER and CHARMM force fields, both in the absence and presence of TMAO. We observed qualitatively similar RNA-TMAO interaction profiles from the simulations using the two force fields. TMAO hydrogen bond interactions are largely depleted around the paired RNA bases and ribose sugars. In contrast, we show that the oxygen atom in TMAO, the hydrogen bond acceptor, preferentially interacts with the hydrogen bond donors in the solvent exposed bases, such as those in the stem-loop, the destabilized base stacks in the unfolded state, especially in the marginally stable 8-nt RNA hairpin. The predicted destabilization mechanism through TMAO-RNA hydrogen bond interactions could be tested using two-dimensional IR spectroscopy. Since TMAO does not significantly interact with the hydroxyl group of the ribose sugars, we predict that similar results must also hold for DNA.
Multi-environment Genome Wide Association Studies of Yield Traits in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Interspecific Advanced Lines at the Humid and Dry Colombian Caribbean Subregions
Authors: López-Hernández Felipe , Burbano-Erazo Esteban , León-Pacheco Rommel Igor , Cordero-Cordero Carina Cecilia , Villanueva-Mejía Diego F. , Tofiño-Rivera Adriana Patricia , Cortés Andrés J. 
Published: August 2022

ABSTRACTGenome Wide Associations Studies GWAS are a powerful strategy for the exploration adaptive genetic variation to drought stress in advanced lines in common bean with interspecific genotypes, yet they still lack behind in the use of arid multi-environments as the subregions of the Colombian Caribbean. In order to bridge this gap, we couple an advanced genotypes panel integrated with Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) interspecific lines with GWAS algorithms to identify novel sources of drought tolerance across the subregions of Colombian Caribbean. One of the most important challenges in agriculture is to achieve food security in environments vulnerable to climate change which worsens with the passing of the years. The common bean, a key product of the food basket of vulnerable regions of the Caribbean is affected by the reduction in yield under drought stress. A total of 87 advanced accessions with interspecific lines were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Five yield traits were developed for each accession and inputted in GWAS algorithms (i.e. FarmCPU, and BLINK) to identify putative associated loci in drought stress. Best-fit models revealed 47 significantly associated alleles distributed in all 11 common bean chromosomes. Flanking candidate genes were identified using 1-kb genomic windows centered in each associated SNP marker. A pathways enriched analysis was carried out using the mapped output in the GWAS step for each yield traits indices. Some of these genes were directly linked to response mechanisms of drought stress to level morphological, physiological, metabolic, signal transduction, and fatty acid and phospholipid metabolism. This work offers putative associated loci for marker-assisted and genomic selection for drought tolerance in common bean. It also demonstrates that it is feasible to identify genome-wide associations with an interspecific panel of genotypes and modern GWAS algorithms in multiples environments.
Convection and extracellular matrix binding control interstitial transport of extracellular vesicles
Authors: Sariano Peter A. , Mizenko Rachel R. , Shirure Venktesh S. , Brandt Abigail , Nesiri Cem , Shergill Bhupinder , Rocke David M. , Carney Randy P. , George Steven C. 
Published: August 2022

AbstractExtracellular vesicles (EVs) influence a host of normal and pathophysiological processes in vivo. Compared to soluble mediators, EVs are relatively large (~30-150 nm) and can traffic a wide range of proteins on their surface including extracellular matrix (ECM) binding proteins. We isolated EVs from the MCF10 series – a model human cell line of breast cancer progression – and demonstrated increasing presence of laminin-binding integrins α3β1 and α6β1 on the EVs as the malignant potential of the MCF10 cells increased. Transport of the EVs within a microfluidic device under controlled physiological interstitial flow (0.15-0.75 μm/s) demonstrated that convection was the dominant mechanism of transport. Binding of the EVs to the ECM enhanced the spatial concentration and gradient, which was partially mitigated by blocking integrins α3β1 and α6β1. Our studies demonstrate that convection and ECM binding are the dominant mechanisms controlling EV interstitial transport and should be leveraged in the design of nanotherapeutics.
Actin architecture steers microtubules in active cytoskeletal composite
Authors: Kučera Ondřej , Gaillard Jérémie , Guérin Christophe , Théry Manuel , Blanchoin Laurent 
Published: August 2022

AbstractCytoskeletal motility assays use surface-immobilised molecular motors to propel cytoskeletal filaments. These assays have been widely employed to characterise the motor properties and interactions of cytoskeletal elements with themselves or with external factors. Moreover, the motility assays are a promising class of bio-inspired active tools for nanotechnological applications. While effective utilisation of these assays involves controlling the filament direction and speed, either as a sensory readout or a functional feature, designing a subtle control embedded in the assay is an ongoing challenge. Here we investigate the interaction between motor-propelled microtubules and networks of actin filaments. We demonstrate that the microtubules respond to a network of actin filaments and that this response depends on the network’s architecture. Both linear actin filaments and a network of actin branched by the Arp2/3 complex decelerate microtubule gliding; however, an unbranched actin network provides additional guidance and effectively steers the microtubules. This effect, which resembles the recognition of cortical actin architecture by microtubules, is a conceptually new means of controlling the filament gliding in the motility assay with potential application in the design of active materials and cytoskeletal nano-devices.
Intra-peritoneal transplantation for efficient and easy generation of experimental acute myeloid leukemia in mice
Authors: Qian Fenghua , Arner Brooke E. , Nettleford Shaneice K. , Paulson Robert F. , Prabhu K. Sandeep 
Published: August 2022

AbstractObjectiveThere is an unmet need for novel therapies to treat acute myeloid leukemia (AML) and the associated relapse that involves leukemia stem cells (LSCs). An experimental AML rodent model to test therapies based on successfully transplanting these cells via retro-orbital injections in recipient mice is fraught with challenges. The aim of this study was to develop an easy, reliable, and consistent method to generate a robust murine model of AML using an intra-peritoneal route.MethodsBone marrow cells were transduced with a retrovirus expressing human MLL-AF9 fusion oncoprotein. Efficiency of lineage negative (Lin−) and Lin−Sca-1+c-Kit+ (LSK) populations as donor LSCs in the development of primary AML was tested. We developed a new method to generate AML by intra-peritoneal injection and discuss the pros and cons of intra-peritoneal and retro-orbital injections in serial transplantations.ResultsBoth Lin− and LSK cells transduced with human MLL-AF9 virus engrafted well in the bone marrow and spleen of recipients, leading to a full-blown AML. Intra-peritoneal injection of donor cells established AML in recipients upon serial transplantation.Infiltration of AML cells was detected in the blood, bone marrow, spleen, and liver of recipients by flow cytometry, qPCR, and histological analyses.ConclusionsIntra-peritoneal injection is an efficient method of AML induction using serial transplantation of donor cells.Delayed homing to bone marrow, seen in intra-peritoneal injection that impedes the progression of AML, can be easily overcome by increasing the number of transplanted donor cells.Increasing the number of transplanted donor cells can also help shorten the duration of onset of AML.Sorting of LSK population within donor cells is not necessary in the generation of MLL-AF9-induced primary AML given that Lin− population are equally efficient as LSK donor cells in primary transplantation of AML in mice.
A new Critically Endangered cloud forest tree Microcos (Grewiaceae-Malvaceae) from the Rumpi Hills of S.W. Region Cameroon
Authors: Cheek Martin , Edwards Sara , Onana Jean Michel 
Published: August 2022

SummaryWe describe Microcos rumpi (Grewiaceae-Malvaceae) as a new species to science from the Rumpi Hills of SW Region Cameroon, a proposed Tropical Important Plant Area. Confined on current evidence to submontane forest, the species is threatened by expanding habitat clearance for farms and is assessed as Critically Endangered. A massive tree, attaining 35 – 40 m height, and 80 cm trunk diameter at 1.3 m above ground, its biomass is calculated as in the range of 7 – 8 metric tonnes. It is the third tree species of the genus recorded from Cameroon and only the fourth recorded west of D.R. Congo. A key to these four species is presented.The concept of Microcos in Africa in relation to Grewia is discussed, and three new combinations are made, transferring three species names from Grewia to Microcos: Microcos louisii (Wilczek) Cheek, M. evrardii(Wilczek) Cheek and M.schmitzii (Wilczek) Cheek.
Molecular formula discovery via bottom-up MS/MS interrogation
Authors: Xing Shipei , Shen Sam , Xu Banghua , Huan Tao 
Published: August 2022

AbstractA substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics. Here we present bottom-up tandem MS (MS/MS) interrogation to illuminate the unidentified features via accurate molecular formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking, and offers false discovery rate estimation. Compared to the existing MS1-initiated formula annotation, our approach shrinks the formula candidate space by 42.8% on average. The superior annotation accuracy of our bottom-up interrogation was demonstrated on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 annotated recurrent unidentified spectra (ARUS), our approach confidently annotated >5,000 novel molecular formulae unarchived in chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global peak annotation. This approach reveals peak interrelationships, allowing the systematic annotation of 37 fatty acid amide molecules in human fecal data, among other applications. All bioinformatics pipelines are available in a standalone software, BUDDY (https://github.com/HuanLab/BUDDY/).