bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke
Authors: Erickson B.A. , Kim B. , Deck B.L. , Pustina D. , DeMarco A.T. , Dickens J.V. , Kelkar A.S. , Turkeltaub P.E. , Medaglia J.D. 
Published: September 2021

AbstractThe severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis.We discovered that the length of bypasses reliably predicted variance in lexical production (R2= .576) and auditory comprehension scores (R2= .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.
Mapping interindividual dynamics of innate immune response at single-cell resolution
Authors: Kumasaka Natsuhiko , Rostom Raghd , Huang Ni , Polanski Krzysztof , Meyer Kerstin B. , Patel Sharad , Boyd Rachel , Gomez Celine , Barnett Sam N. , Panousis Nikolaos I , Schwartzentruber Jeremy , Ghoussaini Maya , Lyons Paul A. , Calero-Nieto Fernando J. , Göttgens Berthold , Barnes Josephine L. , Worlock Kaylee B. , Yoshida Masahiro , Nikolic Marko Z. , Stephenson Emily , Reynolds Gary , Haniffa Muzlifah , Marioni John , Stegle Oliver , Hagai Tzachi , Teichmann Sarah A. 
Published: September 2021

AbstractCommon genetic variants modulate the cellular response to viruses and are implicated in a range of immune pathologies, including infectious and autoimmune diseases. The transcriptional antiviral response is known to vary between infected cells from a single individual, yet how genetic variants across individuals modulate the antiviral response (and its cell-to-cell variability) is not well understood. Here, we triggered the antiviral response in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-seq. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), the first statistical approach designed to identify dynamic eQTLs across a transcriptional trajectory of cell populations, without aggregating single-cell data into pseudo-bulk. This allows us to uncover the underlying architecture and variability of antiviral response across responding cells, and to identify more than two thousands eQTLs modulating the dynamic changes during this response. Many of these eQTLs colocalise with risk loci identified in GWAS of infectious and autoimmune diseases. As a case study, we focus on a COVID-19 susceptibility locus, colocalised with the antiviral OAS1 splicing QTL. We validated it in blood cells from a patient cohort and in the infected nasal cells of a patient with the risk allele, demonstrating the utility of GASPACHO to fine-map and functionally characterise a genetic locus. In summary, our novel analytical approach provides a new framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.
Biochemical characterisation of human transglutaminase 4
Authors: Csobán-Szabó Zsuzsa , Bécsi Bálint , El Alaoui Saïd , Fésüs László , Korponay-Szabó Ilma Rita , Király Róbert 
Published: September 2021

AbstractTransglutaminases are protein modifying enzymes involved in physiological and pathological processes with potent therapeutic possibilities. Human TG4, also called prostate transglutaminase, is frequently associated with pathological symptoms and particularly with cancer invasiveness. Although rodent TG4 is well characterised, bio-chemical characteristics of human TG4 that could help the understanding of its way of action are not published. First, we analysed proteomics databases and found that TG4 protein is present in human tissues beyond the prostate. Then, we studied in vitro the transamidase activity of human TG4 and its regulation using the microtiter plate method. Human TG4 has low transamidase activity which prefers slightly acidic pH and a reducing environment. It is enhanced by submicellar concentrations of SDS suggesting that membrane proximity is an important regulatory event. Human TG4 does not bind GTP as tested by GTP-agarose and BODIPY-FL-GTPγS binding, and its proteolytic activation by dispase or when expressed in AD-293 cells was not observed either. We identified several potential human TG4 glutamine donor substrates in the AD-293 cell extract by biotin-pentylamine incorporation and mass spectrometry. Several of these potential substrates are involved in cell-cell interaction, adhesion and proliferation, suggesting that human TG4 could become an anticancer therapeutic target.
Azacytidine targeting SARS-CoV-2 viral RNA as a potential treatment for COVID-19
Authors: Lin Xian , Ke Xianliang , Xia Lin , Zhang Tianying , Xiong Hualong , Zhao Binghai , Liu Wen , Chen Quanjiao , Tang Chong 
Published: September 2021

AbstractThe COVID-19 pandemic is a global health disaster. Moreover, emerging mutated virus strains present an even greater challenge for existing vaccines and medications. One possible solution is to design drugs based on the properties of virus epigenome, which are more common among coronaviruses.Here, we reported an FDA-approved drug for myelodysplastic syndrome, azacytidine (5Aza), limited virus infection and protected mice against SARS-CoV-2. We demonstrated that this antiviral effect is related to 5Aza incorporation into viral RNA, which disrupt m5C RNA methylation modification profile. This work suggests that targeting viral epigenomes is a viable therapeutic strategy, potentially opening new pathways for treating COVID-19.
Real-time visualization of mRNA synthesis during memory formation in live animals
Authors: Lee Byung Hun , Shim Jae Youn , Moon Hyungseok C. , Kim Dong Wook , Kim Jiwon , Yook Jang Soo , Kim Jinhyun , Park Hye Yoon 
Published: September 2021

AbstractMemories are thought to be encoded in populations of neurons called memory trace or engram cells. However, little is known about the dynamics of these cells because of the difficulty in real-time monitoring of them over long periods of timein vivo. To overcome this limitation, we present a genetically-encoded RNA indicator (GERI) mouse for intravital chronic imaging of endogenousArcmRNA—a popular marker for memory trace cells. We used our GERI to identifyArc-positive neurons in real time without the delay associated with reporter protein expression in conventional approaches. We found that theArc-positive neuronal populations rapidly turned over within two days in the hippocampal CA1 region, whereas ∼4% of neurons in the retrosplenial cortex (RSC) consistently expressedArcfollowing contextual fear conditioning and repeated memory retrievals. Dual imaging of GERI and a calcium indicator in CA1 of mice navigating a virtual reality environment revealed that only the population of neurons expressingArcduring both encoding and retrieval exhibited relatively high calcium activity in a context-specific manner. Thisin vivoRNA imaging approach opens the possibility of unraveling the dynamics of the neuronal population underlying various learning and memory processes.One Sentence SummaryLive-animal imaging ofArcmRNA reveals the dynamics and activity of memory trace cells during memory encoding and retrieval.
Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy
Authors: Manneschi Luca , Gicante Guido , Del Giudice Paolo , Vasilaki Eleni 
Published: September 2021

AbstractWe postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) Signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the “effective” decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.
Analysing pneumococcal invasiveness using Bayesian models of pathogen progression rates
Authors: Løchen Alessandra , Truscott James E. , Croucher Nicholas J. 
Published: September 2021

AbstractThe disease burden attributable to opportunistic pathogens depends on their prevalence in asymptomatic colonisation and the rate at which they progress to cause symptomatic disease. Increases in infections caused by commensals can result from the emergence of “hyperinvasive” strains. Such pathogens can be identified through quantifying progression rates using matched samples of typed microbes from disease cases and healthy carriers. This study describes Bayesian models for analysing such datasets, implemented in an RStan package (https://github.com/nickjcroucher/progressionEstimation). The models converged on stable fits that accurately reproduced observations from meta-analyses of Streptococcus pneumoniae datasets. The estimates of invasiveness, the progression rate from carriage to invasive disease, in cases per carrier per year correlated strongly with the dimensionless values from meta-analysis of odds ratios when sample sizes were large. At smaller sample sizes, the Bayesian models produced more informative estimates. This identified historically rare but high-risk S. pneumoniae serotypes that could be problematic following vaccine-associated disruption of the bacterial population. The package allows for hypothesis testing through model comparisons with Bayes factors. Application to datasets in which strain and serotype information were available for S. pneumoniae found significant evidence for within-strain and within-serotype variation in invasiveness. The heterogeneous geographical distribution of these genotypes is therefore likely to contribute to differences in the impact of vaccination in between locations. Hence genomic surveillance of opportunistic pathogens is crucial for quantifying the effectiveness of public health interventions, and enabling ongoing meta-analyses that can identify new, highly invasive variants.Author summaryOpportunistic pathogens are microbes that are commonly carried by healthy hosts, but can occasionally cause severe disease. The progression rate quantifies the risk of such a pathogen transitioning from a harmless commensal to causing a symptomatic infection. The incidence of infections caused by opportunistic pathogens can rise with the emergence of “hyperinvasive” strains, which have high progression rates. Therefore methods for calculating progression rates of different pathogen strains using surveillance data are crucial for rapidly identifying emerging infectious disease threats. Existing methods typically measure progression rates relative to the overall mix of microbes in the population, but these populations can vary substantially between locations and times, making the outputs challenging to combine across studies. This work presents a new method for estimating progression rates from surveillance data that generates values useful for modelling pathogen populations, even from relatively small sample sizes.
The genomic signatures of natural selection in admixed human populations
Authors: Cuadros Espinoza Sebastian , Laval Guillaume , Quintana-Murci Lluis , Patin Etienne 
Published: September 2021

AbstractAdmixture has been a pervasive phenomenon in human history, shaping extensively the patterns of population genetic diversity. There is increasing evidence to suggest that admixture can also facilitate genetic adaptation to local environments, i.e., admixed populations acquire beneficial mutations from source populations, a process that we refer to as adaptive admixture. However, the role of adaptive admixture in human evolution and the power to detect it are poorly characterized. Here, we use extensive computer simulations to evaluate the power of several neutrality statistics to detect natural selection in the admixed population, accounting for background selection and assuming different admixture scenarios. We show that two statistics based on admixture proportions, Fadm and LAD, show high power to detect mutations that are beneficial in the admixed population, whereas iHS and FST falsely detect neutral mutations that have been selected in the source populations only. By combining Fadm and LAD into a single statistic, we scanned the genomes of 15 worldwide, admixed populations for signatures of adaptive admixture. We confirm that lactase persistence and resistance to malaria have been under adaptive admixture in West Africa and in Madagascar, North Africa and South Asia, respectively. Our approach also uncovers new cases of adaptive admixture, including the APOL1/MYH9 locus in the Fulani nomads and PKN2 in East Indonesians, involved in resistance to infection and metabolism, respectively. Collectively, our study provides new evidence that adaptive admixture has occurred in multiple human populations, whose genetic history is characterized by periods of isolation and spatial expansions resulting in increased gene flow.Author summaryAdaptive introgression, i.e., the acquisition of adaptive traits through hybridization with another species, is a well-documented phenomenon in evolution. Conversely, adaptive admixture, i.e., the acquisition of adaptive traits through admixture between populations of the same species, is poorly described. In this study, we evaluate the importance of adaptive admixture in human recent evolutionary history. We first determine the expected signatures of adaptive admixture on patterns of genomic diversity, using realistic simulations. We then identify the methods that are the most powerful to detect such molecular signatures. Finally, by using the methods identified as the most powerful, we search for cases of adaptive admixture in the genomes of 15 admixed populations from around the globe. We find evidence that adaptive admixture has occurred in several populations from Northeast Africa, Southeast Asia and Oceania. This study suggests that admixture has indeed facilitated human genetic adaptation, particularly at genes involved in metabolism and resistance against pathogens.
Tolerance of Pseudomonas Strain to the 2,4-D Herbicide through a Peroxidase System
Authors: de Oliveira Elizangela Paz , da Silva Rovida Amanda Flávia , Martins Juliane Gabriele , Veiga Pileggi Sônia Alvim , Schemczssen-Graeff Zelinda , Pileggi Marcos 
Published: September 2021

ABSTRACTHerbicides are widely used in agricultural practices for preventing the proliferation of weeds that compete with crops for survival. Upon reaching soil and water, herbicides can damage nontarget organisms, such as bacteria, which need an efficient defense mechanism to tolerate the stress induced by herbicides. 2,4-Dichlorophenoxyacetic acid (2,4-D) is a herbicide that exerts increased oxidative stress among bacterial communities that consequently witness an increased toxicity in their microenvironments. Bacterial isolates were obtained from the biofilm of water that was contaminated with 2,4-D. This biofilm originated from the tanks containing washing water from the packaging of different pesticides, including 2,4-D. Moreover, several isolates were sensitive to biofilm toxicity; however, they remained alive in the presence of 2,4-D. The Pseudomonas sp. CMA-7.3 was selected because of its tolerance against biofilm agrochemicals. Therefore, the objective of this study is to evaluate the antioxidative response system of the Pseudomonas sp. CMA-7.3. This study also analyzed poorly evaluated enzymes, such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and guaiacol peroxidase GPX, in the bacterial systems. The toxic effects of 2,4-D on bacteria were evaluated using mechanisms indicating oxidative stress, such as growth curve, cell viability, peroxide, and malondialdehyde. The Pseudomonas sp. CMA-7.3 was an efficient response system against the activity of antioxidant enzymes such as SOD, CAT, APX, and GPX in balancing the production of H2O2, even at high doses as 25x the field dose of the herbicide, thereby proving the toxicity of 2,4-D for this strain and showing the ability of the strain to tolerate 2,4-D. The adaptation of this microorganism to herbicide exposure is truly relevant for improving future metabolic studies on bacterial communities. The strain showed a great potential in the application and developmental prospects of a new product in the bioremediation process of environments contaminated by these herbicides.
Dual perspective proteomics infectome profiling discoversSalmonellatype III secretion system effector functions in macrophages
Authors: Geddes-McAlister J. , Sukumaran A. , Vogt S.L. , Rowland J.L. , Woodward S.E. , Muselius B. , Gee L. , Roach E.J. , Khursigara C.M. , Raupach B. , Finlay B.B. , Meissner F. 
Published: September 2021

AbstractIntracellular bacterial pathogens have evolved sophisticated infection strategies, including the release and secretion of virulence factors to interfere with host cell functions and to perturb immune responses. ForSalmonella entericaserovar Typhimurium (S. Typhimurium), the type III secretion systems encoded onSalmonellapathogenicity islands (SPI) 1 and 2 mediates invasion of the bacterium into innate immune cells and regulates bacterial replication and survival within the hostile environment of the host, respectively. Here, we explore the temporal and strain-specific dual perspective response of both the host and pathogen during cellular infection via quantitative proteomics. We report time- and pathogenicity island-specific expression and secretion of infection-associated proteins (i.e., SL1344_1263, SL1344_3112, SL1344_1563, and YnhG) and regulated immune response proteins in macrophage, including Cd86, Cd40, Casp4, C3, IL-1α, and Cd69). Through intracellular macrophage andin vivomurine models of infection, we reveal a role in virulence for three of the bacterial proteins (SL1344_1263, SL1344_1563, and YnhG), defining their importance as novel T3SS effectors. We characterize the temporal intra- and extracellular production of the effectors and identify their interaction networks in host cells, representing inhibitory and stimulatory pathways mounted by invading bacterial pathogens.Author SummaryThe relationship between a host and pathogen is intricate, and regulation of the host immune response correlates with the progressive timing of infection and tailored responses to the pathogen. Relying on detection and quantification of protein-level changes using mass spectrometry-based proteomics, we explore the production of known and novel effectors bySalmonelladuring intracellular survival within macrophage. Our results portray a role for these effectors in bacterial virulence using anin vivomurine model of infection, and we define a dynamic network of interaction between the effectors and host proteins. These interactions reveal opportunity for drug design to disrupt modulation of the host by the invading bacterium as a new strategy for combatting infection. Our approach is dynamic and universal, with the power to alter therapeutic discovery against infectious diseases.
CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq
Authors: Cuomo Anna S.E. , Heinen Tobias , Vagiaki Danai , Horta Danilo , Marioni John C. , Stegle Oliver 
Published: September 2021

AbstractSingle cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to perform genetic analyses using scRNA-seq remain based on principles established for bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize heterogeneity in allelic effects across cellular contexts of different granularity, including cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to two recent studies of differentiating iPSCs, where we uncover a previously underappreciated heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.