Biology subjects

Shukla, M.

Publications and source records attributed to Shukla, M..

25 records · Page 2Linked to original sources

bioRxiv2020

Molecular Architecture of Early Dissemination and Evolution of the SARS-CoV-2 Virus in Metropolitan Houston, Texas

We sequenced the genomes of 320 SARS-CoV-2 strains from COVID-19 patients in metropolitan Houston, Texas, an ethnically diverse region with seven million residents. These genomes were from the viruses causing infections in the earliest recognized phase of the pandemic affecting Houston. Substantial viral genomic diversity was identified, which we interpret to mean that the virus was introduced into Houston many times independently by individuals who had traveled from different parts of the country and the world. The majority of viruses are apparent progeny of strains derived from Europe and Asia. We found no significant evidence of more virulent viral types, stressing the linkage between severe disease, underlying medical conditions, and perhaps host genetics. We discovered a signal of selection acting on the spike protein, the primary target of massive vaccine efforts worldwide. The data provide a critical resource for assessing virus evolution, the origin of new outbreaks, and the effect of host immune response. SignificanceCOVID-19, the disease caused by the SARS-CoV-2 virus, is a global pandemic. To better understand the first phase of virus spread in metropolitan Houston, Texas, we sequenced the genomes of 320 SARS-CoV-2 strains recovered from COVID-19 patients early in the Houston viral arc. We identified no evidence that a particular strain or its progeny causes more severe disease, underscoring the connection between severe disease, underlying health conditions, and host genetics. Some amino acid replacements in the spike protein suggest positive immune selection is at work in shaping variation in this protein. Our analysis traces the early molecular architecture of SARS-CoV-2 in Houston, and will help us to understand the origin and trajectory of future infection spikes.

genetics↗

bioRxiv2020

Predicting Antimicrobial Resistance Using Conserved Genes

A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases where the primary AMR mechanism results from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes for use in these models, we show that F1 scores and error rates are stable and have little variance between replicates. Potential biases from strain-specific SNPs, phylogenetic sampling, and imbalances in the phylogenetic distribution of susceptible and resistant strains do not appear to have an impact on this result. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes. Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.

bioinformatics↗

bioRxiv2019

Epigenetic gene silencing by heterochromatin primes fungal resistance

Genes embedded in H3 lysine 9 methylation (H3K9me)-dependent heterochromatin are transcriptionally silenced1-3. In fission yeast, Schizosaccharomyces pombe, H3K9me heterochromatin silencing can be transmitted through cell division provided the counteracting demethylase Epe1 is absent4,5. It is possible that under certain conditions wild-type cells might utilize heterochromatin heritability to form epimutations, phenotypes mediated by unstable silencing rather than changes in DNA6,7. Here we show that resistant heterochromatin-mediated epimutants are formed in response to threshold levels of the external insult caffeine. ChIP-seq analyses of unstable resistant isolates revealed new distinct heterochromatin domains, which in some cases reduce the expression of underlying genes that are known to confer resistance when deleted. Targeting synthetic heterochromatin at implicated loci confirmed that resistance results from heterochromatin-mediated silencing. Our analyses reveal that epigenetic processes allow wild-type fission yeast to adapt to non-favorable environments without altering their genotype. In some isolates, subsequent or co-occurring gene amplification events enhance resistance. Thus, heterochromatin-dependent epimutant formation provides a bet-hedging strategy that allows cells to remain genetically wild-type but transiently adapt to external insults. As unstable caffeine-resistant isolates show cross-resistance to the fungicide clotrimazole it is likely that related heterochromatin-dependent processes contribute to anti-fungal resistance in both plant and human pathogenic fungi.

genetics↗

bioRxiv2019

Hap2-Ino80 facilitated transcription promotes de novo establishment of CENP-A chromatin

Centromeres are maintained epigenetically by the presence of CENP-A, an evolutionarily-conserved histone H3 variant, which directs kinetochore assembly and hence, centromere function. To identify factors that promote assembly of CENP-A chromatin, we affinity selected solubilised fission yeast CENP-ACnp1 chromatin. All subunits of the Ino80 complex were enriched, including the auxiliary subunit Hap2. In addition to a role in maintenance of CENP-ACnp1 chromatin integrity at endogenous centromeres, Hap2 is required for de novo assembly of CENP-ACnp1 chromatin on naive centromere DNA and promotes H3 turnover on centromere regions and other loci prone to CENP-ACnp1 deposition. Prior to CENP-ACnp1 chromatin assembly, Hap2 facilitates transcription from centromere DNA. These analyses suggest that Hap2-Ino80 destabilises H3 nucleosomes on centromere DNA through transcription-coupled histone H3 turnover, driving the replacement of resident H3 nucleosomes with CENP-ACnp1 nucleosomes. These inherent properties define centromere DNA by directing a program that mediates CENP-ACnp1 assembly on appropriate sequences.

molecular biology↗

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↗