bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interface residues that drive allosteric transitions also control the assembly of L-lactate Dehydrogenase
Authors: Chen Jie , Thirumalai D. 
Published: July 2018

AbstractThe allosteric enzyme, L-lactate dehydrogenase (LDH), is activated by fructose 1,6-metaphosphate (FBP) to reduce pyruvate to lactate. The molecular details of the FBP-driven transition between the low affinity T-state to the high affinity R-state in LDH, a tetramer composed of identical subunits, are not known. The dynamics of theT→R allosteric transition, investigated using Brownian dynamics (BD) simulations of the Self-Organized Polymer (SOP) model, revealed that coordinated rotations of the subunits drive the T→R transition. We used the structural perturbation method (SPM), which requires only the static structure, to identify the allostery wiring diagram (AWD), a network of residues that transmits signals across the tetramer, as LDH undergoes the T→R transition. Interestingly, the residues that play a major role in the dynamics, which are predominantly localized at the interfaces, coincide with the AWD identified using the SPM. The conformational changes in the T→R transition start from the region near the active site, comprising of helix αC, helix α1/2G, helix α3G and helix α2F, and proceed to other structural units, thus completing the global motion. Brownian dynamics simulations of the tetramer assembly, triggered by a temperature quench from the fully disrupted conformations, show that the bottleneck for assembly is the formation of the correct orientation between the subunits, requiring contacts between the interface residues. Surprisingly, these residues are part of the AWD, which was identified using the SPM. Taken together, our results show that LDH, and perhaps other multi-domain proteins, may have evolved to stabilize distinct states of allosteric enzymes using precisely the same AWD that also controls the functionally relevant allosteric transitions.
MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota
Authors: Diener Christian , Gibbons Sean M. , Resendis-Antonio Osbaldo 
Published: July 2018

AbstractCompositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM – a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short chain fatty acids (SCFAs) was heterogeneous and highly individual-specific. Model output revealed complex cross-feeding interactions that would be difficult to measurein vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in the open source Python package, which is available athttps://github.com/micom-dev/micom.
Progression of chronic kidney disease in African Americans with type 2 diabetes mellitus using topology learning in electronic medical records
Authors: Wang Li , Zheng Xufei , Huang Lynn S. , Xu Jianzhao , Hsu Fang-Chi , Chen Shyh-Huei , Ng Maggie C.Y. , Bowden Donald W. , Freedman Barry I. , Su Jing 
Published: July 2018

AbstractBackgroundChronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a “real-world” context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions. Genetics also plays an important role. In previous work, we and others have demonstrated that African Americans with high-risk APOL1 genotypes are more likely to develop CKD, tend to develop CKD earlier, and the disease progresses faster. Diabetes, which is more common in African Americans, also significantly increases risk for CKD.Data and MethodElectronic medical records (EMRs) were used to outline the first CKD progression trajectory roadmap for an African American population with type 2 diabetes. By linking participants in 5 genome-wide association study (GWAS) to their clinical records at Wake Forest Baptist Medical Center (WFBMC), an EMR-GWAS cohort was established (n = 1,581). Patients’ health status was described by 18 Essential Clinical Indices across 84,009 clinical encounters. A novel graph learning algorithm, Discriminative Dimensionality Reduction Tree (DDRTree) was implemented, to establish the trajectories of declines in health. Moreover, a prediction model for new patients was proposed along the learned graph structure. We annotated these trajectories with clinical and genomic features including kidney function, other major risk indices of CKD, APOL1 genotypes, and age. The prediction power of the learned disease progression trajectories was further examined using the k-nearest neighbor model.ResultsThe CKD progression trajectory roadmap revealed diverse kidney failure pathways associated with different clinical conditions. Specifically, we identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one was associated with accelerated decline in kidney function. On this roadmap, patients with APOL1 high-risk genotypes were enriched in the high-risk trajectory, suggesting fundamentally different disease progression mechanisms from those without APOL1 risk genotypes. The k-nearest neighbor-based prediction showed effective prediction rate of 87%.ConclusionThe CKD progression trajectory roadmap revealed novel diverse renal failure pathways in African Americans with type 2 diabetes mellitus and highlights disease progression patterns that associate with APOL1 renal-risk genotypes.
Genome location dictates the transcriptional response to PolC-inhibition inClostridium difficile
Authors: van Eijk Erika , Boekhoud Ilse M. , Kuijper Ed J. , Bos-Sanders Ingrid M.J.G. , Wright George , Smits Wiep Klaas 
Published: July 2018

AbstractClostridium difficileis a potentially lethal gut pathogen that causes nosocomial and community acquired infections. Limited treatment options and reports of reduced susceptibility to current treatment emphasize the necessity for novel antimicrobials. The DNA-polymerase of gram-positive organisms is an attractive target for the development of antimicrobials. ACX-362E (N2-(3<,4-Dichlorobenzyl)-7-(2-[1-morpholinyl]ethyl)guanine; MorE-DCBG) is a DNA polymerase inhibitor in pre-clinical development as a novel therapeutic againstC. difficileinfection. This synthetic purine shows preferential activity againstC. difficilePolC over those of other organismsin vitroand is effective in an animal model ofC. difficileinfection. In this study we have determined its efficacy against a large collection of clinical isolates. At concentrations below the minimal inhibitory concentration, the presumed slowing (or stalling) of replication forks due to ACX-362E leads to a growth defect. We have determined the transcriptional response ofC. difficileto replication inhibition and observed an overrepresentation of up-regulated genes near the origin of replication in the presence of PolC-inhibitors, but not when cells were subjected to sub-inhibitory concentrations of other antibiotics. This phenomenon can be explained by a gene dosage shift, as we observed a concomitant increase in the ratio between origin-proximal versus terminus-proximal gene copy number upon exposure to PolC-inhibitors. Moreover, we show that certain genes differentially regulated under PolC-inhibition are controlled by the origin-proximal general stress response regulator sigma factor B. Together, these data suggest that genome location both directly and indirectly determines the transcriptional response to replication inhibition inC. difficile.
Effect of Drought On The Germination of Maize Using PEG (Polyethylene Glycol) As A Substitute For Drought Screening
Authors: Dar I. A. , Dar Kamaluddin. Z. A. , Sofi P.A. , Lone A.A. 
Published: July 2018

AbstractDrought stress is one of the most important environmental factors in reduction of growth, development and production of plants. Germination of each seed is considered as one of the first and most fundamental life stages of a plant so that, the success in growth and yield production is also depending on this stage. To study the effect of PEG stress on germination and early seedling stages on maize, an experiment were laid out at laboratory conditions of Division of Genetics and Plant Breeding SKUAST-K FoA/RRS Wadura. This investigation was performed as factorial experiment under Complete Randomized Design (CRD) with three replications. Polyethylene glycol stress induced in laboratory caused progressive decline in both the parameters across all genotypes with increase in Polyethylene glycol from 0-20% and both the parameters (length of radical and root biomass) had highest value under control.
Divergent Behavior Amid Convergent Evolution: A Case of Four Desert Rodents Learning to Respond to Known and Novel Vipers
Authors: Bleicher Sonny S. , Kotler Burt P. , Shalev Omri , Austin Dixon , Embar Keren , Brown Joel S. 
Published: July 2018

ABSTRACTDesert communities word-wide are used as natural laboratories for the study of convergent evolution, yet inferences drawn from such studies are necessarily indirect. Here, we brought desert organisms together (rodents and vipers) from two deserts (Mojave and Negev). Both predators and prey in the Mojave have adaptations that give them competitive advantage compared to their middle-eastern counterparts. Heteromyid rodents, kangaroo rats and pocket mice, have fur-lined cheek pouches that allow the rodents to carry larger loads under predation risk compared to gerbilline rodents. Sidewinder rattlesnakes have heat-sensing pits, allowing them to hunt better on moonless nights when their Negev sidewinding counterpart, the Saharan horned vipers, are visually impaired. In behavioral-assays, we used giving-up density (GUD) to gage how each species of rodent perceived risk posed by known and novel snakes. We repeated this for the same set of rodents at first encounter and again two months later following intensive “natural” exposure to both snake species. Pre-exposure, all rodents identified their evolutionarily familiar snake as a greater risk than the novel one. However, post-exposure all identified the heat-sensing sidewinder rattlesnake as a greater risk. The heteromyids were more likely to avoid encounters with, and discern the behavioral difference among, snakes than their gerbilline counterparts.
Structural analysis of the interaction between the bacterial cell division proteins FtsQ and FtsB
Authors: Kureisaite-Ciziene Danguole , Varadajan Aravindan , McLaughlin Stephen H. , Glas Marjolein , Silva Alejandro Montón , Luirink Rosa , Mueller Carolin , den Blaauwen Tanneke , Grossmann Tom N. , Luirink Joen , Löwe Jan 
Published: July 2018

AbstractMost bacteria and archaea use similar proteins within their cell division machinery, which uses the tubulin homologue FtsZ as its central organiser. In Gram-negative Escherichia coli bacteria, FtsZ recruits cytosolic, transmembrane, periplasmic and outer membrane proteins, assembling the divisome that facilitates bacterial cell division. One such divisome component, FtsQ, a bitopic membrane protein with a globular domain in the periplasm, has been shown to interact with many other divisome proteins. Despite its otherwise unknown function, it has been shown to be a major divisome interaction hub. Here, we investigated the interactions of FtsQ with FtsB and FtsL, two small bitopic membrane proteins that act immediately downstream of FtsQ. In biochemical assays we show that the periplasmic domains of E. coli FtsB and FtsL interact with FtsQ, but not with each other. Our crystal structure of FtsB bound to the β domain of FtsQ shows that only residues 64-87 of FtsB interact with FtsQ. A synthetic peptide comprising those 24 FtsB residues recapitulates the FtsQ:FtsB interactions. Protein deletions and structure-guided mutant analyses validate the structure. Furthermore, the same structure-guided mutants show cell division defects in vivo that are consistent with our structure of the FtsQ:FtsB complex that shows their interactions as they occur during cell division. Our work provides intricate details of the interactions within the divisome and also provides a tantalising view of a highly conserved protein interaction in the periplasm of bacteria that is an excellent target for cell division inhibitor searches.ImportanceCells in most bacteria and archaea divide through a cell division process that is characterised through its filamentous organiser, FtsZ protein. FtsZ forms a ring structure at the division site and starts the recruitment of 10-20 downstream proteins that together form an elusive multi-protein complex termed divisome. The divisome is thought to facilitate many of the steps required to make two cells out of one. FtsQ and FtsB are part of the divisome, with FtsQ being a central hub, interacting with most of the other divisome components. Here we show for the first time how FtsQ interacts with its downstream partner FtsB and show that mutations that disturb the interface between the two proteins effectively inhibit cell division.
Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics
Authors: Gaspar Héléna A. , Breen Gerome 
Published: July 2018

AbstractPrincipal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM also generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification. This paper is a first application of GTM for ancestry classification models. Our maps and software are available online.Author summaryWith this paper, we seek to encourage researchers working in genetics to use other methods than PCA to visualize ancestry and identify substructures in populations. We propose to use methods which do not only allow visualization of ancestry, but also the estimation of probabilities of belonging to different ancestry groups.
Decoupling the variances of heterosis and inbreeding effects is evidenced in yeast’s life-history and proteomic traits
Authors: Petrizzelli M. , de Vienne D. , Dillmann C. 
Published: July 2018

ABSTRACTHeterosis (hybrid vigor) and inbreeding depression, commonly considered as corollary phenomena, could nevertheless be decoupled under certain assumptions according to theoretical population genetics works. In order to explore this issue on real data, we analyzed the components of genetic variation in a population derived from a half-diallel cross between strains fromSaccharomyces cerevisiaeandS. uvarum, two related yeast species involved in alcoholic fermentation. A large number of phenotypic traits, either molecular (coming from quantitative proteomics) or related to fermentation and life-history, were measured during alcoholic fermentation. Because the parental strains were included in the design, we were able to distinguish between inbreeding effects, which measures phenotypic differences between inbred and hybrids, and heterosis, which measures phenotypic differences between a specific hybrid and the other hybrids sharing a common parent. The sources of phenotypic variation differed depending on the temperature, indicating the predominance of genotype by environment interactions. Decomposing the total genetic variance into variances of additive (intra- and inter-specific) effects, of inbreeding effects and of heterosis (intra- and inter-specific) effects, we showed that the distribution of variance components defined clear-cut groups of proteins and traits. Moreover, it was possible to cluster fermentation and life-history traits into most proteomic groups. Within groups, we observed positive, negative or null correlations between the variances of heterosis and inbreeding effects. To our knowledge, such a decoupling had never been experimentally demonstrated. This result suggests that, despite a common evolutionary history of individuals within a species, the different types of traits have been subject to different selective pressures.
Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models
Authors: Ventorim Ferrão Luis Felipe , Marinho Caillet Dornelles , Munoz Patricio R. , Resende Marcio F. R. 
Published: July 2018

AbstractHybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferredin silicobased on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.
Perinatal outcomes, maternal age, parity and fetal sex – searching for the “optimal” maternal age
Authors: Kaňková Šárka , Flegr Jaroslav , Kuba Radim , Calda Pavel 
Published: July 2018

AbstractBackgroundMaternal age, parity and fetal sex are each known to affect obstetric and birth outcomes. The objective of the present study was to investigate the influence of the combination of maternal age, parity and fetal sex on outcomes of pregnancies._The aim of the study was to analyze the influence of maternal age on perinatal outcomes in both primiparous and multiparous women with different fetal sex.MethodsThe cross-sectional study was performed on data from 11,780 women, who have given birth at the General University Hospital in Prague, Czech Republic between 2008-2012.ResultsMaternal age significantly (P<0.01) influenced pregnancy weight gain, duration of pregnancy, birth weight and birth length. Primiparous women with female newborns aged ≤19 had higher rates of preterm delivery than comparable women 20-39 (P=0.012). Similarly, women with male newborns aged ≥40 had higher rates of preterm delivery than comparable women 20-39 (P=0.003). Women aged ≤24 expressed higher rates of low birth weight than women aged >24 (P<0.001), regardless of parity and fetal sex. The older (≥35) primiparous women with male newborns expressed a higher incidence of macrosomia (P=0.021) compared to other groups of women. The probability of caesarean delivery increased with age (P<0.001) and it was significantly affected by the parity and sex of the newborn with higher rates of caesarean section in primiparous women as well as in mothers carrying male fetuses.ConclusionsOur results indicate that “optimal” maternal age without obstetrics and birth complications is 25-34 years, older age is associated with increased complications with a male fetus, especially in primiparous women. Our data suggests that not just the age of women, but the combination of age, parity, and fetal sex should be taken into consideration during assessment of health risks of pregnancy.
Memory Image Completion (MIC): Establishing a task to behaviorally assess pattern completion in humans
Authors: Vieweg Paula , Riemer Martin , Berron David , Wolbers Thomas 
Published: July 2018

AbstractFor memory retrieval, pattern completion is a crucial process that restores memories from partial or degraded cues. Neurocognitive aging models suggest that the aged memory system is biased toward pattern completion, resulting in a behavioral preference for retrieval over encoding of memories. While there are behavioral tasks to assess the encoding side of these memory differences, pattern completion has received less attention in the literature. Here, we built on our previously developed behavioral recognition memory paradigm – the Memory Image Completion task (MIC) – a task to specifically target pattern completion. First, we used the original design with concurrent eye-tracking in order to rule out perceptual confounds that could interact with recognition performance. Second, we developed parallel versions of the task to accommodate test settings in clinical environments or longitudinal studies. The results show that older adults have a deficit in pattern completion ability with a concurrent bias toward pattern completion – a replication of previously found effects. Importantly, eye-tracking data during encoding could not account for age-related performance differences. At retrieval, spatial viewing patterns for both age groups were more driven by stimulus identity than by response choice, but compared to young adults, older adults’ fixation patterns overlapped more between stimuli that they (wrongly) thought had the same identity. This supports the observation that when making errors older adults choose responses perceived as similar to the correct stimulus, which is interpreted as a bias toward pattern completion. Additionally, two shorter versions of the task yielded comparable results, and no general learning effects were observed for repeated testing. Together, we present evidence that the MIC is a reliable behavioral task that targets pattern completion, that is easily and repeatedly applicable, and that is made freely available online.