Biology subjects

Elliott, J.

Publications and source records attributed to Elliott, J..

3 recordsLinked to original sources

bioRxiv2018

Diagnosing Bacterial Vaginosis with a Novel, Clinically-Actionable 1 Molecular Diagnostic Tool

Abstract/SummaryBacterial vaginosis is a common condition among women of reproductive age and is associated with potentially serious side-effects, including an increased risk of preterm birth. Recent advancements in microbiome sequencing technologies have produced novel insights into the complicated mechanisms underlying bacterial vaginosis and have given rise to new methods of diagnosis. Here we report on the validation of a quantitative, molecular diagnostic algorithm based on the relative abundances of ten potentially pathogenic bacteria and four commensal Lactobacillus species in research subjects (n = 172) classified as symptomatic (n = 149) or asymptomatic (n = 23). We observe a clear and reinforcing pattern among patients diagnosed by the algorithm that is consistent with the current understanding of biological dynamics and dysregulation of the vaginal microbiome during infection. Using this enhanced assessment of the underlying biology of infection, we demonstrate improved diagnostic sensitivity (93%) and specificity (90%) relative to current diagnostic tools. Our algorithm also appears to provide enhanced diagnostic capabilities in ambiguous classes of patients for whom diagnosis and medical decision-making is complicated, including asymptomatic patients and those deemed \"intermediate\" by Nugent scoring. Ultimately, we establish CLS2.0q as a quantitative, sensitive, specific, accurate, robust, and flexible algorithm for the clinical diagnosis of bacterial vaginosis - importantly, one that is also ideal for the differential diagnosis of non-BV infections with clinically similar presentations.

microbiology↗

bioRxiv2017

Conjunction of Factors Triggering Waves of Seasonal Influenza

Understanding the subtle confluence of factors triggering pan-continental, seasonal epidemics of influenza-like illness is an extremely important problem, with the potential to save tens of thousands of lives and billions of dollars every year in the US alone. Beginning with several large, longitudinal datasets on putative factors and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of epidemics. Our analysis included insurance claims for a significant cross-section of the US population in the past decade, human movement patterns inferred from billions of tweets, whole-US weekly weather data covering the same time span as the medical records, data on vaccination coverage over the same period, and sequence variations of key viral proteins. We also explicitly accounted for the spatio-temporal auto-correlations of infectious waves, and a host of socioeconomic and demographic factors. We carried out multiple orthogonal statistical analyses on these diverse, large geo-temporal datasets to bolster and corroborate our findings. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions, the strongest predictor groups are as follows, ranked by importance: (1) the host populations socio- and ethno-demographic properties; (2) weather variables pertaining to relevant area specific humidity, temperature, and solar radiation; (3) the virus antigenic drift over time; (4) the host populations land-based travel habits, and; (5) the spatio-temporal dynamics immediate history, as reflected in the influenza wave autocorrelation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve {approx} 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

epidemiology↗

bioRxiv2017

Tracking Subclonal Mutation Frequencies Throughout Lymphomagenesis Identifies Cancer Drivers in Mouse Models of Lymphoma.

Determining whether recurrent but rare cancer mutations are bona fide driver mutations remains a bottleneck in cancer research. Here we present the most comprehensive analysis of retrovirus driven lymphomagenesis produced to date, sequencing 700,000 mutations from >500 malignancies collected at time points throughout tumor development. This enabled identification of positively selected events, and the first demonstration of negative selection of mutations that may be deleterious to tumor development indicating novel avenues for therapy. Customized sequencing and bioinformatics methodologies were developed to quantify subclonal mutations in both premalignant and malignant tissue, greatly expanding the statistical power for identifying driver mutations and yielding a high-resolution, genome wide map of the selective forces surrounding cancer gene loci. Screening two BCL2 transgenic models confirms known drivers of human B-cell non-Hodgkin lymphoma, and implicates novel candidates including modifiers of immunosurveillance such as co-stimulatory molecules and MHC loci. Correlating mutations with genotypic and phenotypic features also gives robust identification of known cancer genes independently of local variance in mutation density. An online resource http://mulv.lms.mrc.ac.uk allows customized queries of the entire dataset.

cancer biology↗