Biology subjects

Kissler, S. M.

Publications and source records attributed to Kissler, S. M..

4 recordsLinked to original sources

bioRxiv2022

Drug addiction mutations unveil a methylation ceiling in EZH2-mutant lymphoma

Cancer mutations in Polycomb Repressive Complex 2 (PRC2) drive aberrant epigenetic states. Although therapies inhibiting the PRC2 enzymatic component EZH2 are FDA-approved, oncogene-specific dependencies remain to be discovered. Here, we identify mutations that confer both resistance and drug addiction to PRC2 inhibitors in EZH2-mutant lymphoma, resulting in cancer cells that paradoxically depend on drug for survival. Drug addiction is mediated by hypermorphic mutations in the CXC domain of EZH2, which maintain H3K27me3 levels even in the presence of PRC2 inhibitors. Drug removal leads to overspreading of H3K27me3, surpassing a repressive methylation ceiling compatible with lymphoma cell survival. Activating EZH2 cancer mutations establish an epigenetic state precariously close to this ceiling, which we show can be breached by inhibition of SETD2, a PRC2 antagonist, to block lymphoma growth. More broadly, we highlight how approaches to identify drug addiction mutations can be leveraged to discover cancer vulnerabilities.

cancer biology↗

bioRxiv2020

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate and ineffective antibiotic use. However, because such assays infer resistance phenotypes based on the presence or absence of known genetic markers, their utility will wane in response to the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance designed to ensure early detection of novel resistance variants, but efficient strategies to do so remain to be defined. Here, we evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting genetic variants associated with antibiotic resistance or diagnostic escape in Neisseria gonorrhoeae, focusing on this pathogen because of its high burden of disease, the imminent threat of treatment resistance, and the use and ongoing development of genotype-based diagnostics. We show that incorporating patient characteristics, such as demographics, geographic regions, or anatomical sites of isolate collection, into sampling approaches is not a reliable strategy for increasing variant detection efficiency. In contrast, sampling approaches informed by pathogen characteristics, such as genomic diversity and genomic background, are significantly more efficient than random sampling in identifying genetic variants associated with antibiotic resistance and diagnostic escape.

genomics↗

bioRxiv2019

Quantifying the surveillance required to sustain genetic marker-based antibiotic resistance diagnostics

Diagnostics that minimize the time to selection of an appropriate antibiotic treatment represent an important strategy in addressing the challenge of antimicrobial resistance (AMR). Among this class of diagnostics, the use of pathogen genotype to predict AMR phenotype has been facilitated by advances in rapid sequencing platforms. A longstanding objection to this approach, however, is that the emergence of novel resistance mechanisms will inevitably lead to a decline in the sensitivity of these diagnostics. Here, we show that while the sensitivities of some genetic markers of resistance remain stably high, sensitivities of other markers rapidly decline, as expected, due to the emergence of novel resistance variants. We then present a simple mathematical framework that defines the sampling and phenotypic testing rates needed for early detection of novel resistance variants and thus demonstrate how surveillance can help maintain the sensitivity and utility of sequence-based AMR diagnostics.\n\nOne sentence summaryTargeted sampling strategies are necessary for early detection of novel resistance mechanisms and sustainability of genotype-based detection of novel resistance mechanisms and sustainability of genotype-based diagnostics.

microbiology↗

bioRxiv2018

Sparking \"The BBC Four Pandemic\": Leveraging citizen science and mobile phones to model the spread of disease

The nexus of mobile technology, mass media, and public engagement is opening new opportunities for research into the human behaviours relevant to the spread of disease. On 22 March 2018, the British Broadcasting Corporation (BBC) released the documentary "Contagion! The BBC Four Pandemic" to describe the science behind pandemic preparedness in the UK. The authors of this article were responsible for producing a mathematical simulation for that documentary of how a highly contagious respiratory pathogen might spread across the UK. According to the documentary narrative, the outbreak begins in the town of Haslemere, England. To ground the simulation in true human interaction patterns, a three-day citizen science experiment was conducted during which the pairwise distances between 469 volunteers in Haslemere were tracked continuously using a mobile phone app. Here, we offer a scientific companion to the documentary in which we describe the methods behind our simulation and release the pairwise interpersonal distance dataset. We discuss salient features of the dataset, including daily patterns in the clustering and volatility of interpersonal interactions. Our epidemiological analysis of the simulated Haslemere outbreak serves as a springboard to discuss scientific opportunities opened by the Haslemere dataset and others like it. We believe that the Haslemere dataset will productively challenge current strategies for incorporating population structure into disease transmission models, and hope that it will inspire the collection and analysis of other similar datasets in the future.

epidemiology↗