Biology subjects

Lee, R. S.

Publications and source records attributed to Lee, R. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrated transcriptomic and proteomic analyses identify novel biomarkers of bladder outlet obstruction

Bladder outlet obstruction leads to pathological remodeling and emergence of lower urinary tract symptoms. Although relief of obstruction is associated with symptomatic improvement, it is not universally successful, reflecting persistent alterations in the bladder. Reliable surrogate biomarkers of obstruction are lacking, particularly early in the disease course before irreversible damage to the bladder may have occurred. In this study, re-analysis of publicly available transcriptomic datasets from diverse rodent models of obstruction identified tissue transcripts including Cthrc1, Grem1, Ltbp2 and Msn that were induced in response to injury. Candidate markers were validated experimentally in an independent model of neurogenic obstruction demonstrating time-dependent changes. Candidate markers were also attenuated with either surgical removal of obstruction or treatment with anticholinergic medication or inosine. Integrated analysis of tissue transcriptomics data and tissue and urine proteomics data from a model of neurogenic obstruction revealed significant concordance between markers observed in tissue and urine. Urinary proteomics analysis identified a statistically significant increase in MSN in patients with neurogenic bladder compared to unaffected controls. These findings identify tissue and urine biomarkers of both non-neurogenic and neurogenic obstruction that may reflect early changes in obstructive uropathy that could be monitored in a non-invasive manner.

bioinformatics↗

bioRxiv2023

Integrated omics analysis unveils a DNA damage response to neurogenic injury

Spinal cord injury (SCI) evokes profound bladder dysfunction. Current treatments are limited by a lack of molecular data to inform novel therapeutic avenues. Previously, we showed systemic inosine treatment improved bladder function following SCI in rats. Here, we applied multi-omics analysis to explore molecular alterations in the bladder and their sensitivity to inosine following SCI. Canonical pathways regulated by SCI included those associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation. Upstream regulator analysis identified MYC as a key regulator, whereas causal network analysis predicted multiple regulators of DNA damage response signaling following injury, including PARP-1. Staining for both DNA damage ({gamma}H2AX) and PARP activity (poly-ADP-ribose) markers in the bladder was increased following SCI, and attenuated in inosine-treated tissues. Proteomics analysis suggested that SCI induced changes in protein synthesis-, neuroplasticity-, and oxidative stress-associated pathways, a subset of which were shown in transcriptomics data to be inosine-sensitive. These findings provide novel insights into the molecular landscape of the bladder following SCI, and highlight a potential role for PARP inhibition to treat neurogenic bladder dysfunction. Synopsis O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/571015v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@7c3e79org.highwire.dtl.DTLVardef@e998f5org.highwire.dtl.DTLVardef@38fd4aorg.highwire.dtl.DTLVardef@17d3aba_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIEmployed a multi-omics approach, integrating both transcriptomic and proteomic analyses, to investigate the molecular response in a rat model of spinal cord injury (SCI) and the therapeutic effect of inosine. C_LIO_LIDiscovered multiple regulators of the DNA damage response, including PARP-1, using causal network analysis. C_LIO_LIObserved decreased markers of DNA damage and PARP activity in inosine-treated tissues, indicating the therapeutic potential of inosine in neurogenic dysfunction. C_LIO_LIIdentified significant alterations in molecular pathways associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation after SCI, and their modulation by inosine, highlighting its neuroprotective effects beyond DNA damage repair. C_LI

cell biology↗

bioRxiv2023

Histone H3 E50K mutation confers oncogenic activity and supports an EMT phenotype

Sequencing of human patient tumors has identified recurrent missense mutations in genes encoding core histones. We report that mutations that convert histone H3 amino acid 50 from a glutamate to a lysine (H3E50K) support an oncogenic phenotype in human cells. Expression of H3E50K is sufficient to transform human cells as evidenced by a dramatic increase in cell migration and invasion, and a statistically significant increase in proliferation and clonogenicity. H3E50K also increases the invasive phenotype in the context of co-occurring BRAF mutations, which are present in patient tumors characterized by H3E50K. H3E50 lies on the globular domain surface in a region that contacts H4 within the nucleosome. We find that H3E50K perturbs proximal H3 post-translational modifications globally and dysregulates gene expression, activating the epithelial to mesenchymal transition. Functional studies using S. cerevisiae reveal that, while yeast cells that express H3E50K as the sole copy of histone H3 show sensitivity to cellular stressors, including caffeine, H3E50K cells display some genetic interactions that are distinct from the characterized H3K36M oncohistone yeast model. Taken together, these data suggest that additional histone H3 mutations have the potential to be oncogenic drivers and function through distinct mechanisms that dysregulate gene expression. SummaryO_LIRecurrent missense mutation that alter histone H3E50 were identified in patient tumors C_LIO_LIH3E50K expression dysregulates global H3 post-translational modification, gene expression and may govern a transcriptional program associated with metastatic phenotypes C_LIO_LIhht2-E50K yeast cells exhibit growth defects in the presence of DNA damaging agents C_LI

cancer biology↗

bioRxiv2023

Craniofacial diversity across Danionins and the effects of TH status on craniofacial morphology of two Danio species

The model zebrafish (Danio rerio) belongs to the Danioninae subfamily with a range of informative phenotypes. However, the craniofacial diversity across the subfamily is not fully described. To better understand craniofacial phenotypes across Danioninae we used microCT and 3D geometric morphometrics to capture skull shapes from nine species. The Danio species examined showed largely similar skull shapes, although D. aesculapii, the sister species to D. rerio showed a unique morphology. Two non-Danio species examined, Chela dadiburjori and Devario aequipinnatus showed distinct skull morphologies unique from those of other species examined. Thyroid hormone regulates skeletal development and remodeling, and we asked if changes in developmental thyroid hormone metabolism could underlie some of the craniofacial diversity across Danioninae. We reared two Danio species under altered thyroid profiles, finding that hypothyroid individuals from both species showed corresponding morphological shifts in skull shape. Hypothyroid Danios showed skull morphologies closer to that of Chela and unlike any of the examined wild-type Danio species. We provide an examination of the evolved craniofacial diversity across Danioninae, and demonstrate that alterations to thyroid hormone have the capacity to create unique skull phenotypes.

evolutionary biology↗

bioRxiv2023

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

neuroscience↗

bioRxiv2022

Beyond consensus sequence: a quantitative scheme for inferring transmission using deep sequencing in a bacterial transmission model

Genomic surveillance provides a data source complementary to contact tracing to resolve putative transmission chains. However, the role of within-host diversity in transmission is understudied due to a lack of experimental and clinical datasets that capture within-host diversity in both donors and recipients. Here, we assess the utility of deep-sequenced genomic surveillance within a mouse transmission model where the gastrointestinal pathogen Citrobacter rodentium was controllably spread during co-housing of infected and naive animals. We observed that within-host variants were maintained over multiple transmission steps until fixation or elimination. We present a model for inferring the likelihood that a given pair of samples are linked by transmission, by comparing the allelic frequency at variant genomic loci. Our data affirm that within-host single nucleotide variants (iSNVs) can repeatedly pass from donor to recipient along the transmission chain, and the mere sharing of iSNVs between different transmission pairs offers limited confidence in identifying a transmission pair. Beyond the presence and absence of within-host variants, we show that differences arising in the relative abundance of iSNVs can infer transmission pairs with high precision. An important component of our approach is that the inference is based solely on sequence data, without incorporating epidemiological or demographic data for context. Our model, which substantially reduces the number of comparisons a contact tracer needs to consider, may enhance the accuracy of contact tracing and other epidemiological processes, including early detection of emerging transmission clusters.

genomics↗

bioRxiv2022

A vector reward prediction error model explains dopaminergic heterogeneity

The hypothesis that midbrain dopamine (DA) neurons broadcast an error for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1-3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. For instance, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward4. We argue that the previously predominant family of extensions to the RPE model, which replicate the classic model in multiple parallel circuits, are ill-suited to explaining these and other results concerning DA heterogeneity within the VTA. Instead, we introduce a complementary "feature-specific RPE" model positing that DA neurons within VTA report individual RPEs for different elements of a population vector code for an animals state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task and compare the feature-specific RPE derived from the network to population recordings from DA neurons during the same task. The model recapitulates key aspects of VTA DA neuron heterogeneity. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among SNc DA neurons5. Thus, our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.

neuroscience↗

bioRxiv2021

A Budding Yeast Model and Screen to Define the Functional Consequences of Oncogenic Histone Missense Mutations

Somatic missense mutations in histone genes turn these essential proteins into oncohistones, which can drive oncogenesis. Understanding how missense mutations alter histone function is challenging in mammals as mutations occur in a single histone gene. For example, described oncohistone mutations predominantly occur in the histone H3.3 gene, despite the human genome encoding 15 H3 genes. To understand how oncogenic histone missense mutations alter histone function, we leveraged the budding yeast model, which contains only two H3 genes, to explore the functional consequences of oncohistones H3K36M, H3G34W, H3G34L, H3G34R, and H3G34V. Analysis of cells that express each of these variants as the sole copy of H3 reveals that H3K36 mutants show different drug sensitivities compared to H3G34 mutants. This finding suggests that changes to proximal amino acids in the H3 N-terminal tail alter distinct biological pathways. We exploited the caffeine sensitive growth of H3K36 mutant cells to perform a high copy suppressor screen. This screen identified genes linked to histone function and transcriptional regulation, including Esa1, a histone H4/H2A acetyltransferase; Tos4, a forkhead-associated domain-containing gene expression regulator; Pho92, an N6-methyladenosine RNA binding protein and Sgv1/Bur1, a cyclin-dependent kinase. We show that the Esa1 lysine acetyltransferase activity is critical for suppression of the caffeine sensitive growth of H3K36R mutant cells while the previously characterized binding interactions of Tos4 and Pho92 are not required for suppression. This screen identifies pathways that could be altered by oncohistone mutations and highlights the value of yeast genetics to identify pathways altered by such mutations.

genetics↗

bioRxiv2021

Combined PI3K and MLL inhibition synergizes in models of HR+ breast cancer

The high frequency of aberrant PI3K pathway activation in hormone receptor-positive (HR+) breast cancer has led to the development, clinical testing, and approval of the p110-selective PI3K inhibitor alpelisib. The limited clinical efficacy of alpelisib and other PI3K inhibitors is partially attributed to the functional antagonism between PI3K and estrogen receptor (ER) signaling, which is mitigated via combined PI3K inhibition and endocrine therapy. We and others have previously demonstrated a chromatin-associated mechanism by which PI3K supports cancer development and antagonizes ER signaling through the modulation of H3K4 methylation. Here we show that inhibition of the H3K4 histone methyltransferase MLL1 in combination with PI3K inhibition impairs HR+ breast cancer clonogenicity and cell proliferation. While combined PI3K/MLL1 inhibition reduces AKT effector signaling and H3K4 methylation, MLL1 inhibition increases PI3K effector signaling and upregulates the expression of receptor tyrosine kinase signaling cascades upstream of and including AKT. These data reveal a feedback loop between MLL1 and AKT in which MLL1 inhibition reactivates AKT. We additionally show that combined PI3K and MLL1 inhibition synergizes to cause cell death in in vitro and in vivo models of HR+ breast cancer, which is enhanced by the additional genetic ablation of the H3K4 methyltransferase and AKT target MLL4. Together, our data provide evidence of a feedback mechanism connecting histone methylation with AKT and may support the preclinical development and testing of pan-MLL inhibitors. Significance StatementPharmacological inhibition of PI3K provides limited efficacy in PIK3CA-mutated, HR+ breast cancers. Here the authors leverage PI3K/AKT-driven chromatin modification to identify MLL histone methyltransferases as a therapeutic target. Dual PI3K and MLL inhibition synergize to reduce clonogenicity and cell proliferation while enhancing apoptosis in in vitro models, and induces tumor regression in xenograft models of PI3K-activated, HR+ breast cancer. Furthermore, MLL1 inhibition reveals a feedback loop leading to AKT hyperactivation, which is relieved with combined PI3K/MLL inhibition. These findings demonstrate the utility of MLL inhibitors for the treatment of some solid cancers, as patients with HR+ breast cancer characterized by PIK3CA mutation may derive clinical benefit from combined PI3K/MLL inhibition.

cancer biology↗

bioRxiv2020

A Budding Yeast Model for Human Disease Mutations in the EXOSC2 Cap Subunit of the RNA Exosome

RNA exosomopathies, a growing family of tissue-specific diseases, are linked to missense mutations in genes encoding the structural subunits of the conserved 10-subunit exoribonuclease complex, the RNA exosome. Such mutations in the cap subunit gene EXOSC2 cause the novel syndrome SHRF (Short stature, Hearing loss, Retinitis pigmentosa and distinctive Facies). In contrast, exosomopathy mutations in the cap subunit gene EXOSC3 cause pontocerebellar hypoplasia type 1b (PCH1b). Though having strikingly different disease pathologies, EXOSC2 and EXOSC3 exosomopathy mutations result in amino acid substitutions in similar, conserved domains of the cap subunits, suggesting that these exosomopathy mutations have distinct consequences for RNA exosome function. We generated the first in vivo model of the SHRF pathogenic amino acid substitutions using budding yeast by introducing the EXOSC2 mutations in the orthologous S. cerevisiae gene RRP4. The resulting rrp4 mutant cells have defects in cell growth and RNA exosome function. We detect significant transcriptomic changes in both coding and non-coding RNAs in the rrp4 variant, rrp4-G226D, which models EXOSC2 p.Gly198Asp. Comparing this rrp4-G226D mutant to the previously studied S. cerevisiae model of EXOSC3 PCH1b mutation, rrp40-W195R, reveals that these mutants have disparate effects on certain RNA targets, providing the first evidence for different mechanistic consequences of these exosomopathy mutations. Congruently, we detect specific negative genetic interactions between RNA exosome cofactor mutants and rrp4-G226D but not rrp40-W195R. These data provide insight into how SHRF mutations could alter the function of the RNA exosome and allow the first direct comparison of exosomopathy mutations that cause distinct pathologies.

genetics↗

bioRxiv2019

Investigating within-host diversity of Mycobacterium tuberculosis reveals novel super-spreaders in the Canadian North

Tuberculosis disproportionately affects the Canadian Inuit. To address this, it is imperative we understand transmission dynamics in this population. We investigate whether deep sequencing can provide additional resolution compared to standard sequencing, using a well-characterized outbreak from the Arctic (2011-2012, 50 cases). Samples were sequenced to ~500-1000x and reads were aligned to a novel local reference genome generated with PacBio SMRT sequencing. Consensus and heterogeneous variants were identified and compared across genomes. In contrast with previous genomic analyses using ~50x depth, deep sequencing allowed us to identify a novel super-spreader who likely transmitted to up to 17 other cases during the outbreak (35% of all cases that year). It is increasingly evident that within-host diversity should be incorporated into transmission analyses; deep sequencing can facilitate accurately detection of super-spreaders and corresponding transmission clusters. This has implications not only for TB, but all genomic studies of transmission - regardless of pathogen.

genomics↗

bioRxiv2019

Genomic interrogation of the burden and transmission of multidrug-resistant pathogens within and across hospital networks

BackgroundMultidrug-resistant organisms (MDROs) disproportionately affect hospitalized patients due to the combination of comorbidities, frequent antimicrobial use, and in-hospital MDRO transmission. Identification of MDRO transmission by hospital microbiology laboratories is difficult due to limitations of existing typing methods.\n\nMethodsWe conducted a prospective multicenter genomics implementation study (8 hospitals, 2800 beds) from 24th April to 18th June 2017 in Melbourne, Australia. Clinical and screening isolates from hospital inpatients were collected for six MDROs (vanA VRE, MRSA, ESBL E. coli [ESBL-Ec] and Klebsiella pneumoniae [ESBL-Kp], and carbapenem-resistant Pseudomonas aeruginosa [CRPa] and Acinetobacter baumannii [CRAb]), sequenced (Illumina NextSeq) and analyzed using open-source tools. MDRO transmission was assessed by genomics (core SNP phylogeny, grouped by species and ST) and compared to epidemiologic data.\n\nResults408 isolates were collected from 358 patients; 47.5% were screening isolates. ESBL-Ec was most common (52.5%), then MRSA (21.6%), vanA VRE (15.7%) and ESBL-Kp (7.6%).\n\nWe define the transmission rate for each MDRO by genomics and epidemiology; 31.6% of all study patients had potential genomic links to other study isolates; 86% of these were confirmed by epidemiologic links (probable or possible transmission). The highest transmission rates occurred with vanA VRE (88.4% of patients).\n\nConclusionsCombining genomics with high-quality epidemiologic data gives substantial insights into the burden and distribution of critical MDROs in hospitals, including in-hospital transmission. By defining transmission rates by genomics, we hope to enable comparisons over time and between sites, and introduce this as a new outcome measure to assess the efficacy of infection control interventions.

genomics↗

bioRxiv2019

lncRNAKB: A comprehensive knowledgebase of long non-coding RNAs

We have assembled a comprehensive long non-coding RNA knowledgebase (lncRNAKB) of 77,199 annotated human lncRNAs (224,286 transcripts) by methodically integrating widely used lncRNAs resources. To facilitate functional characterization of lncRNAs, we employed Genotype-Tissue Expression (GTEx) project to provide tissue-specific gene expression profiles of lncRNAs in 31 solid organ tissues. Additional information includes network analysis to identify co-expressed gene modules to potentially delineate lncRNA function. Tissue-specificity, phylogenetic conservation scores and coding potential for lncRNAs are included. Finally, using whole genome sequencing data from GTEx, expression quantitative trait loci (cis-eQTL) regulated lncRNAs were calculated in all tissues. lncRNAKB is available at http://www.lncrnakb.org.

bioinformatics↗

bioRxiv2019

Estimating the Relative Probability of Direct Transmission between Infectious Disease Patients

BackgroundEstimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important to understand infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. MethodsUsing a subset of cases with detailed genetic or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. ResultsIn simulations we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. ConclusionsOur method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data. KEY MESSAGESO_LIThis method provides a way to calculate the relative probability that two infectious disease patients are connected by direct transmission using clinical, demographic, geographic, and genetic characteristics. C_LIO_LIWe use a naive Bayes, a machine learning technique to estimate these probabilities using a training set of probable links defined by contact investigation or pathogen WGS data on a subset of cases. C_LIO_LIThese probabilities can be used to explore possible transmission chains, rule out transmission events, and estimate the reproductive number. C_LI

epidemiology↗

bioRxiv2018

Outpatient prescribing of four major antibiotic classes and prevalence of antimicrobial resistance in US adults

BackgroundThere is limited information on the relation between antibiotic use and antibiotic resistance in the US.\n\nMethodsWe used multivariable logistic regression to relate state-specific rates of outpatient prescribing overall for fluoroquinolones, penicillins, macrolides, and cephalosporins between 2011-2014 to state-specific prevalence of resistance for select combinations of antibiotics/bacteria among catheter-associated urinary tract infections (CAUTIs) in the CDC Patient Safety Atlas data between 2011-2014 for adults aged 65+y and 19-64y, adjusting for potential confounders.\n\nResultsRates of fluoroquinolone prescribing were positively associated with prevalence of fluoroquinolone resistance in Escherichia coli and Pseudomonas aeruginosa (both age groups), resistance to extended-spectrum (ES) cephalosporins in E. coli (aged 19-64y), and resistance to methicillin in Staphylococcus aureus (aged 19-64y). Rates of penicillin prescribing were positively associated with prevalence of resistance to fluoroquinolones in E. coli (aged 65+) and P. aeruginosa (both age groups), and resistance to ES cephalosporins in Klebsiella spp. (both age groups). Rates of cephalosporin prescribing were negatively associated with prevalence of resistance to fluoroquinolones in E. coli and resistance to ES cephalosporins in Klebsiella spp. (both age groups). Average annual temperature was positively associated with prevalence of resistance to ES cephalosporins in E. coli and P. aeruginosa, and resistance to fluoroquinolones in E. coli.\n\nConclusionsOur results suggest that prescribing of fluoroquinolones and penicillins to US adults is associated with prevalence of antibiotic resistance, including ESBLs and MRSA. Further work is needed to understand the potential benefit of replacing fluoroquinolones and penicillins by other antibiotics for reducing prevalence of antibiotic resistance.

epidemiology↗

bioRxiv2018

Value representations do not explain movement selectivity in DMS-projecting dopamine neurons

Although midbrain dopamine (DA) neurons have been thought to primarily encode reward prediction error (RPE), recent studies have also found movement-related DAergic signals. For example, we recently reported that DA neurons in mice projecting to dorsomedial striatum are modulated by choices contralateral to the recording side. Here, we introduce, and ultimately reject, a candidate resolution for the puzzling RPE vs movement dichotomy, by showing how seemingly movement-related activity might be explained by an action-specific RPE. By considering both choice and RPE on a trial-by-trial basis, we find that DA signals are modulated by contralateral choice in a manner that is distinct from RPE, implying that choice encoding is better explained by movement direction. This fundamental separation between RPE and movement encoding may help shed light on the diversity of functions and dysfunctions of the DA system.

neuroscience↗

bioRxiv2018

Lineage calling can identify antibiotic resistant clones within minutes

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could impact patient treatment and outcomes. Here we present a method called genomic neighbor typing for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both S. pneumoniae and N. gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in determination of resistance within ten minutes (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from isolates with a representative database) of sequencing starting, and for clinical metagenomic sputum samples (75%/100% for S. pneumoniae), within four hours of sample collection. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

bioinformatics↗

bioRxiv2018

Multiple Exposures, Reinfection, and Risk of Progression to Active Tuberculosis

A recent study reported on a tuberculosis outbreak in a largely Inuit village. Among recently infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential mechanisms by which multiple infections during the first year following initial infection could account for increasing disease risk with increasing exposures. In the reinfection model, multiple exposures have an independent risk of becoming an infection, and infections contribute independently to active disease. In the threshold model, disease risk follows a sigmoidal function with small numbers of exposures conferring a low risk of active disease and large numbers of exposures conferring a high risk. To determine the dynamic impact of reinfection during the early phase of infection, we performed simulations from a modified Reed-Frost model of TB dynamics following spread from an initial number of cases. We parameterized this model with the maximum likelihood estimates from the reinfection and threshold models in addition to the observed distribution of exposures among recent infections. We find that both models can plausibly account for the observed increase in disease risk with increasing exposures, but the threshold model confers a better fit than a nested model without a threshold (p=0.04). Our simulations indicate that multiple exposures during this critical time period can lead to dramatic increases in outbreak size. In order to decrease TB burden in high-prevalence settings, it may be necessary to implement measures aimed at preventing repeated exposures, in addition to preventing primary infection.

epidemiology↗