bluuebunny/crossref_metadata_2025_split huggingface dataset:
Network organization of antibody interactions in sequence and structure space: the RADARS model
Authors: Prechl József 
Published: October 2018

AbstractAdaptive immunity in vertebrates represents a complex self-organizing network of protein interactions that develops throughout the lifetime of an individual. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function.In this paper a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RADARS, is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction representing particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power-law distribution of antibody free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees and B1 cells represent nodes with minimal degree.Thus, the RADARS model implies that antibody structure space develops against an infinite antigen structure space via interactions that are individually immunologically controlled, but on a systems level are organized by thermodynamic probability distributions. The network of interactions, which control B-cell development and differentiation, represent pathways of antigen removal on systems level. Understanding such quantitative network properties of the system should help the organization of sequence-derived structural data, offering the possibility to relate sequence to function in a complex, self-organizing biological system.Graphical AbstractGraphical abstract
The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8
Authors: Chen Shi , Wiewiora Rafal P. , Meng Fanwang , Babault Nicolas , Ma Anqi , Yu Wenyu , Qian Kun , Hu Hao , Zou Hua , Wang Junyi , Fan Shijie , Blum Gil , Pittella-Silva Fabio , Beauchamp Kyle A. , Tempel Wolfram , Jiang Hualiang , Chen Kaixian , Skene Robert , Zheng Y. George , Brown Peter J. , Jin Jian , Luo Cheng , Chodera John D. , Luo Minkui 
Published: October 2018

AbstractElucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.
Knockout of the gene encoding the extracellular matrix protein SNED1 results in early neonatal lethality and craniofacial malformations
Authors: Barqué Anna , Jan Kyleen , De La Fuente Emanuel , Nicholas Christina L. , Hynes Richard O. , Naba Alexandra 
Published: October 2018

AbstractBackgroundThe extracellular matrix (ECM) is a fundamental component of multicellular organisms that orchestrates developmental processes and controls cell and tissue organization. We previously identified the novel ECM protein SNED1 as a promoter of breast cancer metastasis and showed that its level of expression negatively correlated with survival of breast cancer patients. Here we sought to identify the roles of SNED1 during murine development and in physiology.ResultsWe generated two novelSned1knockout mouse strains and showed thatSned1is essential since homozygous ablation of the gene led to ~67% early neonatal lethality. Phenotypic analysis of the surviving knockout mice obtained revealed a role for SNED1 in the development of craniofacial and skeletal structures sinceSned1knockout resulted in growth defects, nasal cavity occlusion, and craniofacial malformations.Sned1is widely expressed in embryos, notably in neural-crest derivatives. We further show here that mice with a neural-crest-cell-specific deletion ofSned1survive, but display facial anomalies partly phenocopying the global knockout mice.ConclusionsOur results demonstrate requisite roles for SNED1 during development and neonatal survival. Importantly, the deletion of 2q37.3 in humans, a region that includes theSNED1locus, has been associated with facial dysmorphism and short stature.
Genome-wide signatures of local adaptation among seven stoneflies species along a nationwide latitudinal gradient in Japan
Authors: Gamboa Maribet , Watanabe Kozo 
Published: October 2018

AbstractBackgroundEnvironmental heterogeneity continuously produces a selective pressure that results in genomic variation among organisms; understanding this relationship remains a challenge in evolutionary biology. Here, we evaluated the degree of genome-environmental association of seven stonefly species across a wide geographic area in Japan and additionally identified putative environmental drivers and their effect on co-existing multiple stonefly species. Double-digest restriction-associated DNA (ddRAD) libraries were independently sequenced for 219 individuals from 23 sites across four geographical regions along a nationwide latitudinal gradient in Japan.ResultsA total of 4,251 candidate single nucleotide polymorphisms (SNPs) strongly associated with local adaptation were discovered using Latent mixed models; of these, 294 SNPs showed strong correlation with environmental variables, specifically precipitation and altitude, using distance-based redundancy analysis. Genome–genome comparison among the seven species revealed a high sequence similarity of candidate SNPs within a geographical region, suggesting the occurrence of a parallel evolution process.ConclusionsOur results revealed genomic signatures of local adaptation and their influence on multiple, co-occurring species. These results can be potentially applied for future studies on river management and climatic stressor impacts.
The allometry of brain size in mammals
Authors: Robert Burger Joseph , Ashaki George Menshian , Leadbetter Claire , Shaikh Farhin 
Published: October 2018

AbstractWhy some animals have big brains and others do not has intrigued scholars for millennia. Yet, the taxonomic scope of brain size research is limited to a few mammal lineages. Here we present a brain size dataset compiled from the literature for 1552 species with representation from 28 extant taxonomic orders. The brain-body size allometry across all mammals is (Brain) = −1.26 (Body)0.75. This relationship shows strong phylogenetic signal as expected due to shared evolutionary histories. Slopes using median species values for each order, family, and genus, to ensure evolutionary independence, approximate ∼0.75 scaling. Why brain size scales to the ¾ power to body size across mammals is, to our knowledge, unknown. Slopes within taxonomic orders exhibiting smaller size ranges are often shallower than 0.75 and range from 0.24 to 0.81 with a median slope of 0.64. Published brain size data is lacking for the majority of extant mammals (>70% of species) with strong bias in representation from Primates, Carnivores, Perrisodactyla, and Australidelphian marsupials (orders Dasyuromorphia, Diprotodontia, Peramelemorphia). Several orders are particularly underrepresented. For example, brain size data are available for less than 20% of species in each of the following speciose lineages: Soricomorpha, Rodentia, Lagomorpha, Didelphimorphia, and Scandentia. Use of museum collections can decrease the current taxonomic bias in mammal brain size data and tests of hypothesis.
Algorithmic biosynthesis of eukaryotic glycans
Authors: Jaiman Anjali , Thattai Mukund 
Published: October 2018

AbstractAn algorithm converts inputs to corresponding unique outputs through a sequence of actions. Algorithms are used as metaphors for complex biological processes such as organismal development. Here we make this metaphor rigorous for glycan biosynthesis. Glycans are branched sugar oligomers that are attached to cell-surface proteins and convey cellular identity. Eukaryotic O-glycans are synthesized by collections of enzymes in Golgi compartments. A compartment can stochastically convert a single input oligomer to a heterogeneous set of possible output oligomers; yet a given type of protein is invariably associated with a narrow and reproducible glycan oligomer profile. Here we resolve this paradox by borrowing from the theory of algorithmic self-assembly. We rigorously enumerate the sources of glycan microheterogeneity: incomplete oligomers via early exit from the reaction compartment; tandem repeat oligomers via runaway reactions; and competing oligomer fates via divergent reactions. We demonstrate how to diagnose and eliminate each of these, thereby obtaining “algorithmic compartments” that convert inputs to corresponding unique outputs. Given an input and a target output we either prove that the output cannot be algorithmically synthesized from the input, or explicitly construct an ordered series of algorithmic compartments that achieves this synthesis. Our theoretical analysis allows us to infer the causes of non-algorithmic microheterogeneity and species-specific diversity in real glycan datasets.
Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease
Authors: Huang Yi-Fei , Siepel Adam 
Published: October 2018

AbstractA central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine-learning model, calledLinearAllele-SpecificSelectionInferencE(LASSIE), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.
Algorithmic improvements for discovery of germline copy number variants in next-generation sequencing data
Authors: O’Fallon Brendan , Durtschi Jacob , Lewis Tracey , Close Devin 
Published: October 2018

AbstractCopy number variants (CNVs) play a significant role in human heredity and disease, however sensitive and specific characterization of CNVs from NGS data has remained challenging. Detection is especially problematic for hybridization-capture data in which read counts are the sole source of copy number information. We describe two algorithmic adaptations that improve CNV detection accuracy in a Hidden Markov Model (HMM) context. First, we present a method for com puting target- and copy number state-specific emission distributions. Second, we demonstrate that the Pointwise Maximum a posteriori (PMAP) HMM decoding procedure yields improved sensitivity for small CNV calls compared to the more common Viterbi HMM decoder. We develop a prototype implementation, called Cobalt, and compare it to other CNV detection tools using sets of simulated and previously detected CNVs with sizes spanning a single exon up to a full chromosome. In both the simulation and previously detected CNV studies Cobalt shows similar sensitivity but significantly improved positive predictive value (PPV) compared to other callers. Overall sensitivity is 80%-90% for deletion CNVs spanning 1-4 targets and 90%-100% for larger deletion events, while sensitivity is somewhat lower for small duplication CNVs. Cobalt demonstrates significantly improved positive predictive value (PPV) compared to other callers with similar sensitivity, typically making 5X fewer total calls overall.
Serum triglycerides in Alzheimer’s disease: Relation to neuroimaging and CSF biomarkers
Authors: Bernath Megan M. , Bhattacharyya Sudeepa , Nho Kwangsik , Barupal Dinesh Kumar , Fiehn Oliver , Baillie Rebecca , Risacher SL , Arnold Matthias , Jacobson Tanner , Trojanowski John Q. , Shaw Leslie M. , Weiner Michael W. , Doraiswamy P. Murali , Kaddurah-Daouk Rima , Saykin Andrew J. ,   for the Alzheimer’s Disease Neuroimaging Initiative,   Alzheimer’s Disease Metabolomics Consortium
Published: October 2018

AbstractObjectiveTo investigate the association of triglyceride (TG) principal component scores with Alzheimer’s disease (AD) and the “A/T/N/V” (Amyloid, Tau, Neurodegeneration, and Cerebrovascular disease) biomarkers for AD.MethodsSerum levels of 84 TG species were measured using untargeted lipid profiling of 689 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort including 190 cognitively normal older adults (CN) and 339 mild cognitive impairment (MCI) and 160 AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [18F]FDG-PET) were assessed using a multivariate generalized linear model (GLM) approach. In both cases, the Bonferroni method of adjustment was employed to correct for multiple comparisons.ResultsThe 84 TGs yielded 9 principal components, two of which consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carryingAPOEε4 allele, these principal components were significantly associated with CSF amyloid-β1-42values and entorhinal cortical thickness.ConclusionsThis study shows PUTG component scores significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced inAPOEε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.
Predicted asymmetrical effects of warming on nocturnal and diurnal ectotherms
Authors: McMunn Marshall S. 
Published: October 2018

AbstractMany ectotherms restrict activity to times and places with favorable temperatures. This widespread pattern of habitat use in fluctuating environments may alter predictions of how climate change will affect ectotherms. By considering time elapsed within a range of suitable temperatures as a resource, I demonstrate that warming is expected to affect thermally restricted nocturnal and diurnal activity windows asymmetrically. Under warming scenarios, thermally restricted nocturnal activity windows lengthen while diurnal activity windows contract. This divergent prediction results from the shape of the function relating time to temperature within a day, which is typically concave during the day and convex during the night. This characteristic shape is nearly universal across terrestrial environments due to the changing angle of the sun throughout each day and exponential decay of overnight temperatures. These predicted asymmetries are exacerbated by expectations of diurnally asymmetric warming (more warming during the night compared to the day). Using example data from a montane ant community, I demonstrate that, as predicted, moderate simulated warming expands activity time available to cool active species and reduces activity time available to warm active species. Together these results suggest that the time of day during which an ectotherms optimal temperature occurs can be an important factor in determining response to warming.
Overdispersed gene expression characterizes schizophrenic brains
Authors: Huang Guangzao , Osorio Daniel , Guan Jinting , Ji Guoli , Cai James J. 
Published: October 2018

AbstractSchizophrenia (SCZ) is a severe, highly heterogeneous psychiatric disorder with varied clinical presentations. The polygenic genetic architecture of SCZ makes identification of causal variants daunting. Gene expression analyses have shown that SCZ may result in part from transcriptional dysregulation of a number of genes. However, most of these studies took the commonly used approach—differential gene expression analysis, assuming people with SCZ are a homogenous group, all with similar expression levels for any given gene. Here we show that the overall gene expression variability in SCZ is higher than that in an unaffected control (CTL) group. Specifically, we applied the test for equality of variances to the normalized expression data generated by the CommonMind Consortium (CMC) and identified 87 genes with significantly higher expression variances in the SCZ group than the CTL group. One of the genes with differential variability,VEGFA, encodes a vascular endothelial growth factor, supporting a vascular-ischemic etiology of SCZ. We also applied a Mahalanobis distance-based test for multivariate homogeneity of group dispersions to gene sets and identified 19 functional gene sets with higher expression variability in the SCZ group than the CTL group. Several of these gene sets are involved in brain development (e.g., development of cerebellar cortex, cerebellar Purkinje cell layer and neuromuscular junction), supporting that structural and functional changes in the cortex cause SCZ. Finally, using expression variability QTL (evQTL) analysis, we show that common genetic variants contribute to the increased expression variability in SCZ. Our results reveal that SCZ brains are characterized by overdispersed gene expression, resulting from dysregulated expression of functional gene sets pertaining to brain development, necrotic cell death, folic acid metabolism, and several other biological processes. Using SCZ as a model of complex genetic disorders with a heterogeneous etiology, our study provides a new conceptual framework for variability-centric analyses. Such a framework is likely to be important in the era of personalized medicine. (313 words)
Omnivory does not preclude strong trophic cascades
Authors: Fahimipour Ashkaan K. , Levin David A. , Anderson Kurt E. 
Published: October 2018

AbstractOmnivory has been cited as an explanation for why trophic cascades are weak in many ecosystems, but empirical support for this prediction is equivocal. Compared to predators that feed only on herbivores, top omnivores — species that feed on both herbivores and primary producers — have been observed generating cascades ranging from strong, to moderate, null, and negative. To gain intuition about the sensitivity of cascades to omnivory, we analyzed models describing systems with top omnivores that display either fixed or flexible diets, two foraging strategies that are supported by empirical observations. We identified regions of parameter space wherein omnivores following a fixed foraging strategy, with herbivores and producers comprising a constant proportion of the diet, non-intuitively generate stronger cascades than predators that are otherwise demographically identical: (i) high productivity relative to herbivore mortality, and (ii) small discrepancies in producer versus herbivore reward create conditions in which cascades are stronger with moderate omnivory. In contrast, flexible omnivores that attempt to optimizeper capitagrowth rates during search never induce cascades that are stronger than the case of predators. Although we focus on simple models, the consistency of these general patterns together with prior empirical evidence suggests that omnivores should not be uniformly ruled out as agents of strong trophic cascades.
Nomenclature Errors in Public 16S rRNA Gene Reference Databases
Authors: Lesack Kyle , Birol Inanc 
Published: October 2018

AbstractBackgroundTargeted gene surveys of the 16S rRNA gene have become a standard method for profiling the membership and biodiversity of microbial communities. These studies rely upon specialized databases that provide reference sequences and their corresponding taxonomic classifications, but few independent evaluations of the nomenclature used in the taxonomic classifications have been performed.ResultsNomenclature data collected from the List of Prokaryotic names with Standing in Nomenclature, Prokaryotic Nomenclature Up-to-Date, and CyanoDB databases were used to validate the nomenclature contained in the taxonomic classifications in the Greengenes, RDP, and SILVA 16S rRNA gene reference databases. Between 82% and 97% of the genus annotations assigned to 16S rRNA gene reference sequences were deemed valid in the reference databases. Between 18% and 97% of the species annotations in Greengenes and SILVA were deemed valid. Misannotations included the use of metadata in place of taxonomic classifications, non-adherence to the binomial nomenclature, and sequences classified as eukaryote organelles or taxa.ConclusionsThe misannotations identified in public 16S rRNA gene databases call into question the reliability of research made using these resources. As targeted gene surveys depend on high quality marker gene databases, imed nomenclature accuracy will be necessary.
Multi-context blind source separation by error-gated Hebbian rule
Authors: Isomura Takuya , Toyoizumi Taro 
Published: October 2018

AbstractAnimals need to adjust their inferences according to the context they are in. This is required for the multi-context blind source separation (BSS) task, where an agent needs to infer hidden sources from their context-dependent mixtures. The agent is expected to invert this mixing process for all contexts. Here, we show that a neural network that implements the error-gated Hebbian rule (EGHR) with sufficiently redundant sensory inputs can successfully learn this task. After training, the network can perform the multi-context BSS without further updating synapses, by retaining memories of all experienced contexts. Finally, if there is a common feature shared across contexts, the EGHR can extract it and generalize the task to even inexperienced contexts. This demonstrates an attractive use of the EGHR for dimensionality reduction by extracting common sources across contexts. The results highlight the utility of the EGHR as a model for perceptual adaptation in animals.