bluuebunny/crossref_metadata_2025_split huggingface dataset:
Floral signals evolve in a predictable way under artificial and pollinator selection inBrassica rapausing a G-matrix
Authors: Zu Pengjuan , Schiestl Florian P. , Gervasi Daniel , Li Xin , Runcie Daniel , Guillaume Frédéric 
Published: June 2019

AbstractBackgroundAngiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them.ResultsWe used data from an artificial and a pollinator (bumblebee, hoverfly) selection experiment with fast cyclingBrassica rapaplants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the responses of most floral traits including volatiles were predicted in the right direction in artificial- and bumblebee-selection experiment, revealing direct and indirect targets of bumblebee selection. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed how G-matrices evolved in the selection processes.ConclusionsOverall, our integrative study shows that floral signals, and especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also needs to be considered for a better understanding of floral trait evolution.
Phosphoprotein-based Biomarkers as Predictors for Cancer Therapy
Authors: Carter Angela M. , Tan Chunfeng , Pozo Karine , Telange Rahul , Molinaro Roberto , Guo Ailan , De Rosa Enrica , Martinez Jonathan O. , Zhang Shanrong , Kumar Nilesh , Takahashi Masaya , Wiederhold Thorsten , Ghayee Hans K. , Oltmann Sarah C. , Pacak Karel , Woltering Eugene A. , Hatanpaa Kimmo J. , Nwariaku Fiemu E. , Grubbs Elizabeth G. , Gill Anthony J , Robinson Bruce , Gillardon Frank , Reddy Sushanth , Jaskula-Sztul Renata , Mobley James A. , Mukhtar M. Shahid , Tasciotti Ennio , Chen Herbert , Bibb James A. 
Published: June 2019

AbstractDisparities in cancer patient responses have prompted widespread searches to identify differences in sensitive vs. non-sensitive populations and form the basis of personalized medicine. This customized approach is dependent upon the development of pathway-specific therapeutics in conjunction with biomarkers that predict patient responses. Here, we show that Cdk5 drives growth in subgroups of human patients with multiple types of neuroendocrine neoplasms. Phosphoproteomics and high throughput screening identified novel phosphorylation sites downstream of Cdk5. These phosphorylation events serve as biomarkers of Cdk5 activity and effectively pinpoint Cdk5-driven tumors. Toward achieving targeted therapy, we demonstrate that mouse models of neuroendocrine cancer are responsive to selective Cdk5 inhibitors and biomimetic nanoparticles are effective vehicles for enhanced tumor targeting and reduction of drug toxicity. Finally, we show that biomarkers of Cdk5-dependent tumors effectively predict response to anti-Cdk5 therapy in patient-derived xenografts. Thus, a phosphoprotein-based diagnostic assay combined with Cdk5-targeted therapy is a rational treatment approach for neuroendocrine malignancies.
BOLD Decoding of Individual Pain Anticipation Biases During Uncertainty
Authors: Kadlec Molly , Tosun Duygu , Strigo Irina 
Published: June 2019

AbstractA prominent model of pain as a predictive cue posits that anticipation shapes pain transmission and ultimately pain experience. Consistent with this model, the neural mechanisms underlying pain anticipation have the power to modulate pain experience thus understanding pain predictions, particularly during uncertainty, may allow us to ascertain measures indicative of intrinsic anticipation biases. Understanding such biases moves way to precision pain management, as it can guide the individualized treatment. To examine individual pain anticipation biases, we applied machine-learning-based neural decoding to functional magnetic resonance imaging (fMRI) data acquired during a pain-anticipation paradigm to identify individualized neural activation patterns differentiating two certain anticipatory conditions, which we then used to decode that individual’s uncertain anticipatory condition. We showed that neural patterns representative of the individualized response during certain anticipatory conditions were differentiable with high accuracy and, across individuals, most commonly involved neural activation patterns within anterior short gyrus of the insula and the nucleus accumbens. Using unsupervised clustering of individualized decodings of anticipatory responses during uncertain conditions, we identified three distinct response profiles representing subjects who, in uncertain situations, consistently anticipated high-pain (i.e., negative bias), subjects who consistently anticipated low-pain (i.e., positive bias), and subjects whose decoded anticipation responses were depended on the intensity of the preceding pain stimulus. The individualized decoded pain anticipation biases during uncertainty were independent of existence or type of diagnosed psychopathology, were stable over one year timespan and were related to underlying insula anatomy. Our results suggest that anticipation behaviors may be intrinsic, stable, and specific to each individual. Understanding individual differences in the neurobiology of pain anticipation has the potential to greatly improve the clinical pain management.
Genome-wide and enzymatic analysis reveals efficient D-galacturonic acid metabolism in the basidiomycete yeastRhodosporidium toruloides
Authors: Protzko Ryan J. , Hach Christina A. , Coradetti Samuel T. , Hackhofer Magdalena A. , Magosch Sonja , Thieme Nils , Arkin Adam P. , Skerker Jeffrey M. , Dueber John E. , Benz J. Philipp 
Published: June 2019

AbstractBiorefining of renewable feedstocks is one of the most promising routes to replace fossil-based products. Since many common fermentation hosts, such asSaccharomyces cerevisiae, are naturally unable to convert many component plant cell wall polysaccharides, the identification of organisms with broad catabolism capabilities represents an opportunity to expand the range of substrates used in fermentation biorefinery approaches. The red basidiomycete yeastRhodosporidium toruloidesis a promising and robust host for lipid and terpene derived chemicals. Previous studies demonstrated assimilation of a range of substrates, from C5/C6-sugars to aromatic molecules similar to lignin monomers. In the current study, we analyzedR. toruloidespotential to assimilate D-galacturonic acid, a major sugar in many pectin-rich agricultural waste streams, including sugar beet pulp and citrus peels. D-galacturonic acid is not a preferred substrate for many fungi, but its metabolism was found to be on par with D-glucose and D-xylose inR. toruloides. A genome-wide analysis by combined RNAseq/RB-TDNAseq revealed those genes with high relevance for fitness on D-galacturonic acid. WhileR. toruloideswas found to utilize the same non-phosphorylative catabolic pathway known from ascomycetes, the maximal velocities of several enzymes exceeded those previously reported. In addition, an efficient downstream glycerol catabolism and a novel transcription factor were found to be important for D-galacturonic acid utilization. These results set the basis for use ofR. toruloidesas a potential host for pectin-rich waste conversions and demonstrate its suitability as a model for metabolic studies in basidiomycetes.ImportanceThe switch from the traditional fossil-based industry to a green and sustainable bio-economy demands the complete utilization of renewable feedstocks. Many currently used bio-conversion hosts are unable to utilize major components of plant biomass, warranting the identification of microorganisms with broader catabolic capacity and characterization of their unique biochemical pathways. D-galacturonic acid is a plant component of bio-conversion interest and is the major backbone sugar of pectin, a plant cell wall polysaccharide abundant in soft and young plant tissues. The red basidiomycete and oleaginous yeastRhodosporidium toruloideshas been previously shown to utilize a range of sugars and aromatic molecules. Using state-of-the-art functional genomic methods, physiological and biochemical assays, we elucidated the molecular basis underlying the efficient metabolism of D-galacturonic acid. This study identifies an efficient pathway for uronic acid conversion to guide future engineering efforts, and represents the first detailed metabolic analysis of pectin metabolism in a basidiomycete fungus.
Stalk Bending Strength is Strongly Associated with Maize Stalk Lodging Incidence Across Multiple Environments
Authors: Sekhon Rajandeep S. , Joyner Chase N. , Ackerman Arlyn J. , McMahan Christopher S. , Cook Douglas D. , Robertson Daniel J. 
Published: June 2019

AbstractStalk lodging in maize results in substantial yield losses worldwide. These losses could be prevented through genetic improvement. However, breeding efforts and genetics studies are hindered by lack of a robust and economical phenotyping method for assessing stalk lodging resistance. A field-based phenotyping platform that induces failure patterns consistent with natural stalk lodging events and measures stalk bending strength in field-grown plants was recently developed. Here we examine the association between data gathered from this new phenotyping platform with counts of stalk lodging incidence on a select group of maize hybrids. For comparative purposes, we examine four additional predictive phenotypes commonly assumed to be related to stalk lodging resistance; namely, rind puncture resistance, cellulose, hemicellulose, and lignin. Historical counts of lodging incidence were gathered on 47 hybrids, grown in 98 distinct environments, spanning four years and 41 unique geographical locations in North America. Using Bayesian generalized linear mixed effects models, we show that stalk lodging incidence is associated with each of the five predictive phenotypes. Further, based on a joint analysis we demonstrate that, among the phenotypes considered, stalk bending strength measured by the new phenotyping platform is the most important predictive phenotype of naturally occurring stalk lodging incidence in maize, followed by rind puncture resistance and cellulose content. This study demonstrates that field-based measurements of stalk bending strength provide a reliable estimate of stalk lodging incidence. The stalk bending strength data acquired from the new phenotyping platform will be valuable for phenotypic selection in breeding programs and for generating mechanistic insights into the genetic regulation of stalk lodging resistance.
Defining the microenvironment landscape of bladder cancer using highly multiplexed spatial genomic and proteomic analysis
Authors: Reeves Jason W , Zhang Zhaojie , Norgaard Zachary K , Zhou Denise M , Gong JingJing , Liang Yan , Das Subhasree , Warren Sarah E , Korpal Manav , Hoang Margaret L , Beechem Joseph M , Kumar Pavan , Rimkunas Victoria 
Published: June 2019

AbstractMuscle-invasive bladder cancer (MIBC) is an aggressive disease with limited therapeutic options. PD-1 pathway targeting immunotherapies have been approved to treat advanced bladder cancer, but most patients exhibit primary resistance, suggesting that immune evasion mechanisms exist. The PPARγ pathway has been identified as a potential therapeutic target in MIBC that is associated with reduced CD8+ T-cell infiltration and increased resistance to immunotherapies. We comprehensively profiled the tumor microenvironment (TME) in formalin-fixed, paraffin-embedded (FFPE) tissues from a cohort of PPARγhigh (n=13) and PPRARγlow (n=12) MIBC, integrating bulk gene expression, targeted mutation sequencing, immunohistochemistry and multiplex spatial profiling of RNA and protein expression on the GeoMx™ Digital Spatial Profiling (DSP) platform. Molecular subtyping was consistent between traditional methods and GeoMx profiling, and, in this cohort, we observed little evidence of spatial heterogeneity in tumor subtyping. The previously characterized T-cell exclusion phenotype of PPARγhigh MIBC was recapitulated on the GeoMx platform and was further extended to show that this is a general phenomenon across immune cell types, supporting potential combination of PPARγ inhibition with ICIs. Furthermore, we found that while immune cells were excluded from PPARγhigh tumors, the stromal compartment from these tumors was not significantly different than those PPARγlow tumors. By preserving spatial relationships during the GeoMx analysis, we also identify a novel association between lower immune cell expression in the tumors and higher expression of β-catenin in the stroma, and differential expression of other WNT pathway members associated with PPARγ activity.One Sentence SummaryA new method for capturing tumor-immune signaling in FFPE tissues explores how the PPARG signaling axis is associated with immune cell exclusion in bladder cancer.
Visual mapping on the brain - caveats for multifocal fMRI
Authors: Crewther David P. , Seixas Shaun A. S. , Crewther Sheila G. 
Published: June 2019

AbstractWhile multifocal electroretinography has become a standard ophthalmological technique, its use in cortical neuroimaging has been lesser. Vanni et al. (2005) presented the first exploration of the multifocal visual mapping methodology with fMRI. This commentary confirms the utility of this method, but also presents empirical results which suggest caveats for the use of the technique. In the current study rapid multifocal fMRI was established using m-sequence pseudo-random binary stimuli applied to visual field mapping in six young adults with normal vision. Nine contiguous regions of visual field – two rings of 4 patches with a central patch, areas scaled for cortical magnification, were pseudo-randomly stimulated, with patterned or grey images. The decorrelation of stimulus patches allowed all 256 volumes to be used for the analysis of each of the nine stimulus areas. Strong localized activation was observed for each of the four peripheral regions with the location of the activation conforming to the expected visual field retinotopy. The inner regions, including the foveal patch, did not significantly activate. We propose, on the basis of a simple correlational model of simulated eye movements, that the loss of signal is due to gaze instability. Thus, while the rapid multifocal method can be successfully applied to fMRI, the results appear quite sensitive to eye movements, the effects of which may have been overlooked by smoothing evoked responses to achieve a retinotopic map.
TET1andTDGsuppress intestinal tumorigenesis by down-regulating the inflammatory and immune response pathways
Authors: Tricarico Rossella , Madzo Jozef , Scher Gabrielle , Maegawa Shinji , Jelinek Jaroslav , Scher Carly , Chang Wen-Chi , Nicolas Emmanuelle , Zhou Yan , Slifker Michael , Devarajan Karthik , Cai Kathy Q. , Nakajima Pamela , Xu Jinfei , Mancuso Pietro , Doneddu Valentina , Bagella Luigi , Ingram Justin , Balachandran Siddharth , Peshkova Iuliia , Koltsova Ekaterina , Grivennikov Sergei , Yen Timothy J. , Issa Jean-Pierre , Bellacosa Alfonso 
Published: June 2019

AbstractIntroductionAberrant DNA methylation is frequently observed in colorectal cancer (CRC), but the underlying mechanisms are poorly understood. Ten-Eleven Translocation (TET) dioxygenases and DNA repair enzyme Thymine DNA Glycosylase (TDG) are involved in active DNA demethylation by generating and removing, respectively, novel oxidized cytosine species. Mutations ofTET1andTDG, and alterations of the levels of oxidized cytosine species have been identified in human CRC cases, but the biological significance of the TET-TDG demethylation axis in intestinal tumorigenesis is unclear.Material and MethodsWe generatedApcMinmice with additional inactivation ofTet1and/orTdg, and characterized the methylome and transcriptome of intestinal adenomas by DREAM and RNA sequencing, respectively.ResultsTet1-and/orTdg-deficientApcMinmice show enhanced intestinal tumorigenesis in comparison to wild typeTet1andTdg ApcMinmice. Specifically,Tet1and/orTdg-deficientApcMinadenomas manifested increased size or features of erosion and stroma activation. Methylome analysis revealed progressive loss of global DNA hypomethylation in colonic adenomas fromTet1-andTdg-deficientApcMinmice, and hypermethylation of CpG islands inTet1-deficientApcMinmice. In addition, RNA sequencing showed upregulation of genes in inflammatory and immune response pathways inTet1-andTdg-mutant colonic adenomas compared to controlApcMinadenomas.ConclusionsTaken together, these findings demonstrate the important role of active DNA demethylation mediated by TET-TDG in reducing intestinal tumor formation, by modulating the epigenome and inflammatory/immune responses. This study highlights a novel mechanism of epigenetic deregulation during intestinal tumorigenesis with diagnostic, therapeutic and prognostic implications.
Sun exposure drives Antarctic cryptoendolithic community structure and composition
Authors: Coleine Claudia , Stajich Jason E. , Zucconi Laura , Onofri Silvano , Selbmann Laura 
Published: June 2019

AbstractThe harsh environmental conditions of the ice-free regions of Continental Antarctica are considered one of the closest Martian analogues on Earth. There, rocks play a pivotal role as substratum for life and endolithism represents a primary habitat for microorganisms when external environmental conditions become incompatible with active life on rock surfaces. Due to the thermal inertia of rock, the internal airspace of lithic substratum is where microbiota find a protected and buffered microenvironment, allowing life to spread throughout these regions with extreme temperatures and low water availability. The high degree of adaptation and specialization of the endolithic communities makes them highly resistant but scarsely resilient to any external perturbation and thus, any shifts in microbial community composition may serve as early-alarm systems of environmental perturbation, including climate change.Previous research concluded that altitude and distance from sea do not play as driving factors in shaping microbial abundance and diversity, while sun exposure was hypothesized as significant parameter influencing endolithic settlement and development. This study aims to explore our hypothesis that changes in sun exposure translate to shifts in community composition and abundances of main biological compartments (fungi, algae and bacteria) in the Antarctic cryptoendolithic communities. We performed a preliminary molecular survey, based on DGGE and qPCR tecniques, of 48 rocks with varying sun exposure, collected in Victoria Land along an altitudinal transect from 834 to 3100 m a.s.l.Our findings demonstrate that differences in sun radiation between north and south exposure influence temperature of rocks surface, availability of water and metabolic activity and also have significant impact on community composition and microbial abundance.
A New Mathematical Model for Tumor Growth, Reduction and Metastasis, Validation with Zebrafish Melanoma and Potential Implications for Dormancy and Recurrence
Authors: Lesi Adeyinka A. , Heilmann Silja , White Richard M. , Rumschitzki David S. 
Published: June 2019

AbstractThe genetic and environmental heterogeneity associated with tumors makes cancer treatment and recovery a difficult and unpredictable process. Patients with initially similar disease can experience vastly different outcomes including sustained recovery, refractory disease or, remarkably, recurrence years after treatment. Mathematical models informed with animal and human data provide tools for theoretical and clinical understanding of cancer progression and of the causes of highly variant disease outcomes. This work postulates a population balance model to describe how populations of a large ensemble of tumors of different sizes evolve in time. Each tumor can grow or reduce in size and metastasize. Gender-segregated, immune-competent and immune-suppressed translucent zebrafish (Casper variant) were inoculated with a transgenic melanoma cell line expressing human BRAF V600E and GFP and observed for tumor progression and metastasis. The model describes both these fish data sets, full histograms of population number vs size at multiple times for both immune states, and a human hepatocellular carcinoma data set also consisting of multiple time histograms very well with a minimum of cancer-specific parameters. The only zebrafish parameter to show strong gender-dependence was the host-dependent tumor reduction (immunity) parameter. This result significantly predicts that men should have far worse outcomes than females, yet similar metastasis rates, which are both indeed the case in human melanomas. Moreover the dynamic growth - reduction interplay, for certain relationships between these processes’ parameters in the model provides a potential mechanism for apparent cancer dormancy and recurrence. Although fish melanoma parameters are not in this range, the model guides future work to try to access it.
A Bayesian Zero-Inflated Binomial Regression and Its Application in Dose-Finding Study
Authors: Wanitjirattikal Puntipa , Shi Chenyang 
Published: June 2019

AbstractIn early phase clinical trial, finding maximum-tolerated dose (MTD) is a very important goal. Many researches show that finding a correct MTD can improve drug efficacy and safety significantly. Usually, dose-finding trials start from very low doses, so in many cases, more than 50% patients or cohorts do not have dose-limiting toxicity (DLT), but DLT may occur suddenly and increase fast along with just two or three doses. Although some fantastic models were built to find MTD, little consideration was given to those ‘0 DLTs’ and the ‘jump’ of DLTs. We developed a Bayesian zero-inflated binomial regression for dose-finding study based on Hall (2000), which analyses dose-finding data from two aspects: 1) observation of only zeros, 2) number of DLTs based on binomial distribution, so it can help us analyse if the cohorts without DLT have potential possibility to have DLT and fit the ‘jump’ of DLTs.