Biology subjects

Malhotra-Kumar, S.

Publications and source records attributed to Malhotra-Kumar, S..

7 recordsLinked to original sources

bioRxiv2021

Polyclonal pathogen populations accelerate the evolution of antibiotic resistance in patients

Antibiotic resistance poses a global health threat, but the within-host drivers of resistance remain poorly understood. Pathogen populations are often assumed to be clonal within hosts, and resistance is thought to emerge due to selection for de novo variants. Here we show that pulmonary populations of the opportunistic pathogen P. aeruginosa are often polyclonal. Crucially, resistance evolves rapidly in patients colonized by polyclonal populations through selection for pre-existing resistant strains. In contrast, resistance evolves sporadically in patients colonized by monoclonal populations due to selection for novel resistance mutations. However, strong trade-offs between resistance and fitness occur in polyclonal populations that can drive the loss of resistant strains. In summary, we show that the within-host diversity of pathogen populations plays a key role in shaping the emergence of resistance in response to treatment. One sentence summaryAntibiotic resistance evolves quickly in patients colonized by polyclonal pathogen populations.

evolutionary biology↗

bioRxiv2020

Molecular pathways to high-level azithromycin resistance in Neisseria gonorrhoeae

ObjectivesThe prevalence of azithromycin resistance in Neisseria gonorrhoeae is increasing in numerous populations worldwide. The aim of this study was to characterize the genetic pathways leading to high-level azithromycin resistance. MethodsA customized morbidostat was used to subject two N. gonorrhoeae reference strains (WHO-F and WHO-X) to dynamically sustained azithromycin pressure. We tracked stepwise evolution of resistance by whole genome sequencing. ResultsWithin 26 days, all cultures evolved high-level azithromycin resistance. Typically, the first step towards resistance was found in transitory mutations in genes rplD, rplV and rpmH (encoding the ribosomal proteins L4, L22 and L34 respectively), followed by mutations in the MtrCDE-encoded efflux pump and the 23S rRNA gene. Low-to high-level resistance was associated with mutations in the ribosomal proteins and MtrCDE-encoded efflux pump. However, high-level resistance was consistently associated with mutations in the 23S ribosomal RNA - mainly the well-known A2059G and C2611T mutations, but also at position A2058G. ConclusionsThis study enabled us to track previously reported mutations and identify novel mutations in ribosomal proteins (L4, L22 and L34) that may play a role in the genesis of azithromycin resistance in N. gonorrhoeae.

microbiology↗

bioRxiv2020

Streptococcus pneumoniae serotypes carried by young children and their association with Acute Otitis Media during the period 2016 - 2019.

BackgroundStreptococcus pneumoniae (Sp) is a major cause of acute otitis media (AOM). Pneumococcal conjugate vaccine (PCV) programs have altered pneumococcal serotype epidemiology in disease and carriage. To establish the clinical picture of AOM in young children exposed to the PCV program in Belgium and the Sp strains they carry, a cross-sectional study started in 2016. Material/methodsIn three collection periods from February 2016 to May 2018, nasopharyngeal swabs and background characteristics were collected from children aged 6-30 months either presenting at their physician with AOM (AOM-group) or healthy and attending day care (DCC-group). Sp was detected, quantified, and characterized using both conventional culture and qPCR. Clinical signs of AOM episodes and treatment were registered by the physicians. ResultsAmong 3264 collected samples, overall pneumococcal carriage and density were similar in AOM (79.2% and 0.50 x106 copies/l) and DCC (77.5% and 0.42 x106 copies/l). Non-vaccine serotypes were most frequent: 23B (AOM: 12.3%; DCC: 17.4%), 11A (AOM: 7.5%; DCC: 7.4%) and 15B (AOM: 7.5%; DCC: 7.1%). Serotypes 3, 6C, 7B, 9N, 12F, 17F and 29 were more frequent in AOM than in DCC, whereas 23A and 23B frequencies were lower. Antibiotic susceptibility of Sp strains was similar in both groups . No predictors of AOM severity were identified, and 77.3% received an antibiotic prescription. ConclusionYoung children with AOM did not carry Sp more frequently or at higher load than healthy children in day care, but some ST were more frequent in AOM and are not included in the currently used vaccines.

immunology↗

bioRxiv2020

Rapid evolution drives the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection

It is well established that antibiotic treatment selects for resistance in pathogenic bacteria. However, the evolutionary responses of pathogen populations to antibiotic treatment during infections remain poorly resolved, especially in acute infections. Here we map the evolutionary responses to treatment in high definition through genomic and phenotypic characterization of >100 isolates from a patient with P. aeruginosa pneumonia. Antibiotic therapy (meropenem, colistin) caused a rapid crash of the P. aeruginosa population in the lung, but this decline was followed by the spread of meropenem resistance mutations that restrict antibiotic uptake (oprD) or modify LPS biosynthesis (wbpM). Low fitness strains with high-level meropenem resistance (oprD) were then replaced by high fitness strains with anti-resistance mutations in the MexAB-OprM efflux pump, causing a rapid decline in resistance to both meropenem and a collateral loss of resistance to a broad spectrum of antibiotics. In contrast, we did not observe any evolutionary responses to antibiotic treatment in the intestinal population of P. aeruginosa. Carbapenem antibiotics are key to the treatment of infections caused by Gram negative pathogens, and our work highlights the ability of natural selection to drive both the rapid rise and fall of carbapenem resistance during acute infections.

evolutionary biology↗

bioRxiv2019

Comparison of biofilm dispersal approaches in Pseudomonas aeruginosa and evaluation of dispersed cells in acute infection mouse model

Biofilm dispersal is biologically significant process to fully understand its consequences during biofilm based infections. The mechanism for biofilm dispersal may involve response to environmental cues and changes in intracellular secondary messengers. Considering range of cues for biofilm dispersal, it is significant to study if dispersed cells generated by different methods have similar phenotype. In the present study, we have compared four type of biofilm dispersal in P. aeruginosa based in response to environment cues (starvation and nutrient rich), c-di-GMP knockdown and signaling molecule NO. We have determined their dispersal efficiency, susceptibility of dispersed cells for antibiotics, their transcriptomic changes compared to planktonic and biofilm. We also determined if dispersed cells can cause acute infections in mouse models. In vitro experiments showed that NO and c-di-GMP based dispersal methods had high biofilm reduction efficiency of 50 and 75% respectively, however, the nutrient induced dispersion showed low efficiency ({approx}30%) and more tolerance to colistin. We also showed that in vitro dispersed cells induced >10-fold transcriptomic expression of genes (at significance level of p < 0.005) related to efflux pumps (mexCD-oprJ), antibiotic resistance (arnDET) in dispersed cells induced by NO and nutrient change, while denitrification pathway and virulence (T3SS, VreI, T2SS) genes in all dispersed cells compared to planktonic and biofilm state. When the ability of the dispersed cells was tested in mouse model of lung infection, c-di-GMP and NO based dispersed cells displayed enhanced infections and haematogenous spread to liver and spleen and higher mortality. Although degree of immune response (cytokines) does not differ based on phenotypes inoculated in our experimental conditions. Based on our data most efficient dispersal methods increase murine mortality. This indicates their capability of making biofilm associated infection more complicated. Our data encourage to be more careful for studies suggesting biofilm dispersal during treatment of biofilm infections.

microbiology↗

bioRxiv2019

The COMPARE Data Hubs

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory, and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats often lead to data not being shared, or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

bioinformatics↗

bioRxiv2019

Quantifying antibiotic impact on within-host dynamics of extended-spectrum beta-lactamase resistance in hospitalized patients

Antibiotic exposure can perturb the human gut microbiome and cause changes in the within-host abundance of the genetic determinants of drug-resistance in bacteria. Such within-host dynamics are expected to play an important role in mediating the relationship between antibiotic use and persistence of drug-resistance within a host and its prevalence within a population. Developing a quantitative representation of these within-host dynamics is an important step towards a detailed mechanistic understanding of the population-level processes by which antibiotics select for resistance. Here we study extended-spectrum beta-lactamase (ESBL) producing organisms of the Enterobacteriaceae bacterial family. These have been identified as a global public health priority and are resistant to most first-line antibiotics for treatment of Enterobacteriaceae infections. We analyse data from 833 rectal swabs from a prospective longitudinal study in three European countries including 133 ESBL-positive hospitalised patients. Quantitative polymerase chain reaction was used to quantify the abundance of the CTX-M gene family - the most wide-spread ESBL gene family - and the 16S rRNA gene as a proxy for bacterial load. We find strong dynamic heterogeneity in CTX-M abundance that is largely explained by the variable nature of the swab sampling. Using information on time-varying antibiotic treatments, we develop a dynamic Bayesian model to decompose the serial data into observational variation and ecological signal and to quantify the potentially causal antibiotic effects. We find an association of treatment with cefuroxime or ceftriaxone with increased CTX-M abundance (approximately 21% and 10% daily increase, respectively), while treatment with meropenem or piperacillin-tazobactam is associated with decreased CTX-M (approximately 8% daily decrease for both). Despite a potential risk for indirect selection, oral ciprofloxacin is also associated with decreasing CTX-M (approximately 8% decrease per day). Using our dynamic model to make forward stochastic simulations of CTX-M dynamics, we generate testable predictions about antibiotic impacts on duration of carriage. We find that a typical course of cefuroxime or ceftriaxone is expected to more than double a patients carriage duration of CTX-M. A typical course of piperacillin-tazobactam or of meropenem - both options to treat hospital acquired infections (HAI) like pneumonia - would reduce CTX-M carriage time relative to ceftriaxone plus amikacin (also an option to treat HAIs) by about 70%. While most antibiotics showed little association with changes in total bacterial abundance, meropenem and piperacillin-tazobactam were associated with decrease in 16S rRNA abundance (3% and 4% daily decrease, respectively). Our study quantifies antibiotic impacts on within-host resistance abundance and resistance carriage, and informs our understanding of how changes in patterns of antibiotic use will affect the prevalence of resistance. This work also provides an analytical framework that can be used more generally to quantify the antibiotic treatment effects on within-host dynamics of determinants of antibiotic resistance using clinical data.

epidemiology↗