bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strain variation and anomalous climate synergistically influence cholera pandemics
Authors: Rodó X. , Bouma M.J. , Rodriguez-Arias M.A. , Roy M. , De Yebra P. , Garcia M. , Pascual M. 
Published: April 2021

SummaryExplanations for the genesis and propagation of recurrent cholera pandemics since 1817 have remained elusive. Evolutionary change of the pathogen is presumed to have been a dominant factor behind the 7th “El Tor” pandemic, but little is known to support this hypothesis for preceding pandemics. We investigate the concomitant roles of climate and putative strain variation for the 6th cholera pandemic (1899-1923; the one with the highest ever associated mortality in India), using newly assembled historical records for climate variables and cholera deaths for Bengal, Assam, Bihar and Bombay provinces in former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. Finally, we ran multi-model climate simulations to infer past and future long-term means of rainfall distributions on the basis of 39 models for 1861-2100, and for different periods of 50 years (1875-1925; 1975-2025 and 2050-2100).The 6th cholera pandemic featured a large scale synchronisation with a delay of a few years in both seasonal and interannual cholera variability over the endemic Bengal region during the El Niño event of 1904-07. Additional evidence supporting the establishment of a new strain includes a shift of cholera incidence to older age groups, an increase in the case fatality rate and the suppression of the spring cholera peak.The 6th cholera pandemic of Indian origin was associated with a novel and particularly invasive strain of new territory, and also with some delay, of endemic parts of India that act as a genetic regional reservoir of the disease. Climate anomalies appear to have played an important role in facilitating the establishment of this invasive strain, with environmental conditions similar to those underlying strain changes associated with ENSO in today’s Bangladesh. The evolutionary change of pathogens can act synergistically with climatic conditions in the replacement and propagation of emerging strains, as was the case in cholera’s 7th pandemic. Increased climate variability and extremes under global warming would thus provide windows of opportunity for emerging new pathogens.
In silicoanalysis of the fast-growing thermophileGeobacillussp. LC300 using a novel genome-scale metabolic model
Authors: Ljungqvist Emil , Gustavsson Martin 
Published: April 2021

AbstractThermophilic microorganisms show high potential for use as biorefinery cell factories. Their high growth temperatures provide fast conversion rates, lower risk of contaminations, and facilitated purification of volatile products. To date, only a few thermophilic species have been utilized for microbial production purposes, and the development of production strains is impeded by the lack of metabolic engineering tools. In this study, we constructed a genome-scale metabolic model,iGEL601, ofGeobacillussp. LC300, an important part of the metabolic engineering pipeline. The model contains 601 genes, 1240 reactions and 1305 metabolites, and the reaction reversibility is based on thermodynamics at the optimum growth temperature. Using flux sampling, the model shows high similarity to experimentally determined reaction fluxes with both glucose and xylose as sole carbon sources. Furthermore, the model predicts previously unidentified by-products, closing the gap in the carbon balance for both carbon sources. Finally,iGEL601was used to suggest metabolic engineering strategies to maximise production of five industrially relevant compounds. The suggested strategies have previously been experimentally verified in other microorganisms, and predicted production rates are on par with or higher than those previously achieved experimentally. The results highlight the biotechnological potential of LC300 and the application ofiGEL601for use as a tool in the metabolic engineering workflow.
Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification
Authors: Song Jian , Zhang Fangfei , Yu Changbin 
Published: April 2021

ABSTRACTIdentification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively.Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data.
Interpretable Artificial Neural Networks incorporating Bayesian Alphabet Models for Genome-wide Prediction and Association Studies
Authors: Zhao Tianjing , Fernando Rohan , Cheng Hao 
Published: April 2021

ABSTRACTIn conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where “NN” stands for neural networks, and “Bayes” stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, Bayesian LASSO, and BayesR. NN-Bayes incorporates Bayesian Alphabet models into non-linear neural networks via hidden layers between SNPs and observed traits. Thus, NN-Bayes attempts to improve the performance of genome-wide prediction and GWAS by accommodating non-linear relationships between the hidden nodes and the observed trait, while maintaining genomic interpretability through the Bayesian regression models that connect the SNPs to the hidden nodes. For genomic interpretability, the posterior distribution of marker effects in NN-Bayes is inferred by Markov chain Monte Carlo (MCMC) approaches and used for inference of association through posterior inclusion probabilities (PIPs) and window posterior probability of association (WPPA). In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude. In real data analyses, for the soy dataset, NN-Bayes achieved significantly higher prediction accuracies than conventional linear models, and results from other four different species showed that NN-Bayes had similar prediction performance to linear models, which is potentially due to the small sample size. Our NN-Bayes is optimized for high-dimensional genomic data and implemented in an open-source package called “JWAS”. NN-Bayes can lead to greater use of Bayesian neural networks to account for non-linear relationships due to its interpretability and computational performance.
Distinct Binding Mechanisms for Allosteric Sodium Ion In Cannabinoid Receptors
Authors: Dutta Soumajit , Selvam Balaji , Shukla Diwakar 
Published: April 2021

AbstractThe therapeutical potential of Cannabinoid receptors is not fully explored due to psychoactive side-effects and lack of selectivity associated with the orthosteric ligands. Allosteric modulators have the potential to become selective therapeutics for cannabinoid receptors. Biochemical experiments have shown the effects of the allosteric Na+binding on cannabinoid receptor activity. However, the Na+coordination site, and binding pathway are still unknown. Here, we perform molecular dynamic simulations to explore Na+binding in the cannabinoid receptors, CB1and CB2. Simulations reveal that Na+binds to the primary binding site from different extracellular sites for CB1and CB2. A distinct secondary Na+coordinate site is identified that is not present in CB2. Furthermore, simulations also show that intracellular Na+could bind to the Na+binding site in CB1. Constructed Markov state models show that the standard free energy of Na+binding is similar to the previously calculated free energy for other class A GPCRs.
PsiNorm: a scalable normalization for single-cell RNA-seq data
Authors: Borella Matteo , Martello Graziano , Risso Davide , Romualdi Chiara 
Published: April 2021

AbstractSingle-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.Here we proposePsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here we show that the Pareto distribution well resembles scRNA-seq data, independently of sequencing depths and technology. Motivated by this result, we implementPsiNorm, a simple and highly scalable normalization method. We benchmarkPsiNormwith other seven methods in terms of cluster identification, concordance and computational resources required. We demonstrate thatPsiNormis among the top performing methods showing a good trade-off between accuracy and scalability. MoreoverPsiNormdoes not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.PsiNormis available as an R package available athttps://github.com/MatteoBlla/PsiNorm
Machine Learning Identifies Ponatinib as a Potent Inhibitor of SARS-CoV2-induced Cytokine Storm
Authors: Chan Marina , Vijay Siddharth , McElrath M. Juliana , Holland Eric C. , Gujral Taranjit S 
Published: April 2021

AbstractAlthough 15-20% of COVID-19 patients experience hyper-inflammation induced by massive cytokine production, cellular triggers of this process and strategies to target them remain poorly understood. Here, we show that the N-terminal domain (NTD) of the spike protein from the SARS-CoV-2 and emerging variants B1.1.7 and B.1.351 substantially induces multiple inflammatory molecules in human monocytes and PBMCs. Further, we identified several protein kinases, including JAK1, EPHA7, IRAK1, MAPK12, and MAP3K8, as essential downstream mediators of NTD-induced cytokine release. Additionally, we found that the FDA-approved, multi-kinase inhibitor Ponatinib is a potent inhibitor of the NTD-mediated cytokine storm. Taken together, we propose that agents targeting multiple kinases required for the SARS-CoV-2-mediated cytokine storm, such as Ponatinib, may represent an attractive therapeutic option for treating moderate to severe COVID-19.
Differing ecological responses of seabirds to invasive species eradication
Authors: Bird Jeremy P. , Fuller Richard A. , Shaw Justine D. S. 
Published: April 2021

ABSTRACTThe impact of invasive species at seabird breeding islands causes a breakdown of important ecological functions such as prey consumption and nutrient transfer, and elevates extinction risk in impacted taxa. Eradicating invasive species from islands can result in substantial short-term recovery of seabird populations and consequently the prevalence of eradication programs as conservation tools is increasing. However, as the scale and complexity of eradications has increased, quantitative data on rates of recovery, especially from larger islands, remain limited. Furthermore, the mechanisms that govern recovery are poorly understood, limiting our ability to forecast outcomes and therefore prioritise effectively. Here, using the world’s largest multi-species vertebrate eradication from Macquarie Island as a case study, we show how responses to invasive species and their eradication differ. Species with broad realised niches whose breeding phenology minimizes time on land and corresponds with summer resource abundance remained extant alongside invasive species while more habitat-specific species present in winter were extirpated. Following eradication, immigration and flexibility to colonise under-utilised optimal habitat appears to be boosting population growth in recolonising species, whereas established populations appear to be tethered to refugial habitats by the influence of philopatry, and their recovery is slower as a result. Unpicking these differential responses and the mechanisms behind them provides valuable information to help predict responses in other systems as future eradications are planned.
Decreased biofilm formation inProteus mirabilisafter short-term exposure to a simulated microgravity environment
Authors: Wang Dapeng , Bai Po , Zhang Bin , Su Xiaolei , Jiang Xuege , Fang Tingzheng , Wang Junfeng , Liu Changting 
Published: April 2021

AbstractBackgroundMicrobes threaten human health in space exploration. Studies have shown thatP. mirabilishas been found in human space habitats. In addition, the biological characteristics ofP. mirabilisin space have been studied unconditionally. The simulated microgravity environment provides a platform for understanding the changes in the biological characteristics ofP. mirabilis.ObjectiveThis study intends to explore the effect of simulated microgravity onP. mirabilis, the formation ofP. mirabilisbiofilm and its related mechanism.MethodsThe strange deformable rods were cultured continuously for 14 days under the microgravity simulated by (HARVs) in a high-aspect ratio vessels. The morphology, growth rate, metabolism and biofilm formation of the strain were measured, and the phenotypic changes ofP. mirabiliswere evaluated. Transcriptome sequencing was used to detect differentially expressed genes under simulated microgravity and compared with phenotype.ResultsThe growth rate, metabolic ability and biofilm forming ability ofP. mirabiliswere lower than those of normal gravity culture under the condition of simulated microgravity. Further analysis showed that the decrease of growth rate, metabolic ability and biofilm forming ability may be caused by the down-regulation of related genes (pstS,sodBandfumC).ConclusionIt provides a certain reference for the prevention and treatment ofP. mirabilisinfection in the future space station by exploring the effect of simulated microgravity exposure onP. mirabilis.
A large and diverse autosomal haplotype is associated with sex-linked colour polymorphism in the guppy
Authors: Paris Josephine R , Whiting James R , Daniel Mitchel J , Obiol Joan Ferrer , Parsons Paul J , van der Zee Mijke J , Wheat Christopher W , Hughes Kimberly A , Fraser Bonnie A 
Published: April 2021

ABSTRACTColour polymorphism provides a tractable trait that can be harnessed to explore the evolution of sexual selection and sexual conflict. Male colour patterns of the Trinidadian guppy (Poecilia reticulata) are governed by both natural and sexual selection, and are typified by extreme pattern colour variation as a result of negative frequency dependent selection. Since guppy colour patterns are often inherited faithfully from fathers to sons, it has been historically presumed that colour genes are physically linked to sex determining loci as a ‘supergene’ on the sex chromosome. Yet the actual identity and genomic location of the colour pattern genes has remained elusive. We phenotyped and genotyped four guppy ‘Iso-Y lines’, where colour was inherited along the patriline, but backcrossed into the stock population every 2 to 3 generations for 40 generations, thereby homogenising the genome at regions unrelated to colour. Using an unbiased phenotyping method to proportion colour pattern differences between and among the Iso-Y lines, we confirmed that the breeding design was successful in producing four distinct colour patterns. Our analysis of genome resequencing data of the four Iso-Y lines uncovered a surprising genetic architecture for colour pattern polymorphism. Genetic differentiation among Iso-Y lines was repeatedly associated with a large and diverse haplotype (∼5Mb) on an autosome (LG1), not the sex chromosome (LG12). Moreover, the LG1 haplotype showed elevated linkage disequilibrium and exhibited evidence of sex-specific diversity when we examined whole-genome sequencing data of the natural source population. We hypothesise that colour pattern polymorphism is driven by Y-autosome epistasis, and conclude that predictions of sexual conflict should focus on incorporating the effects of epistasis in understanding complex adaptive architectures.
Spectroscopic glimpses of the transition state of ATP hydrolysis trapped in a bacterial DnaB helicase
Authors: Malär Alexander A. , Wili Nino , Völker Laura A. , Kozlova Maria I. , Cadalbert Riccardo , Däpp Alexander , Weber Marco E. , Zehnder Johannes , Jeschke Gunnar , Eckert Hellmut , Böckmann Anja , Klose Daniel , Mulkidjanian Armen Y. , Meier Beat H. , Wiegand Thomas 
Published: April 2021

AbstractThe ATP hydrolysis transition state of motor proteins is a weakly populated protein state that can be stabilized and investigated by replacing ATP with chemical mimics. We present atomic-level structural and dynamic insights on a state created by ADP aluminum fluoride binding to the bacterial DnaB helicase fromHelicobacter pylori. We determined the positioning of the metal ion cofactor within the active site using electron paramagnetic resonance, and identified the protein protons coordinating to the phosphate groups of ADP and DNA using proton-detected31P,1H solid-state nuclear magnetic resonance spectroscopy at fast magic-angle spinning > 100 kHz, as well as temperature-dependent proton chemical-shift values to prove their engagements in hydrogen bonds.19F and27Al MAS NMR spectra reveal a highly mobile, fast-rotating aluminum fluoride unit pointing to the capture of a late ATP hydrolysis translation state in which the phosphoryl unit is already detached from the arginine and lysine fingers.
The methyl phosphate capping enzyme Bin3 is a stable component of the fission yeast telomerase holoenzyme
Authors: Porat Jennifer , El Baidouri Moaine , Grigull Jorg , Deragon Jean-Marc , Bayfield Mark A. 
Published: April 2021

AbstractThe telomerase holoenzyme is critical for maintaining eukaryotic genome integrity. In addition to a reverse transcriptase and an RNA template, telomerase contains additional proteins that protect the telomerase RNA and promote holoenzyme assembly. Here we report that the methyl phosphate capping enzyme (MePCE) Bin3 is a stable component of theS. pombetelomerase holoenzyme. Bin3 associates with the telomerase holoenzyme and U6 snRNA through an interaction with the recently described LARP7 family member Pof8, and we demonstrate that these two factors are evolutionarily linked in fungi. Our data suggest that the association of Bin3 with telomerase is independent of its methyltransferase activity, but rather that Bin3 functions in telomerase holoenzyme assembly by promoting TER1 accumulation and Pof8 recruitment to TER1. Taken together, this work yields new insight into the composition, assembly, and regulation of the telomerase holoenzyme in fission yeast as well as the breadth of its evolutionary conservation.
Caregiving quality modulates neuroendocrine and immunological markers in young children in foster care who have experienced early adversity
Authors: Reindl V. , Schippers A. , Tenbrock K. , Job A.-K. , Gerloff C. , Lohaus A. , Heinrichs N. , Konrad K. 
Published: April 2021

AbstractBackgroundEarly adversity is believed to alter the body’s stress response systems, putting children at increased risk for somatic and mental health problems. However, it remains unclear whether such alterations normalize under improved caregiving experiences. Thus, the goal of the present study was to investigate i) whether children in foster care show endocrine and immunological alterations relative to children living with their biological families, ii) whether these alterations change over time spent with the foster family and iii) whether the alterations are modulated by current caregiving experiences.MethodsA total of 94 children in foster care and 157 biological children, aged two to seven years, took part in a longitudinal study with three assessments conducted over a 12-month study period. At the initial assessment, children lived for an average of 18 months with their current foster families. Children’s cortisol, dehydroepiandrosterone (DHEA) and progesterone concentrations and cortisol/DHEA ratios were measured in scalp hair and children’s secretory immunoglobulin A (sIgA) levels in saliva. Caregiving quality was assessed based on caregiver-reports and observational measures of caregiver-child interactions.ResultsChildren in foster care had lower cortisol/DHEA ratios and higher progesterone concentrations than biological children, while no group differences were found for cortisol, DHEA or sIgA. Time spent with the current foster family did not significantly influence the child’s endocrine or immunological markers. Importantly, caregiving quality modulated cortisol/DHEA ratios and sIgA concentrations: children in foster care of lower caregiving quality had lower cortisol/DHEA ratios than children in foster care of higher caregiving quality and showed decreasing, rather than increasing, sIgA concentrations across the study period.ConclusionsOur results indicate that caregiving quality in the foster family may have an important modulating effect on selected indicators of the child’s stress response and could thereby mitigate the possible consequences of early childhood adversity.