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Essential tremor (ET) is one of the most common movement disorders. The etiology of ET remains largely unexplained. Whole genome sequencing (WGS) is likely to be of value in understanding a large proportion of ET with Mendelian and complex disease inheritance patterns. In ET families with Mendelian inheritance patterns, WGS may lead to gene identification where WES analysis failed to identify the causative single nucleotide variant (SNV) or indel due to incomplete coverage of the entire coding region of the genome, in addition to accurate detection of larger structural variants (SVs) and copy number variants (CNVs). Alternatively, in ET families with complex disease inheritance patterns with gene x gene and gene x environment interactions enrichment of functional rare coding and non-coding variants may explain the heritability of ET. We performed WGS in eight ET families (n=40 individuals) enrolled in the Family Study of Essential Tremor. The analysis included filtering WGS data based on allele frequency in population databases, rare SNV and indel classification and association testing using the Mixed-Model Kernel Based Adaptive Cluster (MM-KBAC) test. A separate analysis of rare SV and CNVs segregating within ET families was also performed. Prioritization of candidate genes identified within families was performed using phenolyzer. WGS analysis identified candidate genes for ET in 5/8 (62.5%) of the families analyzed. WES analysis in a subset of these families in our previously published study failed to identify candidate genes. In one family, we identified a deleterious and damaging variant (c.1367G>A, p.(Arg456Gln)) in the candidate gene, CACNA1G, which encodes the pore forming subunit of T-type Ca(2+) channels, CaV3.1, and is expressed in various motor pathways and has been previously implicated in neuronal autorhythmicity and ET. Other candidate genes identified include SLIT3 which encodes an axon guidance molecule and in three families, phenolyzer prioritized genes that are associated with hereditary neuropathies (family A, KARS, family B, KIF5A and family F, NTRK1). Functional studies of CACNA1G and SLIT3 suggest a role for these genes in ET disease pathogenesis.
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Cichlid fishes provide textbook examples of explosive phenotypic diversification and sympatric speciation, thereby making them ideal systems for studying the molecular mechanisms underlying rapid lineage divergence. Despite the fact that gene regulation provides a critical link between diversification in gene function and speciation, many genomic regulatory mechanisms such as miRNAs have received little attention in these rapidly diversifying groups. Therefore, we investigated the post-transcriptional regulatory role of miRNAs in the repeated sympatric divergence of Midas cichlids (Amphilophus spp.) from Nicaraguan crater lakes. Using miRNA and mRNA sequencing of embryos from five Midas species, we first identified miRNA binding sites in mRNAs and highlighted the presences of a surprising number of novel miRNAs in these adaptively radiating species. Then, through analyses of expression levels, we identified putative miRNA/gene target pairs with negatively correlated expression level that were consistent with the role of miRNA in downregulating mRNA. Furthermore, we determined that several miRNA/gene pairs show convergent expression patterns associated with the repeated benthic/limnetic sympatric species divergence implicating these miRNAs as potential molecular mechanisms underlying replicated sympatric divergence. Finally, as these candidate miRNA/gene pairs may play a central role in phenotypic diversification in these cichlids, we characterized the expression domains of selected miRNAs and their target genes via in situ hybridization, providing further evidence that miRNA regulation likely plays a role in the Midas cichlid adaptive radiation. These results provide support for the hypothesis that extremely quickly evolving miRNA regulation can contribute to rapid evolutionary divergence even in the presence of gene flow.
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The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in a freely running cockroach. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle’s leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems.
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Sustained, quantitative observations of nearshore waves and sand levels are essential for testing beach evolution models, but comprehensive datasets are relatively rare. We document beach profiles and concurrent waves monitored at three southern California beaches during 2001-2016. The beaches include offshore reefs, lagoon mouths, hard substrates, and cobble and sandy (medium-grained) sediments. The data span two energetic El Niño winters and four beach nourishments. Quarterly surveys of 165 total cross-shore transects (all sites) at 100m alongshore spacing were made from the backbeach to 8m depth. Monthly surveys of the subaerial beach were obtained at alongshore-oriented transects. The resulting dataset consists of (1) raw sand elevation data, (2) gridded elevations, (3) interpolated elevation maps with error estimates, (4) beach widths, subaerial and total sand volumes, (5) locations of hard substrate and beach nourishments, (6) water levels from a NOAA tide gauge (7) wave conditions from a buoy-driven regional wave model, and (8) time periods and reaches with alongshore uniform bathymetry, suitable for testing 1-dimensional beach profile change models.
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Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.
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Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.
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In deterministic models of epidemics, there is a host abundance threshold, above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, {\it Entomophaga maimaiga}, that infects the gypsy moth, {\it Lymantria dispar}. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on {\it E. maimaiga} epizootics (epidemics in animals). We then used AIC analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects, or only density-dependence effects, do a poor job of explaining the data. Density-dependent transmission in our best model produces a host-density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.
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Some neotropical amphibians, including a few species in Costa Rica, were presumed to be “extinct” after dramatic population declines in the late 1980s but have been rediscovered in isolated populations. Such populations seem to have evolved a resistance/tolerance to Batrachochytrium dendrobatidis (Bd), a fungal pathogen that causes a deadly skin disease and is considered one of the main drivers of worldwide amphibian declines. The skin microbiome is an important component of the host´s innate immune system and is associated with Bd-resistance. However, the way that the bacterial diversity of the skin microbiome confers protection against Bd in surviving species remains unclear. We studied variation in the skin microbiome and the prevalence of putatively anti-Bd bacterial taxa in four co-habiting species in the highlands of the Juan Castro Blanco National Park in Costa Rica using 16S rRNA amplicon sequencing. Lithobates vibicarius, Craugastor escoces, and Isthomohyla rivularis have recently been rediscovered, whereas Isthmohyla pseudopuma has suffered population fluctuations but has never disappeared. To investigate the life stage at which the protective skin microbiome is shaped and when shifts occur in the diversity of putatively anti-Bd bacteria, we studied the skin microbiome of tadpoles, juveniles and adults of L. vibicarius. We show that the skin bacterial composition of sympatric species and hosts with distinct Bd-infection statuses differs at the phyla, family, and genus level. We detected 94 amplicon sequence variants (ASVs) with putative anti-Bd activity pertaining to distinct bacterial taxa, e.g., Pseudomonas spp., Acinetobacter johnsonii, and Stenotrophomonas maltophilia. Bd-uninfected L. vibicarius harbored 79% more putatively anti-Bd ASVs than Bd-infected individuals. Although microbiome composition and structure differed across life stages, the diversity of putative anti-Bd bacteria was similar between pre- and post-metamorphic stages of L. vibicarius. Despite low sample size, our results support the idea that the skin microbiome is dynamic and protects against ongoing Bd presence in endangered species persisting after their presumed extinction. Our study serves as a baseline to understand the microbial patterns in species of high conservation value. Identification of microbial signatures linked to variation in disease susceptibility might, therefore, inform mitigation strategies for combating the global decline of amphibians.
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Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to identify antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To identify antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to identify antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to identify epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, identifying three epitopes recognized by antibodies present in 83% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans revealed seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly revealed six epitopes recognized by >40% of specimens. These common viral and bacterial epitopes exhibited excellent agreement with previously mapped epitopes. Additionally, we identified 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.
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Head and neck squamous cell carcinoma (HNSCC) is a widely prevalent cancer globally with high mortality and morbidity. We report here changes in the genomic landscape in the development of these tumours from potentially premalignant lesions (PPOLS) to malignancy and lymph node metastases. Frequent likely pathological mutations are restricted to a relatively small set of genes including TP53, CDKN2A, FBXW7, FAT1, NOTCH1 and KMT2D; these arise early in tumour progression and are present in PPOLs with NOTCH1 mutations restricted to cell lines from lesions that subsequently progressed to HNSCC. The most frequent genetic changes are of consistent somatic copy number alterations (SCNA). The earliest SCNAs involved deletions of CSMD1 (8p23.2), FHIT (3p14.2) and CDKN2A (9p21.3) together with gains of chromosome 20. CSMD1 deletions or promoter hypermethylation were present in all of the immortal PPOLs and occurred at high frequency in the immortal HNSCC cell lines (promoter hypermethylation ~63%, hemizygous deletions ~75%, homozygous deletions ~18%). Forced expression of CSMD1 in the HNSCC cell line H103 showed significant suppression of proliferation (p=0.0053) and invasion in vitro (p=5.98X10-5) supporting a role for CSMD1 inactivation in early head and neck carcinogenesis. In addition, knockdown of CSMD1 in the CSMD1-expressing BICR16 cell line showed significant stimulation of invasion in vitro (p=1.82 x 10-5) but not cell proliferation (p=0.239). HNSCC with and without nodal metastases showed some clear differences including high copy number gains of CCND1, hsa-miR-548k and TP63 in the metastases group. GISTIC peak SCNA regions showed significant enrichment (adj P<0.01) of genes in multiple KEGG cancer pathways at all stages with disruption of an increasing number of these involved in the progression to lymph node metastases. Sixty-seven genes from regions with statistically significant differences in SCNA/LOH frequency between immortal PPOL and HNSCC cell lines showed correlation with expression including 5 known cancer drivers.
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