bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parallel and Scalable Precise Clustering for Homologous Protein Discovery
Authors: Byma Stuart , Dhasade Akash , Altenhoff Adrian , Dessimoz Christophe , Larus James R. 
Published: August 2019

AbstractThis paper presents a new, parallel implementation of clustering and demonstrates its utility in greatly speeding up the process of identifying homologous proteins. Clustering is a technique to reduce the number of comparison needed to find similar pairs in a set of n elements such as protein sequences. Precise clustering ensures that each pair of similar elements appears together in at least one cluster, so that similarities can be identified by all-to-all comparison in each cluster rather than on the full set. This paper introduces ClusterMerge, a new algorithm for precise clustering that uses transitive relationships among the elements to enable parallel and scalable implementations of this approach.We apply ClusterMerge to the important problem of finding similar amino acid sequences in a collection of proteins. ClusterMerge identifies 99.8% of similar pairs found by a full O (n2) comparison, with only half as many operations. More importantly, ClusterMerge is highly amenable to parallel and distributed computation. Our implementation achieves a speedup of 604 × on 768 cores (1400 × faster than a comparable single-threaded clustering implementation), a strong scaling efficiency of 90%, and a weak scaling efficiency of nearly 100%.
The challenges of estimating the distribution of flight heights from telemetry or altimetry data
Authors: Péron Guillaume , Calabrese Justin M. , Duriez Olivier , Fleming Christen H. , García-Jiménez Ruth , Johnston Alison , Lambertucci Sergio , Safi Kamran , Shepard Emily L.C. 
Published: September 2019

AbstractBackgroundGlobal positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their niche that we need to know to understand their ecology and conservation needs, and to manage our own use of the airspace. However, there are various sources of error in flight height data (“height” above ground, as opposed to “altitude” above a reference like the sea level): vertical error from the devices themselves, error in the ground elevation below the tracked animals, and error in the horizontal position of the animals and thus the predicted ground elevation below them.MethodsWe used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management.ResultsErrors of a magnitude of 20 meters appear in benign conditions (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative recorded flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences.ConclusionThe state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to raw flight height records, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.
Tudor-SN promotes early replication of dengue virus in the Aedes aegypti midgut
Authors: Hélène Merkling Sarah , Raquin Vincent , Dabo Stéphanie , Moltini-Conclois Isabelle , Frangeul Lionel , Varet Hugo , Saleh Maria-Carla , Lambrechts Louis 
Published: August 2019

AbstractDiseases caused by mosquito-borne viruses have been on the rise for the last decades, despite the implementation of vector control methods primarily based on insecticides. An alternative control method currently in development is the use of lab-engineered mosquitoes that are incapable to carry viruses. This has stimulated efforts to identify optimal target genes that are naturally involved in mosquito antiviral defenses or required for viral replication. Although several antiviral immune pathways such as RNA interference (RNAi) have been previously characterized in mosquitoes, the genes that prevent or promote early viral replication in the midgut remain elusive. Here, we investigated the role of a member of the Tudor protein family, Tudor-SN, upon dengue virus infection in the mosquito Aedes aegypti. Tudor-SN expression was upregulated early after dengue virus infection and was subsequently positively correlated with viral loads in the midgut. Using RNAi-mediated knockdown, we showed that the loss of Tudor-SN reduced dengue virus replication in the Ae. aegypti derived cell line Aag2 and in the midgut of Ae. aegypti females in vivo. Using immunofluorescence assays, we found that Tudor-SN localizes to the nucleolus in both Ae. aegypti and Aedes albopictus cells. Finally, we used a reporter assay to demonstrate that Tudor-SN was not required for RNAi function in vivo. Collectively, these results define a novel proviral role for Tudor-SN upon early dengue virus infection of the Ae. aegypti midgut.
qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data
Authors: Koçhan Necla , Tütüncü Gözde Y. , Smyth Gordon K. , Gandolfo Luke C. , Giner Göknur 
Published: August 2019

AbstractClassification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available.
Biological network topology features predict gene dependencies in cancer cell lines
Authors: Benstead-Hume Graeme , Wooller Sarah K. , Dias Samantha , Woodbine Lisa , Carr Anthony M. , Pearl Frances M. G. 
Published: September 2019

AbstractIn this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene essentiality data, human protein-protein interaction (PPI) network data and individual cell-line genomic alteration data we have built a range of machine learning classification models to predict cell line specific acquired essential genes. Genetic alterations found in each individual cell line were modelled by removing protein nodes to reflect loss of function mutations and changing the weights of edges in each PPI to reflect gain of function mutations and gene expression changes.We found that PPI networks can be used to successfully classify human cell line specific acquired essential genes within individual cell lines and between cell lines, even across tissue types with AUC ROC scores of between 0.75 and 0.85. Our novel perturbed PPI network models further improved prediction power compared to the base PPI model and are shown to be more sensitive to genes on which the cell becomes dependent as a result of other changes. These improvements offer opportunities for personalised therapy with each individual’s cancer cell dependencies presenting a potential tailored drug target.The overriding motivation for predicting cancer cell line specific acquired essential genes is to provide a low-cost approach to identifying personalised cancer drug targets without the cost of exhaustive loss of function screening.
Parallel Concerted Evolution of Ribosomal Protein Genes in Fungi and Its Adaptive Significance
Authors: Mullis Alison , Lu Zhaolian , Zhan Yu , Wang Tzi-Yuan , Rodriguez Judith , Rajeh Ahmad , Chatrath Ajay , Lin Zhenguo 
Published: August 2019

ABSTRACTRibosomal proteins (RPs) genes encode structure components of ribosomes, the cellular machinery for protein synthesis. A single functional copy has been maintained in most of 78-80 RP families in animals due to evolutionary constraints imposed by gene dosage balance. Some fungal species have maintained duplicate copies in most RP families. How the RP genes were duplicated and maintained in these fungal species, and their functional significance remains unresolved. To address these questions, we identified all RP genes from 295 fungi and inferred the timing and nature of gene duplication for all RP families. We found that massive duplications of RP genes have independently occurred by different mechanisms in three distantly related lineages. The RP duplicates in two of them, budding yeast and Mucoromycota, were mainly created by whole genome duplication (WGD) events. However, in fission yeasts, duplicate RP genes were likely generated by retroposition, which is unexpected considering their dosage sensitivity. The sequences of most RP paralogs in each species have been homogenized by repeated gene conversion, demonstrating parallel concerted evolution, which might have facilitated the retention of their duplicates. Transcriptomic data suggest that the duplication and retention of RP genes increased RP transcription abundance. Physiological data indicate that increased ribosome biogenesis allowed these organisms to rapidly consuming sugars through fermentation while maintaining high growth rates, providing selective advantages to these species in sugar-rich environments.
Computing 3D chromatin configurations from contact probability maps by Inverse Brownian Dynamics
Authors: Kumari K. , Duenweg B. , Padinhateeri R. , Prakash J. R. 
Published: August 2019

ABSTRACTThe three-dimensional organization of chromatin, on the length scale of a few genes, is crucial in determining the functional state — accessibility and the amount of gene expression — of the chromatin. Recent advances in chromosome conformation capture experiments provide partial information on the chromatin organization in a cell population, namely the contact count between any segment pairs. However, given the contact matrix, determining the complete 3D organization of the whole chromatin polymer is an inverse problem. In the present work, an Inverse Brownian Dynamics (IBD) method has been proposed to compute the optimal interaction strengths between different segments of chromatin such that the experimentally measured contact count probability constraints are satisfied. Applying this method to the α-globin gene locus in two different cell types, we predict the 3D organizations corresponding to active and repressed states of chromatin at the locus. We show that the average distance between any two segments of the region has a broad distribution and cannot be computed as a simple inverse relation based on the contact probability alone. We also address the normalization problem of the contact count matrix and argue that extra measurements of polymer properties such as radius of gyration may be required to resolve the problem.SIGNIFICANCEChromosome conformation capture experiments such as 5C and Hi-C provide information on the contact counts between different segments of chromatin, but not the interaction strengths that lead to these counts. Here a methodology is proposed by which this inverse problem can be solved, namely, given the contact probabilities between all segment pairs, what is the pair-wise interaction strength that leads to this value? With the knowledge of pair-wise interactions determined in this manner, it is then possible to evaluate the 3D organization of chromatin and to determine the true relationship between contact probabilities and spatial distances.
Predicted effects of severing enzymes on the length distribution and total mass of microtubules
Authors: Kuo Y.-W. , Trottier O. , Howard J. 
Published: September 2019

AbstractMicrotubules are dynamic cytoskeletal polymers whose growth and shrinkage are highly regulated as eukaryotic cells change shape, move and divide. One family of microtubule regulators includes the ATP-hydrolyzing enzymes spastin, katanin and fidgetin, which sever microtubule polymers into shorter fragments. Paradoxically, severases can increase microtubule number and mass in cells. Recent work with purified spastin and katanin accounts for this phenotype by showing that, in addition to severing, these enzymes modulate microtubule dynamics by accelerating the conversion of microtubules to the growing state and thereby promoting their regrowth. This leads to the observed exponential increase in microtubule mass. Spastin also influences the steady-state distribution of microtubule lengths, changing it from an exponential, as predicted by models of microtubule dynamic instability, to a peaked distribution. This effect of severing and regrowth by spastin on the microtubule length distribution has not been explained theoretically. To solve this problem, we formulated and solved a master equation for the time evolution of microtubule lengths in the presence of severing and microtubule dynamic instability. We then obtained numerical solutions to the steady-state length distribution and showed that the rate of severing and the speed of microtubule growth are the dominant parameters determining the steady-state length distribution. Furthermore, we found that the amplification rate is predicted to increase with severing, which is a new result. Our results establish a theoretical basis for how severing and dynamics together can serve to nucleate new microtubules, constituting a versatile mechanism to regulate microtubule length and mass.SignificanceThe numbers and lengths of microtubules are tightly regulated in cells. Severing enzymes fragment microtubules into shorter filaments and are important for cell division and tissue development. Previous work has shown that severing can lead to an increase in total microtubule number and mass, but the effect of severing on microtubule length is not understood quantitatively. Combining mathematical modeling and computational simulation, we solve the microtubule length distribution in the presence of severing enzymes and explore how severing activity and microtubule dynamics collectively control microtubule number and length. These results advance our understanding of the physical basis of severing as a regulatory mechanism shaping the cellular cytoskeletal network.
Boundary-Driven Oscillations Rescue PdsA-cells
Authors: Eckstein T. , Vidal-Henriquez E. , Gholami A. 
Published: August 2019

ABSTRACTDictyostelium discoideumamoeba aggregate if deprived of nutrients, producing cAMP waves at precisely timed intervals. Degradation of extracellular cAMP by the enzyme phosphodiesterase PdsA is fundamental to successfully producing waves, regulating the external cAMP gradient field and preventing the accumulation of cAMP. The knockout mutant PdsA-produces no or a greatly reduced amount of main extracellular phosphodiesterase, therefore failing to relay cAMP waves and aggregate under starvation conditions. Using a microfluidic channel, we show how an advective flow can partially recover signaling in a population of starving PdsA-cells. Above a minimum flow velocity, decaying waves are induced, with a decay length that increases with the imposed flow velocity. Interestingly, after stopping the advecting flow, the cells continue to signal, showing wave propagation and aggregation, although with a wave period much higher than in wild type cells. We performed extensive numerical simulations and showed that these waves have a boundary-driven origin, where the lack of cAMP in the upstream flow destabilizes the system. We explored the properties of these waves and the parameter region where they exist, with good agreement with our experimental observations. These boundary-driven waves dominate the system dynamics in the velocity range where they exist, while at higher flow velocities the natural wave period of 6 min recovers. These results provide experimental confirmation of the destabilizing effect of the upstream boundary in an otherwise stable reaction-diffusion system. We expect this mechanism to be relevant for wave creation in other oscillatory or excitable systems that are incapable of normal pattern formation.SIGNIFICANCE STATEMENTWe present experimental evidence for the existence of boundary-driven instabilities in a reaction-diffusion-advection system. In our theoretical prediction (1), we have shown that imposing an absorbing boundary condition on the upstream end of a flow-through channel filled with signaling cells creates an instability capable of periodically producing wave trains which are advected downstream. Under starvation, these cells secret the signaling molecule cAMP as well as the degrading agent phosphodiestrase that degrades cAMP. This instability was predicted to exist at lower degradation rates of cAMP and thus was expected to provide a mechanism for wave creation in phosphodiesterase deficient systems, such as PdsA-cells. Our experiments confirm the importance of the upstream boundary condition and show that boundary-driven oscillations are relevant in reaction-diffusion systems.
Manner of death and demographic effects on microbial community composition in organs of the human cadaver
Authors: Lutz Holly , Vangelatos Alexandria , Gottel Neil , Speed Emily , Osculati Antonio , Visona Silvia , Finley Sheree J. , Tuomisto Sari , Karhunen Pekka , Gilbert Jack A. , Javan Gulnaz T. 
Published: August 2019

AbstractThe microbiome serves important functions in human health, and postmortem, the microbial signatures of colonized organ tissue could be useful in helping to predict the manner of death in cases where this information is not known. We surveyed the microbiota (16S rRNA V4 amplicon sequencing) of 265 organ tissue samples including liver, blood, brain, heart, prostate, spleen and uterus from cadavers in Italy, Finland and the United States with confirmed manners of death comprising either accidental death, natural death, homicide, and suicide. Geographic locality (i.e. nationality) had a strong effect on observed microbial composition. Differing PERMANOVA results between unweighted and weighted UniFrac (nearly inverse results) suggest that specific bacteria may be associated with ethnicity and age, but that these differences are negligible when taking into account the relative abundance of bacterial taxa; weighted UniFrac measures suggest that although taxonomic composition may not vary significantly between different manners of death, PMI, or BMI categories, the relative abundance of specific taxa vary significantly. Various tissues exhibit differential associations with bacteria, and prostate and uterus were substantially different compared to other organs. For example, in Italian cadavers, the bacteria MLE1-12 permeated nearly all tissues, except the prostate and uterus. We identified specific bacterial ASVs as biomarkers of either natural or accidental death and suicide, but not for homicide. While the manner of death may have an impact on microbial associations, further investigation under more controlled conditions will be needed to validate whether these associations are predictive in forensic determinations.ImportanceThe utilization of microbial data in the context of forensic investigations holds great promise for the field of forensic science. Identification of taxa that are associated with postmortem interval (PMI), specific manners of death (MOD), or other traits such as age, sex, ethnicity, and nationality may allow investigators to refine the circumstantial details surrounding the death of an individual. In this study we find nationality (geographic location of cadaver) to be a dominant predictor of cadaver microbiome composition. We also identify a number of cadaver-specific traits to be associated with microbial alpha- and beta diversity, as well as bacterial taxa that are differentially associated with these traits.
SPINT2 inhibits proteases involved in activation of both influenza viruses and metapneumoviruses
Authors: Straus Marco R. , Kinder Jonathan T. , Segall Michal , Dutch Rebecca Ellis , Whittaker Gary R. 
Published: August 2019

AbstractViruses possessing class I fusion proteins require proteolytic activation by host cell proteases to mediate fusion with the host cell membrane. The mammalian SPINT2 gene encodes a protease inhibitor that targets trypsin-like serine proteases. Here we show the protease inhibitor, SPINT2, restricts cleavage-activation efficiently for a range of influenza viruses and for human metapneumovirus (HMPV). SPINT2 treatment resulted in the cleavage and fusion inhibition of full-length influenza A/CA/04/09 (H1N1) HA, A/Aichi/68 (H3N2) HA, A/Shanghai/2/2013 (H7N9) HA and HMPV F when activated by trypsin, recombinant matriptase or KLK5. We also demonstrate that SPINT2 was able to reduce viral growth of influenza A/CA/04/09 H1N1 and A/X31 H3N2 in cell culture by inhibiting matriptase or TMPRSS2. Moreover, inhibition efficacy did not differ whether SPINT2 was added at the time of infection or 24 hours post-infection. Our data suggest that the SPINT2 inhibitor has a strong potential to serve as a novel broad-spectrum antiviral.
High throughput, high fidelity genotyping andde novodiscovery of allelic variants at the self-incompatibility locus in natural populations of Brassicaceae from short read sequencing data
Authors: Genete Mathieu , Castric Vincent , Vekemans Xavier 
Published: August 2019

AbstractPlant self-incompatibility (SI) is a genetic system that prevents selfing and enforces outcrossing. Because of strong balancing selection, the genes encoding SI are predicted to maintain extraordinary high levels of polymorphism, both in terms of the number of S-alleles that segregate in SI species and in terms of nucleotide sequence divergence among distinct S-allelic lines. However, because of these two combined features, documenting polymorphism of these genes also presents important methodological challenges that have so far largely prevented the comprehensive analysis of complete allelic series in natural populations, and also precluded the obtention of complete genic sequences for many S-alleles. Here, we present a novel methodological approach based on a computationally optimized comparison of short Illumina sequencing reads from genomic DNA to a database of known nucleotide sequences of the extracellular domain ofSRK (eSRK). By examining mapping patterns along the reference sequences, we obtain highly reliable predictions of S-genotypes from individuals collected in natural populations ofArabidopsis halleri. Furthermore, using ade novoassembly approach of the filtered short reads, we obtain full length sequences of eSRK even when the initial sequence in the database was only partial, and we discover newSRKalleles that were not initially present in the database. When including those new alleles in the reference database, we were able to resolve the complete diploid SI genotypes of all individuals. Beyond the specific case of Brassicaceae S-alleles, our approach can be readily applied to other polymorphic loci, given reference allelic sequences are available.