bluuebunny/crossref_metadata_2025_split huggingface dataset:
Environmental factors dominate over host genetics in shaping human gut microbiota composition
Authors: Rothschild Daphna , Weissbrod Omer , Barkan Elad , Korem Tal , Zeevi David , Costea Paul I , Godneva Anastasia , Kalka Iris , Bar Noam , Zmora Niv , Pevsner-Fischer Meirav , Israeli David , Kosower Noa , Malka Gal , Wolf Bat Chen , Avnit-Sagi Tali , Lotan-Pompan Maya , Weinberger Adina , Halpern Zamir , Carmi Shai , Elinav Eran , Segal Eran 
Published: June 2017

AbstractHuman gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the termbiome-explainabilityas the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.
Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly
Authors: Stanek Kimberly A. , Mura Cameron 
Published: June 2017

AbstractHfq is a bacterial RNA-binding protein that plays key roles in the post–transcriptional regulation of gene expression. Like other Sm proteins, Hfq assembles into toroidal discs that bind RNAs with varying affinities and degrees of sequence specificity. By simultaneously binding to a regulatory small RNA (sRNA) and an mRNA target, Hfq hexamers facilitate productive RNA⋯RNA interactions; the generic nature of this chaperone-like functionality makes Hfq a hub in many sRNA-based regulatory networks. That Hfq is crucial in diverse cellular pathways—including stress response, quorum sensing and biofilm formation— has motivated genetic and ‘RNAomic’ studies of its function and physiology (in vivo), as well as biochemical and structural analyses of Hfq⋯RNA interactions (in vitro). Indeed, crystallographic and bio-physical studies first established Hfq as a member of the phylogenetically-conserved Sm superfamily. Crystallography and other biophysical methodologies enable the RNA-binding properties of Hfq to be elucidated in atomic detail, but such approaches have stringent sample requirements,viz.: reconstituting and characterizing an Hfq•RNA complex requires ample quantities of well-behaved (sufficient purity, homogeneity) specimens of Hfq and RNA (sRNA, mRNA fragments, short oligoribonucleotides, or even single nucleotides). The production of such materials is covered in this Chapter, with a particular focus on recombinant Hfq proteins for crystallization experiments.Abbreviations3Dthree-dimensionalAUasymmetric unitCVcolumn volumeDEPCdiethyl pyrocarbonateHDVhepatitis δ virusHDVDhanging-drop vapor diffusionIMACimmobilized metal affinity chromatographyMWmolecular weightMWCOmolecular weight cut-offntnucleotidePDBProtein Data BankRNPribonucleoproteinRTroom temperatureSDVDsitting-drop vapor diffusionJournal formatMethods in Molecular Biology(Springer Protocolsseries); this volume is entitled “Bacterial Regulatory RNA: Methods and Protocols”; an author guide is linked athttp://www.springer.com/series/7651
Selecting among three basic fitness landscape models: additive, multiplicative and stickbreaking
Authors: Miller Craig R. , Van Leuven James T. , Wichman Holly A. , Joyce Paul 
Published: June 2017

AbstractFitness landscapes map genotypes to organismal fitness. Their topography depends on how mutational effects interact–epistasis–and is important for understanding evolutionary processes such as speciation, the rate of adaptation, the advantage of recombination, and predictability versus stochasticity of evolution. The growing amount of empirical data has made it possible to better test landscape models empirically. We argue that this endeavor will benefit from the development and use of meaningful null models against which to compare more complex models. Here we develop statistical and computational methods for fitting fitness data from mutation combinatorial networks to three simple models: additive, multiplicative and stickbreaking. We employ a Bayesian framework for doing model selection. Using simulations, we demonstrate that our methods work and we explore their statistical performance: bias, error, and the power to discriminate among models. We then illustrate our approach and its flexibility by analyzing several previously published datasets. An R-package that implements our methods is available in the CRAN repository under the nameStickbreaker.
The SCWISh network is essential for survival under mechanical pressure
Authors: Delarue Morgan , Poterewicz Gregory , Hoxha Ori , Choi Jessica , Yoo Wonjung , Kayser Jona , Holt Liam , Hallatschek Oskar 
Published: June 2017

Cells that proliferate within a confined environment build up mechanical compressive stress. For example, mechanical pressure emerges in the naturally space-limited tumor environment. However, little is known about how cells sense and respond to mechanical compression. We developed microfluidic bioreactors to enable the investigation of the effects of compressive stress on the growth of the genetically tractable model organism Saccharomyces cerevisiae. We used this system to determine that compressive stress is partly partly sensed through a module consisting of the mucin Msb2, and the cell wall protein Sho1, which act together as a sensor module in one of the two major osmosensing pathways in budding yeast. This signal is transmitted via the MAPKKK kinase Ste11. Thus, we term this mechanosensitive pathway theSMuShpathway, forSte11 throughMucin /Sho1 pathway. The SMuSh pathway delays cells in the G1 phase of the cell cycle and improves cell survival in response to growth-induced pressure. We also found that the Cell Wall Integrity (CWI) pathway contributes to the response to mechanical compressive stress. These latter results are confirmed in complimentary experiments in the accompanying manuscript from Mishra et al. When both the SMuSh and the CWI pathways are deleted, cells fail to adapt to compressive stress and all cells lyse at relatively low pressure when grown in confinement. Thus, we define a network that is essential for cell survival during growth under pressure. We term this new mechanosensory system the SCWISh (Survival through the CWI and SMuSh) network.Significance StatementGrowth in confined environments leads to the build up of compressive mechanical stresses, which are relevant to diverse fields, from cancer to microbiology. In contrast to tensile stress, little is known about the molecular integration of compressive stresses. In this study, we elucidate the SMuSh pathway, which, together with the Cell Wall Integrity pathway, is essential for viability of the budding yeastS. cerevisiaewhen growing under mechanical pressure. Pressure-sensing requires the transmembrane mucin, Msb2, which is linked to the actin cortex. Our result raises the intriguing question of whether mucins, widely conserved in eukaryotes and frequently misregulated in cancers, might sense compressive stresses in other organisms, including humans.
The U1 snRNP subunit LUC7 controls plant development and stress response through alternative splicing regulation
Authors: Amorim Marcella de Francisco , Willing Eva-Maria , Francisco-Mangilet Anchilie G. , Droste-Borel Irina , Maček Boris , Schneeberger Korbinian , Laubinger Sascha 
Published: June 2017

AbstractIntrons are removed by the spliceosome, a large macromolecular complex composed of five ribonucleoprotein subcomplexes (U snRNP). The U1 snRNP, which binds to 5’ splice sites, plays an essential role in early steps of the splicing reaction. Here, we show that Arabidopsis LUC7 proteins, which are encoded by a three-member gene family in Arabidopsis, are important for plant development and stress resistance. We show that LUC7 are U1 snRNP accessory proteins by RNA immunoprecipitation experiments and LUC7 protein complex purifications. Transcriptome analyses revealed that LUC7 proteins are not only important for constitutive splicing, but also affects hundreds of alternative splicing events. Interestingly, LUC7 proteins specifically promote splicing of a subset of terminal introns. Splicing of LUC7-dependent introns is a prerequisite for nuclear export and some splicing events are modulated by stress in a LUC7-dependent manner. Taken together our results highlight the importance of the U1 snRNP component LUC7 in splicing regulation and suggest a previously unrecognized role of a U1 snRNP accessory factor in terminal intron splicing.
Crosslinkers both drive and brake cytoskeletal remodeling and furrowing in cytokinesis
Authors: Descovich Carlos Patino , Cortes Daniel B. , Ryan Sean , Nash Jazmine , Zhang Li , Maddox Paul S. , Nedelec Francois , Maddox Amy Shaub 
Published: June 2017

AbstractCytokinesis and other cell shape changes are driven by the actomyosin contractile cytoskeleton. The molecular rearrangements that bring about contractility in non-muscle cells are currently debated. Specifically, both filament sliding by myosin motors, as well as cytoskeletal crosslinking by myosins and non-motor crosslinkers, are thought to promote contractility. Here, we examined how the abundance of motor and non-motor crosslinkers controls the speed of cytokinetic furrowing. We built a minimal model to simulate the contractile dynamics of theC. eleganszygote cytokinetic ring. This model predicted that intermediate levels of non-motor crosslinkers would allow maximal contraction speed, which we found to be the case for the scaffold protein anillin,in vivo. Our model also demonstrated a non-linear relationship between the abundance of motor ensembles and contraction speed.In vivo, thorough depletion of non-muscle myosin II delayed furrow initiation, slowed F-actin alignment, and reduced maximum contraction speed, but partial depletion allowed faster-than-expected kinetics. Thus, both motor and non-motor crosslinkers promote cytokinetic ring closure when present at low levels, but act as a brake when present at higher levels. Together, our findings extend the growing appreciation for the roles of crosslinkers, but reveal that they not only drive but also brake cytoskeletal remodeling.
Mechanisms of transmission ratio distortion at hybrid sterility loci within and betweenMimulusspecies
Authors: Kerwin Rachel E. , Sweigart Andrea L. 
Published: June 2017

ABSTRACTHybrid incompatibilities are a common correlate of genomic divergence and a potentially important contributor to reproductive isolation. However, we do not yet have a detailed understanding of how hybrid incompatibility loci function and evolve within their native species, or why they are dysfunctional in hybrids. Here, we explore these issues for a well-studied, two-locus hybrid incompatibility betweenhybrid male sterility 1(hms1) andhybrid male sterility 2(hms2) in the closely related yellow monkeyflower speciesMimulus guttatusandM. nasutus. By performing reciprocal backcrosses with introgression lines, we find evidence for gametic expression of thehms1-hms2incompatibility. Surprisingly, however, hybrid transmission ratios athms1do not reflect this incompatibility, suggesting additional mechanisms counteract the effects of gametic sterility. Indeed, our backcross experiment shows hybrid transmission bias towardM. guttatusthrough both pollen and ovules, an effect that is particularly strong whenhms2is homozygous forM. nasutusalleles. In contrast, we find little evidence forhms1transmission bias in crosses withinM. guttatus, providing no indication of selfish evolution at this locus. Although we do not yet have sufficient genetic resolution to determine if hybrid sterility and transmission ratio distortion map to the same loci, our preliminary fine-mapping uncovers a genetically independent hybrid lethality system involving at least two loci linked tohms1. This fine-scale dissection of transmission ratio distortion athms1andhms2provides insight into genomic differentiation between closely relatedMimulusspecies and reveals multiple mechanisms of hybrid dysfunction.
Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus
Authors: Djordjevic Djordje , Tang Joshua Y. S. , Chen Yun Xin , Shannon Kwan Shu Lun , Ling Raymond W. K. , Qian Gordon , Woo Chelsea Y. Y. , Ellis Samuel J. , Ho Joshua W. K. 
Published: June 2017

AbstractThere exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI’s Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO’s free text metadata to annotate the experimental type and sample type, this database remains diﬃcult to harness computationally without significant manual intervention.In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network.GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available athttps://github.com/VCCRI/GEOracle.
Final amendment: Ambiguous specification of EGFR mutations compounded by nil or negligible fragmented gene counts and erroneous application of the Kappa statistic reiterates doubts on the veracity of the TEP-study
Authors: Chakraborty Sandeep 
Published: June 2017

AbstractFinal amendment noteThis paper had raised two issues - the error-prone classification and mistaken application of the Kappa statistic. The classification critique still holds, and is being taken up with other criticisms athttp://www.biorxiv.org/content/early/2017/07/02/146134. The Kappa statistic was an error on my part since I had failed to see another page in Table S1. Please consider this pre-print closed.Original abstractThe use of RNA-seq from tumor-educated platelets (TEP) as a ‘liquid biopsy’ source [1] has been refuted recently (http://biorxiv.org/content/early/2017/06/05/146134, not peer-reviewed). The TEP-study also mentioned that mutant epidermal growth factor receptor (EGFR) was ‘accurately distinguished using surrogate TEP mRNA profiles’, which is contested here. It is shown that only 10 out of 24 (a smaller sample set, original study has 60) non-small cell lung carcinoma (NSCLC) samples here has any expression at all. Even there the number of reads (101 bp) are [1, 4, 1, 14, 9, 1, 2, 19, 21, 6], and do not even add up to one complete EGFR gene (about 6000 bp). EGFR mutations have been painstakingly collated inwww.mycancergenome.org/content/disease/lung-cancer/egfr. In stark contrast, the TEP study has no specification of the EGFR mutant used. The TEP study found EGFR mutations in 17/21 (81%), and EGFR wild-type in 4/39 (10%) for NSCLC samples (Table S7, reflected in Fig 3, Panel E in percentages). A major flaw is the assumption that a non “EGFR wild-type” is a “EGFR mutant” since cases zero with EGFR reads (which are almost half of the samples) could be either. The application of the Kappa statistic to this data is erroneous for two reasons. First, the Kappa statistic does not handle “unknowns”, as is the case for samples with zero expression. Secondly, ‘interobserver variation can be measured in any situation in which two or more independent observers are evaluating the same thing’ [2]. The 90% (Fig 3, Panel E) is just the percentage of samples (35/39) that are not “EGFT WT” in one observation. It is not qualified to be in the Kappa matrix, where it translates to 35, leading to a Kappa=0.707, which implies “substantial agreement” [2]. The other observation (looking for EGFR mutation) is in a different set. To summarize, this work reiterates negligible expression of EGFR reads in NSCLC samples, and finds serious shortcomings in the statistical analysis of subsequent mutational analysis from these reads in the TEP-study.
Scalable composition frameworks for multicellular logic
Authors: Guiziou Sarah , Mayonove Pauline , Ulliana Federico , Moreau Violaine , Leclère Michel , Bonnet Jéróme 
Published: June 2017

ABSTRACTA major goal of synthetic biology is to reprogram living organisms to solve pressing challenges in manufacturing, environmental remediation, or healthcare. While many types of genetic logic gates have been engineered, their scalability remains limited. Previous work demonstrated that Distributed Multicellular Computation (DMC) enables the implementation of complex logic within cellular consortia. However, current DMC systems require spatial separation of cellular subpopulation to be scalable to N-inputs, and need cell-cell communication channels to operate. Here we present scalable composition frameworks for the systematic design of multicellular consortia performing recombinase-based Boolean or history-dependent logic, and integrating an arbitrary number of inputs. The theoretical designs for both Boolean and history-dependent logic are based on reduced sets of computational modules implemented into specific cellular subpopulations which can then be combined in various manners to implement all logic functions. Our systems mark a departure from previous DMC architectures as they do not require either cell-cell communication nor spatial separation, greatly facilitating their implementation and making them fully autonomous. Due to their scalability and composability, we anticipate that the design strategies presented here will help researchers and engineers to reprogram cellular behavior for various applications in a streamlined manner. We provide an online tool for automated design of DNA architectures allowing the implementation of multicellular N-inputs logic functions at:http://synbio.cbs.cnrs.fr/calin.
Dynamic interactions between top-down expectations and conscious awareness
Authors: Meijs Erik L. , Slagter Heleen A. , de Lange Floris P. , van Gaal Simon 
Published: June 2017

AbstractIt is well known that top-down expectations affect perceptual processes. Yet, remarkably little is known about the relationship between expectations and conscious awareness We address three crucial questions that are outstanding: 1) How do predictions affect the likelihood of conscious stimulus perception?; 2) Does the brain register violations of predictions nonconsciously?; and 3) Do predictions need to be conscious to influence perceptual decisions? We performed three experiments in which we manipulated stimulus predictability within the attentional blink paradigm, while combining visual psychophysics with electrophysiological recordings. We found that valid stimulus expectations increase the likelihood of conscious access of stimuli. Furthermore, our findings suggest a clear dissociation in the interaction between expectations and consciousness: conscious awareness seems crucial for the implementation of top-down predictions, but not for the bottom-up generation of stimulus-evoked prediction errors. These results constrain and update influential theories about the role of consciousness in the predictive brain.
Platform for rapid nanobody discoveryin vitro
Authors: McMahon Conor , Baier Alexander S. , Zheng Sanduo , Pascolutti Roberta , Ong Janice X. , Erlandson Sarah C. , Hilger Daniel , Ring Aaron M. , Manglik Aashish , Kruse Andrew C. 
Published: June 2017

AbstractCamelid single-domain antibody fragments (“nanobodies”) provide the remarkable specificity of antibodies within a single immunoglobulin VHHdomain. This unique feature enables applications ranging from their use as biochemical tools to therapeutic agents. Virtually all nanobodies reported to date have been obtained by animal immunization, a bottleneck restricting many applications of this technology. To solve this problem, we developed a fullyin vitroplatform for nanobody discovery based on yeast surface display of a synthetic nanobody scaffold. This platform provides a facile and cost-effective method for rapidly isolating nanobodies targeting a diverse range of antigens. We provide a blueprint for identifying nanobodies starting from both purified and non-purified antigens, and in addition, we demonstrate application of the platform to discover rare conformationally-selective nanobodies to a lipid flippase and a G protein-coupled receptor. To facilitate broad deployment of this platform, we have made the library and all associated protocols publicly available.
Out-of-equilibrium statistical dynamics of spatial pattern generating cellular automata
Authors: Olimpio Eduardo P. , Youk Hyun 
Published: June 2017

ABSTRACTHow living systems generate order from disorder is a fundamental question1-5. Metrics and ideas from physical systems have elucidated order-generating collective dynamics of mechanical, motile, and electrical living systems such as bird flocks and neuronal networks6-8. But suitable metrics and principles remain elusive for many networks of cells such as tissues that collectively generate spatial patterns via chemical signals, genetic circuits, and dynamics representable by cellular automata1,9-11. Here we reveal such principles through a statistical mechanics-type framework for cellular automata dynamics in which cells with ubiquitous genetic circuits generate spatial patterns by switching on and off each other’s genes with diffusing signalling molecules. Lattices of cells behave as particles stochastically rolling down a pseudo-energy landscape – defined by a spin glass-like Hamiltonian – that is shaped by “macrostate” functions and genetic circuits. Decreasing the pseudo-energy increases the spatial patterns’ orderliness. A new kinetic trapping mechanism – “pathway trapping” – yields metastable spatial patterns by preventing minimization of the particle’s pseudo-energy. Noise in cellular automata reduces the trapping, thus further increases the spatial order. We generalize our framework to lattices with multiple types of cells and signals. Our work shows that establishing statistical mechanics of computational algorithms can reveal collective dynamics of signal-processing in biological and physical networks.