Biology subjects

Dotson, G. A.

Publications and source records attributed to Dotson, G. A..

6 recordsLinked to original sources

bioRxiv2022

Obesity disrupts innate-adaptive immune network patterning in adipose tissue

ObjectiveObesity-induced metabolic dysfunction, tissue remodelling, and chronic inflammation in visceral white adipose-tissue (WAT) are correlated with insulin resistance, type II diabetes, and metabolic disease pathogenesis [1]. In this work, we sought to establish spatio-temporal context of adipose tissue macrophage (ATM) reprogramming during obesity. MethodsWe captured single-cell RNA-sequencing, spatial transcriptomics, and histological imagining of murine WAT over the course of diet-induced obesity to study macrophage phenotype dynamics. We developed a straightforward mathematical approach to integrating multi-modal data to quantify obesityinduced changes to WAT organization. We aligned ATM phenotypes with crown-like structures (CLS) in early obesity and used spatial network analysis to uncover signalling mechanisms implicated in CLS formation. ResultsWe identified novel diversity of the lipid-associated macrophage (LAM) phenotype, whose transcriptional profile, signaling mechanisms, and spatial context serve as indicators of CLS formation in early obesity. We demonstrated that dysregulation of lipid-metabolic signalling is a critical turning point in the monocyte-LAM lineage and identified novel ligand-receptor mechanisms including Apoe, Lrp1, Lpl and App that serve as hallmarks of nascent CLS in WAT. ConclusionsMulti-modal spatio-temporal profiling demonstrates that LAMs disproportionately accumulate in CLS and are preceded by a transition-state macrophage phenotype with monocytic origins. We identified novel ligand-receptor interactions implicated in nascent CLS regions which may guide future cellular-reprogramming interventions for obesity-related sequelae. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/482134v6_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1d7ac5borg.highwire.dtl.DTLVardef@74c5c7org.highwire.dtl.DTLVardef@64b722org.highwire.dtl.DTLVardef@33392e_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- We characterize a novel lipid-associated macrophage (LAM) phenotype along the monocyte-LAM lineage - Integrated imaging, single-cell sequencing and spatial transcriptomics data show that LAMs accumulate at nascent CLS - Analysis of spatial transcriptomics data reveals a novel set of ligands and receptors that implicate immature LAMs in shaping the CLS microenvironment in early obesity - We present a simple mathematical framework for studying dynamics of tissue-structure over time

cell biology↗

bioRxiv2021

OptiFit: an improved method for fitting amplicon sequences to existing OTUs

Assigning amplicon sequences to operational taxonomic units (OTUs) is often an important step in characterizing the composition of microbial communities across large datasets. OptiClust, a de novo OTU clustering method, has been shown to produce higher quality OTU assignments than other methods and at comparable or faster speeds. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added to a dataset. However, in some cases one may wish to incorporate new samples into a previously clustered dataset without performing clustering again on all sequences, such as when comparing across datasets or deploying machine learning models where OTUs are features. Existing reference-based clustering methods produce consistent OTUs, but they only consider the similarity of each query sequence to a single reference sequence in an OTU, thus resulting in OTU assignments that are significantly worse than those generated by de novo methods. To provide an efficient and robust method to fit amplicon sequence data to existing OTUs, we developed the OptiFit algorithm. Inspired by OptiClust, OptiFit considers the similarity of all pairs of reference and query sequences in an OTU to produce OTUs of the best possible quality. We tested OptiFit using four microbiome datasets with two different strategies: by clustering to an external reference database or by splitting the dataset into a reference and query set and clustering the query sequences to the reference set after clustering it using OptiClust. The result is an improved implementation of closed and open-reference clustering. OptiFit produces OTUs of similar quality as OptiClust and at faster speeds when using the split dataset strategy, although the OTU quality and processing speed depends on the database chosen when using the external database strategy. OptiFit provides a suitable option for users who require consistent OTU assignments at the same quality afforded by de novo clustering methods. ImportanceAdvancements in DNA sequencing technology have allowed researchers to affordably generate millions of sequence reads from microorganisms in diverse environments. Efficient and robust software tools are needed to assign microbial sequences into taxonomic groups for characterization and comparison of communities. The OptiClust algorithm produces high quality groups by comparing sequences to each other, but the assignments can change when new sequences are added to a dataset, making it difficult to compare different studies. Other approaches assign sequences to groups by comparing them to sequences in a reference database to produce consistent assignments, but the quality of the groups produced is reduced compared to OptiClust. We developed OptiFit, a new reference-based algorithm that produces consistent yet high quality assignments like OptiClust. OptiFit allows researchers to compare microbial communities across different studies or add new data to existing studies without sacrificing the quality of the group assignments.

microbiology↗

bioRxiv2021

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

scientific communication and education↗

bioRxiv2021

Cohesin Releasing Factor WAPL Regulates Genome Structure and Function of Mature T Cells

The cohesin complex modulates gene expression and cellular functions by shaping three-dimensional (3D) organization of chromatin. WAPL, cohesins DNA release factor, regulates 3D chromatin architecture. The 3D genome structure and its relevance to mature T cell functions in vivo is not well understood. We show that in vivo lymphopenic expansion, and allo-antigen driven proliferation, alters the 3D structure and function of the genome in mature T cells. Conditional deletion of Wapl in T cells reduced long-range genomic interactions, altered chromatin A/B compartments and interactions within topologically associating domains (TADs) of the chromatin in T cells at baseline. Comparison of chromatin structure in normal and WAPL-deficient T cells after lymphopenic and allo-antigen driven stimulation revealed reduced loop extensions with changes in cell cycling genes. WAPL-mediated changes in 3D architecture of chromatin regulated activation, cycling and proliferation of T cells in vitro and in vivo. Finally, WAPL-deficient T cells demonstrated reduced severity of graft-versus-host disease (GVHD) following experimental allogeneic hematopoietic stem cell transplantation. These data collectively characterize 3D genomic architecture of T cells in vivo and demonstrate biological and clinical implications for its disruption by cohesin release factor WAPL.

molecular biology↗

bioRxiv2020

TCF7L2 Silencing Reprograms the 4D Nucleome of Colorectal Cancer Cells

Canonical Wnt signaling is crucial for intestinal homeostasis as the major Wnt signaling effector in the intestines, TCF4, is required for stem cell maintenance. The capability of TCF4 to maintain the stem cell phenotype is contingent upon {beta}-catenin, a potent transcriptional activator which interacts with histone acetyltransferases and chromatin remodeling complexes. In colorectal cancer, mutations result in high levels of nuclear {beta}-catenin causing aberrant cell growth. Here, we used RNAi to explore the influence of TCF4 on chromatin structure (Hi-C) and gene expression (RNA sequencing) across a 72-hour time series in colorectal cancer. We found that TCF4 reduction results in a disproportionate upregulation of gene expression genome-wide, including a powerful induction of SOX2. Hi-C analysis revealed a general increase in chromatin compaction across the entire time series, though this did not influence gene expression. Analysis of local chromosome organization demonstrated a TAD boundary loss which influenced the expression of a cluster of CEACAM genes on chromosome 19. Four-dimensional nucleome (4DN) analysis, which integrates structural (Hi-C) and functional (RNA sequencing) data, identified EMT and E2F as the two most deregulated pathways and LUM, TMPO, and AURKA as highly influential genes in these networks. Results from gene expression, chromatin structure, and centrality analyses were then integrated to generate a list of candidate transcription factors for reprogramming of colorectal cancer cells to a vulnerable state. The top ranked transcription factor in our analysis was c-JUN, an oncoprotein known to interact with TCF4 and {beta}-catenin.

cancer biology↗

bioRxiv2018

Definitive demonstration by synthesis of genome annotation completeness

Bacteriophage oX174 was the first DNA genome to be sequenced. The genome is well studied by classical methods and is known to encode 11 essential genes. At least 23 closely-related Bullavirinae genome sequences are now available. We identified 315 potential open reading frames (ORFs) within the genome via bioinformatic analysis, and a subset of 82 highly-conserved ORFs that have no known gene products or functions. Using genome scale design and synthesis we made a mutant genome in which all 11 essential genes are simultaneously disrupted, leaving intact only the 82 conserved-but-cryptic ORFs. The resulting genome is not viable, as expected. Cell-free gene expression followed by mass spectrometry revealed only a single peptide expressed from both the cryptic-ORF and wild-type genomes, suggesting a potential new gene. A second synthetic genome in which 71 conserved cryptic ORFs were simultaneously disrupted is viable but with ~50% reduced fitness relative to the wild type. However, rather than finding any new genes, repeated evolutionary adaptation revealed a single point mutation modulating translation of gene H, a known essential gene, that fully suppressed the fitness defect. Taken together, we conclude that the annotation of ORFs for the oX174 genome is formally complete. Sequencing and bioinformatics followed by synthesis-enabled reverse genomics, proteomics, and evolutionary adaptation can definitely establish the sufficiency and completeness of natural genome annotations.

synthetic biology↗