Biology subjects

Miller, M.

Publications and source records attributed to Miller, M..

At least 91 records · Page 5Linked to original sources

bioRxiv2020

Innate immune gene expression in Acropora palmata is consistent despite variance in yearly disease events

Coral disease outbreaks are expected to increase in prevalence, frequency and severity due to climate change and other anthropogenic stressors. This is especially worrying for the Caribbean branching Acropora palmata which has already seen an 80% decrease in its coral cover, with this primarily due to disease. Despite the importance of this species, there has yet to be a characterization of its transcriptomic response to disease exposure. In this study we provide the first transcriptomic analysis of 12 A. palmata genotypes, and their symbiont Symbiodiniaceae, exposed to disease in 2016 and 2017. Year was the primary driver of sample variance for A. palmata and the Symbiodiniaceae. Lower expression of ribosomal genes in the coral, and higher expression of transmembrane ion transport genes in the Symbiodiniaceae indicate that the increased virulence in 2017 may have been due to a dysbiosis between the coral and Symbiodiniaceae. We also identified a conserved suite of innate immune genes responding to the disease challenge that was activated in both years. This included genes from the Toll-like receptor and lectin pathways, and antimicrobial peptides. Co-expression analysis identified a module positively correlated to disease exposure rich in innate immune genes, with D-amino acid oxidase, a gene implicated in phagocytosis and microbiome homeostasis, as the hub gene. The role of D-amino acid oxidase in coral immunity has not been characterized but holds potential as an important enzyme for responding to disease. Our results indicate that A. palmata mounts a similar immune response to disease exposure as other coral species previously studied, but with unique features that may be critical to the survival of this keystone Caribbean species.

molecular biology↗

bioRxiv2019

Genome-wide association study for maize leaf cuticular conductance identifies candidate genes involved in the regulation of cuticle development

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of gc of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA in 2016 and 2017). Five genomic regions significantly associated with gc were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and {beta}-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of gc in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of gc and have the potential to help breeders more effectively develop drought-tolerant maize for target environments.\n\nArticle summaryThe cuticle serves as the major barrier to water loss when stomata are closed at night and under water-limited conditions and potentially relevant to drought tolerance in crops. We performed a genome-wide association study to elucidate the genetic architecture of natural variation for maize leaf cuticular conductance. We identified epidermally expressed candidate genes that are potentially involved in cuticle biosynthesis, trafficking and deposition, cutin polymerization, and cell wall modification. Finally, we observed moderately high predictive abilities for whole-genome prediction of leaf cuticular conductance. Collectively, these findings may help breeders more effectively develop drought-tolerant maize.

genetics↗

bioRxiv2019

Profiling the endothelial translatome in vivo using ‘AngioTag’ zebrafish

Vascular endothelial cells in vivo are exquisitely regulated by their local environment, which is disrupted or absent when using methods such as FACS sorting of cells isolated from animals or in vitro cell culture. Here, we profile the gene expression patterns of undisturbed endothelial cells in living animals using a novel "AngioTag" zebrafish transgenic line that permits isolation of actively translating mRNAs from endothelial cells in their native environment. This transgenic line uses the endothelial cell-specific kdrl promoter to drive expression of an epitope tagged Rpl10a 60S ribosomal subunit protein, allowing for Translating Ribosome Affinity Purification (TRAP) of actively translating endothelial cell mRNAs. By performing TRAP-RNAseq on AngioTag animals, we demonstrate strong enrichment of endothelial specific genes and uncover novel endothelial genes and unique endothelial gene expression signatures for different adult organs. Finally, we generated a versatile "UAS:RiboTag" transgenic line to allow a wider array of different zebrafish cell and tissue types to be examined using TRAP-RNAseq methods. These new tools offer an unparalleled resource to study the molecular identity of cells in their normal in vivo context.

developmental biology↗

bioRxiv2019

Emergence of the Ug99 lineage of the wheat stem rust pathogen through somatic hybridisation

Parasexuality contributes to diversity and adaptive evolution of haploid (monokaryotic) fungi. However non-sexual genetic exchange mechanisms are not defined in dikaryotic fungi (containing two distinct haploid nuclei). Newly emerged strains of the wheat stem rust pathogen, Puccinia graminis f. sp. tritici (Pgt), such as Ug99, are a major threat to global food security. Here we show that Ug99 arose by somatic hybridisation and nuclear exchange between dikaryons. Fully haplotype-resolved genome assembly and DNA proximity analysis revealed that Ug99 shares one haploid nucleus genotype with a much older African lineage of Pgt, with no recombination or reassortment. Generation of genetic variation by nuclear exchange may favour the evolution of dikaryotism by providing an advantage over diploidy.

genetics↗

bioRxiv2019

Passenger Hotspot Mutations in Cancer

Hotspots, or mutations that recur at the same genomic site across multiple tumors, have been conventionally interpreted as strong universal evidence of somatic positive selection, unequivocally pinpointing genes driving tumorigenesis. Here, we demonstrate that this convention is falsely premised on an inaccurate statistical model of background mutagenesis. Many hotspots are in fact passenger events, recurring at sites that are simply inherently more mutable rather than under positive selection, which current background models do not account for. We thus detail a log-normal-Poisson (LNP) background model that accounts for variation in site-specific mutability in a manner consistent with models of mutagenesis, use this model to show that the tendency to generate passenger hotspots pervades all common mutational processes, and apply it to a ~10, 000 patient cohort from The Cancer Genome Atlas to nominate driver hotspots with far fewer false positives compared to conventional methods. As the biomedical community faces critical decisions in prioritizing putative driver mutations for deep experimental characterization to assess therapeutic potential, we offer our findings as a guide to avoid wasting valuable scientific resources on passenger hotspots.

genomics↗

bioRxiv2019

Phylo-comparative analyses reveal the dual role of drift and selection in reproductive character displacement.

When incipient species meet in secondary contact, natural selection can rapidly reduce costly reproductive interactions by directly targeting reproductive traits. This process, called reproductive character displacement (RCD), leaves a characteristic pattern of geographic variation where divergence of traits between species is greater in sympatry than allopatry. However, because other forces can also cause similar patterns, care must be given in separating pattern from process. Here we show how the phylo-comparative method together with genomic data can be used to evaluate evolutionary processes at the population level in closely related species. Using this framework, we test the role of RCD in speciation of two cricket species endemic to Anatolian mountains by quantifying patterns of character displacement, rates of evolution and adaptive divergence. Our results show differing patterns of character displacement between species for reproductive vs. non-reproductive characters and strong patterns of asymmetric divergence. We demonstrate diversification results from rapid divergence of reproductive traits towards multiple optima under the dual influence of strong drift and selection. These results present the first solid evidence for RCD in Anatolian mountains, quantify the amount of drift and selection necessary for RCD to lead to speciation, and demonstrate the utility of phylo-comparative methods for quantifying evolutionary parameters at the population level.

evolutionary biology↗

bioRxiv2019

EM-LDDMM for 3D to 2D registration

We examine the problem of mapping dense 3D atlases onto censored, sparsely sampled 2D target sections at micron and meso scales. We introduce a new class of large deformation diffeomorphic metric mapping (LD-DMM) algorithms for generating dense atlas correspondences onto sparse 2D samples by introducing a field of hidden variables which must be estimated representing a large class of target image uncertainties including (i) unknown parameters representing cross stain contrasts, (ii) censoring of tissue due to localized measurements of target subvolumes and (iii) sparse sampling of target tissue sections. For prediction of the hidden fields we introduce the generalized expectation-maximization algorithm (EM) for which the E-step calculates the conditional mean of the hidden variates simultaneously combined with the diffeomorphic correspondences between atlas and target coordinate systems. The algorithm is run to fixed points guaranteeing estimators satisfy the necessary maximizer conditions when interpreted as likelihood estimators. The dense mapping is an injective correspondence to the sparse targets implying all of the 3D variations are performed only on the atlas side with variation in the targets only 2D manipulations.

neuroscience↗

bioRxiv2019

Neural Correlates of Attitudes and Risk Perception for Food Technology Topics

Food technologies provide numerous benefits to society and are extensively vetted for safety. However, many technological innovations still face high levels of skepticism from consumers. To promote development and use of food technologies, it is critical to understand the psychological and neurobiological processes associated with consumer acceptability concerns. The current study uses a neuroscience-based approach to understand consumer attitudes and perceptions of risk associated with food technologies and investigate how such attitudes impact consumers processing of information related to food technologies. We used functional magnetic resonance imaging (fMRI) to measure brain activation while participants processed infographics related to food technology topics. For technology topics perceived as riskier (antibiotics and hormones), activation was higher in areas of the lateral prefrontal cortex that are associated with decisional uncertainty. In contrast, technology topics that were viewed more favorably (sustainability and animal welfare) tended to activate the ventromedial prefrontal cortex, a region that processes positive affect and subjective value. Moreover, for information about hormones, the lateral PFC activation was associated with individual differences in resistance to change in risk perception. These results reveal how attitudes and risk perception relate to how the brain processes information about food technologies and how people respond to information about such technologies.

neuroscience↗

bioRxiv2019

fuNTRp: Identifying protein positions for variation driven functional tuning

Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. Understanding corresponding sequence changes is also fundamental for synthetic protein design and stability assessments. However, the performance gain of variant effect predictors observed in recent years is not in line with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene/protein features for modeling variant effect, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the in vivo observable variation is arguably weaker in its impact and, thus, requires evaluation at a higher level of resolution. Here we describe function Neutral/Toggle/Rheostat predictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types reflect experimentally determined functional effects and can thus improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

bioinformatics↗

bioRxiv2019

Tumor mutational landscape is a record of the pre-malignant state

Chromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

genomics↗

bioRxiv2018

Identifying Changepoints in Biomarkers During the Preclinical Phase of AD

ObjectiveSeveral models have been proposed for the evolution of Alzheimers disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD.\n\nMethodsWe examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset.\n\nResultsAll nine measures had significant changepoints, all of which preceded symptom onset, however the timing of these changepoints varied considerably. A single measure, CSF-tau, had an early changepoint (40 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF-Abeta and phosphorylated tau) and all cognitive tests had changepoints 10-15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a five-year time difference between the right and left side (respectively nine and three years prior to symptom onset).\n\nConclusionsThese findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.

neuroscience↗

bioRxiv2018

Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer’s disease

This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity, together with pose and deformation parameters. Missing data is accommodated via a multiple atlas selection procedure where several atlases may be of homogeneous intensity and correspond to \"background\" or \"artifact\". The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We show results for 3D reconstruction of digital pathology and MRI in standard atlas coordinates. In conjunction with convolutional neural networks, we quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimers disease postmortem specimen.\n\nAuthor summaryOur work in Alzheimers disease (AD) is attempting to connect histopathology at autopsy and longitudinal clinical magnetic resonance imaging (MRI), combining the strengths of each modality in a common coordinate system. We are bridging this gap by using post mortem high resolution MRI to reconstruct digital pathology in 3D. This image registration problem is challenging because it combines images from different modalities in the presence of missing tissue and artifacts. We overcome this challenge by developing a new registration technique that simultaneously classifies each pixel as \"good data\" / \"missing tissue\" / \"artifact\", learns a contrast transformation between modalities, and computes deformation parameters. We name this technique \"(D)eformable (R)egistration and (I)ntensity (T)ransformation with (M)issing (D)ata\", pronounced as \"Dr. It, M.D.\". In conjunction with convolutional neural networks, we use this technique to map the three dimensional distribution of tau tangles in the medial temporal lobe of an AD postmortem specimen.

neuroscience↗

bioRxiv2018

Lack of detectable neoantigen depletion in the untreated cancer genome

Somatic mutations in cancer can result in the presentation of mutated peptides on the cell surface, eliciting an immune response. Mutant peptides are presented via HLA molecules and are known as neoantigens. It has been suggested that selection acts against the underlying mutations, leading to neoantigen depletion. Knowing the extent of this specific form of immunoediting may provide fundamental insights into tumour-immune interactions during tumour evolution. Here, we quantified the extent of neoantigen depletion in a wide range of human cancers by studying somatic mutations in the HLA-binding annotated exome, i.e. genomic regions that can be translated into presented peptides. We initially observed reduced non-synonymous mutation rates in presented regions, suggestive of neoantigen depletion. However, when compared to the expected mutation rates from a trinucleotide-based mutational signature model, depletion signals were negligible. This is explained by correlative relationships between the likelihood of mutagenesis in different nucleotide sequences and predicted HLA affinities for corresponding peptides. Our results suggest that signals of immunogenic negative selection are weak or absent in cancer genomics data and that other mechanisms to escape immune responses early during tumour evolution might be more efficient.

genomics↗

bioRxiv2018

A Versatile Rapture (RAD-Capture) Platform for Genotyping Marine Turtles

Advances in high-throughput sequencing (HTS) technologies coupled with increased interdisciplinary collaboration is rapidly expanding capacity in the scope and scale of wildlife genetic studies. While existing HTS methods can be directly applied to address some evolutionary and ecological questions, certain research goals necessitate tailoring methods to specific study organisms, such as high-throughput genotyping of the same loci that are comparable over large spatial and temporal scales. These needs are particularly common for studies of highly mobile species of conservation concern like marine turtles, where life history traits, limited financial resources and other constraints require affordable, adaptable methods for HTS genotyping to meet a variety of study goals. Here, we present a versatile marine turtle HTS targeted enrichment platform adapted from the recently developed Rapture (RAD-Capture) method specifically designed to meet these research needs. Our results demonstrate consistent enrichment of targeted regions throughout the genome and discovery of candidate variants in all species examined for use in various conservation genetics applications. Accurate species identification confirmed the ability of our platform to genotype over 1,000 multiplexed samples, and identified areas for future methodological improvement such as optimization for low initial concentration samples. Finally, analyses within green turtles supported the ability of this platform to identify informative SNPs for stock structure, population assignment and other applications over a broad geographic range of interest to management. This platform provides an additional tool for marine turtle genetic studies and broadens capacity for future large-scale initiatives such as collaborative global marine turtle genetic databases.

genomics↗

bioRxiv2018

Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

The tumour microenvironment comprises complex cellular compositions and interactions between cancer, immune, and stromal components which all play crucial roles in cancer. Various computational approaches have been developed during the last decade that estimate the relative abundance of different cell types in an unbiased manner using bulk tumour RNA data. However, a comparison that objectively evaluates the performance of these approaches against one another has not been conducted. Here we benchmarked six widely used tools and gene sets: Bindea et al. gene sets, Davoli et al. gene sets, CIBERSORT, MCP-counter, TIMER, and xCell. We also introduce ConsensusTME, a consensus approach that uses the union of genes that the six tools used for cell estimation, and corrects for tumour-type specificity. We benchmarked the seven tools using TCGA DNA-derived purity scores (33 tumour-types), methylation-derived leukocyte scores (30 tumour-types), and H&E deep learning derived lymphocyte counts (13 tumour types), and individual benchmark data sets (PBMCs and 2 tumour types). Although none of the seven tools outperformed others in every benchmark, ConsensusTME ranked consistently well in all cancer-related benchmarks making it the top performing method overall. Computational methods that provide robust and accurate estimates of non-cancerous cell populations in the tumour microenvironment from tumour bulk expression data are important tools that can advance our understanding of tumour, immune, and stroma interactions, with potential clinical application if high accuracy estimates are achieved.

bioinformatics↗

bioRxiv2018

Asymmetrically Positioned Flagellar Control Units Regulate Human Sperm Rotation

The ability of sperm to fertilize an egg is controlled by ion channels, one of which is the pH-dependent calcium channel of sperm CatSper. For CatSper to be fully activated, the cytoplasmic pH must be alkaline, which is accomplished by either proton transporters, or a faster mechanism, such as the voltage-gated proton channel Hv1. To ensure effective regulation, these channels and regulatory proteins must be tightly compartmentalized. Here, we characterize human sperm nanodomains that are comprised of Hv1, CatSper and regulatory protein ABHD2. Super-resolution microscopy revealed that Hv1 forms asymmetrically positioned bilaterally distributed longitudinal lines that span the entire length of the sperm tail. Such a distribution provides a direct structural basis for the selective activation of CatSper, and subsequent flagellar rotation along the long axis that, together with hyperactivated motility, enhances sperm fertility. Indeed, Hv1 inhibition leads to a decrease in sperm rotation. Thus, sperm ion channels are organized in distinct regulatory nanodomains that control hyperactivated motility and rotation.

cell biology↗

bioRxiv2018

Detection of race-specific resistance against Puccinia coronata f. sp. avenae in Brachypodium species

Oat crown rust caused by Puccinia coronata f. sp. avenae is the most destructive foliar disease of cultivated oat. Characterization of genetic factors controlling resistance responses to Puccinia coronata f. sp. avenae in non-host species could provide new resources for developing disease protection strategies in oat. We examined symptom development and fungal colonization levels of a collection of Brachypodium distachyon and B. hybridum accessions infected with three North American P. coronata f. sp. avenae isolates. Our results demonstrated that colonization phenotypes are dependent on both host and pathogen genotypes, indicating a role for race-specific responses in these interactions. These responses were independent of the accumulation of reactive oxygen species. Expression analysis of several defense-related genes suggested that salicylic acid and ethylene-mediated signaling, but not jasmonic acid are components of resistance reaction to P. coronata f. sp. avenae. Our findings provide the basis to conduct a genetic inheritance study to examine if effector-triggered immunity contributes to non-host resistance to P. coronata f. sp. avenae in Brachypodium species.

pathology↗

bioRxiv2018

On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 m meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.\n\nAuthor SummaryNew developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain. Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brains neural circuitry and shape. This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial. Typically, neuroanatomists use reference volumes of the same subject (e.g. MRI) to guide reconstruction. However, obtaining reference imagery is often non-standard, as in high-throughput animal models like mouse histology. Others have proposed using anatomical atlases as guides, but have not accounted for the intrinsic nonlinear shape difference from atlas to subject. Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation. This accounts for intrinsic shape differences and enables rigorous, direct comparisons of atlas and subject coordinates. Using simulations, we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping. We also demonstrate our method on the Mouse Brain Architecture dataset, successfully mapping and reconstructing over 500 brains.

neuroscience↗