Biology subjects

Walters, P.

Publications and source records attributed to Walters, P..

4 recordsLinked to original sources

bioRxiv2021

Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the Phylum

An overwhelming majority of bacterial life remains uncharacterized. Recent efforts to assemble genomes from metagenomes have provided invaluable insights into these yet-uncultured bacterial lineages. We report on the characterization of 30 genomes belonging to three novel classes within the phylum Desulfobacterota. One class (proposed name Candidatus "Anaeroferrophillalia") was characterized by the capacity for heterotrophic growth, either fermentatively or utilizing polysulfide, tetrathionate and thiosulfate as electron acceptors. Autotrophic growth using the Wood Ljungdahl pathway and hydrogen or Fe(II) as an electron donor could also occur in absence of organic carbon sources. The second class (proposed name Candidatus "Anaeropigmentia") was characterized by its capacity for fermentative or aerobic growth at low oxygen thresholds using a broad range of sugars and amino acids, and the capacity to synthesize the methyl/alkyl carrier CoM, an ability that is prevalent in the archaeal but rare in the bacterial domain. Pigmentation is inferred from the capacity for carotenoids (lycopene) production, as well as the occurrence of the majority of genes involved in bacteriochlorophyll a biosynthesis. The third class (proposed name Candidatus "Zymogenia") was characterized by the capacity for heterotrophic growth fermentatively using broad sugars and amino acids as carbon sources, and the adaptation of some of its members to hypersaline habitats. Analysis of the distribution pattern of all three classes showed their occurrence as rare community members in multiple habitats, with preferences for anaerobic terrestrial (e.g. hydrocarbon contaminated environments, wetlands, bioreactors), freshwater (e.g. ground water and gas-saturated temperate lakes), and marine (e.g. hydrothermal vents, marine sediments, and coastal sediments) environments, over oxygenated (e.g. pelagic ocean and agricultural land) settings. Special preference for some members of the class Candidatus "Zymogenia" to hypersaline environments, e.g. hypersaline microbial mats and lagoons was observed. ImportanceCulture-independent diversity surveys conducted in the last three decades have clearly demonstrated that the scope of microbial diversity is much broader than that inferred from isolation efforts. Multiple reasons have been put forth to explain the refractiveness of a wide range of the earths microbiome to isolation efforts. Documenting the scope of high-rank phylogenetic diversity on earth, as well as deciphering and documenting the metabolic capacities, physiological preferences, and putative ecological roles of these yet-uncultured lineages represents one of the central goals in current microbial ecology research. Recent efforts to assemble genomes from metagenomes have provided invaluable insights into these yet-uncultured lineages. This study expands our knowledge of the phylum Desulfobacterota through the characterization of 30 genomes belonging to three novel classes. The analyzed genomes were either recovered from Zodletone Spring in southwestern Oklahoma in this study, or recently binned from public metagenomes as part of the Global Earth Microbiome initiative. Our results expand the high-rank diversity within the bacterial tree of life by describing three novel classes within the phylum Desulfobacterota, document the utilization of multiple metabolic processes, e.g. iron-oxidation, aromatic hydrocarbon degradation, reduction of sulfur-cycling intermediates, and features, e.g. coenzyme M biosynthesis, and pigmentation, as salient characteristics in these novel Desulfobacterota classes.

microbiology↗

bioRxiv2019

Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via \"mapping\" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

bioinformatics↗

bioRxiv2018

Resource: Scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires

Essential features of cancer tissue cellular heterogeneity such as negatively selected genome topologies, sub-clonal mutation patterns and genome replication states can only effectively be studied by sequencing single-cell genomes at scale and high fidelity. Using an amplification-free single-cell genome sequencing approach implemented on commodity hardware (DLP+) coupled with a cloud-based computational platform, we define a resource of 40,000 single-cell genomes characterized by their genome states, across a wide range of tissue types and conditions. We show that shallow sequencing across thousands of genomes permits reconstruction of clonal genomes to single nucleotide resolution through aggregation analysis of cells sharing higher order genome structure. From large-scale population analysis over thousands of cells, we identify rare cells exhibiting mitotic mis-segregation of whole chromosomes. We observe that tissue derived scWGS libraries exhibit lower rates of whole chromosome anueploidy than cell lines, and loss of p53 results in a shift in event type, but not overall prevalence in breast epithelium. Finally, we demonstrate that the replication states of genomes can be identified, allowing the number and proportion of replicating cells, as well as the chromosomal pattern of replication to be unambiguously identified in single-cell genome sequencing experiments. The combined annotated resource and approach provide a re-implementable large scale platform for studying lineages and tissue heterogeneity.

genomics↗

bioRxiv2018

clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers

Measuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we present clonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell population. We apply clonealign to triple-negative breast cancer patient derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either DNA-Seq or RNA-Seq alone.

bioinformatics↗