bluuebunny/crossref_metadata_2025_split huggingface dataset:
Illuminating protein space with a programmable generative model
Authors: Ingraham John , Baranov Max , Costello Zak , Frappier Vincent , Ismail Ahmed , Tie Shan , Wang Wujie , Xue Vincent , Obermeyer Fritz , Beam Andrew , Grigoryan Gevorg 
Published: December 2022

AbstractThree billion years of evolution have produced a tremendous diversity of protein molecules, and yet the full potential of this molecular class is likely far greater. Accessing this potential has been challenging for computation and experiments because the space of possible protein molecules is much larger than the space of those likely to host function. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. We suggest that Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.
The type 1 diabetes-associated lncRNAARGIparticipates in virus-induced pancreatic β cell inflammation
Authors: González-Moro Itziar , Garcia-Etxebarria Koldo , Mendoza Luis Manuel , Fernández-Jiménez Nora , Mentxaka-Salgado Jon , Olazagoitia-Garmendia Ane , Arroyo María Nicol , Sawatani Toshiaki , de Beek Anne Op , Cnop Miriam , Igoillo-Esteve Mariana , Santin Izortze 
Published: December 2022

SummaryType 1 diabetes-associated single nucleotide polymorphisms are mainly located in non-coding regions of the human genome. Single nucleotide polymorphisms located in long non-coding RNAs may result in the disruption of their secondary structure, affecting their function.Here, we functionally characterized the virus-induced type 1 diabetes-associated lncRNAARGI(Antiviral Response Gene Inducer).ARGIupregulation in pancreatic β cells leads to the transcriptional activation of antiviral and pro-inflammatory genes. Upon a viral insult,ARGIis upregulated in the nuclei of pancreatic β cells and binds to CTCF to interact with the regulatory regions ofIFNβand interferon-stimulated genes, promoting their transcriptional activation in an allele-specific manner.The presence of the risk allele for type 1 diabetes inARGIinduces an hyperactivation of type I IFN response in β cells, an expression signature that is present in the pancreas of diabetic patients. These data shed light on the molecular mechanisms by which type 1 diabetes-related single nucleotide polymorphisms in long non-coding RNAs influence pathogenesis at the pancreatic β cell level.
Integrated genetic and metabolic characterisation of diverse Latin American cassava (Manihot esculentaCrantz) germplasm; implications for future breeding strategies
Authors: Perez-Fons L , Ovalle TM , Drapal M , Ospina MA , Bohorquez-Chaux A , Becerra Lopez-Lavalle LA , Fraser PD 
Published: December 2022

AbstractCassava is an important staple crop for food security in Africa and South America. The present study describes an integrated genomic and metabolomic approach to the characterisation of Latin American cassava germplasm. Classification based on genotyping and the leaf metabolome correlates, the key finding being the adaption to specific eco-geographical environments. In contrast the root metabolome does not relate to the genotypic clustering, suggesting different spatial regulation of this tissue’s metabolome. The data has been used to generate pan-metabolomes for specific tissues and the inclusion of phenotypic data has enabled the identification of metabolic sectors underlying traits of interest. For example, tolerance to whiteflies was not linked to cyanide content but to cell wall related phenylpropanoids or apocarotenoids. Collectively, these data advance the communities resources and provides a valuable insight into new parental breeding materials with traits of interest directly related to combating food security.Significance statementCassava is a staple crop in developing countries of sub-tropical regions. Traditionally, cassava has been considered as a subsistence crop. However recently it has become a sustainable solution to fulfil both hunger and malnutrition needs, and drive economic development. Varietal improvement via classic breeding has successfully delivered products into the Asian market by including/exchanging germplasm from original Latin American collections. Conversely, modest progress has been achieved in Sub-Saharan countries since genetic resources are biased towards exploitation of local landraces and uncharacterised parental material. The present work explores the genetic and metabolic diversity of Latin American cassava’s genebank, one of the largest and most complete worldwide. These data provide a robust characterisation of valuable germplasm that can be exploited in breeding programmes.
WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins
Authors: González-Delgado Javier , Sagar Amin , Zanon Christophe , Lindorff-Larsen Kresten , Bernadó Pau , Neuvial Pierre , Cortés Juan 
Published: December 2022

AbstractThe structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, being one ensemble characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available athttps://gitlab.laas.fr/moma/WASCO.
RNAseq-based gene expression analysis ofMelolontha hippocastanihindgut pockets and the surrounding hindgut wall tissue
Authors: Alonso-Pernas Pol , Boland Wilhelm 
Published: December 2022

AbstractIn this study, the metatranscriptome of newly-discovered structures attached at the distal end of the hindgut of the larvae of a coleopteran (Melolontha hippocastani), is compared with that of the surrounding hindgut wall. Larvae were collected in their natural habitat, RNA was extracted using a commercial kit and sequenced in a Illumina HiSeq2500 platform. 250 bp paired-end reads were used to de novo assemble the transcriptomes. Contig annotation was carried out with BLASTx and Blast2GO PRO and differential expression analysis was performed in edgeR. Contigs aligned mainly toAchromobactersp. in the pockets and to the Firmicutes phylum in hindgut wall. Host RNAs were expressed in the pockets in higher amounts than in hindgut wall. Gene expression suggest that pocket bacteria undergo aerobic metabolism and are exposed to higher levels of oxidative stress than the population of the hindgut wall. Hypothetical functions for the pocket might be immune-stimulation and regulation of host development, while the hindgut wall appears to be devoted to degradation of dietary polysaccharides and host nitrogenous wastes. Further research is necessary to experimentally prove these suggested roles.
DYNATE: Localizing Rare-Variant Association Regions via Multiple Testing Embedded in an Aggregation Tree
Authors: Li Xuechan , Pura John , Allen Andrew , Owzar Kouros , Lu Jianfeng , Harms Matthew , Xie Jichun 
Published: December 2022

AbstractRare variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNATE (DYNamic Aggregation TEsting). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with controlled weighted false discovery rate. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared to existing methods. Importantly, DYNATE-identified regions have higher enrichment levels of disease-associated RVs. We applied DYNATE to an amyotrophic lateral sclerosis (ALS) study and identified a new gene,EPG5, harboringpossibly pathogenic mutations.
Myofibroblast Ccn3 is regulated by Yap and Wwtr1 and contributes to adverse cardiac outcomes
Authors: Flinn Michael A. , Alvarez-Argote Santiago , Knas Makenna C. , Almeida Victor Alencar , Paddock Samantha J. , Zhou Xiaoxu , Buddell Tyler , Jamal Ayana , Liu Pengyuan , Drnevich Jenny , Patterson Michaela , Link Brian A. , O’Meara Caitlin C. 
Published: December 2022

AbstractWhile Yap and Wwtr1 regulate resident cardiac fibroblast to myofibroblast differentiation following cardiac injury, their role specifically in activated myofibroblasts remains unexplored. Here we assess the pathophysiological and cellular consequence of genetic depletion of Yap alone (Yapfl/fl;PostnMCM) or Yap and Wwtr1 (Yapfl/fl;Wwtr1fl/+;PostnMCM) in adult mouse myofibroblasts following myocardial infarction and identify and validate novel downstream factors specifically in cardiac myofibroblasts that mediate pathological remodeling. Following myocardial infarction, depletion of Yap in myofibroblasts had minimal effect on heart function while depletion of Yap/Wwtr1 resulted in smaller scars, reduced interstitial fibrosis, and improved ejection fraction and fractional shortening. Single cell RNA sequencing of interstitial cardiac cells 7 days post infarction showed suppression of pro-fibrotic genes in fibroblasts derived fromYapfl/fl,Wwtr1fl/+;PostnMCMhearts. In vivo myofibroblast depletion of Yap/Wwtr1 as well in vitro knockdown of Yap/Wwtr1 dramatically decreased RNA and protein expression of the matricellular factor Ccn3. Administration of recombinant CCN3 to adult mice following myocardial infarction remarkably aggravated cardiac function and scarring. CCN3 administration drove myocardial gene expression of pro-fibrotic genes in infarcted left ventricles implicating CCN3 as a novel driver of cardiac fibrotic processes following myocardial infarction.
Visualization & Quality Control Tools for Large-scale Multiplex Tissue Analysis in TissUUmaps 3
Authors: Behanova Andrea , Avenel Christophe , Andersson Axel , Chelebian Eduard , Klemm Anna , Wik Lina , Östman Arne , Wählby Carolina 
Published: December 2022

AbstractLarge-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in e.g. tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users’ trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.
Network topology in brain tumor patients with and without structural epilepsy- a prospective MEG-study
Authors: Ladisich Barbara , Rampp Stefan , Trinka Eugen , Weisz Nathan , Schwartz Christoph , Kraus Theo , Sherif Camillo , Marhold Franz , Demarchi Gianpaolo 
Published: December 2022

AbstractIt has been proposed that functional connectivity (FC) and network topology (NT) are altered in patients with glial brain tumors. So far there is no consensus on the pattern of these changes, furthermore data on FC in patients with brain metastases (BMs) as well as on the presence and absence of tumor-related epilepsy is lacking.We aimed to analyze preoperative NT of newly diagnosed, singular, supratentorial glial brain tumors (GBTs) and BMs with and without structural epilepsy.FC and NT derived from resting state magnetoencephalography (MEG) were compared between patients (PAs) and matched healthy controls (HCs), between GBTs and BMs as well as between patients with and without structural epilepsy. We investigated all patients, who met our inclusion criteria from February 2019 to March 2021. Clinical data was collected from patients’ electronic medical charts. We analyzed whole brain (wb) connectivity in six frequency bands, calculated three different network topological parameters (node degree (ND), shortest path length (L), clustering coefficient (CC)) and performed a stratification, where differences in the power were to be found. For data analysis we used Fieldtrip, Brain Connectivity Matlab toolboxes and in-house built scripts.We consecutively included 41 patients (21 men), mean age 60.1ys (range 23-82), who were operated on at our institution. Tumor histology included high-grade gliomas (n=18), low-grade gliomas (n=4), dysembryoplastic neuroepithelial tumor (DNET, n=1), BMs (n=14) and others (n=4). Statistical analysis revealed a significant decrease for wb ND in patients compared to healthy controls in every frequency range analyzed at the descriptive and corrected level (p1-30Hz=0.002, pγ=0.002, pβ=0.002, pα=0.002, pθ=0.024, pδ=0.002). Furthermore, at the descriptive level, we found a significant augmentation for wb CC (p1-30Hz=0.031, pδ=0.013) in PAs compared to HCs, which did not persist the false discovery rate (FDR) correction. There were no differences in the networks of glial brain tumors and metastases identified. However, we found a significant increase in wb CC in patients with structural epilepsy (pθ= 0.048), and significantly lower wb ND (pα= 0.039) at the uncorrected level.Our data suggests that network topology is altered in brain tumor patients, which is in line with previous studies. Tumor histology per se might not influence the brain’s functional network, however, tumor-related epilepsy seems to do so. Longitudinal studies and in-depth analysis of possible factors and confounders might be helpful to further substantiate these findings.
MLcps: Machine Learning Cumulative Performance Score for classification problems
Authors: Akshay Akshay , Abedi Masoud , Shekarchizadeh Navid , Burkhard Fiona C. , Katoch Mitali , Bigger-Allen Alex , Adam Rosalyn M. , Monastyrskaya Katia , Gheinani Ali Hashemi 
Published: December 2022

ABSTRACTMotivationA performance metric is a tool to measure the correctness of a trained Machine Learning (ML) model. Numerous performance metrics have been developed for classification problems making it overwhelming to select the appropriate one since each of them represents a particular aspect of the model. Furthermore, selection of a performance metric becomes harder for problems with imbalanced and/or small datasets. Therefore, in clinical studies where datasets are frequently imbalanced and, in situations when the prevalence of a disease is low or the collection of patient samples is difficult, deciding on a suitable metric for performance evaluation of an ML model becomes quite challenging. The most common approach to address this problem is measuring multiple metrics and compare them to identify the best-performing ML model. However, comparison of multiple metrics is laborious and prone to user preference bias. Furthermore, evaluation metrics are also required by ML model optimization techniques such as hyperparameter tuning, where we train many models, each with different parameters, and compare their performances to identify the best-performing parameters. In such situations, it becomes almost impossible to assess different models by comparing multiple metrics.ResultsHere, we propose a new metric called Machine Learning Cumulative Performance Score (MLcps) as a Python package for classification problems. MLcps combines multiple pre-computed performance metrics into one metric that conserves the essence of all pre-computed metrics for a particular model. We tested MLcps on 4 different publicly available biological datasets and the results reveal that it provides a comprehensive picture of overall model robustness.AvailabilityMLcps is available athttps://pypi.org/project/MLcps/and cases of use are available athttps://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main.Supplementary informationSupplementary data are available at Bioinformatics online.
Heterochromatin and RNAi act independently to ensure genome stability in Mucorales human fungal pathogens
Authors: Navarro-Mendoza María Isabel , Pérez-Arques Carlos , Heitman Joseph 
Published: December 2022

AbstractChromatin modifications play a fundamental role in controlling transcription and genome stability and yet despite their importance, are poorly understood in early-diverging fungi. We present the first comprehensive study of histone-lysine and DNA methyltransferases across the Mucoromycota, emphasizing heterochromatin formation pathways that rely on the Clr4 complex involved in H3K9-methylation, the Polycomb-repressive complex 2 driving H3K27-methylation, or DNMT1-like methyl-transferases that catalyze 5mC DNA methylation. Our analysis uncovered H3K9-methylated heterochromatin as the major chromatin modification repressing transcription in these fungi, which lack both Polycomb silencing and cytosine methylation. Although small RNAs generated by RNAi pathways facilitate the formation of heterochromatin in many eukaryotic organisms, we show that RNAi is not required to maintain either genomic or centromeric heterochromatin inMucor. H3K9-methylation and RNAi act independently to control centromeric regions, suggesting a functional sub-specialization. Whereas the H3K9 methyltransferase Clr4 and heterochromatin formation are essential for cell viability, RNAi is dispensable for viability yet acts as the main epigenetic, regulatory force repressing transposition of centromeric GremLINE1 elements. Mutations inactivating canonical RNAi lead to rampant transposition and insertional inactivation of targets resulting in antimicrobial drug resistance. This fine-tuned, Rdrp2-dependent RNAi activity is critical for genome stability, restricting GremLINE1 retroelements to the centromeres where they occupy long heterochromatic islands. Taken together, our results suggest that RNAi and heterochromatin formation are independent genome defense and regulatory mechanisms in the Mucorales, contributing to a paradigm shift from the co-transcriptional gene silencing observed in fission yeasts to models in which heterochromatin and RNAi operate independently in early-diverging fungi.
A generalized adaptive harvesting model exhibits cusp bifurcation, noise, and rate-associated tipping pathways
Authors: Tekwa E.W. , Junquera Victoria 
Published: December 2022

AbstractThe sustainability of renewable resource harvesting may be threatened by environmental and socioeconomic changes that induce tipping points. Here, we propose a synthetic harvesting model with a comprehensive set of socioecological factors that have not been explored together, including market price and stock value, effort and processing costs, labour and natural capital elasticities, societal risk aversion, maximum sustainable yield (MSY), and population growth shape. We solve for harvest rate and stock biomass solutions by applying a timescale-separation between fast ecological dynamics and slow institutional adaptation that responds myopically to short-term net profit. The result is a cusp bifurcation with two composite bifurcation parameters: 1. consumptive scarcityλcor the ratio of market price-to-processing cost divided byMSY(leading to a pitchfork), and 2. non-consumptive scarcityλnor the stock value minus a scaled effort cost (leading to saddle-nodes or folds). Together, consumptive and non-consumptive scarcities create a cusp catastrophe. We further identify four tipping phenomena: 1. process (harvest rate) noise-induced tipping; 2. exogenous (λc) rate+process noise-induced tipping; 3. exogenous noise-induced reduction in tipping; and 4. exogenous cycle-induced reduction in tipping. Case 2 represents the first mechanistically motivated example of rate-associated tipping in socioecological systems, while cases 3 and 4 resemble noise-induced stability. We discuss the empirical relevance of catastrophe and tipping in natural resource management. Our work shows that human institutional behaviour coupled with changing socioecological conditions can cause counterintuitive sustainability and resilience outcomes.