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56 results for “within-host”
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy.
<p>Data used for the publication of a paper entitled: <strong>The within-host population dynamics of <em>Mycobacterium tuberculosis</em> vary with treatment efficacy.</strong></p> <p>The data were derived from:</p> <p>1. the deep sequencing of serial sputum samples from 12 TB patients,</p> <p>2. the deep sequencing of liquid cultures derived from the expansion of individual colonies <em>in vitro</em>,</p> <p>3. <em>In </em><em>silico</em> simulations of DNA sequencing, populations and mutagenesis.</p> <p>The analytical scripts associated with the generation of the data can be found at:</p> <p>https://github.com/swisstph/TBRU_serialTB/</p> <p><strong>Paper Abstract:</strong></p> <p><strong>Background:</strong></p> <p>Combination therapy is one of the most effective tools for limiting the emergence of drug resistance. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We addressed this question by studying the population dynamics of <em>Mycobacterium tuberculosis</em> within tuberculosis patients undergoing treatment with different combinations of antibiotics.</p> <p><strong>Results:</strong></p> <p>By combining very deep whole genome sequencing (~1,000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy had a clear impact on the population dynamics: sufficient drug pressure bore a clear signature of purifying selection leading to apparent genetic stability. In contrast, <em>M. tuberculosis</em> populations subject to less drug pressure showed markedly different dynamics, including cases of acquisition of additional drug resistance.</p> <p><strong>Conclusions:</strong></p> <p>Our findings show that for a pathogen like <em>M. tuberculosis</em>, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.</p>
The impact of within-host coinfection interactions on between-host parasite transmission dynamics varies with spatial scale
<p>Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and applied a novel spatially explicit analysis to parasite infection data from a wild wood mouse (<em>Apodemus sylvaticus</em>) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode <em>Heligmosomoides polygyrus,</em> and the coccidian <em>Eimeria hungaryensis</em>, using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of <em>E. hungaryensis</em>, but only within spatially-restricted neighbourhoods around each host. However, for the closely related species <em>E. apionodes</em>, which experiments show does not interact strongly with <em>H. polygyrus</em>, we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood.</p>
A model of within-host interactions between host resources, macroparasite infection and immune response
<p>This project was designed to mathematically investigate the of different host parasite-mitigation strategies on host condition. The R code herein comprises:</p> <ul> <li>An ODE model of within-host interactions between a macroparasite (e.g. helminth) infection, host resource levels and host immune response, and the consequent effects on host condition. In brief, resources are ingested and utilised by the host, leading to inceased condition. The host is infected by a parasite, which matures and establishes within the host; both age stages cause harm to the host, decreasing host condition. The presence of the parasite stimulates an immune response, which can either target larval or adult parasites (a resistance strategy), or ameliorate the harm they cause (a tolerance strategy). The host can also reduce resource intake in order to also reduce ingestion of parasite infective stages (an avoidance strategy). Resistance responses have an associated immunopathology, in that the immune response also harms the host.</li> <li>Code to plot model trajectories over time.</li> <li>Code to plot multiple trajectories as a heat map, in which the x-axis is time and the y-axis is a parameter representing the host investment in its parasite-mitigation strategy.</li> <li>Code to calculate the optimum host investment for each strategy, over various sets of parameter values, as determined by maximising mean host condition over a given timeframe, and to plot the output.</li> <li>The same are also provided for an ODE model in which the total immune response is allocated between the two resistance responses and tolerance (a combined strategy). The optimisation code optimises both the total investment in immune repsonse, and how much is allocated to the three different individual strategies.</li> </ul> <p>The model and results are described in detail in the associated manuscript. We also provide here the simulated datasets in which the optimum host investments were calculated over a range of different parameter values, as these take several hours to run on a standard desktop computer..</p>
Fig. 4 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 4. Total number of a) genotype-I and b) genotype-II myxospores produced per actinospore, as a measure of parasite success, in fish from single and mixedgenotype treatments. Black bars denote genotype-I only, white denote genotype-II only, and grey denote mixed-genotype treatments. Letters indicate treatments that differed (Tukey's HSD tests, α = 0.05).
Fig. 3 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 3. Parasite copy number, as a measure of parasite competition in mixed-genotype treatments, in a) gill tissue sampled at 7d (t7), b) gill tissue sampled at 14d (t14) c) intestinal tissue sampled at 7d, and d) intestinal tissue sampled at 14d. Black bars denote genotype-I only, white denote genotype-II only, and grey denote mixedgenotype treatments. Inset striped grey bars represent total genotype I copy numbers, based on the proportion of genotype I in sequenced DNA samples (genotype II comprises the remainderthe solid grey bar). Letters indicate treatments that differed (Tukey's HSD tests, α = 0.05). Total number of genotype-I (black circles) and genotype-II (white circles) myxospores produced per actinospore, as a measure of parasite success in fish overlaid on parasite copy number in intestinal tissue sampled at 14d.
Fig. 2 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 2. Median day to death, as a measure of parasite virulence, in treatment groups. Black bars denote genotype-I only, white denote genotype-II only, and grey denote mixed-genotype treatments. Letters indicate treatments that differed (Tukey's HSD tests, α = 0.05).
Fig. 1 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 1. Experimental schematic and timeline. Timeline begins at t-3 when density of parasites in polychaete cultures (inset a) was estimated in replicate water samples to calculate dose administered on t0 and t6. Specific-pathogen-free (SPF) well water ("W") was used as a negative control and a mock exposure t0 and t6 in treatments that received no parasites on those exposure dates "W"- denotes water, "I: denotes genotype-I and "II" denotes genotype-II (inset b). * denote treatments used for cytokine and immunoglobulin assays (b).
The first arriving virus shapes within-host viral diversity during natural epidemics
Viral diversity has been discovered across scales from host individuals to populations. However, the drivers of viral community assembly are still largely unknown. Within-host viral communities are formed through coinfections, where the interval between the arrival times of viruses may vary. Priority effects describe the timing and order in which species arrive in an environment, and how early colonizers impact subsequent community assembly. To study the effect of the first-arriving virus on subsequent infection patterns of five focal viruses, we set up a field experiment using naïve Plantago lanceolata plants as sentinels during a seasonal virus epidemic. Using joint species distribution modelling, we find both positive and negative effects of early season viral infection on late season viral colonization patterns. The direction of the effect depends on both the host genotype and which virus colonized the host early in the season. It is well-established that co-occurring viruses may change the virulence and transmission of viral infections. However, our results show that priority effects may also play an important, previously unquantified role in viral community assembly. The assessment of these temporal dynamics within a community ecological framework will improve our ability to understand and predict viral diversity in natural systems.
Data from: Is the local environment more important than within-host interactions in determining coinfection?
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A model of within-host interactions between host resources, macroparasite infection and immune response
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Host controls of within-host disease dynamics: insight from an invertebrate system
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The first arriving virus shapes within-host viral diversity during natural epidemics
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The impact of within-host coinfection interactions on between-host parasite transmission dynamics varies with spatial scale
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Drivers of within-host genetic diversity in acute infections of viruses
<p>Mutations frequencies of HIV, RSV and CMV samples taken from acute infections.</p>
(Extended Data) Amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial
<p>These extended data accompany the manuscript: Targeted Amplicon deep sequencing of ama1 and mdr1 to track within-host <em>P. falciparum</em> diversity throughout treatment in a clinical drug trial</p> <p><strong>Table S1: Concentration ratios and resulting parasitemia in artificial dna mixtures of P. falciparum Lab Isolates 3D7 and Dd2.</strong> This table presents the parasitemia for the artificial mixtures of P. falciparum lab isolates 3D7 and Dd2. Each mixture was prepared at varying ratios of 3D7 to Dd2, starting from equal proportions to a complete presence of only 3D7. The original concentration of each isolate was approximately 50,000 parasites per microliter (pf/μl), and the table displays the proportion of each strain in the mixture and the resulting total parasitemia concentration.</p> <p><strong>Table S2. List of PCR and deep sequencing primers.</strong> This table shows the list of forward and reverse primers used for deep sequencing. In boldface are the MID tags, while in the regular face are the forward primers</p> <p><strong>Table S3. The relative frequencies of each ama1 variant and the number of samples with each variant.</strong> The relative frequencies (%) of the 33 AMA1 variants in pre-and post-treatment samples (n = 330) are shown as a 33 amino acid sequence. The frequencies were calculated by dividing the number of reads of each microhaplotype by the total number of reads obtained per sample (116,187,131).</p> <p><strong>Table S4. Distribution of microhaplotypes among samples.</strong> This table shows the occurrence of microhaplotypes across all participants, both with monoclonal and multiclonal ama1 infections. It presents the ama1 clonality – monoclonal or multiclonal (column 1) - participant IDs (column 2), microhaplotype IDs (column 3), and the relative frequencies of these microhaplotypes across timepoints from 0 to 1008 hours (day 42) (column 3). Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S5. Distribution of rare microhaplotypes among samples.</strong> This table shows the occurrence of rare microhaplotypes in various samples. It presents participant IDs (column 1), microhaplotype IDs (column 2), and the relative frequencies of these microhaplotypes across time points from 0 to 1008 hours (day 42) (column 3). Samples containing rare microhaplotypes - specifically from PID10, PID32, PID38, PID40, PID49, PID60, PID63, and PID65 - are shown in orange, along with the corresponding rare microhaplotypes and their time points of occurrence. Furthermore, participants are categorised by shared microhaplotypes to indicate instances of rarity and commonality. Except for one microhaplotype unique to PID30, rare microhaplotypes were detected in several samples, frequently exceeding a 5% relative frequency. Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S6. The parasitemia levels associated with each ama1 microhaplotype per timepoint.</strong> This table shows the parasitemia for each ama1 microhaplotype per timepoint and each participant. “Patient ID” represents the patient ID, “AMA1 COI at 0h” represents the complexity of infection (COI) for each participant at baseline, based on ama1 while subsequent columns represent the parasitemia for each ama1 microhaplotype from timepoint 0h to 1008h. Parasitemia was back-calculated using the COI and total parasitemia for each time point. For time points with a COI > 1, parasitemia for the respective ama1 microhaplotypes are separated by commas, cells in red indicate timepoints without sequencing data (ND = not determined). In contrast, cells in grey indicate time points where microhaplotypes were detected below 10 parasites/μl, hence at risk of falling below the sampling limit.</p> <p><strong>Figure S1. Performance of AmpSeq in the sequencing controls.</strong> Six aliquots were prepared for each control set to ensure sufficient control data in case of PCR or sequencing failure. The median read depth in the lab controls was 5,658 (range 4,310 – 12,603) and 704 (291 – 1,676). The x-axis represents the aliquot identifier across the five mixtures, starting from 1 to 6, while the y-axis represents the proportions of each variant across all aliquots. For ama1 (A), two variants (3D7 and Dd2) were detected, whereas in mdr1 (B), two variants were detected YY, FY and NY following amplification of Dd2 Copy I, Dd2 Copy II and 3D7, respectively. For ama1, sequencing failed for aliquot 6 of control set 1, while for mdr1, sequencing failed for aliquot 2 and 6 of control set 3, aliquots 1 and 6 of control set 4 and aliquots 1 and 5 of control set 5. Under the mdr1 control set 4, the Dd2 copy II (86F, 184Y) was not identified, possibly due to having very low concentrations that were not picked up in this aliquot. Based on our control mixtures, the minimum variant frequency we could detect was 5%.</p> <p><strong>Figure S2. Heatmaps of the successfully PCR amplified and sequenced samples for ama1 (A) and mdr1 (B).</strong> The rows represent the study participants, while the columns represent time in hours. Successfully sequenced samples are shown in blue, those that failed PCR are shown in red and those that failed sequencing are in black. The timepoint “ Rec” represents unscheduled visits where a recurrent sample was collected. The unshaded areas with "-" are time points where samples were not collected. For each time point, the number of samples successfully sequenced (n Successful) is indicated in the last row of each panel. The table in panel C shows the groupings of samples based on parasitemia, high (> 5,000), moderate (100-5,000) and low (< 100 parasites per microlitre). Many samples collected between 0h-12h had high parasitemia, samples collected between 18h–30h had moderate parasitemia, while samples collected after 30h were primarily of low parasitemia.</p> <p><strong>Figure S3. The mean complexity of infection (COI) by AMA1 throughout treatment.</strong> The mean COI (red diamonds) appeared to be stable (between 1.5 - 2) from baseline (0h) up to 72h and thereafter fluctuated due to the small sample sizes (<5) in the post-treatment samples. The black dots represent the COI per sample.</p> <p> </p>
Dataset for the manuscript: Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages.
<p>Dataset for the manuscript:</p> <p>Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages. preprint, bioRxiv, 2022. DOI: 10.1101/2022.05.06.490883</p> <p>Each zip file contains the raw computational data (as a gzip compressed tarball), YAML input files, as well as Python plotting scripts. The Python plotting scripts have dependencies on the packages: <em>tarfile</em>, <em>multiprocessing</em>, <em>numpy</em>, <em>scipy</em>, and <em>matplotlib</em>. Note that the Python plotting scripts plot directly from the gzip compressed tarballs.</p> <p>The corresponding code can be found on GitHub: https://github.com/Ruth-Bowness-Group/CAModel</p>
Short-term fitness consequences of parasitism depend on host genotype and within-host parasite community
<p>Multi-parasite communities inhabiting individual hosts are common and often consist of parasites from multiple taxa. The effects of parasite community composition and complexity on host fitness are critical for understanding how host-parasite coevolution is affected by parasite diversity. To test how naturally-occurring parasites affect host fitness of multiple host genotypes, we performed a common-garden experiment where we inoculated four genotypes of host plant <em>Plantago lanceolata</em> with six microbial parasite treatments: three single parasite treatments, a fungal mixture, a viral mixture, and a cross-kingdom treatment. Seed production was affected by both host genotype and parasite treatment, and their interaction jointly determined growth of the hosts. Fungal parasites had more consistent negative effects than viruses in both single mixed parasite treatments. These results demonstrate that parasite communities have the potential to affect the evolution and ecology of host populations through their effects on host growth and reproduction. Moreover, the results highlight the importance of accounting for the diversity of parasites as well as host genotypes when aiming to predict the consequences of parasites for epidemics as the effects of multi-parasitism are not necessarily additive of single parasite effects, nor uniform across all host genotypes.</p>
Parasite responses to resource provisioning can be altered by within-host co-infection interactions
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Short-term fitness consequences of parasitism depend on host genotype and within-host parasite community
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OpenNeuro
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