scholarly journals Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI)

Science ◽  
2017 ◽  
Vol 356 (6334) ◽  
pp. 189-194 ◽  
Author(s):  
Chongyi Chen ◽  
Dong Xing ◽  
Longzhi Tan ◽  
Heng Li ◽  
Guangyu Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Whole Genome ◽  
Linear Amplification ◽  
Transposon Insertion ◽  
Genome Analyses

scholarly journals Conjugative plasmids: vessels of the communal gene pool

2009 ◽  
Vol 364 (1527) ◽  
pp. 2275-2289 ◽  
Author(s):  
Anders Norman ◽  
Lars H. Hansen ◽  
Søren J. Sørensen
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Significant Contribution ◽  
Prokaryotic Genome ◽  
Whole Genome ◽  
Adaptive Traits ◽  
Conjugative Plasmids ◽  
Environmental Setting ◽  
Genome Analyses ◽  
Insight Into

Comparative whole-genome analyses have demonstrated that horizontal gene transfer (HGT) provides a significant contribution to prokaryotic genome innovation. The evolution of specific prokaryotes is therefore tightly linked to the environment in which they live and the communal pool of genes available within that environment. Here we use the term supergenome to describe the set of all genes that a prokaryotic ‘individual’ can draw on within a particular environmental setting. Conjugative plasmids can be considered particularly successful entities within the communal pool, which have enabled HGT over large taxonomic distances. These plasmids are collections of discrete regions of genes that function as ‘backbone modules’ to undertake different aspects of overall plasmid maintenance and propagation. Conjugative plasmids often carry suites of ‘accessory elements’ that contribute adaptive traits to the hosts and, potentially, other resident prokaryotes within specific environmental niches. Insight into the evolution of plasmid modules therefore contributes to our knowledge of gene dissemination and evolution within prokaryotic communities. This communal pool provides the prokaryotes with an important mechanistic framework for obtaining adaptability and functional diversity that alleviates the need for large genomes of specialized ‘private genes’.

scholarly journals MBRS-59. SINGLE-CELL WHOLE-GENOME SEQUENCING DISSECTS INTRA-TUMOURAL GENOMIC HETEROGENEITY AND CLONAL EVOLUTION IN CHILDHOOD MEDULLOBLASTOMA

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii408-iii408
Author(s):  
Marina Danilenko ◽  
Masood Zaka ◽  
Claire Keeling ◽  
Stephen Crosier ◽  
Rafiqul Hussain ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Single Cell ◽  
Genome Sequencing ◽  
Copy Number ◽  
Single Cell Analysis ◽  
Mutational Analysis ◽  
Single Cells ◽  
Clonal Evolution ◽  
Whole Genome ◽  
Clinically Significant

Abstract Medulloblastomas harbor clinically-significant intra-tumoral heterogeneity for key biomarkers (e.g. MYC/MYCN, β-catenin). Recent studies have characterized transcriptional heterogeneity at the single-cell level, however the underlying genomic copy number and mutational architecture remains to be resolved. We therefore sought to establish the intra-tumoural genomic heterogeneity of medulloblastoma at single-cell resolution. Copy number patterns were dissected by whole-genome sequencing in 1024 single cells isolated from multiple distinct tumour regions within 16 snap-frozen medulloblastomas, representing the major molecular subgroups (WNT, SHH, Group3, Group4) and genotypes (i.e. MYC amplification, TP53 mutation). Common copy number driver and subclonal events were identified, providing clear evidence of copy number evolution in medulloblastoma development. Moreover, subclonal whole-arm and focal copy number alterations covering important genomic loci (e.g. on chr10 of SHH patients) were detected in single tumour cells, yet undetectable at the bulk-tumor level. Spatial copy number heterogeneity was also common, with differences between clonal and subclonal events detected in distinct regions of individual tumours. Mutational analysis of the cells allowed dissection of spatial and clonal heterogeneity patterns for key medulloblastoma mutations (e.g. CTNNB1, TP53, SMARCA4, PTCH1) within our cohort. Integrated copy number and mutational analysis is underway to establish their inter-relationships and relative contributions to clonal evolution during tumourigenesis. In summary, single-cell analysis has enabled the resolution of common mutational and copy number drivers, alongside sub-clonal events and distinct patterns of clonal and spatial evolution, in medulloblastoma development. We anticipate these findings will provide a critical foundation for future improved biomarker selection, and the development of targeted therapies.

scholarly journals A whole-genome screen identifies Salmonella enterica serovar Typhi genes involved in fluoroquinolone susceptibility

2020 ◽  
Vol 75 (9) ◽  
pp. 2516-2525
Author(s):  
A Keith Turner ◽  
Sabine E Eckert ◽  
Daniel J Turner ◽  
Muhammud Yasir ◽  
Mark A Webber ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Insertion Site ◽  
Whole Genome ◽  
Genome Screen ◽  
Polysaccharide Biosynthesis ◽  
Salmonella Enterica Serovar Typhi ◽  
Transposon Insertion ◽  
Associated Functions ◽  
The Impact ◽  
Insertion Mutations

Abstract Objectives A whole-genome screen at sub-gene resolution was performed to identify candidate loci that contribute to enhanced or diminished ciprofloxacin susceptibility in Salmonella enterica serovar Typhi. Methods A pool of over 1 million transposon insertion mutants of an S. Typhi Ty2 derivative were grown in a sub-MIC concentration of ciprofloxacin, or without ciprofloxacin. Transposon-directed insertion site sequencing (TraDIS) identified relative differences between the mutants that grew following the ciprofloxacin treatment compared with the untreated mutant pool, thereby indicating which mutations contribute to gain or loss of ciprofloxacin susceptibility. Results Approximately 88% of the S. Typhi strain’s 4895 annotated genes were assayed, and at least 116 were identified as contributing to gain or loss of ciprofloxacin susceptibility. Many of the identified genes are known to influence susceptibility to ciprofloxacin, thereby providing method validation. Genes were identified that were not known previously to be involved in susceptibility, and some of these had no previously known phenotype. Susceptibility to ciprofloxacin was enhanced by insertion mutations in genes coding for efflux, other surface-associated functions, DNA repair and expression regulation, including phoP, barA and marA. Insertion mutations that diminished susceptibility were predominantly in genes coding for surface polysaccharide biosynthesis and regulatory genes, including slyA, emrR, envZ and cpxR. Conclusions A genomics approach has identified novel contributors to gain or loss of ciprofloxacin susceptibility in S. Typhi, expanding our understanding of the impact of fluoroquinolones on bacteria and of mechanisms that may contribute to resistance. The data also demonstrate the power of the TraDIS technology for antibacterial research.

scholarly journals Identifying tumor clones in sparse single-cell mutation data

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i186-i193
Author(s):  
Matthew A Myers ◽  
Simone Zaccaria ◽  
Benjamin J Raphael
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Sequencing Coverage ◽  
Sequencing Technologies ◽  
Low Coverage ◽  
Clonal Composition ◽  
Cancer Studies

Abstract Motivation Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. Results We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. Availability and implementation SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. Supplementary information Supplementary data are available at Bioinformatics online.

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