bluuebunny/crossref_metadata_2025_split huggingface dataset:
PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles
Authors: Sora Valentina , Tiberti Matteo , Robbani Shahriyar Mahdi , Rubin Joshua , Papaleo Elena 
Published: November 2020

AbstractMotivationProtein dynamic is essential for cellular functions. Due to the complex nature of non-covalent interactions and their long-range effects, the analysis of protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) rely on different philosophies, and the currently available tools suffer from limitations in terms of input formats, supported network models, and version control. Another issue is the precise definition of cutoffs for the network calculations and the assessment of the stability of the parameters, which ultimately affect the outcome of the analyses.ResultsWe provide two open-source software packages, i.e., PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a harmonized, reproducible, and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and calculates a diverse range of network models with the possibility to integrate them into a macro-network and perform further downstream graph analyses. PyInKnife2 is a standalone package that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. Several functionalities are based on MDAnalysis and NetworkX, including parallelization, and are available for Python 3.7. PyInteraph2 underwent a massive restructuring in terms of setup, installation, and test support compared to the original PyInteraph software.ConclusionsWe foresee that the modular structure of the code and the version control system of GitHub will promote the transition to a community-driven effort, boost reproducibility, and establish harmonized protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and maintenance of the package.AvailabilityThe packages are available athttps://github.com/ELELAB/pyinteraph2andhttps://github.com/ELELAB/PyInKnife2with guides provided within the packages.
Inferring a spatial code of cell-cell interactions across a whole animal body
Authors: Armingol Erick , Ghaddar Abbas , Joshi Chintan J. , Baghdassarian Hratch , Shamie Isaac , Chan Jason , Her Hsuan-Lin , O’Rourke Eyleen J. , Lewis Nathan E. 
Published: November 2020

AbstractCell-cell interactions shape cellular function and ultimately organismal phenotype. However, the code embedded in the molecular interactions driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our toolcell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across theCaenorhabditis elegans’ body. Leveraging a 3D atlas ofC. elegans’ cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred withcell2celland the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.
A representational similarity analysis of cognitive control during color-word Stroop
Authors: Freund Michael C. , Bugg Julie M. , Braver Todd S. 
Published: November 2020

AbstractProgress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For this paradigm, models have postulated complementary coding schemes: dorsomedial frontal cortex (DMFC) is proposed to evaluate the demand for control via incongruency-related coding, whereas dorsolateral prefrontal cortex (DLPFC) is proposed to implement control via goal and target-related coding. Yet, mapping these theorized schemes to measured neural activity within this task has been challenging. Here, we tested for these coding schemes relatively directly, by decomposing an event-related color-word Stroop task via representational similarity analysis (RSA). Three neural coding models were fit to the similarity structure of multi-voxel patterns of human fMRI activity, acquired from 65 healthy, young-adult males and females. Incongruency coding was predominant in DMFC, whereas both target and incongruency coding were present with indistinguishable strength in DLPFC. In contrast, distractor coding was strongly encoded within early visual cortex. Further, these coding schemes were differentially related to behavior: individuals with stronger DLPFC (and lateral posterior parietal cortex) target coding, but weaker DMFC incongruency coding, exhibited less behavioral Stroop interference. These results highlight the utility of the RSA framework for investigating neural mechanisms of cognitive control and point to several promising directions to extend the Stroop paradigm.Significant StatementHow the human brain enables cognitive control — the ability to override behavioral habits to pursue internal goals — has been a major focus of neuroscience research. This ability has been frequently investigated by using the Stroop color-word naming task. With the Stroop as a test-bed, many theories have proposed specific neuroanatomical dissociations, in which medial and lateral frontal brain regions underlie cognitive control by encoding distinct types of information. Yet providing a direct confirmation of these claims has been challenging. Here, we demonstrate that representational similarity analysis (RSA), which estimates and models the similarity structure of brain activity patterns, can successfully establish the hypothesized functional dissociations within the Stroop task. RSA may provide a useful approach for investigating cognitive control mechanisms.
Operating principles of circular toggle polygons
Authors: Hati Souvadra , Duddu Atchuta Srinivas , Jolly Mohit Kumar 
Published: November 2020

AbstractDecoding the dynamics of cellular decision-making and cell differentiation is a central question in cell and developmental biology. A common network motif involved in many cell-fate decisions is a mutually inhibitory feedback loop between two self-activating ‘master regulators’ A and B, also called as toggle switch. Typically, it can allow for three stable states – (high A, low B), (low A, high B) and (medium A, medium B). A toggle triad – three mutually repressing regulators A, B and C, i.e. three toggle switches arranged circularly (between A and B, between B and C, and between A and C) – can allow for six stable states: three ‘single positive’ and three ‘double positive’ ones. However, the operating principles of larger toggle polygons, i.e. toggle switches arranged circularly to form a polygon, remain unclear. Here, we simulate using both discrete and continuous methods the dynamics of different sized toggle polygons. We observed a pattern in their steady state frequency depending on whether the polygon was an even or odd numbered one. The even-numbered toggle polygons result in two dominant states with consecutive components of the network expressing alternating high and low levels. The odd-numbered toggle polygons, on the other hand, enable more number of states, usually twice the number of components with the states that follow ‘circular permutation’ patterns in their composition. Incorporating self-activations preserved these trends while increasing the frequency of multistability in the corresponding network. Our results offer insights into design principles of circular arrangement of regulatory units involved in cell-fate decision making, and can offer design strategies for synthesizing genetic circuits.
Genome Wide Association Studies on 7 Yield-related Traits of 183 Rice Varieties in Bangladesh
Authors: Roy Nilanjan , Kabir Acramul Haque , Zahan Nourin , Mouna Shahba Tasmiya , Chakravarty Sakshar , Rahman Atif Hasan , Bayzid Md. Shamsuzzoha 
Published: November 2020

AbstractMotivationRice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties.ResultsWe performed Genome Wide Association Studies (GWAS) on 7 rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width and leaf angle) based on a population of 183 rice landraces of Bangladesh. Our GWA studies reveal various chromosomal regions and candidate genes that are associated with different traits in Bangladeshi rice varieties. Noteworthy was the recurrent implication of chromosome 10 in all three grain shape related traits (grain length, grain width, and grain weight), indicating its pivotal role in shaping rice grain morphology. Our study also underscores the involvement of transposon gene families across these three traits. For leaf related traits, chromosome 10 was found to harbor regions that are significantly associated with leaf length and leaf width. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice.ConclusionsThis is the first known GWAS study on various yield-related traits in the varieties ofOryza sativaavailable in Bangladesh – the fourth largest rice-producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.
A probabilistic model for indel evolution: differentiating insertions from deletions
Authors: Loewenthal Gil , Rapoport Dana , Avram Oren , Moshe Asher , Itzkovitch Alon , Israeli Omer , Azouri Dana , Cartwright Reed A. , Mayrose Itay , Pupko Tal 
Published: November 2020

AbstractInsertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.
Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods
Authors: Yan Haidong , Lee Jiyoung , Song Qi , Li Qi , Schiefelbein John , Zhao Bingyu , Li Song 
Published: November 2020

SummaryAn essential step in the analysis of single-cell RNA sequencing data is to classify specific cell types with marker genes. In this study, we have developed a machine learning pipeline called Single cell Predictive markers (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root.Unlike traditional approaches, our method uses interpretable machine learning methods to select marker genes. We have demonstrated that our method can (1) assign cell types based on cells that were labeled using published methods, (2) project cell types identified by trajectory analysis from one dataset to other datasets, and (3) assign cell types based on internal GFP markers.Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, we have found more orthologous genes of these new marker genes in corresponding rice single cell clusters. We have also found 172 new marker genes for trichoblast in five non-Arabidopsis species, which expands number of marker genes for this cell type by 35-154%.Our results represent a new approach to identify cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.
Language network connectivity increases in prodromal Alzheimer’s disease
Authors: Pistono A. , Senoussi M. , Guerrier L. , Rafiq M. , Gimeno M. , Péran P. , Jucla M. , Pariente J. 
Published: November 2020

AbstractLanguage production deficits occur early in the course of Alzheimer’s disease (AD); however, only few studies have focused on language functional networks in prodromal AD. The current study aims to uncover the extent of language alteration at a prodromal stage, on a behavioral, structural and functional level, using univariate and multivariate analyses. Twenty-four AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; atrophy within the executive control networks, as well as the pattern of functional connectivity within the language network; and functional connectivity within executive control networks. AD participants had language impairment during standardized language tasks and connected-speech production. Univariate analyses were not able to discriminate participants at this stage, while multivariate pattern analyses could significantly predict the group membership of prodromal patients and healthy controls, both when classifying atrophy patterns or connectivity patterns of the language network. Language functional networks could discriminate AD participants better than executive control networks. Most notably, they revealed an increased connectivity at a prodromal stage. Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.HighlightsLanguage network connectivity discriminates prodromal AD from healthy controlsLanguage network connectivity increases in prodromal ADAtrophy patterns in the language network do not correlate with connectivity patterns in AD
ER bodies are induced byPseudomonas syringaeand negatively regulate immunity
Authors: Rufián José S. , Elmore James M. , Bejarano Eduardo R. , Beuzon Carmen R. , Coaker Gitta L. 
Published: November 2020

ABSTRACTER bodies are endoplasmic reticulum-derived organelles present in plants belonging to theBrassicalesorder. InArabidopsis thaliana, ER bodies are ubiquitous in cotyledons and roots, and present only in certain cell types in rosette leaves. However, both wounding and jasmonic acid treatment induce the formation of ER bodies in leaves. Formation of this structure is dependent on the transcription factorNAI1. The main components of the ER bodies are β-glucosidases (BGLUs), enzymes that hydrolyze specialized compounds. InArabidopsis, PYK10 (BGLU23) and BGLU18 are the most abundant ER body proteins. In this work, we found that ER bodies are downregulated as a consequence of the immune responses induced by bacterial flagellin perception.Arabidopsismutants defective in ER body formation show enhanced responses upon flagellin perception and enhanced resistance to bacterial infections. Furthermore, the bacterial toxin coronatine induces the formation ofde novoER bodies in leaves and its virulence function is partially dependent on this structure. Finally, we show that performance of the polyphagous beet armyworm herbivore,Spodoptera exigua, increases in plants lacking ER bodies. Altogether, we provide new evidence for the role of the ER bodies in plant immune responses.
Uncovering transposable element variants and their potential adaptive impact in urban populations of the malaria vectorAnopheles coluzzii
Authors: Vargas-Chavez Carlos , Longo Pendy Neil Michel , Nsango Sandrine E. , Aguilera Laura , Ayala Diego , González Josefa 
Published: November 2020

AbstractBackgroundAnopheles coluzziiis one of the primary vectors of human malaria in sub-Saharan Africa. Recently, it has colonized the main cities of Central Africa threatening vector control programs. The adaptation ofAn. coluzziito urban environments is partly due to an increased tolerance to organic pollution and insecticides. While some of the molecular mechanisms for ecological adaptation, including chromosome rearrangements and introgressions, are known, the role of transposable elements (TEs) in the adaptive processes of this species has not been studied yet. To assess the role of TEs in rapid urban adaptation, the first step is to accurately annotate TE insertions in the genomes of natural populations collected in urban settings.ResultsWe sequenced using long-reads sixAn. coluzziigenomes from natural breeding sites in two major Central Africa cities. Wede novoannotated the complete set of TEs in these genomes and in an additional high-qualityAn. coluzziigenome available and identified 64 previously undescribed TE families. TEs were non-randomly distributed throughout the genome with significant differences in the number of insertions of several superfamilies across the studied genomes. We identified seven putatively active families with insertions near genes with functions related to vectorial capacity. Moreover, we identified several TE insertions providing promoter and transcription factor binding sites to insecticide resistance and immune-related genes.ConclusionsThe analysis of multiple genomes sequenced using long-read technologies allowed us to generate the most comprehensive TE annotations in this species to date. We identified several TE insertions that could potentially impact both genome architecture and the regulation of functionally relevant genes inAn. coluzzii. These results provide a basis for future studies of the impact of TEs on the biology ofAn. coluzzii.
Inhibition of Biofilm Formation by Modified Oxylipins from the Shipworm SymbiontTeredinibacter turnerae
Authors: Lacerna Noel M. , V. Ramones Cydee Marie , D. Robes Jose Miguel , D. Picart Myra Ruth , O. Tun Jortan , Miller Bailey W. , Haygood Margo G. , Schmidt Eric W. , Salvador-Reyes Lilibeth A. , Concepcion Gisela P. 
Published: November 2020

AbstractBioactivity-guided purification of the culture broth of the shipworm endosymbiontTeredinibacter turnerae991H.S.0a.06 yielded a new fatty acid, turneroic acid (1), and two previously described oxylipins (2-3). Turneroic acid (1) is an 18-carbon fatty acid decorated by a hydroxy group and an epoxide ring. Compounds1-3inhibited bacterial biofilm formation inStaphylococcus epidermidis, while only3showed antimicrobial activity against planktonicS. epidermidis. Comparison of the bioactivity of1-3with structurally related compounds indicated the importance of the epoxide moiety for selective and potent biofilm inhibition.Abstract Figure
Vaccine-induced, but not natural immunity, against the Streptococcal Inhibitor of Complement protects against invasive disease
Authors: Tan Lionel K. K. , Reglinski Mark , Teo Daryl , Reza Nada , Lamb Lucy E. M. , Nageshwaran Vaitehi , Turner Claire E. , Wikstrom Mats , Frick Inga-Maria , Bjorck Lars , Sriskandan Shiranee 
Published: November 2020

AbstractHighly pathogenicemm1 Streptococcus pyogenesstrains secrete the multidomain Streptococcal inhibitor of complement (SIC) that binds and inactivates components of the innate immune response. We aimed to determine if naturally occurring or vaccine-induced antibodies to SIC are protective against invasiveS. pyogenesinfection. Immunisation with full length SIC protected mice against systemic bacterial dissemination following intranasal or intramuscular infection withemm1 S. pyogenes.Vaccine-induced rabbit anti-SIC antibodies, but not naturally occurring human anti-SIC antibodies, enhanced bacterial clearance in an ex vivo whole blood assay. SIC vaccination of both mice and rabbits resulted in antibody recognition of all domains of SIC, whereas naturally occurring human anti-SIC antibodies recognised the proline-rich region of SIC only. We therefore propose a model whereby natural infection withS. pyogenesgenerates non-protective antibodies against the proline-rich region of SIC, while vaccination with full length SIC permits development of protective antibodies against all SIC domains.
Principles of 3D Compartmentalization of the Human Genome
Authors: Nichols Michael H. , Corces Victor G. 
Published: November 2020

AbstractChromatin is organized in the nucleus by CTCF loops and compartmental domains, the latter of which contain sequences bound by proteins capable of mediating interactions among themselves. While compartmental domains are one of the most prominent features of genome 3D organization at the chromosome scale, we lack a nuanced understanding of the different types of compartmental domains present in chromosomes and a mechanistic knowledge of the forces responsible for their formation. In this study, we compared different cell types to identify distinct paradigms of compartmental domain formation in human tissues. We identified and quantified compartmental forces correlated with histone modifications characteristic of transcriptional activity as well as previously underappreciated roles for compartmental domains correlated with the presence of H3K9me3, H3K27me3, or none of these histone modifications. We present a simple computer simulation model capable of predicting compartmental organization based on the biochemical characteristics of independent chromatin features. Using this computational model, we show that the underlying forces responsible for compartmental domain formation in human cells are conserved and that the diverse compartmentalization patterns seen across cells are due to differences in chromatin features. We extend these findings to Drosophila to suggest that the same fundamental forces are at work beyond humans. These results offer mechanistic insights into the fundamental forces driving the 3D organization of the genome.