bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulating brain signals with predefined mutual correlations – a technical note
Authors: Moiseev Alexander 
Published: June 2021

AbstractObjectiveWhen modeling task-related human brain activity it is often necessary to simulate brain signals with specific mutual correlations between them. The signals should resemble those observed in practice, and consist of an “evoked” (“phase-locked”) component and a random oscillatory part. To be neurophysiologically plausible their waveforms must be shaped in a certain way or exhibit specific global features; in technical terms - they should be modulated by a certain envelope function. The goal of this technical note is to describe a simple way of how such signal sets can be obtained.MethodsWe derive a procedure which allows generating multi-epoch signals with the above properties. This is done by mixing a “seed” set of waveforms typically reflecting particular qualities of the target brain activity. As an example, the seed set can consist of realizations of colored noise with desired power spectrum, or can be obtained from real brain measurements.ResultsThe algorithm yields a set of n multi-epoch signals with specified mutual correlations. Evoked parts, oscillatory parts and global envelopes of the signals can be controlled independently in order to obtain desired properties of the generated time courses.ConclusionThe procedure provides versatile sets of mutually correlated signals suitable for modeling task-related brain activity.SignificanceIn contrast to other methods often relying on complicated computations, the suggested approach is straightforward and easy to apply in everyday practical work, yet yielding realistic “functionally connected” simulated brain signals.
The Rab11-Family Interacting Proteins reveal selective interaction of mammalian recycling endosomes with the Toxoplasma parasitophorous vacuole in a Rab11- and Arf6-dependent manner
Authors: Hartman Eric J. , Romano Julia D. , Coppens Isabelle 
Published: June 2021

AbstractAfter invasion of mammalian cells, the parasite Toxoplasma gondii multiplies in a self-made membrane-bound compartment, the parasitophorous vacuole (PV). We previously showed that intravacuolar Toxoplasma interacts with many host cell organelles, especially recycling endosomes, and further manipulates the host endocytic recycling through the sequestration of Rab11 vesicles into the PV. Mammalian Rab-PV interactions are likely mediated by Toxoplasma and host proteins that remain to be identified. In this context, we have examined the specificity of host Rab vesicle interaction with the PV by monitoring the recruitment of subtypes of Rab11 vesicles differing in their composition in Rab11-Family Interacting Proteins (FIPs). We found that vesicles with FIPs from Class I (FIP1C, FIP2, FIP5) or Class II (FIP3, FIP4) are distributed at the PV and detected to varying degrees inside the PV. The PV delivery of vesicles with FIPs from Class I, but not Class II, is Rab11-dependent. In addition to Rab11, FIP3 binds to Arf6, and vesicles associated with FIP3-Arf6 complexes are observed within the PV. Binding of FIP3 to either Rab11 or Arf6 significantly increases the internalization of vesicles into the PV. These data point to a selective process of host recycling endosome recognition and scavenging mediated by Toxoplasma.
Structural Dynamics and Catalytic Mechanism of ATP13A2 (PARK9) from Simulations
Authors: Mateeva Teodora , Klähn Marco , Rosta Edina 
Published: June 2021

AbstractATP13A2 is a gene encoding a protein of the P5B subfamily of ATPases and is a PARK gene. Molecular defects of the gene are mainly associated with variations of Parkinson’s Disease (PD). Despite the established importance of the protein in regulating neuronal integrity, the three-dimensional structure of the protein currently remains unresolved crystallographically. We have modelled the structure and reactivity of the full-length protein in its E1-ATP state. Using Molecular Dynamics (MD), Quantum cluster and Quantum Mechanical/Molecular mechanical (QM/MM) methods, we aimed at describing the main catalytic reaction, leading to the phosphorylation of Asp513. Our MD simulations suggest that two positively charged Mg2+ cations are present at the active site during the catalytic reaction, stabilizing a specific triphosphate binding mode. Using QM/MM calculations, we subsequently calculated the reaction profiles for the phosphoryl transfer step in the presence of one and two Mg2+ cations. The calculated barrier heights in both cases are found to be ∼12.5 and 7.0 kcal mol-1, respectively. We elucidated details of the catalytically competent ATP conformation and the binding mode of the second Mg2+ cofactor. We also examined the role of the conserved Arg686 and Lys859 catalytic residues. We observed that by lowering significantly the barrier height of the ATP cleavage reaction, Arg686 had significant effect on the reaction. The removal of Arg686 increased the barrier height for the ATP cleavage by more than 5.0 kcal mol-1 while the removal of key electrostatic interactions created by Lys859 to the γ-phosphate and Asp513 destabilizes the reactant state. When missense mutations occur in close proximity to an active site residue, they can interfere with the barrier height of the reaction, which can halt the normal enzymatic rate of the protein. We also found large binding pockets in the full-length structure, including a transmembrane domain pocket, which is likely where ATP13A2 cargo binds.
PLK1 inhibition selectively kills ARID1A deficient cells through uncoupling of oxygen consumption from ATP production
Authors: Srinivas Upadhyayula S. , Ramachandran Gokula K. , Wardyn Joanna D. , Hoppe Michal M. , Peng Yanfen , Lim Sherlly , Lee May Yin , Peethala Praveen C. , An Omer , Shendre Akshay , Tan Bryce W.Q. , Norbert Tay S.C. , Jaynes Patrick , Hu Longyu , Jakhar Rekha , Sachaphibulkij Karishma , Lim Lina H.K. , Crasta Karen , Yang Henry , Tan Patrick , Kappei Dennis , Peng Yong Wei , Tan David S.P. , Pervaiz Shazib , Bordi Matteo , Campello Silvia , Tam Wai Leong , Frezza Christian , Jeyasekharan Anand D. 
Published: June 2021

AbstractInhibitors of the mitotic kinase PLK1 yield objective responses in a subset of refractory cancers. However, PLK1 overexpression in cancer does not correlate with drug sensitivity, and the clinical development of PLK1 inhibitors has been hampered by the lack of patient selection marker. Using a high-throughput chemical screen, we discovered that cells deficient for the tumor suppressor ARID1A are highly sensitive to PLK1 inhibition. Interestingly this sensitivity was unrelated to canonical functions of PLK1 in mediating G2-M cell cycle transition. Instead, a whole-genome CRISPR screen revealed PLK1 inhibitor sensitivity in ARID1A deficient cells to be dependent on the mitochondrial translation machinery. We find that ARID1A knocked-out (KO) cells have an unusual mitochondrial phenotype with aberrant biogenesis, increased oxygen consumption/ expression of oxidative phosphorylation genes, but without increased ATP production. Using expansion microscopy and biochemical fractionation, we see that a subset of PLK1 localizes to the mitochondria in interphase cells. Inhibition of PLK1 in ARID1A KO cells further uncouples oxygen consumption from ATP production, with subsequent membrane depolarization and apoptosis. Knockdown of a key subunit of the mitochondrial ribosome reverses PLK1-inhibitor induced apoptosis in ARID1A deficient cells, confirming specificity of the phenotype. Together, these findings highlight a novel interphase role for PLK1 in maintaining mitochondrial fitness under metabolic stress, and a strategy for therapeutic use of PLK1 inhibitors. To translate these findings, we describe a quantitative microscopy assay for assessment of ARID1A protein loss, which could offer a novel patient selection strategy for the clinical development of PLK1 inhibitors in cancer.Statement of significanceCurrently, no predictive biomarkers have been identified for PLK1 inhibitors in cancer treatment. We show that ARID1A loss sensitizes cells to PLK1 inhibitors through a previously unrecognized vulnerability in mitochondrial oxygen metabolism.
scRegulocity: Detection of local RNA velocity patterns in embeddings of single cell RNA-Seq data
Authors: Harmanci Akdes Serin , Harmanci Arif O , Zhou Xiaobo , Deneen Benjamin , Rao Ganesh , Klisch Tiemo , Patel Akash 
Published: June 2021

AbstractSingle cell RNA-sequencing has revolutionized transcriptome analysis. ScRNA-seq provides a massive resource for studying biological phenomena at single cell level. One of the most important applications of scRNA-seq is the inference of dynamic cell states through modeling of transcriptional dynamics. Understanding the full transcriptional dynamics using the concept named RNA Velocity enables us to identify cell states, regimes of regulatory changes in cell states, and putative drivers within these states. We present scRegulocity that integrates RNA-velocity estimates with locality information from cell embedding coordinates. scRegulocity focuses on velocity switching patterns, local patterns where velocity of nearby cells change abruptly. These different transcriptional dynamics patterns can be indicative of transitioning cell states. scRegulocity annotates these patterns with genes and enriched pathways and also analyzes and visualizes the velocity switching patterns at the regulatory network level. scRegulocity also combines velocity estimation, pattern detection and visualization steps.
Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging
Authors: Andrianov Grigorii V. , Ong Wern Juin Gabriel , Serebriiskii Ilya , Karanicolas John 
Published: June 2021

AbstractIn early stage drug discovery, the hit-to-lead optimization (or “hit expansion”) stage entails starting from a newly-identified active compound, and improving its potency or other properties. Traditionally this process relies on synthesizing and evaluating a series of analogs to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogs with improved potency. Our protocol begins from an inhibitor of the target kinase, and generalizes the synthetic route used to access it. By searching for commercially-available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions – or billions – of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well-described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into a single new compounds, and which are not. Further, the use of the pairwise approximation allows interaction energies to be assigned to each compound in the library, without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFRT790M, and ACK1. In each example, the enumerated library includes additional analogs reported by the original study to have activity, and these analogs are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.Graphical Abstract
A subset of CB002 xanthine analogues bypass p53-signaling to restore a p53 transcriptome and target an S-phase cell cycle checkpoint in tumors with mutated-p53
Authors: Hernández Borrero Liz J. , Dicker David T. , Santiago John , Sanders Jennifer , Tian Xiaobing , Ahsan Nagib , Lev Avital , Zhou Lanlan , El-Deiry Wafik S. 
Published: June 2021

AbstractMutations in TP53 occur commonly in the majority of human tumors and confer aggressive tumor phenotypes including metastasis and therapy resistance. CB002 and structural-analogues restore p53 signaling in tumors with mutant-p53 but we find that unlike other xanthines such as caffeine, pentoxifylline, and theophylline, they do not deregulate the G2-checkpoint. Novel CB002-analogues induce pro-apoptotic Noxa protein in an ATF3/4-dependent manner, whereas caffeine, pentoxifylline, and theophylline do not. By contrast to caffeine, CB002-analogues target an S-phase checkpoint associated with increased p-RPA/RPA2, p-ATR, decreased Cyclin A, p-histone H3 expression and downregulation of essential proteins in DNA-synthesis and -repair. CB002-analogue #4 enhances cell death, and decreases Ki-67 in patient-derived tumor-organoids without toxicity to normal human cells. Preliminaryin vivostudies demonstrate anti-tumor efficacy in mice. Thus, a novel class of anti-cancer drugs show activation of p53 pathway signaling in tumors with mutated p53, and target an S-phase checkpoint.
Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping
Authors: Li Brian , Cotner Kristen L. , Liu Nathaniel K. , Hinz Stefan , LaBarge Mark A. , Sohn Lydia L. 
Published: June 2021

AbstractCellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform single-cell mechanical testing with high throughput. However, prior work has performed only limited characterization of these platforms’ technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, which is a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform’s measurements of single-cell mechanical properties, and we demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different labs with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.
Significant (Z|−4|) admixture signal with a source from ancient Wusun observed in contemporary Bulgarians
Authors: Stamov Svetoslav 
Published: June 2021

AbstractWe report the presence of significant Central Asian ancestry in both contemporary Bulgarians and in early medieval population from SMC (Saltovo Mayaky Culture).The existence of Chalcolithic-Iran (Hajj-Fruz) and Wusun related ancestral component in contemporary Bulgarians comes as a surprise and sheds light on both migration route and ethnic origins of Proto-Bulgarians. We interpret these results as an evidence for a Central –Asian connection for the tribes, constituting the population of SMC and Kubrat’s Old Great Bulgaria in Pontic steppe from 6th-7th century AD.We identify Central Asian Wusun tribes as carriers of this component on the base from the results from f3 and f4 statistics. We suggest that Wusun-related tribes must have played role (or might have even been the backbone) in what became known as the Hunnic migration to Europe during 3rd-5th century AD. Same population must have taken part in the formation of the SMC (Saltovo-Mayaki Culture) and Great Old Bulgarian during 6th-9th century AD in Pontic – Caspian steppe.We also explore the genomic origins of Thracians and their relations to contemporary Europeans. We conclude that contemporary Bulgarians do not harbor Thracian-specific ancestry, since ancient Thracian samples share more SNPs with contemporary Greeks and even contemporary Icelanders than with contemporary Bulgarians.
Free energies of stalk formation in the lipidomics era
Authors: Poojari Chetan S. , Scherer Katharina C. , Hub Jochen S. 
Published: June 2021

AbstractMany biological membranes are asymmetric and exhibit complex lipid composition, comprising hundreds of distinct chemical species. Identifying the biological function and advantage of this complexity is a central goal of membrane biology. Here, we study how membrane complexity controls the energetics of the first steps of membrane fusions, that is, the formation of a stalk. We first present a computationally efficient method for simulating thermodynamically reversible pathways of stalk formation at near-atomic resolution. The new method reveals that the inner leaflet of a typical plasma membrane is far more fusogenic than the outer leaflet, which is likely an adaptation to evolutionary pressure. To rationalize these findings by the distinct lipid compositions, we computed ~200 free energies of stalk formation in membranes with different lipid head groups, tail lengths, tail unsaturations, and sterol content. In summary, the simulations reveal a drastic influence of the lipid composition on stalk formation and a comprehensive fusogenicity map of many biologically relevant lipid classes.
Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference
Authors: Masharipov Ruslan , Nikolaev Yaroslav , Korotkov Alexander , Didur Michael , Cherednichenko Denis , Kireev Maxim 
Published: June 2021

AbstractClassical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. Moreover, studies with a sufficiently large sample size will find statistically significant results even when the effect is negligible and may be considered practically equivalent to the ‘null effect’. This leads to a publication bias against the null hypothesis. There are two main approaches to assess ‘null effects’: shifting from the point-null to the interval-null hypothesis and considering the practical significance in the frequentist approach; using the Bayesian parameter inference based on posterior probabilities, or the Bayesian model inference based on Bayes factors. Herein, we discuss these statistical methods with particular focus on the application of the Bayesian parameter inference, as it is conceptually connected to both frequentist and Bayesian model inferences. Although Bayesian methods have been theoretically elaborated and implemented in commonly used neuroimaging software, they are not widely used for ‘null effect’ assessment. To demonstrate the advantages of using the Bayesian parameter inference, we compared it with classical null hypothesis significance testing for fMRI data group analysis. We also consider the problem of choosing a threshold for a practically significant effect and discuss possible applications of Bayesian parameter inference in fMRI studies. We argue that Bayesian inference, which directly provides evidence for both the null and alternative hypotheses, may be more intuitive and convenient for practical use than frequentist inference, which only provides evidence against the null hypothesis. Moreover, it may indicate that the obtained data are not sufficient to make a confident inference. Because interim analysis is easy to perform using Bayesian inference, one can evaluate the data as the sample size increases and decide to terminate the experiment if the obtained data are sufficient to make a confident inference. To facilitate the application of the Bayesian parameter inference to ‘null effect’ assessment, scripts with a simple GUI were developed.
Characterizing Hydrogen Bonds in Intact RNA from MS2 Bacteriophage Using Solid State Magic Angle Spinning NMR
Authors: Lusky Orr Simon , Meir Moran , Goldbourt Amir 
Published: June 2021

AbstractRibonucleic acid (RNA) is a polymer with pivotal functions in many biological processes. RNA structure determination is thus a vital step towards understanding its function. The secondary structure of RNA is stabilized by hydrogen bonds formed between nucleotide base pairs and it defines the positions and shapes of functional stem-loops, internal loops, bulges, and other functional and structural elements. In this work we present a methodology for studying large intact RNA biomolecules using homonuclear 15N solid state nuclear magnetic resonance (NMR) spectroscopy. We show that Proton Driven Spin Diffusion (PDSD) experiments with long mixing times, up to 16s, improved by the incorporation of multiple rotor-synchronous 1H inversion pulses (termed Radiofrequency Dipolar Recoupling, RFDR, pulses), reveal key hydrogen-bond contacts. In the full-length RNA isolated from MS2 phage, we observed strong and dominant contributions of G-C Watson-Crick base pairs, and beyond these common interactions, we observe a significant contribution of the G-U wobble base pairs. Moreover, we can differentiate base-paired and non-base-paired nitrogen atoms. Using the improved technique facilitates characterization of hydrogen-bond types in intact large-scale RNA using solid-state NMR. It can be highly useful to guide secondary structure prediction techniques, and possibly structure determination methods.
Using top-down modulation to optimally balance shared versus separated task representations
Authors: Verbeke Pieter , Verguts Tom 
Published: June 2021

AbstractHuman adaptive behavior requires continually learning and performing a wide variety of tasks, often with very little practice. To accomplish this, it is crucial to separate neural representations of different tasks in order to avoid interference. At the same time, sharing neural representations supports generalization and allows faster learning. Therefore, a crucial challenge is to find an optimal balance between shared versus separated representations. Typically, models of human cognition employ top- down modulatory signals to separate task representations, but there exist surprisingly little systematic computational investigations of how such modulation is best implemented. We identify and systematically evaluate two crucial features of modulatory signals. First, top-down input can be processed in an additive or multiplicative manner. Second, the modulatory signals can be adaptive (learned) or non-adaptive (random). We cross these two features, resulting in four modulation networks which are tested on a variety of input datasets and tasks with different degrees of stimulus-action mapping overlap. The multiplicative adaptive modulation network outperforms all other networks in terms of accuracy. Moreover, this network develops hidden units that optimally share representations between tasks. Specifically, different than the binary approach of currently popular latent state models, it exploits partial overlap between tasks.