bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evidence of independent acquisition and adaption of ultra-small bacteria to human hosts across the highly diverse yet reduced genomes of the phylum Saccharibacteria
Authors: McLean Jeffrey S. , Bor Batbileg , To Thao T. , Liu Quanhui , Kerns Kristopher A. , Solden Lindsey , Wrighton Kelly , He Xuesong , Shi Wenyuan 
Published: February 2018

ABSTRACTRecently, we discovered that a member of the Saccharibacteria/TM7 phylum (strain TM7x) isolated from the human oral cavity, has an ultra-small cell size (200-300nm), a highly reduced genome (705 Kbp) with limited de novo biosynthetic capabilities, and a very novel lifestyle as an obligate epibiont on the surface of another bacterium 1. There has been considerable interest in uncultivated phyla, particularly those that are now classified as the proposed candidate phyla radiation (CPR) reported to include 35 or more phyla and are estimated to make up nearly 15% of the domain Bacteria. Most members of the larger CPR group share genomic properties with Saccharibacteria including reduced genomes (<1Mbp) and lack of biosynthetic capabilities, yet to date, strain TM7x represents the only member of the CPR that has been cultivated and is one of only three CPR routinely detected in the human body. Through small subunit ribosomal RNA (SSU rRNA) gene surveys, members of the Saccharibacteria phylum are reported in many environments as well as within a diversity of host species and have been shown to increase dramatically in human oral and gut diseases. With a single copy of the 16S rRNA gene resolved on a few limited genomes, their absolute abundance is most often underestimated and their potential role in disease pathogenesis is therefore underappreciated. Despite being an obligate parasite dependent on other bacteria, six groups (G1-G6) are recognized using SSU rRNA gene phylogeny in the oral cavity alone. At present, only genomes from the G1 group, which includes related and remarkably syntenic environmental and human oral associated representatives1, have been uncovered to date. In this study we systematically captured the spectrum of known diversity in this phylum by reconstructing completely novel Class level genomes belonging to groups G3, G6 and G5 through cultivation enrichment and/or metagenomic binning from humans and mammalian rumen. Additional genomes for representatives of G1 were also obtained from modern oral plaque and ancient dental calculus. Comparative analysis revealed remarkable divergence in the host-associated members across this phylum. Within the human oral cavity alone, variation in as much as 70% of the genes from nearest oral clade (AAI 50%) as well as wide GC content variation is evident in these newly captured divergent members (G3, G5 and G6) with no environmental relatives. Comparative analyses suggest independent episodes of transmission of these TM7 groups into humans and convergent evolution of several key functions during adaptation within hosts. In addition, we provide evidence from in vivo collected samples that each of these major groups are ultra-small in size and are found attached to larger cells.
Single cell network analysis with a mixture of Nested Effects Models
Authors: Pirkl Martin , Beerenwinkel Niko 
Published: February 2018

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/.Contactmartin.pirkl@bsse.ethz.ch, niko.beerenwinkel@bsse.ethz.chSupplementary informationSupplementary data are available.online.
BDQC: a general-purpose analytics tool for domain-blind validation of Big Data
Authors: Deutsch Eric W. , Kramer Roger , Ames Joseph , Bauman Andrew , Campbell David S. , Chard Kyle , Clark Kristi , D’Arcy Mike , Dinov Ivo D. , Donovan Rory , Foster Ian , Heavner Benjamin D. , Hood Leroy E. , Kesselman Carl , Madduri Ravi , Mi Huaiyu , Muruganujan Anushya , Pa Judy , Price Nathan D. , Robinson Max , Sepehrband Farshid , Toga Arthur W. , Van Horn John , Zhao Lu , Glusman Gustavo 
Published: February 2018

AbstractTranslational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected “ signal” in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.
Activity of Antimicrobial Peptides Decreases with Increased Cell Membrane Crossing Free Energy Cost
Authors: Zou Rongfeng , Zhu Xiaomin , Tu Yaoquan , Wu Junchen , Landry Markita P. 
Published: February 2018

ABSTRACTAntimicrobial peptides (AMPs) are a promising alternative to mitigating bacterial infections in light of increasing bacterial resistance to antibiotics. However, predicting, understanding, and controlling the antibacterial activity of AMPs remains a significant challenge. While peptide intramolecular interactions are known to modulate AMP antimi-crobial activity, peptide intermolecular interactions remain elusive in their impact on peptide bioactivity. Herein, we test the relationship between AMP intermolecular interactions and antibacterial efficacy by controlling AMP intermolecular hydrophobic and hydrogen bonding interactions. Molecular dynamics simulations and Gibbs free energy calculations in concert with experimental assays show that increasing intermolecular interactions via inter-peptide aggregation increases the energy cost for the peptide to cross the bacterial cell membrane, which in turn decreases the AMP antibacterial activity. Our findings provide a route for predicting and controlling the antibacterial activity of AMPs against Gramnegative bacteria via reductions of intermolecular AMP interactions.
Early candidate urine biomarkers for detecting Alzheimer’s disease before beta amyloid plaque deposition in an APP (swe)/PSEN1dE9transgenic mouse model
Authors: Zhang Fanshuang , Wei Jing , Li Xundou , Ma Chao , Gao Youhe 
Published: February 2018

AbstractAlzheimer’s disease (AD) is an incurable age-associated neurodegenerative disorder that is characterized by irreversible progressive cognitive deficits and extensive brain damage. The identification of candidate biomarkers before beta amyloid plaque deposition occurs is therefore of great importance for the early intervention of AD. Urine, which is not regulated by homeostatic mechanisms, theoretically accumulates changes associated with AD earlier than cerebrospinal fluid and blood. In this study, an APP (swe)/PSEN1dE9 transgenic mouse model was used to identify candidate biomarkers for early AD. Urine samples were collected from 4-, 6-, and 8-month-old transgenic mouse models, and the urinary proteomes were profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The levels of 33 proteins differed significantly between wild-type and 4-month-old mice, which had not started to deposit beta amyloid plaque. Among these proteins, 16 have been associated with the mechanisms of AD, while 9 have been suggested as AD biomarkers. Our results indicated that urine proteins enable detecting AD before beta amyloid plaque deposition, which may present an opportunity for intervention.
Sent to the Corner: xylem vessel anatomy not surface area determines megaphyll hydraulics inCecropia obtusaTrécul (Urticaceae)
Authors: Levionnois Sébastien , Coste Sabrina , Nicolini Eric , Stahl Clément , Morel Hélène , Heuret Patrick 
Published: February 2018

AbstractCorners rule predicts a positive correlation between leaf dimensions and the cross-sectional area of the primary stem. Although this relationship is usually explained by hydraulic and mechanical requirements, these hypotheses have never been tested empirically. However, Corner’s rule is tricky to investigate since rapid secondary growth of the stem prevents a rigorous link being established between a given leaf and the supporting stem. We chose a twig-like leaf model since petiole anatomy is only linked to the attached lamina. We tested the hypothesis that anatomical adjustments to hydraulic requirements related to vessel size enable reduced investment in tissue in the framework of Corner’s rule. We conducted a functional, mechanistic and intraspecific investigation ofCecropia obtusaTrécul, a Neotropical pioneer tree, by integrating morphological, anatomical and theoretical hydraulic traits around the lamina-petiole size relationship. The twig-like structure of the leaf and the strong lamina-petiole correlation of this model tree species made it possible to use the leaf-level model for a rigorous investigation of the functional implications of Corner’s rule. We found a positive correlation between petiole size, lamina size, the ratio of mean vessel area to vessel frequency in the petiole xylem and theoretical specific conductivity in the petiole xylem. Hydraulic function supports Corner’s rule to a lesser extent than previously thought. Variations in vessel dimensions mainly drive xylem hydraulic performances and avoid disproportionate petiole cross-sections to answer to hydraulic requirements associated with lamina size.
Genomes of trombidid mites reveal novel predicted allergens and laterally-transferred genes associated with secondary metabolism
Authors: Dong Xiaofeng , Chaisiri Kittipong , Xia Dong , Armstrong Stuart D. , Fang Yongxiang , Donnelly Martin J. , Kadowaki Tatsuhiko , McGarry John W. , Darby Alistair C. , Makepeace Benjamin L. 
Published: February 2018

AbstractBackgroundTrombidid mites have a unique lifecycle in which only the larval stage is ectoparasitic. In the superfamily Trombiculoidea (“chiggers”), the larvae feed preferentially on vertebrates, including humans. Species in the genusLeptotrombidiumare vectors of a potentially fatal bacterial infection, scrub typhus, which affects 1 million people annually. Moreover, chiggers can cause pruritic dermatitis (trombiculiasis) in humans and domesticated animals. In the Trombidioidea (velvet mites), the larvae feed on other arthropods and are potential biological control agents for agricultural pests. Here, we present the first trombidid mites genomes, obtained both for a chigger,Leptotrombidium deliense, and for a velvet mite,Dinothrombium tinctorium.ResultsSequencing was performed using Illumina technology. A 180 Mb draft assembly forD. tinctoriumwas generated from two paired-end and one mate-pair library using a single adult specimen. ForL. deliense, a lower-coverage draft assembly (117 Mb) was obtained using pooled, engorged larvae with a single paired-end library. Remarkably, both genomes exhibited evidence of ancient lateral gene transfer from soil-derived bacteria or fungi. The transferred genes confer functions that are rare in animals, including terpene and carotenoid synthesis. Thirty-seven allergenic protein families were predicted in theL. deliensegenome, of which nine were unique. Preliminary proteomic analyses identified several of these putative allergens in larvae.ConclusionsTrombidid mite genomes appear to be more dynamic than those of other acariform mites. A priority for future research is to determine the biological function of terpene synthesis in this taxon and its potential for exploitation in disease control.
Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126
Authors: Mesnage Robin , Biserni Martina , Balu Sucharitha , Frainay Clément , Poupin Nathalie , Jourdan Fabien , Wozniak Eva , Xenakis Theodoros , Mein Charles A , Antoniou Michael N 
Published: February 2018

AbstractChemical pollutant exposure is a risk factor contributing to the growing epidemic of nonalcoholic fatty liver disease (NAFLD) affecting human populations that consume a Western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here we describe the alterations in gene expression profiles and metabolite levels in the human hepatocyte HepaRG cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments.
INKA, an integrative data analysis pipeline for phosphoproteomic inference of active phosphokinases
Authors: Pham Thang V. , Beekhof Robin , van Alphen Carolien , Knol Jaco C. , Henneman Alex A. , Rolfs Frank , Labots Mariette , Henneberry Evan , Le Large Tessa Y.S. , de Haas Richard R. , Piersma Sander R. , Verheul Henk M.W. , Jimenez Connie R. 
Published: February 2018

AbstractIdentifying (hyper)active kinases in cancer patient tumors is crucial to enable individualized treatment with specific inhibitors. Conceptually, kinase activity can be gleaned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific kinases to identify (hyper)active kinases that may fuel growth/progression of individual tumors. Approaches have hitherto focused on phosphorylation of either kinases or their substrates. Here, we combine kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. INKA utilizes label-free quantification of phosphopeptides derived from kinases, kinase activation loops, kinase substrates deduced from prior experimental knowledge, and kinase substrates predicted from sequence motifs, yielding a single score. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate relation networks in a biological sample. As a proof of concept, INKA scoring of phosphoproteomic data for different oncogene-driven cancer cell lines inferred top activity of implicated driver kinases, and relevant quantitative changes upon perturbation. These analyses show the ability of INKA scoring to identify (hyper)active kinases, with potential clinical significance.
Xist Intron 1 Repression by TALE Transcriptional Factor Improves Somatic Cell Reprogamming in Mice
Authors: Zhang Jindun , Gao Xuefei , Yang Jian , Fan Xiaoying , Wang Wei , Liang Yanfeng , Fan Lihong , Han Hongmei , Xu Xiaorong , Tang Fuchou , Bao Siqin , Liu Pentao , Li Xihe 
Published: February 2018

SummaryXist is the master regulator of X chromosome inactivation (XCI). In order to further understand the Xist locus in reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) and in somatic cell nuclear transfer (SCNT), we tested transcription-factor-like effectors (TALE)-based designer transcriptional factors (dTFs), which were specific to numerous regions at the Xist locus. We report that the selected dTF repressor 6 (R6) binding the intron 1 of Xist, which did not affect Xist expression in mouse embryonic fibroblasts (MEFs), substantially improved the iPSC generation and the SCNT preimplantation embryo development. Conversely, the dTF activator targeting the same genomic region of R6 decreased iPSC formation, and blocked SCNT-embryo development. These results thus uncover the critical requirement for the Xist locus in epigenetic resetting, which is not directly related to Xist transcription. This may provide a unique route to improving the reprogramming.
Doxorubicin induces caspase-mediated proteolysis of KV7.1
Authors: Strigli Anne , Raab Christian , Hessler Sabine , Huth Tobias , Schuldt Adam J. T. , Alzheimer Christian , Friedrich Thomas , Burridge Paul W. , Luedde Mark , Schwake Michael 
Published: February 2018

AbstractThe voltage-gated potassium channel Kv7.1 (KCNQ1) co-assembles with KCNE1 to generate the cardiac potassium current IKs. Gain- and loss-of-function mutations in KCNQ1 are associated with atrial fibrillation and long-QT (LQT) syndrome, respectively, highlighting the importance of modulating IKS activity for proper cardiac function. On a post-translational level, IKS can be regulated by phosphorylation, ubiquitination and sumoylation. Here, we report proteolysis of Kv7.1 as a novel, irreversible posttranslational modification. The identification of two C-terminal fragments (CTF1 and CTF2) of Kv7.1 led us to identify an aspartate critical for the generation of CTF2 and caspases as responsible for mediating Kv7.1 proteolysis. Activating caspases by apoptotic stimuli significantly reduced Kv7.1/KCNE1 currents, which was abrogated in cells expressing caspase-resistant Kv7.1 D459A/KCNE1 channels. An increase in cleavage of Kv7.1 could be detected in the case of LQT mutation G460S, which is located adjacent to the cleavage site. Application of apoptotic stimuli or doxorubicin-induced cardiotoxicity provoked caspase-mediated cleavage of endogenous Kv7.1 in human cardiomyocytes. In summary, our findings establish caspases as novel regulatory components for modulating Kv7.1 activity which may have important implications for the molecular mechanism of doxorubicin-induced cardiotoxicity.Non-standard Abbreviations and AcronymsCamcalmodulinEBCequilibrium buffer contentLQT syndromelong QT syndromeNRVMNeonatal rat ventricular cardiomyocyteshiPSC-CMshuman induced pluripotent stem cell-derived cardiomyocytes
Predicting CTCF-mediated chromatin loops using CTCF-MP
Authors: Zhang Ruochi , Wang Yuchuan , Yang Yang , Zhang Yang , Ma Jian 
Published: February 2018

AbstractThe three dimensional organization of chromosomes within the cell nucleus is highly regulated. It is known that CTCF is an important architectural protein to mediate long-range chromatin loops. Recent studies have shown that the majority of CTCF binding motif pairs at chromatin loop anchor regions are in convergent orientation. However, it remains unknown whether the genomic context at the sequence level can determine if a convergent CTCF motif pair is able to form chromatin loop. In this paper, we directly ask whether and what sequence-based features (other than the motif itself) may be important to establish CTCF-mediated chromatin loops. We found that motif conservation measured by “branch-of-origin” that accounts for motif turn-over in evolution is an important feature. We developed a new machine learning algorithm called CTCF-MP based on word2vec to demonstrate that sequence-based features alone have the capability to predict if a pair of convergent CTCF motifs would form a loop. Together with functional genomic signals from CTCF ChIP-seq and DNase-seq, CTCF-MP is able to make highly accurate predictions on whether a convergent CTCF motif pair would form a loop in a single cell type and also across different cell types. Our work represents an important step further to understand the sequence determinants that may guide the formation of complex chromatin architectures.