bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parallel altitudinal clines reveal trends adaptive evolution of genome size inZea mays
Authors: Bilinski Paul , Albert Patrice S. , Berg Jeremy J , Birchler James A , Grote Mark , Lorant Anne , Quezada Juvenal , Swarts Kelly , Yang Jinliang , Ross-Ibarra Jeffrey 
Published: May 2017

AbstractWhile the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.Author summaryGenome size in plants can vary by orders of magnitude, but this variation has long been considered to be of little to no functional consequence. Studying three independent adaptations to high altitude inZea mays, we find that genome size experiences parallel pressures from natural selection, causing a linear reduction in genome size with increasing altitude. Though reductions in repetitive content are responsible for the genome size change, we find that only those individual loci contributing most to the variation in genome size are individually targeted by selection. To identify the phenotype influenced by genome size, we study how variation in genome size within a single teosinte population impacts leaf growth and cell division. We find that genome size variation correlates negatively with the rate of cell division, suggesting that individuals with larger genomes require longer to complete a mitotic cycle. Finally, we reanalyze data from maize inbreds to show that faster cell division is correlated with earlier flowering, connecting observed variation in genome size to an important adaptive phenotype.
Vimentin plays a functional role in mammary gland regeneration
Authors: Virtakoivu Reetta , Peuhu Emilia , Mai Anja , Wärri Anni , Ivaska Johanna 
Published: May 2017

AbstractIn the mammary gland, vimentin intermediate filaments are expressed in stromal cells and in basal epithelial cell populations including gland-reconstituting mammary stem cells (MaSC), with largely undefined functions. Here, we studied how vimentin deficiency affects mouse mammary gland development. Our results demonstrate that in adult vimentin knockout mice (Vim-/-) mammary ductal outgrowth is delayed. The adultVim-/-glands are characterised by dilated ducts, an imbalance in the proportion of basal to luminal mammary epithelial cells and a reduction in cells expressing Slug (Snai2), an established MaSC regulator. All of these features are indicative of reduced progenitor cell activity. Accordingly, isolatedVim-/-mammary epithelial cells display reduced capacity to form mammospheres, and altered organoid structure, compared to wt counterparts, when plated in a 3D matrixin vitro. Importantly, altered basal epithelial cell number translates into defects inVim-/-mammary gland regenerationin vivoin cleared fat pad transplantation studies. Furthermore, we show that vimentin contributes to stem-like cell properties in triple negative MDA-MB-231 breast cancer cells, wherein vimentin depletion reduces tumorsphere formation and alters expression of breast cancer stem cell-associated surface markers. Together, our findings identify vimentin as a positive regulator of stemness in the developing mouse mammary gland and in breast cancer cells.
Consequences of natural perturbations in the human plasma proteome
Authors: Sun Benjamin B. , Maranville Joseph C. , Peters James E. , Stacey David , Staley James R. , Blackshaw James , Burgess Stephen , Jiang Tao , Paige Ellie , Surendran Praveen , Oliver-Williams Clare , Kamat Mihir A. , Prins Bram P. , Wilcox Sheri K. , Zimmerman Erik S. , Chi An , Bansal Narinder , Spain Sarah L. , Wood Angela M. , Morrell Nicholas W. , Bradley John R. , Janjic Nebojsa , Roberts David J. , Ouwehand Willem H. , Todd John A. , Soranzo Nicole , Suhre Karsten , Paul Dirk S. , Fox Caroline S. , Plenge Robert M. , Danesh John , Runz Heiko , Butterworth Adam S. 
Published: May 2017

AbstractProteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, includingtransassociations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.
Mathematical model for Plant-Insect interaction with dynamic response to PAD4-BIK1 interaction and effect of BIK1 inhibition
Authors: Sanjay  , Ahmad Sabahuddin , Siddiqi M. I. , Raza Khalid 
Published: May 2017

AbstractPlant-insect interaction system has been a widely studied model of the ecosystem. Attempts have long been made to understand the numerical behaviour of this counter system and make improvements in it from initial simple analogy based approach with predator-prey model to the recently developed mathematical interpretation of plant-insect interaction including concept of plant immune interventions Caughley and Lawton (1981). In our current work, we propose an improvement in the model, based on molecular interactions behind plant defense mechanism and it’s effect on the plant growth and insect herbivory. Motivated from an interaction network of plant biomolecules given by Louis and Shah (2014) and extending the model of Chattopadhyay, et al (2001), we propose here a mathematical model to show how plant insect interaction system is governed by the molecular components inside. Insect infestation mediated induction of Botrytis Induced Kinase-1 (BIK-1) protein causes inhibition of Phyto Alexin Deficient-4 (PAD4) protein. Lowered PAD4, being responsible for initiating plant defense mechanism, results in degraded plant immune potential and thus causes loss of plant quality. We adapt these interactions in our model to show how they influence the plant insect interaction system and also to reveal how silencing BIK-1 may aid in enhanced production of plant biomass by increasing plant immunity mediated by increase in PAD4 and associated antixenotic effects. We hypothesize the significance of BIK-1 inhibition which could result in the improvement of the plant quality. We explain the interaction system in BIK-1 inhibition using mathematical model. Further, we adopted the plethora of computational modeling and simulations techniques to identify the mechanisms of molecular inhibition.
A deep learning and novelty detection framework for rapid phenotyping in high-content screening
Authors: Sommer Christoph , Hoefler Rudolf , Samwer Matthias , Gerlich Daniel W. 
Published: May 2017

AbstractSupervised machine learning is a powerful and widely used method to analyze high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence ona prioriknowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations withCellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates thatCellCognition Explorerenables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.
Fine stratification of microbial communities through a metagenomic profile of the photic zone
Authors: Haro-Moreno Jose M. , López-Pérez Mario , de la Torre José R. , Picazo Antonio , Camacho Antonio , Rodríguez-Valera Francisco 
Published: May 2017

ABSTRACTMost marine metagenomic studies of the marine photic zone analyze only samples taken at one or two depths. However, when the water column is stratified, physicochemical parameters change dramatically over relatively short depth intervals. We sampled the photic water column every 15m depth at a single point of an off-shore Mediterranean site during a period of strong stratification (early autumn) to evaluate the effects of small depth increases on the microbiome. Using genomic assembly and metagenomic read recruitment, we found major shifts in the community structure over small variations of depth, with most microbes showing a distribution limited to layers approximately 30 meters thick (stenobathic). Only some representatives of the SAR11 clade and the Sphingomonadaceae appeared to be eurybathic, spanning a greater range of depths. These results were confirmed by studying a single gene (rhodopsin) for which we also found narrow depth distributions. Our results highlight the importance of considering vertical distribution as a major element when analyzing the presence of marine clades and species or comparing the microbiome present at different locations.
How to normalize metatranscriptomic count data for differential expression analysis
Authors: Klingenberg Heiner , Meinicke Peter 
Published: May 2017

ABSTRACTBACKGROUNDDifferential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it is not clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. The potential side effects that may result from direct application of transcriptomic tools to metatranscriptomic count data have not been studied so far.METHODSWe propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script.RESULTSWhen applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data the organism-independent (global) scaling of counts implies a high risk of falsely predicted functional differences. In simulation studies we show that incorrect normalization not only tends to loose significant differences but especially can produce a large number of false positives. In contrast, taxon-specific scaling can equalize the variation of relative library sizes from different organisms and therefore shows a reliable detection of significant differences in all simulations. On real metatranscriptomic data the results from taxon-specific and global scaling can largely differ. In our study, global scaling shows a high number of extra predictions which are not supported by single transcriptome analyses. Inspection of the scaling error suggests that these extra predictions may actually correspond to artifacts of an incorrect normalization.CONCLUSIONSAs in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore effectively prevents the risk of false predictions due to incorrect normalization.
Omics-based Hybrid Prediction in Maize
Authors: Westhues Matthias , Schrag Tobias A. , Heuer Claas , Thaller Georg , Friedrich Utz H. , Schipprack Wolfgang , Thiemann Alexander , Seifert Felix , Ehret Anita , Schlereth Armin , Stitt Mark , Nikoloski Zoran , Willmitzer Lothar , Schön Chris C. , Scholten Stefan , Melchinger Albrecht E. 
Published: May 2017

AbstractAccurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction (WGP) has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream “omics” data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of “omics” data. Here, we evaluate several “omics” predictors — genomic, transcriptomic and metabolic data — measured on parent lines at early developmental stages, and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream “omics” data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.Key messageComplementing genomic data with other “omics” predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.Conflict of InterestThe authors declare that they have no conflict of interest.
Concurrent duplication of the Cid and Cenp-C genes in the Drosophila subgenus with signatures of subfunctionalization and male germline-biased expression
Authors: Teixeira José R. , Dias Guilherme B. , Svartman Marta , Ruiz Alfredo , Kuhn Gustavo C. S. 
Published: May 2017

AbstractDespite their essential role in the process of chromosome segregation in eukaryotes, kinetochore proteins are highly diverse across species, being lost, duplicated, created, or diversified during evolution. Based on comparative genomics, the duplication of the inner kinetochore proteins CenH3 and Cenp-C, which are interdependent in their roles of stablishing centromere identity and function, can be said to be rare in animals. Surprisingly, theDrosophila CenH3homologCidunderwent four independent duplication events during evolution. Particularly interesting are the highly diverged and subfunctionalizedCid1andCid5paralogs of theDrosophilasubgenus, which show that over one thousandDrosophilaspecies may encode twoCidgenes, making those with a single copy a minority. Given that CenH3 and Cenp-C likely co-evolve as a functional unit, we investigated the molecular evolution ofCenp-Cin species ofDrosophila. We report yet anotherCidduplication within theDrosophilasubgenus and show that not onlyCid, but alsoCenp-Cis duplicated in the entire subgenus. TheCenp-Cparalogs, which we namedCenp-C1andCenp-C2, are highly divergent. The retention of key motifs involved in centromere localization and function by both Cenp-C1 and Cenp-C2 makes neofunctionalization unlikely. In contrast, the alternate conservation of some functional motifs between the proteins is indicative of subfunctionalization. Interestingly, bothCid5andCenp-C2are male germline-biased and evolved adaptively. Our findings point towards a specific inner kinetochore composition in a specific context (i.e., spermatogenesis), which could prove valuable for the understanding of how the extensive kinetochore diversity is related to essential cellular functions.
The effect of Nipped-B-Like (Nipbl) haploinsufficiency on genome-wide cohesin binding and target gene expression: modeling Cornelia de Lange Syndrome
Authors: Newkirk Daniel A. , Chen Yen-Yun , Chien Richard , Zeng Weihua , Biesinger Jacob , Flowers Ebony , Kawauchi Shimako , Santos Rosaysela , Calof Anne L. , Lander Arthur D. , Xie Xiaohui , Yokomori Kyoko 
Published: May 2017

ABSTRACTCornelia de Lange Syndrome (CdLS) is a multisystem developmental disorder frequently associated with heterozygous loss-of-function mutations of Nipped-B-like (NIPBL), the human homolog of Drosophila Nipped-B. NIPBL loads cohesin onto chromatin. Cohesin mediates sister chromatid cohesion important for mitosis, but is also increasingly recognized as a regulator of gene expression. In CdLS patient cells and animal models, the presence of multiple gene expression changes with little or no sister chromatid cohesion defect suggests that disruption of gene regulation underlies this disorder. However, the effect of NIPBL haploinsufficiency on cohesin binding, and how this relates to the clinical presentation of CdLS, has not been fully investigated. Nipbl haploinsufficiency causes CdLS-like phenotype in mice. We examined genome-wide cohesin binding and its relationship to gene expression using mouse embryonic fibroblasts (MEFs) from Nipbl +/- mice that recapitulate the CdLS phenotype. We found a global decrease in cohesin binding, including at CCCTC-binding factor (CTCF) binding sites and repeat regions. Cohesin-bound genes were found to be enriched for histone H3 lysine 4 trimethylation (H3K4me3) at their promoters; were disproportionately downregulated in Nipbl mutant MEFs; and displayed evidence of reduced promoter-enhancer interaction. The results suggest that gene activation is the primary cohesin function sensitive to Nipbl reduction. Over 50% of significantly dysregulated transcripts in mutant MEFs come from cohesin target genes, including genes involved in adipogenesis that have been implicated in contributing to the CdLS phenotype. Thus, decreased cohesin binding at the gene regions directly contributes to disease-specific expression changes. Taken together, our Nipbl haploinsufficiency model allows us to analyze the dosage effect of cohesin loading on CdLS development.
Antecedents and consequences of electronic supply chain management diffusion
Authors: Lin Hsiu-Fen 
Published: May 2017

PurposeGrounded in the resource-based view and social exchange theory, the purpose of this paper is to develop a research model that offers a comprehensive understanding of the antecedents and consequences of electronic supply chain management (e-SCM) diffusion.Design/methodology/approachSurvey data from 142 managers (in charge of e-SCM projects in their companies) of large Taiwanese firms were collected and used to test the hypotheses using hierarchical moderated regression analysis.FindingsThe results indicate that information technology deployment capability, operational capability, human resource capability, and knowledge sharing are important antecedents of e-SCM diffusion. In turn, higher levels of e-SCM diffusion lead to greater competitive performance. This study also finds that knowledge sharing plays a moderating role by strengthening the relationship between organizational capabilities (e.g. operational capability and human resource capability) and e-SCM diffusion.Practical implicationsManagers should recognize that human resource development activities (recruiting, training, and managing valuable e-SCM personnel) are an important source of e-SCM diffusion. Similarly, managers must establish the connection between human resource capabilities and e-SCM diffusion (i.e. “soft-side” e-SCM) such as hiring and retaining skilled e-SCM personnel, training and development for e-SCM personnel, and measuring e-SCM personnel’s global mindset over time.Originality/valueTheoretically, this study aims to provide a research model that is capable of understanding the antecedents and consequences of e-SCM diffusion. From the managerial perspective, the findings of this study provide valuable decision guides for practitioners to help them identify and develop firm internal capabilities and social mechanisms that foster e-SCM diffusion.
Collaborative process design
Authors: Swanson David , Jin Yao Henry , Fawcett Amydee M. , Fawcett Stanley E. 
Published: May 2017

PurposeOver the past two decades, technological advances have spurred companies to design collaborative processes. Yet most such efforts are difficult to implement, with only a few resulting in sustained competitive advantages. The purpose of this paper is to leverage the tenets of socio-technical theory to examine how collaborative process design may lead to improved collaborative performance.Design/methodology/approachThe authors employ a multi-method – survey and interview – approach to examine the roles of technical and social initiatives in mitigating resistors to collaborative performance, and identify both the short-term appeals of technology investments and long-term social resistors that inhibit additional performance gains.FindingsWhile initial investments in information technology yield alluring gains, performance benefits diminish as social resistors create limiting conditions. The dynamic capability for firms to recognize and respond to the dual and integrative nature of technical and social systems is required for firms to overcome powerful limiting conditions and change resistors through collaborative process design in order to cultivate new value-creation processes.Originality/valueThis study is the first in the discipline to utilize socio-technical systems theory to examine an issue in supply chain process redesign. The multi-method approach elaborates the difficulty inherent in cultivating new value-creation processes. The results collectively illustrate a need for recognizing the influence of both the reinforcing and limiting processes. Whereas, technical initiatives enable new capabilities, social initiatives remove fear, create vision, and inculcate skills, enabling technology adoption and process change.