bluuebunny/crossref_metadata_2025_split huggingface dataset:
Industrialization is associated with elevated rates of horizontal gene transfer in the human microbiome
Authors: Groussin Mathieu , Poyet Mathilde , Sistiaga Ainara , Kearney Sean M. , Moniz Katya , Noel Mary , Hooker Jeff , Gibbons Sean M. , Segurel Laure , Froment Alain , Said Mohamed Rihlat , Fezeu Alain , Juimo Vanessa A. , Girard Catherine , Nguyen Le Thanh Tu , Shapiro B. Jesse , Lehtimäki Jenni M. S. , Ruokolainen Lasse , Kettunen Pinja P. , Vatanen Tommi , Sigwazi Shani , Mabulla Audax , Domínguez-Rodrigo Manuel , Summons Roger E. , Xavier Ramnik J. , Alm Eric J. 
Published: January 2020

AbstractHorizontal Gene Transfer, the process by which bacteria acquire new genes and functions from non-parental sources, is common in the human microbiome1,2. If the timescale of HGT is rapid compared to the timescale of human colonization, then it could have the effect of ‘personalizing’ bacterial genomes by providing incoming strains with the genes necessary to adapt to the diet or lifestyle of a new host. The extent to which HGT occurs on the timescale of human colonization, however, remains unclear. Here, we analyzed 6,188 newly isolated and sequenced gut bacteria from 34 individuals in 9 human populations, and show that HGT is more common among bacteria isolated from the same human host, indicating that the timescale of transfer is short compared to the timescale of human colonization. Comparing across 9 human populations reveals that high rates of transfer may be a recent development in human history linked to industrialization and urbanization. In addition, we find that the genes involved in transfer reflect the lifestyle of the human hosts, with elevated transfer of carbohydrate metabolism genes in hunter gatherer populations, and transfer of antibiotic resistance genes among pastoralists who live in close contact with livestock. These results suggest that host-associated bacterial genomes are not static within individuals, but continuously acquire new functionality based on host diet and lifestyle.
Polarization of microbial communities between competitive and cooperative metabolism
Authors: Machado Daniel , Maistrenko Oleksandr M. , Andrejev Sergej , Kim Yongkyu , Bork Peer , Patil Kaustubh R. , Patil Kiran R. 
Published: January 2020

AbstractResource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet, the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modeling to assess resource competition and metabolic cooperation potential in large co-occurring groups, with up to 40 member species, across thousands of habitats. Our analysis revealed two distinct community types, clustering at opposite ends in a trade-off landscape between competition and cooperation. On one end lie highly cooperative communities, characterized by smaller genomes and multiple auxotrophies, reminiscent of the black queen hypothesis. At the other end lie highly competitive communities, conforming to the red queen hypothesis, featuring larger genomes and overlapping nutritional requirements. While the latter are mainly present in soils, the former are found both in free-living and host-associated habitats. Community-scale flux simulations showed that, while the competitive communities can better resist species invasion but not nutrient shift, the cooperative communities are susceptible to species invasion but resilient to nutrient change. In accord, we show, through analyzing an additional independent dataset, the colonization of the human gut by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our analysis highlights the bifurcation between competition and cooperation in the assembly of natural communities and its implications for community modulation.
Parametrising diffusion-taxis equations from animal movement trajectories using step selection analysis
Authors: Potts Jonathan R. , Schlägel Ulrike E. 
Published: January 2020

AbstractMathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data.To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrise a class of PDEs, calleddiffusion-taxismodels, from an animal’s trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion-taxis models in two ways. First, we derive the steady-state utilisation distribution in a closed analytic form. Second, we give a simple recipe for deriving spatial pattern formation properties that emerge from inferred movement-and-interaction processes: specifically, do those processes lead to heterogeneous spatial distributions and if so, do these distributions oscillate in perpetuity or eventually stabilise? The second question is demonstrated by application to data on concurrently-tracked bank voles (Myodes glareolus).Our results show that SSA can accurately parametrise diffusion-taxis equations from location data, providing the frequency of the data is not too low. We show that the steady-state distribution of our diffusion-taxis model, where it exists, has an identical functional form to the utilisation distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA-PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA.Our methods give a user-friendly way in to the world of PDEs, via a well-used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non-speculative link between observed movement behaviours and space use patterns on larger spatio-temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions.
Microbiome structure of ecologically important bioeroding sponges (family Clionaidae): The role of host phylogeny and environmental plasticity
Authors: Sacristán-Soriano Oriol , Turon Xavier , Hill Malcolm 
Published: January 2020

AbstractThe potential of increased bioerosion by excavating sponges in future environmental scenarios represents a potential threat to coral reef structure and function. If we are to predict changes to coral reef habitats, it is important to understand the biology of these sponges. Little is known about prokaryotic associations in excavating sponges despite the fact that evidence indicates they contribute to the sponge growth through their heterotrophic metabolism and may even act as microborers. Here, we provide the first detailed description of the microbial community of multiple bioeroding sponges from the Clionaidae family (Cliona varians,C. tumula,C. delitrix,Spheciospongia vesparium,Cervicornia cuspidifera) collected in inshore and offshore coral reefs in the Florida Keys. A total of 6,811 prokaryote OTUs identified using 16S rRNA gene sequencing was detected in the samples studied, including ambient water, belonging to 39 bacterial phyla and 3 archaeal phyla. The microbiomes of species harboringSymbiodinium(Cliona varians,C. tumula,Cervicornia cuspidifera) and the azooxanthellateS. vespariumwere dominated by Alphaproteobacteria that represented from 83 to 96% of total sequences. These clionaid sponges presented species-specific core microbiomes, with 4 OTUs being shared by all sponge samples, albeit with species-specific enrichments. The microbiomes ofC. variansandS. vespariumwere stable but showed certain plasticity between offshore and inshore reefs. The distantly relatedCliona delitrixdoes not harborSymbiodinium,and had a microbiome dominated by Gammaproteobacteria, which represented 82% of all sequences. Most of the sponge-exclusive OTUs are found in low abundance and belong to the “rare biosphere” category, highlighting the potential importance of these microbes in the ecology of the holobiont. Sponge microbiomes may enhance functional redundancy for the sponge holobiont and allow it to respond to shifting environments over much short time scales than evolutionary change would permit. This work establishes the basis for future research to explore how microbial shifts in bioeroding sponges contribute to bioerosion in the face of a changing environment.
A single-cell transcriptomic and anatomic atlas of mouse dorsal raphePet1neurons
Authors: Okaty Benjamin W. , Sturrock Nikita , Lozoya Yasmin Escobedo , Chang YoonJeung , Senft Rebecca A. , Lyon Krissy A. , Alekseyenko Olga V. , Dymecki Susan M. 
Published: January 2020

AbstractAmong the brainstem raphe nuclei, the dorsal raphe nucleus (DR) contains the greatest number ofPet1-lineage neurons, a predominantly serotonergic group distributed throughout DR subdomains. These neurons collectively regulate diverse physiology and behavior and are often therapeutically targeted to treat affective disorders. CharacterizingPet1neuron molecular heterogeneity and relating it to anatomy is vital for understanding DR functional organization, with potential to inform therapeutic separability. Here we use high-throughput and DR subdomain-targeted single-cell transcriptomics and intersectional genetic tools to map molecular and anatomical diversity of DR-Pet1neurons. We describe up to fourteen neuron subtypes, many showing biased cell body distributions across the DR. We further show thatP2ry1-Pet1DR neurons – the most molecularly distinct subtype – possess unique efferent projections and electrophysiological properties. These data complement and extend previous DR characterizations, combining intersectional genetics with multiple transcriptomic modalities to achieve fine-scale molecular and anatomic identification ofPet1neuron subtypes.
FMR1loss results in early changes to intrinsic membrane excitability in human cellular models
Authors: Susco Sara G. , Arias-Garcia Mario A. , Lopez-Huerta Violeta G. , Beccard Amanda , Bara Anne M. , Moffitt Jessica , Korn Justin , Fu Zhanyan , Barrett Lindy E. 
Published: January 2020

AbstractFragile X mental retardation 1 (FMR1)encodes the RNA binding protein FMRP. Loss of FMRP drives Fragile X syndrome (FXS), the leading inherited cause of intellectual disability and a leading monogenic cause of autism. Cortical hyperexcitability is a hallmark of FXS, however, the underlying mechanisms reported, including alterations in synaptic transmission and ion channel expression and properties, are heterogeneous and at times contradictory. Here, we generated isogenicFMR1y/+andFMR1y/-human pluripotent stem cell (hPSC) lines using CRISPR-Cas9, differentiated these stem cell tools into excitatory cortical neurons and systematically assessed the impact of FMRP loss on intrinsic membrane and synaptic properties over the course ofin vitrodifferentiation. Using whole-cell patch clamp analyses at five separate time-points, we observed significant changes in multiple metrics following FMRP loss, including decreased membrane resistance, increased capacitance, decreased action potential half-width and higher maximum frequency, consistent withFMR1y/-neurons overall showing an increased intrinsic membrane excitability compared with age-matchedFMR1y/+controls. Surprisingly, a majority of these changes emerged early duringin vitrodifferentiation and some were not stable over time. Although we detected significant differences in intrinsic properties, no discernable alterations were observed in synaptic transmission. Collectively, this study provides a new isogenic hPSC model to study the mechanisms ofFMR1gene function, identifies electrophysiological impacts of FMRP loss on human excitatory cortical neurons over timein vitro, and underscores that early developmental changes to intrinsic membrane properties may be a critical cellular pathology contributing to cortical hyperexcitability in FXS.
Protein evolution is structure dependent and non-homogeneous across the tree of life
Authors: Pandey Akanksha , Braun Edward L. 
Published: January 2020

AbstractMotivationProtein sequence evolution is a complex process that varies among-sites within proteins and across the tree of life. Comparisons of evolutionary rate matrices for specific taxa (‘clade-specific models’) have the potential to reveal this variation and provide information about the underlying reasons for those changes. To study changes in patterns of protein sequence evolution we estimated and compared clade-specific models in a way that acknowledged variation within proteins due to structure.ResultsClade-specific model fit was able to correctly classify proteins from four specific groups (vertebrates, plants, oomycetes, and yeasts) more than 70% of the time. This was true whether we used mixture models that incorporate relative solvent accessibility or simple models that treat sites as homogeneous. Thus, protein evolution is non-homogeneous over the tree of life. However, a small number of dimensions could explain the differences among models (for mixture models ~50% of the variance reflected relative solvent accessibility and ~25% reflected clade). Relaxed purifying selection in taxa with lower long-term effective population sizes appears to explain much of the among clade variance. Relaxed selection on solvent-exposed sites was correlated with changes in amino acid side-chain volume; other differences among models were more complex. Beyond the information they reveal about protein evolution, our clade-specific models also represent tools for phylogenomic inference.AvailabilityModel files are available fromhttps://github.com/ebraun68/clade_specific_prot_models.Contactebraun68@ufl.eduSupplementary informationSupplementary data are appended to this preprint.
Modifiers and Mediators of Craniosynostosis Severity Revealed by Differential Gene Expression
Authors: Dudakovic Amel , Nam Hwa Kyung , vanWijnen Andre , Hatch Nan E. 
Published: January 2020

AbstractSeverity of craniosynostosis in humans varies widely even in patients with identical genetic mutations. In this study we compared RNA sequencing data from cranial tissues of a severe form of Crouzon craniosynostosis syndrome (C57BL/6 FGFR2C342Y/+mice) with those of a less severe form of Crouzon craniosynostosis (BALB/c FGFR2C342Y/+mice) to identify genetic modifiers that influence craniosynostosis phenotype severity. Comparison of the mice revealed neonatal onset of coronal suture fusion in the form of suture obliteration in C57BL/6 mice (88% incidence, p<.001 between genotypes). Coronal suture fusion in the form of point fusions across the suture occurred at approximately 4 weeks after birth, with less severe skull shape abnormalities, in BALB/c mice. Substantially fewer genes were differentially expressed in BALB/c FGFR2+/+vs. FGFR2C342Y/+mice (87 out of 15,893 expressed genes) than C57BL/6 FGFR2C+/+vs. FGFR2C342Y/+mice (2,043 out of 19,097 expressed genes). Further investigation revealed differential expression of coronal suture fusion associated genes, eph/ephrin boundary genes, cell proliferation genes, osteoblast differentiation genes and epigenetic regulators, among others. The most striking pattern in the data was the minimal change in gene expression seen for most genes in BALB/c FGFR2+/+vs. FGFR2C342Y/+mice. Analysis of protein processing and lysosomal components support the hypothesis that the craniosynostosis phenotype is less severe in BALB/c mice because the mutant FGFR2C342Yprotein is not expressed to the same extent as that seen in C57BL/6 mice. Together, these results suggest that a strategy aimed at increasing degradation of the mutant receptor or downstream signaling inhibition could lead to diminished phenotype severity.
The Landscape of Receptor-Mediated Precision Cancer Combination Therapy: A Single-Cell Perspective
Authors: Ahmadi Saba , Sukprasert Pattara , Vegesna Rahulsimham , Sinha Sanju , Schischlik Fiorella , Artzi Natalie , Khuller Samir , Schäffer Alejandro A. , Ruppin Eytan 
Published: January 2020

AbstractThe availability of single-cell transcriptomics data opens new opportunities for rational design of combination cancer treatments. Mining such data, we employed combinatorial optimization techniques to explore the landscape of optimal combination therapies in solid tumors including brain, head and neck, melanoma, lung, breast and colon cancers. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. As a baseline case, we studied the killing of at least 80% of the tumor cells while sparing more than 90% of the non-tumor cells in each patient, as a putative regimen. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient. However, the number of distinct targets that one would need to assemble to treat all patients in a cohort accordingly would be around 10 in most cases. Further requiring that the target genes be also lowly expressed in healthy tissues uncovers qualitatively similar trends. However, as one asks for more stringent and selective killing beyond the baseline regimen we focused on, we find that the number of targets needed rises rapidly. Emerging individual promising receptor targets includePTPRZ1, which is frequently found in the optimal combinations for brain and head and neck cancers, andEGFR, a recurring target in multiple tumor types. In sum, this systematic single-cell based characterization of the landscape of combinatorial receptor-mediated cancer treatments establishes first of their kind estimates on the number of targets needed, identifying promising ones for future development.
Pharmacodynamic Markers of LRRK2 Inhibition in Biofluids
Authors: Wang Shijie , Kelly Kaela , Schussler Nathalie , Boularand Sylviane , Dubois Laurent , Hsieh Frank , Tengstrand Elizabeth , Brotchie Jonathan M. , Koprich James B. , West Andrew B. 
Published: January 2020

AbstractHyper-activated LRRK2 is linked to Parkinson’s disease susceptibility and progression. Quantitative measures of LRRK2 inhibition, especially in the brain, may be critical in the clinical development of successful LRRK2-targeting therapeutics. In this study, three different brain-penetrant and selective LRRK2 small-molecule kinase inhibitors (PFE-360, MLi2, and RA283) were orally administered to groups of cynomolgus macaques at different doses. Biofluid markers with proposed pharmacodynamic properties for assessing LRRK2 inhibition were measured from samples of blood, urine, and cerebral-spinal fluid (CSF). LRRK2 kinase inhibition led to consistent reduced pS935-LRRK2 and pRab10 proteins in blood mononuclear cells, reduced exosome LRRK2 protein and di-docosahexaenoyl (22:6) bis (monoacylglycerol) phosphate in urine, and reduced exosome LRRK2 and autophosphorylated pS1292-LRRK2 protein in CSF. Incomplete LRRK2 kinase inhibition reduced LRRK2 protein secretion in exosomes whereas high drug exposures may reduce both exosome and tissue levels of LRRK2 protein. These orthogonal markers for LRRK2 inhibition in urine and CSF can be used in combination with blood markers to non-invasively monitor the potency of LRRK2-targeting therapeutics in the brain and periphery.
Trimmed Constrained Mixed Effects Models: Formulations and Algorithms
Authors: Zheng Peng , Barber Ryan , Sorensen Reed , Murray Christopher , Aravkin Aleksandr 
Published: January 2020

AbstractMixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects component is linear. We then develop an efficient approach for a broad problem class that allows nonlinear measurements, priors, and constraints, and finds robust estimates in all of these cases using trimming in the associated marginal likelihood.The software accompanying this paper is disseminated as an open-source Python package calledLimeTr.LimeTris able to recover results more accurately in the presence of outliers compared to available packages for both standard longitudinal analysis and meta-analysis, and is also more computationally efficient than competing robust alternatives. Supplementary materials that reproduce the simulations, as well as runLimeTrand third party code are available online. We also present analyses of global health data, where we use advanced functionality ofLimeTr, including constraints to impose monotonicity and concavity for dose-response relationships. Nonlinear observation models allow new analyses in place of classic approximations, such as log-linear models. Robust extensions in all analyses ensure that spurious data points do not drive our understanding of either mean relationships or between-study heterogeneity.
Hierarchical Network Model Excitatory-Inhibitory Tone Shapes Alternative Strategies for Different Degrees of Uncertainty in Multi-Attribute Decisions
Authors: Pettine Warren Woodrich , Louie Kenway , Murray John D , Wang Xiao Jing 
Published: January 2020

AbstractWe investigated two-attribute, two-alternative decision-making in a hierarchical neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a choice layer producing the decision. Depending on intermediate layer excitatory-inhibitory (E/I) tone, the network displays three distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option’s attribute information is additively integrated. To maximize reward at low environmental uncertainty, the system should operate in regime I. At high environmental uncertainty, reward maximization is achieved in regime III, with each attribute module selecting a favored alternative, and the ultimate decision based upon comparison between outputs of attribute processing modules. We then use these principles to examine multi-attribute decisions with autism-related deficits in E/I balance, leading to predictions of different choice patterns and overall performance between autism and neurotypicals.