bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative analysis of CAKUT multi-omics data
Authors: Bayjanov Jumamurat R. , Doornbos Cenna , Ozisik Ozan , Shin Woosub , Queralt-Rosinach Núria , Wijnbergen Daphne , Saulnier Blache Jean-Sébastien , Schanstra Joost P. , Fernández José M. , Kaliyaperumal Rajaram , Baudot Anaïs , ’t Hoen Peter A.C. , Ehrhart Friederike 
Published: July 2023

AbstractCongenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood end-stage renal disease and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. Multi-omics experiments, including amniotic fluid miRNome, peptidome, and proteome analyses, can shed light on foetal kidney development in non-severe CAKUT patients compared to severe CAKUT cases. We performed FAIRification of these omics data sets to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.
Definitive environmental DNA research on aquatic insects: Analysis optimization using the recently developed MtInsects-16S primers set
Authors: Takenaka Masaki , Hasebe Yuta , Yano Koki , Okamoto Seiya , Tojo Koji 
Published: July 2023

AbstractLong-term biodiversity monitoring is necessary for the conservation and management of water resources. Notably, aquatic insects have been used as an indicator of water quality because they provide important basic information about freshwater ecosystems and water resources. Although environmental DNA (eDNA) surveys can enable easy and effective biomonitoring of aquatic insects, previous studies have not successfully detected all insect species, and there has been frequent amplification of nontarget taxa (e.g., algae and diatoms). Therefore, we developed a universal primers set, MtInsects-16S, for eDNA analyses of insects in the mtDNA 16SrRNA region. Furthermore, a well-established database of aquatic insects, especially the MtInsects-16S DNA region of Ephemeroptera, Plecoptera, and Trichoptera in Kanagawa Prefecture, which was the target area in this study, was constructed. Therefore, in this study, we conducted eDNA analyses using a universal primers set and using a well-established database. We conducted and compared capture surveys at the same sites to examine the detection capability of eDNA for Insecta. As a result, eDNA analyses using MtInsects- 16S not only detected almost all of the captured species but also detected many more species without amplifying nontarget taxa. This study demonstrated the application of eDNA analyses with unprecedented accuracy and reliability. It was also shown that community structure by eDNA reflected a relatively narrow range at the water sampling point. Although the data accumulation for constructing locally specific databases is an urgent issue, using the MtInsects-16S region is expected to be a breakthrough in the metabarcoding of insects.
Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study
Authors: Marzetti Laura , Basti Alessio , Guidotti Roberto , Baldassarre Antonello , Metsomaa Johanna , Zrenner Christoph , D’Andrea Antea , Makkinayeri Saeed , Pieramico Giulia , Ilmoniemi Risto J. , Ziemann Ulf , Romani Gian Luca , Pizzella Vittorio 
Published: July 2023

AbstractState-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks.This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor µ-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS.HighlightsEEG pre-stimulus connectivity of left M1 largely corresponds to the motor networkStronger motor network (MN) connectivity corresponds to greater MEP amplitudesLinear regression models based on MN connectivity predicts MEP amplitude
Competition between induced fit- and conformational selection binding: how different flux-based approaches help clarifying their interplay
Authors: Vauquelin Georges , Maes Dominique 
Published: July 2023

SummaryBackgroundBinding kinetics has become a popular discipline in pharmacology. Yet, it was recommended a decade ago to look on ligand-target binding in terms of fluxes instead of rate constants. Like product formation in enzymology, these binding fluxes stand for the rate/velocity by which the target converts form one state into another. Several thereon-based approaches can be used for studying the functioning binding mechanisms.ObjectivesAlthough the relative contribution/importance of the induced fit and conformational selection binding pathways within a thermodynamic cycle is still hotly debated, only a single flux-based approach is customarily addressed for such calculations. There is a need to better understand the relevance of this peculiar approach.MethodsThe present findings relied on differential equation-based simulations, using the provided forward- and reverse rate constants for four reported cases as input.ResultsBoth pathways may act in unison and even in tandem rather than being mutually exclusive. Also, calculating their relative contribution by distinct flux-based approaches may yield different outcomes for some of the cases under pre-equilibrium as well as equilibrium conditions. Additional flux-based approaches offer a rationale for the occurrence of those disparities.ConclusionsThe interplay between the induced fit- and conformational selection pathways is even more intricate than hitherto expected. It may me preferable to quantitate their relative contribution in terms of target occupancies instead of binding fluxes when genuine equilibrium can be reached.PerspectivesCompared to the often-used elaborate algebraic equations, combining different binding flux-based approaches may offer better intuitive and visual insight into the functioning of complex binding mechanisms. More attention should also be paid to physiologically more relevant non-equilibrium situations.
TrkB-dependent regulation of molecular signaling across septal cell types
Authors: Rodriguez Lionel A. , Tran Matthew Nguyen , Garcia-Flores Renee , Pattie Elizabeth A. , Divecha Heena R. , Kim Sun Hong , Shin Joo Heon , Lee Yong Kyu , Montoya Carly , Jaffe Andrew E. , Collado-Torres Leonardo , Page Stephanie C. , Martinowich Keri 
Published: July 2023

AbstractThe lateral septum (LS), a GABAergic structure located in the basal forebrain, is implicated in social behavior, learning and memory. We previously demonstrated that expression of tropomyosin kinase receptor B (TrkB) in LS neurons is required for social novelty recognition. To better understand molecular mechanisms by which TrkB signaling controls behavior, we locally knocked down TrkB in LS and used bulk RNA-sequencing to identify changes in gene expression downstream of TrkB. TrkB knockdown induces upregulation of genes associated with inflammation and immune responses, and downregulation of genes associated with synaptic signaling and plasticity. Next, we generated one of the first atlases of molecular profiles for LS cell types using single nucleus RNA-sequencing (snRNA-seq). We identified markers for the septum broadly, and the LS specifically, as well as for all neuronal cell types. We then investigated whether the differentially expressed genes (DEGs) induced by TrkB knockdown map to specific LS cell types. Enrichment testing identified that downregulated DEGs are broadly expressed across neuronal clusters. Enrichment analyses of these DEGs demonstrated that downregulated genes are uniquely expressed in the LS, and associated with either synaptic plasticity or neurodevelopmental disorders. Upregulated genes are enriched in LS microglia, associated with immune response and inflammation, and linked to both neurodegenerative disease and neuropsychiatric disorders. In addition, many of these genes are implicated in regulating social behaviors. In summary, the findings implicate TrkB signaling in the LS as a critical regulator of gene networks associated with psychiatric disorders that display social deficits, including schizophrenia and autism, and with neurodegenerative diseases, including Alzheimer’s.
Stochastic model of Alzheimer’s Disease progression using two-state Markov chains
Authors: Parks Meaghan 
Published: July 2023

AbstractIn 2016, Hao and Friedman developed a deterministic model of Alzheimer’s disease progression using a system of partial differential equations. This model describes the general behavior of the disease, however, it does not incorporate the molecular and cellular stochasticity intrinsic to the underlying disease processes. Here we extend the Hao and Friedman model by modeling each event in disease progression as a stochastic Markov process. This model identifies stochasticity in disease progression, as well as changes to the mean dynamics of key agents. We find that the pace of neuron death increases whereas the production of the two key measures of progression, Tau and Amyloid beta proteins, decelerates when stochasticity is incorporated into the model. These results suggest that the non-constant reactions and time-steps have a significant effect on the overall progression of the disease.
Microvascular plasticity in stroke recovery: Longitudinal snapshots, network statistical analysis, and dynamics
Authors: Mihelic Samuel A , Engelmann Shaun A , Sadr Mahdi , Jafari Chakameh Z , Zhou Annie , Williamson Michael R , Dunn Andrew K 
Published: July 2023

AbstractThis research article quantitatively investigates neuro-microvascular network remodeling dynamics following stroke using a novel in vivo two-photon angiography (cubic millimeter volume, weekly snapshots) and high throughput (thousands of connected capillaries) vascular vectorization method. The results suggest distinct temporal patterns of cere-brovascular plasticity, with acute remodeling peaking at one week post-stroke. The network architecture then gradually stabilizes, returning to a new steady state after four weeks. These findings align with previous literature on neuronal plasticity, highlighting the correlation between neuronal and neurovascular remodeling. Quantitative analysis of neurovascular networks using length- and strand-based statistical measures reveals intri-cate changes in network anatomy and topology. The distance and strand-length statistics show significant alterations, with a peak of plasticity observed at one week post-stroke, followed by a gradual return to baseline. The orientation statistic plasticity peaks at two weeks, gradually approaching the (conserved across subjects) stroke signature. The underlying mechanism of the vascular response (angiogenesis vs. tissue deformation), however, is yet unelucidated, requiring network registration advancements. Overall, the combination of two-photon angiography, vectorization, reconstruction/visualization, and statistical analysis enables both qualitative and quantitative assessments of neu-rovascular remodeling dynamics, demonstrating an impactful method for investigating neuro-microvascular network disorders and the therapeutic modes of action thereof. Understanding the timing and nature of neurovascular remodeling allows for optimized interventions, including personalized medicine for stroke rehabilitation. Additionally, the evaluation of pharmaceutical interventions using these tools may facilitate targeted drug development. Furthermore, neurovascular coupling dynamics have implications for neurodegenerative diseases, brain aging, and the field of brain-computer interfaces.
Evolutionary dynamics and ecological influences on Felidae
Authors: Porto Lucas M. V. , de Olveira Gabriel P. , Fiedler Maico S. 
Published: July 2023

ABSTRACTIn the field of evolutionary biology, researchers have long been interested in comprehending the patterns of diversification and elucidating the mechanisms responsible for the impressive variety of lifeforms observed within taxonomic groups. Based on the fossil records of Felidae, and using a Bayesian framework, we assess here how speciation and extinction rates vary across the last 25 Myr, and how it can potentially be associated with changes in two major ecomorphological traits of Felidae, body mass and carnassial teeth, over such time span. We found two coupled independent increases in both traits along Felidae evolution, one in Machairodontinae, which gradually increases over the last 15 Myr, and the second inPanthera, which rapidly increases within 5 million years. Furthermore, gradual reductions in body mass and carnassial teeth were also observed for some genera such asLeopardus, Lynx, andFelis. Diversification rates showed a gradual reduction throughout the Miocene and Pliocene, with significant rate shifts occurring around 9 and 2 Myr. We suggest that felids were influenced by major environmental changes over the last 17 Myr that probably shaped both traits studied here in order to hunt larger herbivores, but also to explore new niches that became available.
RNF185 control of COL3A1 expression limits prostate cancer migration and metastatic potential
Authors: Van Espen Benjamin , Oo Htoo Zarni , Collins Colin , Fazli Ladan , Molinolo Alfredo , Murad Rabi , Gleave Martin , Ronai Ze’ev A. 
Published: July 2023

AbstractRNF185 is a RING finger domain-containing ubiquitin ligase implicated in ER-associated degradation. Prostate tumor patient data analysis revealed a negative correlation between RNF185 expression and prostate cancer progression and metastasis. Likewise, several prostate cancer cell lines exhibited greater migration and invasion capabilities in culture upon RNF185 depletion. Subcutaneous inoculation of mouse prostate cancer MPC3 cells stably expressing shRNA against RNF185 into mice resulted in larger tumors and more frequent lung metastases. RNA-sequencing and Ingenuity Pathway Analysis identified wound healing and cellular movement among the most significant pathways upregulated in RNF185-depleted, compared to control prostate cancer cells. Gene Set Enrichment Analyses performed in samples from patients harboring low RNF185 expression and in RNF185-depleted lines confirmed the deregulation of genes implicated in EMT. Among those, COL3A1 was identified as the primary mediator of RNF185’s ability to impact migration phenotypes. Correspondingly, enhanced migration and metastasis of RNF185 KD prostate cancer cells were attenuated upon co-inhibition of COL3A1. Our results identify RNF185 as a gatekeeper of prostate cancer metastasis, partlyviaits control of COL3A1 availability.
A more expressive spline representation for SBML models improves code generation performance in AMICI
Authors: Contento Lorenzo , Stapor Paul , Weindl Daniel , Hasenauer Jan 
Published: July 2023

AbstractsSpline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very complex and difficult to derive by hand. Tools to create such formulas exist but only deal with numeric data and thus cannot be used for function estimation. Similarly, simulation tools suffer from several limitations when handling splines. For example, in the AMICI library splines with large numbers of nodes lead to long model import times.We have developed a set of SBML annotations to mark assignment rules as spline formulas. These compact representations are human-readable and easy to edit, in contrast to the piecewise representation. Different boundary conditions and extrapolation methods can also be specified. By extending AMICI to create and recognize these annotations, model import can be sped up significantly. This allows practitioners to increase the expressivity of their models.While the performance improvement is limited to AMICI, our tools for creating spline formulas can be used for other tools as well and our syntax for compact spline representation may be a starting point for an SBML-native way to represent spline interpolants.
XPA tumor variants lead to defects in NER that sensitize cells to cisplatin
Authors: Blee Alexandra M. , Gallagher Kaitlyn S. , Kim Hyun-Suk , Kim Mihyun , Troll Christina R. , D’Souza Areetha , Park Jiyoung , Neufer P. Drew , Schärer Orlando D. , Chazin Walter J. 
Published: July 2023

AbstractNucleotide excision repair (NER) neutralizes treatment with platinum (Pt)-based chemotherapy by removing Pt lesions from DNA. Previous study has identified that missense mutation or loss of either of the NER genes Excision Repair Cross Complementation Group 1 and 2 (ERCC1andERCC2) leads to improved patient outcomes after treatment with Pt-based chemotherapies. Although most NER gene alterations found in patient tumors are missense mutations, the impact of such mutations in the remaining nearly 20 NER genes is unknown. Towards this goal, we previously developed a machine learning strategy to predict genetic variants in an essential NER scaffold protein, Xeroderma Pigmentosum Complementation Group A (XPA), that disrupt repair activity on a UV-damaged substrate. In this study, we report in-depth analyses of a subset of the predicted NER-deficient XPA variants, includingin vitroanalyses of purified recombinant protein and cell-based assays to test Pt agent sensitivity in cells and determine mechanisms of NER dysfunction. The most NER deficient variant Y148D had reduced protein stability, weaker DNA binding, disrupted recruitment to damage, and degradation resulting from tumor missense mutation. Our findings demonstrate that tumor mutations in XPA impact cell survival after cisplatin treatment and provide valuable mechanistic insights to further improve variant effect prediction efforts. More broadly, these findings suggest XPA tumor variants should be considered when predicting patient response to Pt-based chemotherapy.SignificanceA destabilized, readily degraded tumor variant identified in the NER scaffold protein XPA sensitizes cells to cisplatin, suggesting that XPA variants can be used to predict response to chemotherapy.
Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer
Authors: Chu Lee-Shin , Ruffolo Jeffrey A. , Harmalkar Ameya , Gray Jeffrey J. 
Published: July 2023

AbstractConventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi-track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. For a benchmark set of rigid targets, GeoDock obtains a 41% success rate, outperforming all the other tested methods. For a more challenging benchmark set of flexible targets, GeoDock achieves a similar number of top-model successes as the traditional method ClusPro [1], but fewer than ReplicaDock2 [2]. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available athttps://github.com/Graylab/GeoDock.
Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections
Authors: Wan Guojia , Liao Minghui , Zhao Dong , Wang Zengmao , Pan Shirui , Du Bo 
Published: July 2023

AbstractMotivationReconstructing neuron-level brain circuit network is a universally recognized formidable task. A significant impediment involves discerning the intricate interconnections among multitudinous neurons in a complex brain network. However, the majority of current methodologies only rely on learning local visual synapse features while neglecting the incorporation of comprehensive global topological connectivity information. In this paper, we consider the perspective of network connectivity and introduce graph neural networks to learn the topological features of brain networks. As a result, we propose Neuronal Circuit Prediction Network (NCPNet), a simple and effective model to jointly learn node structural representation and neighborhood representation, constructing neuronal connection pair feature for inferring neuron-level connections in a brain circuit network.ResultsWe use a small number of connections randomly selected from a single brain circuit network as training data, expecting NCPNet to extrapolate known connections to unseen instances. We evaluated our model onDrosophilaconnectome andC. elegansworm connectome. The numerical results demonstrate that our model achieves a prediction accuracy of 91.88% for neuronal connections in theDrosophilaconnectome when utilizing only 5% of known connections. Similarly, under the condition of 5% known connections inC. elegans, our model achieves an accuracy of 93.79%. Additional qualitative analysis conducted on the learned representation vectors of Kenyon cells indicates that NCPNet successfully acquires meaningful features that enable the discrimination of neuronal sub-types. Our project is available athttps://github.com/mxz12119/NCPNet.