Biology subjects

Shukla, M.

Publications and source records attributed to Shukla, M..

3 recordsLinked to original sources

bioRxiv2018

Inter-species conservation of organisation and function between non-homologous regional centromeres

Despite the conserved essential function of centromeres, centromeric DNA itself is not conserved1-4. The histone-H3 variant, CENP-A, is the epigenetic mark that specifies centromere identity5-8. Paradoxically, CENP-A normally assembles on particular sequences at specific genomic locations. To gain insight into the specification of complex centromeres we took an evolutionary approach, fully assembling genomes and centromeres of related fission yeasts. Centromere domain organization, but not sequence, is conserved between Schizosaccharomyces pombe, S. octosporus and S. cryophilus with a central CENP-ACnp1 domain flanked by heterochromatic outer-repeat regions. Conserved syntenic clusters of tRNA genes and 5S rRNA genes occur across the centromeres of S. octosporus and S. cryophilus, suggesting conserved function. Remarkably, non-homologous centromere central-core sequences from S. octosporus are recognized in S. pombe, resulting in cross-species establishment of CENP-ACnp1 chromatin and functional kinetochores. Therefore, despite the lack of sequence conservation, Schizosaccharomyces centromere DNA possesses intrinsic conserved properties that promote assembly of CENP-A chromatin. Thus, centromere DNA can be recognized and function over unprecedented evolutionary timescales.

genomics↗

bioRxiv2017

Centromeric DNA destabilizes H3 nucleosomes to promote CENP-A deposition during the cell cycle

Active centromeres are defined by the presence of nucleosomes containing CENP-A, a histone H3 variant, which alone is sufficient to direct kinetochore assembly. Once assembled at a location CENP-A chromatin and kinetochores are maintained at that location though a positive feedback loop where kinetochore proteins recruited by CENP-A itself promote deposition of new CENP-A following replication. Although CENP-A chromatin itself is a heritable entity, it is normally associated with specific sequences. Intrinsic properties of centromeric DNA may favour the assembly of CENP-A rather than H3 nucleosomes. Here we investigate histone dynamics on centromeric DNA. We show that during S-phase histone H3 is deposited as a placeholder at fission yeast centromeres and is subsequently evicted in G2 when we detect deposition of the majority of new CENP-ACnp1. We also find that centromeric DNA has an innate property of driving high rates of turnover of H3 containing nucleosomes resulting in low nucleosome occupancy. When placed at an ectopic chromosomal location in the absence of any CENP-ACnp1 assembly, centromeric DNA retains its ability to impose S-phase deposition and G2 eviction of H3, suggesting that features within this DNA program H3 dynamics. As RNAPII occupancy on this centromere DNA coincides with H3 eviction in G2, we propose a model in which RNAPII-coupled chromatin remodelling promotes replacement of H3 with CENP-ACnp1 nucleosomes.

cell biology↗

bioRxiv2017

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

bioinformatics↗