bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mate selection provides similar genetic progress and average inbreeding than optimum contribution selection in the long-term
Authors: Yoshida Grazyella Massako , Yáñez José Manuel , Queiroz Sandra Aidar de , Carvalheiro Roberto 
Published: January 2020

AbstractOptimum contribution selection (OCS) and mate selection (MS) are alternative strategies to maximize genetic gain under controlled rates of inbreeding. There is evidence in the literature that MS outperforms OCS in controlling inbreeding under the same expected genetic gain in the short-term. It is unclear, however, if the same would occur in the long-term. This study aimed to compare OCS and MS regarding short and long-term genetic progress and inbreeding, using simulated data. The structure of the simulated population aimed to mimic an aquaculture breeding program. Twenty discrete generations were simulated, considering 50 families and 2,000 offspring per generation, and a trait with a heritability of 0.3. OCS and MS were applied using a differential evolution (DE) algorithm, under an objective function that accounted for genetic merit, inbreeding of the future progeny and coancestry among selection candidates. For OCS, the optimization process consisted of selection based on optimum contribution followed by minimum inbreeding mating. Objective functions using different weights on coancestry were tested. For each application, 20 replicates were simulated and the results were compared based on their average. Both strategies, OCS and MS, were very effective in controlling inbreeding over the generations. In the short-term, MS was more efficient than OCS in controlling inbreeding under the same genetic gain. In the long-term, OCS and MS resulted in similar genetic progress and average inbreeding, under the same penalty on coancestry.
Effect of Cysteine, Yeast Extract, pH Regulation and Gas Flow on Acetate and Ethanol Formation and Growth Profiles ofClostridium ljungdahliiSyngas Fermentation
Authors: Infantes Alba , Kugel Michaela , Neumann Anke 
Published: January 2020

ABSTRACTThe fermentation of synthesis gas, or syngas, which consists mainly of CO, CO2and H2by acetogenic bacteria has the potential to help in transitioning from a fossil-fuel-based to a renewable bio-economy.Clostridium ljungdahlii, one of such microorganisms, has as main fermentation products acetate and ethanol. Multiple research efforts have been directed towards understanding how the metabolism and the product formation of this, and other acetogenic bacteria, can be directed towards increasing productivities and yields; nonetheless, transferring those findings to a particular set-up can prove challenging. This study used a well-established and robust fed-batch fermentation system withC. ljungdahliito look into the effects of different fermentation pH profiles, gas flow, and the supplementation with additional yeast extract or cysteine on growth, product formation ratios, yields, and productivities, as well as gas consumption. Neither yeast extract nor cysteine supplementation had a noticeable impact on cell growth, product formation or overall gas consumption. The lowering of the pH proved mainly detrimental, with decreased productivities and no improvement in ethanol ratios. The most notable shift towards ethanol was achieved by the combination of lowering both the pH and the gas flow after 24 h, but with the caveat of lower productivity. The obtained results, unexpected to some extent, highlight the necessity for a better understanding of the physiology and the metabolic regulation of acetogenic bacteria in order for this process to become more industrially relevant.
Early EEG and behavioral alterations in Dravet mice
Authors: Fadila Saja , Quinn Shir , Maia Ana Turchetti , Yakubovich Daniel , Anderson Karen L. , Rubinstein Moran 
Published: January 2020

AbstractDravet Syndrome (Dravet) is a severe childhood epileptic encephalopathy. The disease begins around the age of six months, with a febrile stage, characterized by febrile seizures with otherwise normal development. By the end of the first year of life, the disease progresses to the worsening stage, featuring recurrent intractable seizures and the appearance of additional comorbidities, including global developmental delay, cognitive deficits, hyperactivity and motor problems. Later, in early school years, Dravet reaches the stabilization stage, in which seizure burden decreases, while Dravet-associated comorbidities persist. Dravet syndrome mouse models (DS) faithfully recapitulate the three stages of the human syndrome. Here, we performed power spectral analyses of background EEG activity in DS and their wild-type (WT) littermates, demonstrating disease stage-related alterations. Specifically, while the febrile stage activity resembled that of WT mice, we observed a marked reduction in total power during the worsening stage and a smaller reduction during the stabilization stage. Moreover, low EEG power at the worsening stage correlated with increased risk for premature death, suggesting that such measurements can potentially be used as a marker for Dravet severity. With normal development at the febrile stage and the presentation of developmental delay at the worsening stage, the contribution of recurrent seizures to the emergence of Dravet-associated comorbidities is still debated. Thus, we further characterized the behavior of WT and DS mice during the different stages of Dravet. At the febrile stage, despite their normal background EEG patterns, DS mice already demonstrated motor impairment and hyperactivity in the open field, that persisted to the worsening and stabilization stages. Conversely, clear evidence for deficits in working memory emerged later in life, during the worsening stage. These results indicate that despite the mild epilepsy at the febrile stage, DS development is already altered, suggesting that the pathophysiological mechanisms governing the appearance of some Dravet behavioral comorbidities may be independent of the epileptic phenotype.HighlightsReduction in background EEG power in DravetLow EEG power correlates with the risk of premature deathMotor deficits and hyperactivity are evident as early as the febrile stageCognitive deficits and detection of increased anxiety begin at the worsening stage
Differential functional connectivity underlying asymmetric reward-related activity in human and non-human primates
Authors: Lopez-Persem Alizée , Roumazeilles Léa , Folloni Davide , Marche Kévin , Fouragnan Elsa F. , Khalighinejad Nima , Rushworth Matthew F. S. , Sallet Jérôme 
Published: January 2020

AbstractThe orbitofrontal cortex (OFC) is a key brain region involved in complex cognitive functions such as reward processing and decision-making. Neuroimaging studies have shown unilateral OFC response to reward-related variables, however, those studies rarely discussed the lateralization of this effect. Yet, some lesion studies suggest that the left and right OFC contribute differently to cognitive processes. We hypothesized that the OFC asymmetrical response to reward could reflect underlying hemispherical difference in OFC functional connectivity. Using restingstate and reward-related MRI data from humans and from rhesus macaques, we first identified a specific asymmetrical response of the lateral OFC to reward in both species. Crucially, the subregion showing the highest reward-related asymmetry (RRA) overlapped with the region showing the highest functional connectivity asymmetry (FCA). Furthermore, the two types of functional asymmetries were found to be significantly correlated across humans. Altogether, our results suggest a similar pattern of functional specialization between the left and right OFC is present in two primate species.
The completeness and stratification in yeast genotype-phenotype space
Authors: Wang Jianguo , He Xionglei 
Published: January 2020

AbstractGenotype and phenotype are two themes of modern biology. While the running principles in genotype has been well understood (e.g., DNA double helix structure, genetic code, central dogma, etc.), much less is known about the rules in phenotype. In this study we examine a yeast phenotype space that is represented by 405 quantitative traits. We show that the space is convergent with limited latent dimensions, which form surprisingly long-distance chains such that all traits are interconnected with each other. As a consequence, statistically uncorrelated traits are linearly dependent in the multi-dimensional phenotype space and can be precisely inferred from each other. Meanwhile, the performance is much poorer for similar trait inferences but from the genotype space (including DNA and mRNA), highlighting the dimension stratification between genotype space and phenotype space. Since the world we’re living is primarily phenotypic and what we truly care is phenotype, these findings call for phenotype-centered biology as a complement for the cross-space genetic thinking in current biology.
SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation
Authors: Hu Zhirui , Zu Songpeng , Liu Jun S. 
Published: January 2020

AbstractA main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called “dropout”), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.
Crystal structure of NirF: Insights into its role in hemed1biosynthesis
Authors: Klünemann Thomas , Nimtz Manfred , Jänsch Lothar , Layer Gunhild , Blankenfeldt Wulf 
Published: January 2020

AbstractCertain facultative anaerobes such as the opportunistic human pathogenPseudomonas aeruginosacan respire on nitrate, a process generally known as denitrification. This enables denitrifying bacteria to survive in anoxic environments and contributes e.g. to the formation of biofilm, hence increasing difficulties in eradicatingP. aeruginosainfections. A central step in denitrification is the reduction of nitrite to nitrous oxide by nitrite reductase NirS, an enzyme that requires the unique cofactor hemed1. While hemed1biosynthesis is mostly understood, the role of the essential periplasmatic protein NirF in this pathway remains unclear. Here, we have determined crystal structures of NirF and its complex with dihydrohemed1, the last intermediate of hemed1biosynthesis. We found that NirF forms a bottom-to-bottom β-propeller homodimer and confirmed this by multi-angle light and small-angle X-ray scattering. The N-termini are immediately neighbored and project away from the core structure, which hints at simultaneous membrane anchoringviaboth N-termini. Further, the complex with dihydrohemed1allowed us to probe the importance of specific residues in the vicinity of the ligand binding site, revealing residues not required for binding or stability of NirF but essential for denitrification in experiments with complemented mutants of a ΔnirFstrain ofP. aeruginosa. Together, these data implicate that NirF possesses a yet unknown enzymatic activity and is not simply a binding protein of hemed1derivatives.
How quickly can we predict trimethoprim resistance using alchemical free energy methods?
Authors: Fowler Philip W 
Published: January 2020

AbstractThe emergence of antimicrobial resistance (AMR) threatens modern medicine and necessitates more personalised treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run.We show that a large number (N=15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically-derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.
Cell-type specific effects of genetic variation on chromatin accessibility during human neuronal differentiation
Authors: Liang Dan , Elwell Angela L. , Aygün Nil , Lafferty Michael J. , Krupa Oleh , Cheek Kerry E. , Courtney Kenan P. , Yusupova Marianna , Garrett Melanie E. , Ashley-Koch Allison , Crawford Gregory E. , Love Michael I. , de la Torre-Ubieta Luis , Geschwind Daniel H. , Stein Jason L. 
Published: January 2020

SummaryCommon genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, the mechanisms mediating the effects of genetic variants on gene regulation are poorly understood. To determine the functional impact of common genetic variation on the non-coding genome longitudinally during human cortical development, we performed a chromatin accessibility quantitative trait loci (caQTL) analysis in neural progenitor cells and their differentiated neuronal progeny from 92 donors. We identified 8,111 caQTLs in progenitors and 3,676 caQTLs in neurons, with highly temporal, cell-type specific effects. A subset (∼20%) of caQTLs were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type specific caQTLs and brain-relevant genome-wide association data, we were able to fine-map loci and identify regulatory mechanisms underlying non-coding neuropsychiatric disorder risk variants.HighlightsGenetic variation alters chromatin architecture during human cortical developmentGenetic effects on chromatin accessibility are highly cell-type specificAlleles disrupting TF motifs generally decrease accessibility, except for repressorscaQTLs facilitate fine-mapping and inference of regulatory mechanisms of GWAS loci
Regulated repression, and not activation, governs the cell fate promoter controlling yeast meiosis
Authors: Tam Janis , van Werven Folkert J. 
Published: January 2020

AbstractIntrinsic signals and cues from the external environment drive cell fate decisions. In budding yeast, the decision to enter meiosis is controlled by nutrient and mating-type signals that regulate expression of the master transcription factor for meiotic entry,IME1. How nutrient signals controlIME1expression remains poorly understood. Here we show thatIME1transcription is regulated by multiple sequence-specific transcription factors that mediate association of Tup1-Cyc8 co-repressor to its promoter. We find that at least eight transcription factors bind theIME1promoter when nutrients are ample. Remarkably, association of these transcription factors is highly regulated by different nutrient cues. Mutant cells lacking three transcription factors (Sok2/Phd1/Yap6) displayed reduced Tup1-Cyc8 association, increasedIME1expression and earlier onset of meiosis. Our data demonstrate that the promoter of a master regulator is primed for rapid activation while repression by multiple transcription factors mediating Tup1-Cyc8 recruitment dictates the fate decision to enter meiosis.
Heterosis of leaf and rhizosphere microbiomes in field-grown maize
Authors: Wagner Maggie R. , Roberts Joe H. , Balint-Kurti Peter , Holland James B. 
Published: January 2020

SummaryMacroorganisms’ genotypes shape their phenotypes, which in turn shape the habitat available to potential microbial symbionts. This influence of host genotype on microbiome composition has been demonstrated in many systems; however, most previous studies have either compared unrelated genotypes or delved into molecular mechanisms. As a result, it is currently unclear whether the heritability of host-associated microbiomes follows similar patterns to the heritability of other complex traits.We take a new approach to this question by comparing the microbiomes of diverse maize inbred lines and their F1hybrid offspring, which we quantified in both rhizosphere and leaves of field-grown plants using 16S-v4 and ITS1 amplicon sequencing.We show that inbred lines and hybrids differ consistently in composition of bacterial and fungal rhizosphere communities, as well as leaf-associated fungal communities. A wide range of microbiome features display heterosis within individual crosses, consistent with patterns for non-microbial maize phenotypes. For leaf microbiomes, these results were supported by the observation that broad-sense heritability in hybrids was substantially higher than narrow-sense heritability.Our results support our hypothesis that at least some heterotic host traits affect microbiome composition in maize.
Genetic effects on educational attainment in Hungary
Authors: Ujma Péter P. , Eszlári Nóra , Millinghoffer András , Bruncsics Bence , Petschner Péter , Antal Péter , Deakin Bill , Breen Gerome , Bagdy György , Juhász Gabriella 
Published: January 2020

AbstractEducational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. We found that the PGS is significantly associated with highest educational level attained as well as the number of years in education in a sample of Hungarian volunteers (N=829). In an English (N=976) comparison sample with identical measurement protocols the same PGS had a stronger association with educational level, but not with years in education. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. Our results provide evidence that polygenic scores for educational attainment are valid in diverse European populations.
Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS
Authors: Aksenov Alexander A. , Laponogov Ivan , Zhang Zheng , Doran Sophie LF , Belluomo Ilaria , Veselkov Dennis , Bittremieux Wout , Nothias Louis Felix , Nothias-Esposito Mélissa , Maloney Katherine N. , Misra Biswapriya B. , Melnik Alexey V. , Jones Kenneth L. , Dorrestein Kathleen , Panitchpakdi Morgan , Ernst Madeleine , van der Hooft Justin J.J. , Gonzalez Mabel , Carazzone Chiara , Amézquita Adolfo , Callewaert Chris , Morton James , Quinn Robert , Bouslimani Amina , Albarracín Orio Andrea , Petras Daniel , Smania Andrea M. , Couvillion Sneha P. , Burnet Meagan C. , Nicora Carrie D. , Zink Erika , Metz Thomas O. , Artaev Viatcheslav , Humston-Fulmer Elizabeth , Gregor Rachel , Meijler Michael M. , Mizrahi Itzhak , Eyal Stav , Anderson Brooke , Dutton Rachel , Lugan Raphaël , Boulch Pauline Le , Guitton Yann , Prevost Stephanie , Poirier Audrey , Dervilly Gaud , Bizec Bruno Le , Fait Aaron , Persi Noga Sikron , Song Chao , Gashu Kelem , Coras Roxana , Guma Monica , Manasson Julia , Scher Jose U. , Barupal Dinesh , Alseekh Saleh , Fernie Alisdair , Mirnezami Reza , Vasiliou Vasilis , Schmid Robin , Borisov Roman S. , Kulikova Larisa N. , Knight Rob , Wang Mingxun , Hanna George B , Dorrestein Pieter C. , Veselkov Kirill 
Published: January 2020

AbstractGas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patternsviaunsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.