Biology subjects

Cohen, T.

Publications and source records attributed to Cohen, T..

4 recordsLinked to original sources

bioRxiv2018

Evaluation of tuberculosis treatment response with serial C-reactive protein measurements

BackgroundNovel biomarkers are needed to assess response to antituberculosis therapy in smear-negative patients.\n\nMethodsTo evaluate the utility of CRP in monitoring response to antituberculosis therapy we conducted a post-hoc analysis on a cohort of adults with symptoms of tuberculosis and negative sputum smears in a high tuberculosis and HIV prevalence setting in KwaZulu-Natal, South Africa. Serial changes in CRP, weight, and haemoglobin were evaluated over 8 weeks.\n\nResults421 participants with suspected smear-negative tuberculosis were enrolled and 33 excluded. 295 were treated for tuberculosis (137 confirmed, 158 possible), and 93 did not have tuberculosis. 185 of 215 (86%) participants who agreed to HIV testing were HIV-positive. At week 8, the on-treatment median CRP reduction in the tuberculosis group was 79.5% (IQR 25.4; 91.7), median weight gain 2.3% (IQR -1.0; 5.6), and median haemoglobin increase 7.0% (IQR 0.8; 18.9) (p-value <0.0001 for baseline to week 8 comparison of absolute median values). Only CRP changed significantly at week 2 (median reduction of 75.1% (IQR 46.9; 89.2) in the group with confirmed tuberculosis and 49.0% (IQR -0.4; 80.9) in the possible tuberculosis group. Failure of CRP to reduce to [≤]55% of the baseline value at week 2 predicted hospitalization or death in both tuberculosis groups, with 99% negative predictive value.\n\nConclusionChange in CRP may have utility in early evaluation of response to antituberculosis treatment and to identify those at increased risk of adverse outcomes.\n\nKey pointsC-reactive protein (CRP) falls by 80% after eight weeks of antituberculosis treatment. At two weeks sustained CRP elevation is associated with death or hospitalization.

microbiology↗

bioRxiv2018

How molecular mechanisms of resistance affect resistance evolution

Combatting antibiotic resistance will require both new antibiotics and strategies to preserve the effectiveness of existing drugs. Both approaches would benefit from predicting optimal dosing of antibiotics based on drug-target binding parameters that can be measured early in drug development and that can change when bacteria become resistant. This would avoid the currently frequently employed trial-and-error approaches and might reduce the number of antibiotic candidates that fail late in drug development.\n\nHere, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that leverages accessible biochemical parameters to quantitatively predict antibiotic dose-response relationships. We validate our model with MICs of a range of quinolone antibiotics in clinical isolates demonstrating that antibiotic efficacy can be predicted from drug-target binding (R2 > 0.9). To further challenge our approach, we do not only predict antibiotic efficacy from biochemical parameters, but also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We experimentally demonstrate that changes in drug-target binding can be predicted from antibiotic dose-response curves with 92-94 % accuracy by exposing bacteria overexpressing target molecules to ciprofloxacin. To test the generality of COMBAT, we apply it to a different antibiotic class, the beta-lactam ampicillin, and can again predict binding parameters from dose-response curves with 90 % accuracy. We then apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations.\n\nOur goal here is dual: First, we address a fundamental biological question and demonstrate that drug-target binding determines bacterial response to antibiotics, although antibiotic action involves many additional effects downstream of drug-target binding. Second, we create a tool that can help accelerate drug development by predicting optimal dosing and preserve the efficacy of existing antibiotics by predicting optimal treatment for possible resistant mutants.

biochemistry↗

bioRxiv2018

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as \"transmission clusters\". The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.

genomics↗

bioRxiv2018

Trends in CRP, D-dimer and fibrinogen during therapy for HIV associated multidrug resistant tuberculosis

BackgroundHIV positive adults on treatment for multidrug-resistant tuberculosis (MDR-TB) experience high mortality. Biomarkers of HIV/MDR-TB treatment response may enable earlier treatment modifications that improve outcomes.\n\nMethodsTo determine whether trends in C-reactive protein (CRP), D-dimer and fibrinogen predict treatment outcome among those with HIV/MDR-TB co-infection we studied 20 HIV positive participants initiating therapy for MDR-TB. Serum CRP, fibrinogen, and D-dimer were measured at baseline and serially while on treatment. Results: At baseline, all biomarkers were elevated with median CRP 86.15 mg/L (IQR 29.25-149.32), D-dimer 0.85 g/mL (IQR 0.34-1.80) and fibrinogen 4.11 g/L (IQR 3.75-6.31). CRP decreased significantly within 10 days of treatment initiation and fibrinogen within 28 days; D-dimer did not change significantly. 5 (25%) participants died. Older age (median age of 38y among survivors and 54y among deceased, p=0.008) and higher baseline fibrinogen (3.86 g/L among survivors and 6.37 g/L among deceased, p=0.02) were significantly associated with death. Higher CRP concentrations at the beginning of each measurement interval were significantly associated with a higher risk of death during that interval.\n\nConclusionTrends in fibrinogen and CRP may be useful for evaluating early response to treatment among individuals with HIV/MDR-TB co-infection.

immunology↗