bluuebunny/crossref_metadata_2025_split huggingface dataset:
Yeast proteins reversibly aggregate like amphiphilic molecules
Authors: Dasmeh Pouria , Wagner Andreas 
Published: March 2021

AbstractMore than a hundred proteins in yeast reversibly aggregate and phase-separate in response to various stressors, such as nutrient depletion and heat shock. We know little about the sequence and structural features behind this ability, which has not been characterized on a proteome-wide level. To identify the distinctive features of aggregation-prone regions, we apply machine learning algorithms to genome-scale limited proteolysis-mass spectrometry data from 96 yeast proteins that phase-separate upon heat shock. We find that the aggregation-prone regions (APRs) of our study proteins are significantly enriched in aliphatic residues and depleted in positively charged amino acids. Aggregator proteins with longer APRs show a greater propensity to aggregate, a relationship that can be explained by equilibrium statistical thermodynamics. Altogether, our observations suggest that proteome-wide reversible protein aggregation is mediated by sequence-encoded properties. Aggregating proteins resemble supra-molecular amphiphiles, where APRs are the hydrophobic parts, and non-APRs are the hydrophilic parts.
DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types
Authors: Das Jyotirmoy , Idh Nina , Sikkeland Liv Ingunn Bjoner , Paues Jakob , Lerm Maria 
Published: March 2021

AbstractBackgroundFlow cytometry is a classical approach used to define cell types in peripheral blood. While DNA methylation signatures have been extensively employed in recent years as an alternative to flow cytometry to define cell populations in peripheral blood, this approach has not been tested in lung-derived samples. Here, we compared bronchoalveolar lavage with a more cost-effective and less invasive technique based on sputum induction and developed a DNA methylome-based algorithm that can be used to deconvolute the cell types in such samples.ResultsWe analyzed the DNA methylome profiles of alveolar macrophages and lymphocytes cells isolated from the pulmonary compartment. The cells were isolated using two different methods, sputum induction and bronchoalveolar lavage. A strong positive correlation between the DNA methylome profiles of cells obtained with the two isolation methods was observed, and in two of the donors, in which the correlation was best, a later analyses demonstrated that those subjects the samples were consistently derived from the lower part of the lungs. We also identified unique patterns of CpG methylation in DNA obtained from the two cell populations, which can be used as a signature to discriminate between the alveolar macrophages and lymphocytes by means of open-source algorithms. We validated our findings with external data and obtained results consistent with the previous findings.ConclusionsOur analysis opens up a new possibility to identify different cell populations from lung samples and promotes sputum induction as a tool to study immune cell populations from the lung.
Discriminative feature of cells characterizes cell populations of interest by a small subset of genes
Authors: Fujii Takeru , Maehara Kazumitsu , Fujita Masatoshi , Ohkawa Yasuyuki 
Published: March 2021

ABSTRACTDifferentially expressed gene-based analysis have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis in single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), implemented by logistic regression with an adaptive LASSO penalty. Adaptive LASSO selects a small subset of genes to discriminate a population of interest. DFC well captured non-independent structure of expression patterns such as cell-type-specific markers, specific composition of gene expression, and subcategories of this cell population. DFC may complement existing methods for interpreting large data sets.
TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry
Authors: Frässle Stefan , Aponte Eduardo A. , Bollmann Saskia , Brodersen Kay H. , Do Cao T. , Harrison Olivia K. , Harrison Samuel J. , Heinzle Jakob , Iglesias Sandra , Kasper Lars , Lomakina Ekaterina I. , Mathys Christoph , Müller-Schrader Matthias , Pereira Inês , Petzschner Frederike H. , Raman Sudhir , Schöbi Dario , Toussaint Birte , Weber Lilian A. , Yao Yu , Stephan Klaas E. 
Published: March 2021

ABSTRACTPsychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use.In this paper, we present theTranslationalAlgorithms forPsychiatry-AdvancingScience (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
Immunoprofiling reveals novel mast cell receptors and a continuous nature of human lung mast cell heterogeneity
Authors: Rönnberg Elin , Boey Daryl Zhong Hao , Ravindran Avinash , Säfholm Jesper , Orre Ann-Charlotte , Al-Ameri Mamdoh , Adner Mikael , Dahlén Sven-Erik , Dahlin Joakim S. , Nilsson Gunnar 
Published: March 2021

AbstractBackgroundImmunohistochemical analysis of granule-associated proteases have revealed that human lungs mast cells constitute a heterogeneous population of cells, with distinct subpopulations identified. However, a systematic and comprehensive analysis of cell surface markers to study human lung mast cell heterogeneity is yet to be performed.MethodsHuman lung mast cells were obtained from lung lobectomies and the expression of 332 cell surface markers were analyzed using flow cytometry and the LEGENDScreen™kit. Markers that exhibited a high variance were selected for additional analyses to reveal whether they correlated and if discrete mast cell subpopulations were discernable.ResultsWe identified expression of 102 surface markers on human lung mast cells. Several markers showed a high continuous variation of expression within the mast cell population. Six of these markers correlated: SUSD2, CD49a, CD326, CD34, CD66 and HLA-DR. The expression of these markers also correlated to the size and granularity of the mast cells. However, no marker produced an expression profile consistent with a bi- or multimodal distribution.ConclusionsLEGENDScreen analysis identified more than 100 cell surface markers on mast cells, out of which 23 have to our knowledge not previously described on human mast cells. Several of the newly described markers are known to be involved in sensing the microenvironment, and their identification can shed new light on mast cell functions. The exhaustive expression profiling of the 332 surface markers failed to detect distinct mast cell subpopulations. Instead, we demonstrate a continuous nature of human lung mast cell heterogeneity.
Niche specificity, polygeny, and pleiotropy in herbivorous insects
Authors: Hardy Nate B , Forister Matt 
Published: March 2021

AbstractWhat causes host-use specificity in herbivorous insects? Population genetic models predict specialization when habitat preference can evolve and there is antagonistic pleiotropy at a performance-affecting locus. But empirically for herbivorous insects, host-use performance is governed by many genetic loci, and antagonistic pleiotropy seems to be rare. Here, we use individual-based quantitative genetic simulation models to investigate the role of pleiotropy in the evolution of sympatric host-use specialization when performance and preference are quantitative traits. We look first at pleiotropies affecting only host-use performance. We find that when the host environment changes slowly the evolution of host-use specialization requires levels of antagonistic pleiotropy much higher than what has been observed in nature. On the other hand, with rapid environmental change or pronounced asymmetries in productivity across host species, the evolution of host-use specialization readily occurs without pleiotropy. When pleiotropies affect preference as well as performance, even with slow environmental change and host species of equal productivity, we observe fluctuations in host-use breadth, with mean specificity increasing with the pervasiveness of antagonistic pleiotropy. So, our simulations show that pleiotropy is not necessary for specialization, although it can be sufficient, provided it is extensive or multifarious.
Associations Between Vocal Emotion Recognition and Socio-emotional Adjustment in Children
Authors: Neves Leonor , Martins Marta , Correia Ana Isabel , Castro São Luís , Lima César F. 
Published: March 2021

AbstractThe human voice is a primary channel for emotional communication. It is often presumed that being able to recognise vocal emotions is important for everyday socio-emotional functioning, but direct empirical evidence for this remains scarce. Here, we examined relationships between vocal emotion recognition and socio-emotional adjustment in children. The sample included 6 to 8-year-old children (N= 141). The emotion tasks required them to categorise five emotions conveyed by nonverbal vocalisations (e.g., laughter, crying) and speech prosody: anger, disgust, fear, happiness, sadness, plus neutrality. Socio-emotional adjustment was independently evaluated by the children’s teachers using a multi-dimensional questionnaire of self-regulation and social behaviour. Based on frequentist and Bayesian analyses, we found that higher emotion recognition in speech prosody related to better general socio-emotional adjustment. This association remained significant even after accounting for the children’s general cognitive ability, age, sex, and parental education in multiple regressions. Follow-up analyses indicated that the advantages were particularly robust for the socio-emotional dimensions prosocial behaviour and cognitive and behavioural self-regulation. For emotion recognition in nonverbal vocalisations, no associations with socio-emotional adjustment were found. Overall, these results support the close link between children’s emotional prosody recognition skills and their everyday social behaviour.
Genomic epidemiology ofMycobacterium bovisinfection in sympatric badger and cattle populations in Northern Ireland
Authors: Akhmetova Assel , Guerrero Jimena , McAdam Paul , Salvador Liliana C.M. , Crispell Joseph , Lavery John , Presho Eleanor , Kao Rowland R. , Biek Roman , Menzies Fraser , Trimble Nigel , Harwood Roland , Pepler P. Theo , Oravcova Katarina , Graham Jordon , Skuce Robin , du Plessis Louis , Thompson Suzan , Wright Lorraine , Byrne Andrew , Allen Adrian R. 
Published: March 2021

AbstractBackgroundBovine tuberculosis (bTB) is a costly, epidemiologically complex, multi-host, endemic disease. Pathogen whole genome sequencing can improve the resolution of epidemiological tracing. We genome sequenced an exceptional data set of 619Mycobacterium bovisisolates from badgers and cattle in a 100km2bTB ‘hotspot’. Historical molecular subtyping data permitted the targeting of an endemic pathogen lineage, whose long-term persistence provided an opportunity to study genome epidemiology in detail. To assess whether badger population genetic structure was associated with the spatial distribution of pathogen genetic diversity, we microsatellite genotyped hair samples from 769 badgers trapped in this area.ResultsEight lineages ofM. boviswere circulating in the study area, seven of which were likely non-endemic, and imported by animal movement. The endemic lineage exhibited low genetic diversity with an average inter-isolate genetic distance of 7.6 SNPs (s.d. ± 4.0), consistent with contemporary transmission. Bayesian phylogenetic methods determined an evolutionary rate of 0.30 substitutions per genome per year for this lineage, estimating its emergence 40-50 years before present, while Bayesian Skyline analysis identified significant population expansion of the endemic lineage in the 1990s and again in 2011-2012. The phylogeny revealed distinct sub-lineages, all of which contained isolates from both cattle and badger hosts, indicative of the sharing of closely related strains and inter-species transmission. However, the presence of significant badger population genetic structure was not associated with the spatial distribution ofM. bovisgenetic diversity.ConclusionsOur data provided unparalleled detail on the evolutionary history of an endemicM. bovislineage. Findings are consistent with ongoing interspecies transmission in the study area but suggest that badger intra-species transmission may not be a major driver of persistence in this area. In addition, the data collected permitted the tracking of incursions of novel pathogen lineages into the study area and means to determine if they were involved in disease transmission.
Tree phyllospheres are a habitat for diverse populations of CO-oxidising bacteria
Authors: Palmer Jess L. , Hilton Sally , Picot Emma , Bending Gary D. , Schäfer Hendrik 
Published: March 2021

AbstractBackgroundCarbon monoxide (CO) is a naturally occurring and ubiquitous trace gas in the atmosphere. As a product of combustion processes, it can reach concentrations in the mg/m3range in urban areas, contributing to air pollution. Aerobic CO-degrading microorganisms have been identified previously and are thought to remove ~370 Tg of CO in soils and oceans per year. Based on the presence of genes encoding subunits of the enzyme carbon monoxide dehydrogenase in metagenomes, a large fraction of soil bacteria may have the potential for CO degradation. The activity and diversity of CO-degrading microorganisms in above ground habitats such as the phyllosphere has not been addressed, however, and their potential role in global CO cycling remains unknown.ResultsMonitoring of CO-degradation in leaf washes of two common British trees,Ilex aquifoliumandCrataegus monogyna, demonstrated CO uptake in all samples investigated. Leaf washes ofI. aquifoliumhad significantly higher CO oxidation rates than those ofC. monogyna. A diverse range of bacterial taxa were identified as candidate CO-oxidising taxa based on high-throughput sequencing and multivariate statistical analysis of 16S rRNA amplicon data, as well as functional diversity analysis based oncoxL, the gene encoding the large subunit of CO-dehydrogenase. Candidate CO-oxidising taxa included a range of Rhizobiales and Burkholderiales, of which the Burkholderiales OTUs were abundant colonisers of the phyllosphere at the time of sampling, as indicated by 16S rRNA gene sequencing. In addition, an estimated 12.4% of leaf OTUs in samples of this study containedcoxLhomologues, based on their predicted genomes. We also mined data of publicly available phyllosphere metagenomes for genes encoding subunits of CO-dehydrogenase which indicated that, on average, 25% of phyllosphere bacteria contained CO-dehydrogenase gene homologues. A CO-oxidising Phyllobacteriaceae strain was isolated from phyllosphere samples which contains genes encoding both CODH as well as a RuBisCO.ConclusionsThe phyllosphere, a vast microbial habitat, supports diverse and potentially abundant CO-oxidising bacteria. These findings identify tree phyllosphere bacteria as a potential sink for atmospheric CO and highlight the need for a more detailed assessment of phyllosphere microbial communities in the global cycle of CO.
Performance comparison and in-silico harmonisation of commercial platforms for DNA methylome analysis by targeted bisulfite sequencing
Authors: Tanić Miljana , Moghul Ismail , Rodney Simon , Dhami Pawan , Vaikkinen Heli , Ambrose John , Barrett James , Feber Andrew , Beck Stephan 
Published: March 2021

AbstractDNA methylation is a key epigenetic modification in the regulation of cell fate and differentiation, and its analysis is gaining increasing importance in both basic and clinical research. Targeted Bisulfite Sequencing (TBS) has become the method of choice for the cost-effective, targeted analysis of the human methylome at base-pair resolution. Here we benchmarked five commercially available TBS platforms, including three hybridization capture-based (Agilent, Roche, and Illumina) and two RRBS-based (Diagenode and NuGen), across 16 samples. A subset of these were also compared to whole-genome DNA methylation sequencing with the Illumina and Oxford Nanopore platforms. We assessed performance with respect to workflow complexity, on/off-target performance, coverage, accuracy and reproducibility. We find all platforms able to produce usable data but major differences for some performance criteria, especially in the number and identity of the CpG sites covered, which affects the interoperability of datasets generated on these different platforms. To overcome this limitation, we used imputation and show that it improves the interoperability from an average of 10.35% (0.8M CpG sites) to 97% (7.6M CpG sites). Our study provides cross-validated guidance on which TBS platform to use for different features of the methylome and offers an imputation-based harmonization solution for improved interoperability between platforms, allowing comparative and integrative analysis.
SGI: Automatic clinical subgroup identification in omics datasets
Authors: Buyukozkan Mustafa , Suhre Karsten , Krumsiek Jan 
Published: March 2021

SummaryThe ‘Subgroup Identification’ (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this paper, we describe the functionality of the toolbox and demonstrate an application example on a blood metabolomics dataset with various clinical biochemistry readouts in a type 2 diabetes case-control study.Availability and implementationSGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available athttps://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded fromhttps://doi.org/10.6084/m9.figshare.5904022.
ExiguobacteriumSp. HA2, isolated from the Ilam Mountains of Iran
Authors: Heidari Reza , Akbariqomi Mostafa , Tavoosidana Gholamreza , Rigi Garshasb 
Published: March 2021

ABSTRACTA motile, Gram-stain-positive, rod-shaped, non-sporing, tolerate up to 5% NaCl, grew at 0–25 °C, designatedExiguobacteriumsp. HA2 was isolated from the soil of the Ilam Mountains of Iran during October 2016. The major isoprenoid quinone is MK-7 and in the smaller amount are MK-6 and MK-8. Polar lipids included diphosphatidylglycerol, phosphatidylglycerol, phosphatidylserine, phosphatidylinositol, phosphatidylethanolamine. Major fatty acids (>10 %) are isoC13:0, isoC15:0and C16:0. The bacterial cell wall peptidoglycan layer was lysine-glycine. The 16S rRNA sequence was analyzed at the phylogenetic levels. Also, A supplemental comparison was made between five other genes includingcsp, gyrB, hsp70, rpoB,andcitC.According to the results of genotypic and phenotypic characteristics, the strain was categorized in the genusExiguobacterium.This bacterium had the closest relation withExiguobacterium undae,and thus was dubbedExiguobacteriumsp. HA2. The different in the Phenotypic, functional characteristics and phylogenetic indicatedExiguobacteriumsp. HA2 can be regarded as representing considered a novel species within the genusExiguobacterium.
Next-generation biocomputing: mimicking artificial neural network with genetic circuits
Authors: Seak Leo Chi U , Lo Owen Lok In , Suen Wade Chun-Wai , Wu Ming-Tsung 
Published: March 2021

AbstractArtificial neural network (ANN) is nowadays one of the most used and researched computational methods. In the field of biocomputing, however, synthetic biologists are still using logic gate technologies to design genetic circuits. We here propose and computationally validate a novel method to mimic ANN with genetic circuits. We first describe the flow and the mathematical expression of this genetic circuit design and then we providein silicoproof to support the functionality of our method in regression and classification analyses. We believe that thisde novogenetic circuit design method would have wide applications in biotechnology.