Background: The NCOA7 gene product is an estrogen receptor associated protein that is highly similar to the human OXR1 gene product, which functions in oxidation resistance. OXR genes are conserved among all sequenced eukaryotes from yeast to humans. In this study we examine if NCOA7 has an oxidation resistance function similar to that demonstrated for OXR1. We also examine NCOA7 expression in response to oxidative stress and its subcellular localization in human cells, comparing these properties with those of OXR1.

Background Deep sequencing of targeted genomic regions is becoming a common tool for understanding the dynamics and complexity of Plasmodium infections, but its lower limit of detection is currently unknown. Here, a new amplicon analysis tool, the Parallel Amplicon Sequencing Error Correction (PASEC) pipeline, is used to evaluate the performance of amplicon sequencing on low-density Plasmodium DNA samples. Illumina-based sequencing of two Plasmodium falciparum genomic regions ( CSP and SERA2 ) was performed on two types of samples: in vitro DNA mixtures mimicking low-density infections (1–200 genomes/μl) and extracted blood spots from a combination of symptomatic and asymptomatic individuals (44–653,080 parasites/μl). Three additional analysis tools—DADA2, HaplotypR, and SeekDeep—were applied to both datasets and the precision and sensitivity of each tool were evaluated. Results Amplicon sequencing can contend with low-density samples, showing reasonable detection accuracy down to a concentration of 5 Plasmodium genomes/μl. Due to increased stochasticity and background noise, however, all four tools showed reduced sensitivity and precision on samples with very low parasitaemia (< 5 copies/μl) or low read count (< 100 reads per amplicon). PASEC could distinguish major from minor haplotypes with an accuracy of 90% in samples with at least 30 Plasmodium genomes/μl, but only 61% at low Plasmodium concentrations (< 5 genomes/μl) and 46% at very low read counts (< 25 reads per amplicon). The four tools were additionally used on a panel of extracted parasite-positive blood spots from natural malaria infections. While all four identified concordant patterns of complexity of infection (COI) across four sub-Saharan African countries, COI values obtained for individual samples differed in some cases. Conclusions Amplicon deep sequencing can be used to determine the complexity and diversity of low-density Plasmodium infections. Despite differences in their approach, four state-of-the-art tools resolved known haplotype mixtures with similar sensitivity and precision. Researchers can therefore choose from multiple robust approaches for analysing amplicon data, however, error filtration approaches should not be uniformly applied across samples of varying parasitaemia. Samples with very low parasitaemia and very low read count have higher false positive rates and call for read count thresholds that are higher than current default recommendations. Electronic supplementary material The online version of this article (10.1186/s12936-019-2856-1) contains supplementary material, which is available to authorized users.

Background: In 2006, the Senegalese National Malaria Control Programme recommended artemisinin-based combination therapy (ACT) with artemether-lumefantrine as the first-line treatment for uncomplicated Plasmodium falciparum malaria. To date, multiple mutations associated with artemisinin delayed parasite clearance have been described in Southeast Asia in the Pfk13 gene, such as Y493H, R539T, I543T and C580Y. Even though ACT remains clinically and parasitologically efficacious in Senegal, the spread of resistance is possible as shown by the earlier emergence of resistance to chloroquine in Southeast Asia that subsequently spread to Africa. Therefore, surveillance of artemisinin resistance in malaria endemic regions is crucial and requires the implementation of sensitive tools, such as next-generation sequencing (NGS) which can detect novel mutations at low frequency. Methods: Here, an amplicon sequencing approach was used to identify mutations in the Pfk13 gene in eighty-one P. falciparum isolates collected from three different regions of Senegal. Results: In total, 10 SNPs around the propeller domain were identified; one synonymous SNP and nine non-synonymous SNPs, and two insertions. Three of these SNPs (T478T, A578S and V637I) were located in the propeller domain. A578S, is the most frequent mutation observed in Africa, but has not previously been reported in Senegal. A previous study has suggested that A578S could disrupt the function of the Pfk13 propeller region. Conclusion: As the genetic basis of possible artemisinin resistance may be distinct in Africa and Southeast Asia, further studies are necessary to assess the new SNPs reported in this study.

Background: Deep sequencing of targeted genomic regions is becoming a common tool for understanding the dynamics and complexity of Plasmodium infections, but its lower limit of detection is currently unknown. Here, a new amplicon analysis tool, the Parallel Amplicon Sequencing Error Correction (PASEC) pipeline, is used to evaluate the performance of amplicon sequencing on low-density Plasmodium DNA samples. Illumina-based sequencing of two P. falciparum genomic regions (CSP and SERA2) was performed on two types of samples: in vitro DNA mixtures mimicking low-density infections (1-200 genomes/μl) and extracted blood spots from a combination of symptomatic and asymptomatic individuals (44-653,080 parasites/μl). Three additional analysis tools-DADA2, HaplotypR, and SeekDeep-were applied to both datasets and the precision and sensitivity of each tool were evaluated.. Results:Amplicon sequencing can contend with low-density samples, showing reasonable detection accuracy down to a concentration of 5 Plasmodium genomes/μl. Due to increased stochasticity and background noise, however, all four tools showed reduced sensitivity and precision on samples with very low parasitemia (<5 copies/μl) or low read count (<100 reads per amplicon). PASEC could distinguish major from minor haplotypes with an accuracy of 90% in samples with at least 30 Plasmodium genomes/μl, but only 61% at low Plasmodium concentrations (<5 genomes/μl) and 46% at very low read counts (<25 reads per amplicon). The four tools were additionally used on a panel of extracted parasitepositive blood spots from natural malaria infections. While all four identified concordant patterns of complexity of infection (COI) across four sub-Saharan African countries, the COI values obtained for individual samples differed in some cases. Conclusions:Amplicon deep sequencing can be used to determine the complexity and diversity of low-density Plasmodium infections. Despite differences in their approach, four state-of-the-art tools resolved known haplotype mixtures with similar sensitivity and precision. Researchers can therefore choose from multiple robust approaches for analyzing amplicon data, however, error filtration approaches should not be uniformly applied across samples of varying parasitemia. Samples with very low parasitemia and very low read count have higher false positive rates and call for read count thresholds that are higher than current recommendations.

Background: Whole-genome sequencing (WGS) is well established as a high-resolution method for measuring bacterial relatedness to better understand infection transmission in cases of healthcare-associated infections (HAIs). However, sequencing is still rarely used in HAI investigations due to a lack of access to computational analysis platforms with actionable turnaround times. Single-nucleotide polymorphism (SNP) analysis is typically used to determine bacterial relatedness. However, SNP-based methods often require a suite of bioinformatics tools that can be difficult to use and interpret without the expertise of a trained computational biologist. These obstacles become more significant in the case of prospective, real-time surveillance of HAIs, which can require the analysis of a large number of isolates. To enable the use of WGS for proactive determination of infection outbreaks, a rapid, automated method that can scale to large data sets is needed. Methods: Here, we demonstrate the capabilities of ksim, a novel automated algorithm to determine the clonality of bacterial samples using WGS. ksim measures the number of shared kmers (genomic subsequences of length k) between bacterial samples to determine their relatedness. ksim also filters out accessory genomic regions, such as plasmids, that can confound genetic relatedness estimates. We benchmarked the accuracy and speed of ksim relative to an SNP-based pipeline on simulated data sets (with sequencing reads generated in silico) and on 9 clinical-cluster data sets (6 publicly available and 3 real-time data sets from Massachusetts General Hospital [MGH]). We also used ksim to determine the relatedness of >5,000 historical clinical bacterial isolates from MGH, collected between 2015 and 2019. Results: ksim first preprocesses raw sequencing data to generate a common data structure, after which it computes the genomic distance between bacterial samples in ∼0.2 seconds in simple cases and in ∼4 seconds in complex cases when accessory genome filtering is required. In simulations across 5 species, ksim determined clonality (defined as <40 SNPs) with high accuracy (sensitivity, 99.7% and specificity, 99.6%). ksim performance on 9 clinical HAI data sets demonstrated its sensitivity (99.4%) and specificity (90.8%) compared to an SNP-based pipeline. ksim efficiently analyzed >5,000 clinical samples from MGH and found previously unidentified transmission clusters. Conclusions:ksim shows promise for rapid clonality determination in HAI outbreaks and has the potential to scale to tens of thousands of samples. This method could enable infection control teams to use WGS for prospective outbreak detection via an automated computational pipeline without the need for specialized computational biology training.Funding: Day Zero Diagnostics and the NIH provided Funding: for this study.Disclosures: Mohamad Sater reports salary from Day Zero Diagnostics.