Biology subjects

Kelley, D. R.

Publications and source records attributed to Kelley, D. R..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

The Class VIII myosin ATM1 is required for root apical meristem function

Myosins are evolutionarily conserved motor proteins that interact with actin filaments to regulate organelle transport, cytoplasmic streaming and cell growth. Plant-specific Class XI myosin proteins direct cell division and root organogenesis. However, the roles of plantspecific Class VIII myosin proteins in plant growth and development are less understood. Here, we investigated the function of an auxin-regulated Class VIII myosin, Arabidopsis thaliana Myosin 1 (ATM1), using genetics, transcriptomics, and live cell microscopy. ATM1 is expressed in the primary root, adventitious roots and throughout lateral root development. ATM1 is a plasma membrane localized protein that is enriched in actively dividing cells in the root apical meristem (RAM). Loss of ATM1 function results in impaired primary root growth due to decreased RAM size and reduced cell proliferation in a sugar-dependent manner. In ATM1 loss-of-function roots, columella reporter gene expression is diminished, and fewer columella stem cell divisions occur. In addition, atm1-1 roots displayed reduced auxin responses and auxin marker gene expression. Complementation of atm1-1 with a tagged ATM1 driven under the native ATM1 promoter restored root growth and cell cycle progression in the root meristem. Collectively, these results provide novel evidence that ATM1 functions to influence cell proliferation and columella differentiation in primary roots in response to auxin and sugar cues.

plant biology↗

bioRxiv2022

The complete cell atlas of an aging multicellular organism

Here we describe a single-cell atlas of aging for the nematode Caenorhabditis elegans. This unique resource describes the expression across adulthood of over 20,000 genes among 211 groups of cells that correspond to virtually every cell type in this organism. Our findings suggest that C. elegans aging is not random and stochastic in nature, but rather characterized by coordinated changes in functionally related metabolic and stress-response genes in a highly cell-type specific fashion. Aging signatures of different cell types are largely different from one another, downregulation of energy metabolism being the only nearly universal change. Some biological pathways, such as genes associated with translation, DNA repair and the ER unfolded protein response, exhibited strong (in some cases opposite) changes in subsets of cell types, but many more were limited to a single cell type. Similarly, the rates at which cells aged, measured as genome-wide expression changes, differed between cell types; some of these differences were tested and validated in vivo by measuring age-dependent changes in mitochondrial morphology. In some, but not all, cell types, aging was characterized by an increase in cell-to-cell variance. Finally, we identified a set of transcription factors whose activities changed coordinately across many cell types with age. This set was strongly enriched for stress-resistance TFs known to influence the rate of aging. We tested other members of this set, and discovered that some, such as GEI-3, likely also regulate the rate of aging. Our dataset can be accessed and queried at c.elegans.aging.atlas.research.calicolabs.com/.

genomics↗

bioRxiv2022

The genetic and biochemical determinants of mRNA degradation rates in mammals

BackgroundDegradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable a more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. ResultsHere, we establish a compendium of 54 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. ConclusionsOur work produces a more robust "ground truth" with regards to transcriptome-wide mRNA half-lives in mammalian cells. Using these consolidated measurements, we trained a model that is over 50% more accurate in predicting half-life from sequence than existing models. Our best model, Saluki, succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

genomics↗

bioRxiv2022

Temporal and spatial auxin responsive networks in maize primary roots

Auxin is a key regulator of root morphogenesis across angiosperms. To better understand auxin regulated networks underlying maize root development we have characterized auxin responsive transcription across two time points (30 and 120 minutes) and four regions of the primary root: the meristematic zone, elongation zone, cortex, and stele. Hundreds of auxin-regulated genes involved in diverse biological processes were quantified in these different root regions. In general, most auxin regulated genes are region unique and are predominantly observed in differentiated tissues compared to the root meristem. Auxin gene regulatory networks (GRNs) were reconstructed with these data to identify key transcription factors that may underlie auxin responses in maize roots. Additionally, Auxin Response Factor (ARF) subnetworks were generated to identify target genes which exhibit tissue or temporal specificity in response to auxin. These networks describe novel molecular connections underlying maize root development and provide a foundation for functional genomic studies in a key crop.

plant biology↗

bioRxiv2021

scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks

1Single cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.

bioinformatics↗

bioRxiv2021

Revisiting the Hayflick Limit: Insights from an Integrated Analysis of Changing Transcripts, Proteins, Metabolites and Chromatin

The process wherein dividing cells exhaust proliferative capacity and enter into replicative senescence has become a prominent model for cellular aging in vitro. Despite decades of study, this cellular state is not fully understood in culture and even much less so during aging. Here, we revisit Leonard Hayflicks original observation of replicative senescence in WI-38 human lung fibroblasts equipped with a battery of modern techniques including RNA-seq, single cell RNA-seq, proteomics, metabolomics, and ATAC-seq. We find evidence that the transition to a senescent state manifests early, increases gradually, and corresponds to a concomitant global increase in DNA accessibility in nucleolar and lamin associated domains. Furthermore, we demonstrate that senescent WI-38 cells acquire a striking resemblance to myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT) that is regulated by the transcription factors YAP1/TEAD1 and TGF-{beta}2. Lastly, we show that verteporfin inhibition of YAP1/TEAD1 activity in aged WI-38 cells robustly attenuates this gene expression program.

genomics↗

bioRxiv2021

Effective gene expression prediction from sequence by integrating long-range interactions

The next phase of genome biology research requires understanding how DNA sequence encodes phenotypes, from the molecular to organismal levels. How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequence through the use of a new deep learning architecture called Enformer that is able to integrate long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Notably, Enformer outperformed the best team on the critical assessment of genome interpretation (CAGI5) challenge for noncoding variant interpretation with no additional training. Furthermore, Enformer learned to predict promoter-enhancer interactions directly from DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of growing human disease associations to cell-type-specific gene regulatory mechanisms and provide a framework to interpret cis-regulatory evolution. To foster these downstream applications, we have made the pre-trained Enformer model openly available, and provide pre-computed effect predictions for all common variants in the 1000 Genomes dataset. One-sentence summaryImproved noncoding variant effect prediction and candidate enhancer prioritization from a more accurate sequence to expression model driven by extended long-range interaction modelling.

genomics↗

bioRxiv2021

slim shady is a novel allele of PHYTOCHROMEB present in the T-DNA line SALK_015201

Auxin is a hormone that is required for hypocotyl elongation during seedling development. In response to auxin rapid changes in transcript and protein abundance occur in hypocotyls and some auxin responsive gene expression is linked to hypocotyl growth. To functionally validate proteomic studies, a reverse genetics screen was performed on mutants in auxin-regulated proteins to identify novel regulators of plant growth. This uncovered a long hypocotyl mutant, which we called slim shady, in an annotated insertion line in IMMUNOREGULATORY RNA-BINDING PROTEIN (IRR). Overexpression of the IRR gene failed to rescue the slim shady phenotype and characterization of a second T-DNA allele of IRR found that it had a wild-type hypocotyl length. The slim shady mutant has an elevated expression of numerous genes associated with the brassinosteroid-auxin-phytochrome (BAP) regulatory module compared to wild-type, including transcription factors that regulate brassinosteroid, auxin and phytochrome pathways. Additionally, slim shady seedlings fail to exhibit a strong transcriptional response to auxin. Using whole genome sequence and transcriptomics data for SALK_015201C we determined that a novel single nucleotide polymorphism in PHYTOCHROME B was responsible for the slim shady phenotype. This is predicted to convert induce a frameshift and premature stop codon at leucine 1125, within the histidine kinase-related domain of the carboxy terminus of PHYB, which is required for phytochrome signaling and function. Genetic complementation analyses with phyb-9 confirmed that slim shady is a mutant allele of PHYB. This study advances our understanding of the molecular mechanisms in seedling development, by furthering our understanding of how light signaling is linked to auxin dependent cell elongation. Furthermore, this study highlights the importance of confirming the genetic identity of research material before attributing phenotypes to known mutations sourced from T-DNA stocks.

plant biology↗

bioRxiv2021

The landscape of alternative polyadenylation in single cells of the developing mouse embryo

3' untranslated regions (3' UTRs) post-transcriptionally regulate mRNA stability, localization, and translation rate. While 3'-UTR isoforms have been globally quantified in limited cell types using bulk measurements, their differential usage among cell types during mammalian development remains poorly characterized. In this study, we examined a dataset comprising [~]2 million cells spanning E9.5-E13.5 of mouse embryonic development to quantify transcriptome-wide changes in alternative polyadenylation (APA). We observe a global lengthening of 3' UTRs across embryonic stages in all cell types, although we detect shorter 3' UTRs in hematopoietic lineages and longer 3' UTRs in neuronal cell types within each stage. An analysis of RBP dynamics identifies ELAV-like family members, which are concomitantly induced in neuronal lineages and developmental stages experiencing 3'-UTR lengthening, as putative regulators of APA. By measuring 3'-UTR isoforms in an expansive single cell dataset, our work provides a transcriptome-wide and organism-wide map of the dynamic landscape of alternative polyadenylation during mammalian organogenesis.

genomics↗

bioRxiv2021

Quantitative proteomics reveals extensive lysine ubiquitination in the Arabidopsis root proteome and uncovers novel transcription factor stability states

Protein activity, abundance, and stability can be regulated by posttranslational modification including ubiquitination. Ubiquitination is conserved among eukaryotes and plays a central role in modulating cellular function and yet we lack comprehensive catalogs of proteins that are modified by ubiquitin in plants. In this study, we describe an antibody-based approach to enrich peptides containing the di-glycine (diGly) remnant of ubiquitin and coupled that with isobaric labeling to enable quantification, from up to 16-multiplexed samples, for plant tissues. Collectively, we identified 7,130 diGly-modified lysine residues sites arising from 3,178 proteins in Arabidopsis primary roots. These data include ubiquitin proteasome dependent ubiquitination events as well as ubiquitination events associated with auxin treatment. Gene Ontology analysis indicated that ubiquitinated proteins are associated with numerous biological processes including hormone signaling, plant defense, protein homeostasis, and root morphogenesis. We determined the ubiquitinated lysine residues that directly regulate the stability of the transcription factors CRYPTOCHROME-INTERACTING BASIC-HELIX-LOOP-HELIX 1 (CIB1), CIB1 LIKE PROTEIN 2 (CIL2), and SENSITIVE TO PROTON RHIZOTOXICITY (STOP1) using site directed mutagenesis and in vivo degradation assays. These comprehensive site-level ubiquitinome profiles provide a wealth of data for future studies related to modulation of biological processes mediated by this posttranslational modification in plants.

plant biology↗

bioRxiv2020

Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6,121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.

genomics↗

bioRxiv2020

scNym: Semi-supervised adversarial neural networks for single cell classification

Annotating cell identities is a common bottleneck in the analysis of single cell genomics experiments. Here, we present scNym, a semi-supervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled datasets and new, unlabeled datasets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing methods. We also show that scNym models can synthesize information from multiple training and target datasets to improve performance. In addition to high performance, we show that scNym models are well-calibrated and interpretable with saliency methods.

bioinformatics↗

bioRxiv2020

Differentiation reveals the plasticity of age-related change in murine muscle progenitors

Skeletal muscle experiences a decline in lean mass and regenerative potential with age, in part due to intrinsic changes in progenitor cells. However, it remains unclear if age-related changes in progenitors persist across a differentiation trajectory or if new age-related changes manifest in differentiated cells. To investigate this possibility, we performed single cell RNA-seq on muscle mononuclear cells from young and aged mice and profiled muscle stem cells (MuSCs) and fibro/adipose progenitors (FAPs) after differentiation. Differentiation increased the magnitude of age-related change in MuSCs and FAPs, but also masked a subset of age-related changes present in progenitors. Using a dynamical systems approach and RNA velocity, we found that aged MuSCs follow the same differentiation trajectory as young cells, but stall in differentiation near a commitment decision. Our results suggest that age-related changes are plastic across differentiation trajectories and that fate commitment decisions are delayed in aged myogenic cells.

systems biology↗

bioRxiv2019

Solo: doublet identification via semi-supervised deep learning

AO_SCPLOWBSTRACTC_SCPLOWSingle cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these "doublets" violate the fundamental premise of single cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells beyond any previous approach.

bioinformatics↗

bioRxiv2019

Predicting 3D genome folding from DNA sequence

In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Here we present a deep convolutional neural network, Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of CTCF and reveal a complex grammar underlying genome folding. Akita enables rapid in silico predictions for sequence mutagenesis, genome folding across species, and genetic variants.

genomics↗

bioRxiv2019

Evaluating the informativeness of deep learning annotations for human complex diseases

Deep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotations informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results - only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

genetics↗

bioRxiv2019

A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging

BackgroundAging is a pleiotropic process affecting many aspects of organismal and cellular physiology. Mammalian organisms are composed of a constellation of distinct cell type and state identities residing within different tissue environments. Due to technological limitations, the study of aging has traditionally focused on changes within individual cell types, or the aggregate changes across cell types within a tissue. The influence of cell identity and tissue environment on the trajectory of aging therefore remains unclear.\n\nResultsHere, we perform single cell RNA-seq on >50,000 individual cells across three tissues in young and aged mice. These molecular profiles allow for comparison of aging phenotypes across cell types and tissue environments. We find transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we compute a trajectory and magnitude of aging for each cell type. We find that cell type exerts a larger influence on these measures than tissue environment.\n\nConclusionIn this study, we use single cell RNA-seq to dissect the influence of cell identity and tissue environment on the aging process. Single cell analysis reveals that cell identities age in unique ways, with some common features of aging shared across identities. We find that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.

bioinformatics↗

bioRxiv2019

Cross-species regulatory sequence activity prediction

Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, model organisms have been less explored. Model organism genomes offer both additional training sequences and unique annotations describing tissue and cell states unavailable in humans. Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple genomes and apply it to learn sequence predictors for large compendia of human and mouse data. Training on both genomes improves gene expression prediction accuracy on held out sequences. We further demonstrate a novel and powerful transfer learning approach to use mouse regulatory models to analyze human genetic variants associated with molecular phenotypes and disease. Together these techniques unleash thousands of non-human epigenetic and transcriptional profiles toward more effective investigation of how gene regulation affects human disease.

bioinformatics↗