Predicting Plasmodium falciparum infection status in blood using a multiplexed bead-based antigen detection assay and machine learning approaches.

BACKGROUND: Plasmodium blood-stage infections can be identified by assaying for protein products expressed by the parasites. While the binary result of an antigen test is sufficient for a clinical result, greater nuance can be gathered for malaria infection status based on quantitative and sensitive detection of Plasmodium antigens and machine learning analytical approaches. METHODS: Three independent malaria studies performed in Angola and Haiti enrolled persons at health facilities and collected a blood sample. Presence and parasite density of P. falciparum infection was determined by microscopy for a study in Angola in 2015 (n = 193), by qRT-PCR for a 2016 study in Angola (n = 208), and by qPCR for a 2012-2013 Haiti study (n = 425). All samples also had bead-based detection and quantification of three Plasmodium antigens: pAldolase, pLDH, and HRP2. Decision trees and principal component analysis (PCA) were conducted in attempt to categorize P. falciparum parasitemia density status based on continuous antigen concentrations. RESULTS: Conditional inference trees were trained using the known P. falciparum infection status and corresponding antigen concentrations, and PCR infection status was predicted with accuracies ranging from 73-96%, while level of parasite density was predicted with accuracies ranging from 59-72%. Multiple decision nodes were created for both pAldolase and HRP2 antigens. For all datasets, dichotomous infectious status was more accurately predicted when compared to categorization of different levels of parasite densities. PCA was able to account for a high level of variance (>80%), and distinct clustering was found in both dichotomous and categorical infection status. CONCLUSIONS: This pilot study offers a proof-of-principle of the utility of machine learning approaches to assess P. falciparum infection status based on continuous concentrations of multiple Plasmodium antigens.

Early Emergence Phase of SARS-CoV-2 Delta Variant in Florida, US.

SARS-CoV-2, the causative agent of COVID-19, emerged in late 2019. The highly contagious B.1.617.2 (Delta) variant of concern (VOC) was first identified in October 2020 in India and subsequently disseminated worldwide, later becoming the dominant lineage in the US. Understanding the local transmission dynamics of early SARS-CoV-2 introductions may inform actionable mitigation efforts during subsequent pandemic waves. Yet, despite considerable genomic analysis of SARS-CoV-2 in the US, several gaps remain. Here, we explore the early emergence of the Delta variant in Florida, US using phylogenetic analysis of representative Florida and globally sampled genomes. We find multiple independent introductions into Florida primarily from North America and Europe, with a minority originating from Asia. These introductions led to three distinct clades that demonstrated varying relative rates of transmission and possessed five distinct substitutions that were 3-21 times more prevalent in the Florida sample as compared to the global sample. Our results underscore the benefits of routine viral genomic surveillance to monitor epidemic spread and support the need for more comprehensive genomic epidemiology studies of emerging variants. In addition, we provide a model of epidemic spread of newly emerging VOCs that can inform future public health responses.

Therapeutic Efficacy of Artemisinin-Based Combination Therapies in Democratic Republic of the Congo and Investigation of Molecular Markers of Antimalarial Resistance.

Routine assessment of the efficacy of artemisinin-based combination therapies (ACTs) is critical for the early detection of antimalarial resistance. We evaluated the efficacy of ACTs recommended for treatment of uncomplicated malaria in five sites in Democratic Republic of the Congo (DRC): artemether-lumefantrine (AL), artesunate-amodiaquine (ASAQ), and dihydroartemisinin-piperaquine (DP). Children aged 6-59 months with confirmed Plasmodium falciparum malaria were treated with one of the three ACTs and monitored. The primary endpoints were uncorrected and polymerase chain reaction (PCR)-corrected 28-day (AL and ASAQ) or 42-day (DP) cumulative efficacy. Molecular markers of resistance were investigated. Across the sites, uncorrected efficacy estimates ranged from 63% to 88% for AL, 73% to 100% for ASAQ, and 56% to 91% for DP. PCR-corrected efficacy estimates ranged from 86% to 98% for AL, 91% to 100% for ASAQ, and 84% to 100% for DP. No pfk13 mutations previously found to be associated with ACT resistance were observed. Statistically significant associations were found between certain pfmdr1 and pfcrt genotypes and treatment outcome. There is evidence of efficacy below the 90% cutoff recommended by WHO to consider a change in first-line treatment recommendations of two ACTs in one site not far from a monitoring site in Angola that has shown similar reduced efficacy for AL. Confirmation of these findings in future therapeutic efficacy monitoring in DRC is warranted.

Plasmodium falciparum kelch 13 Mutations, 9 Countries in Africa, 2014-2018.

The spread of drug resistance to antimalarial treatments poses a serious public health risk globally. To combat this risk, molecular surveillance of drug resistance is imperative. We report the prevalence of mutations in the Plasmodium falciparum kelch 13 propeller domain associated with partial artemisinin resistance, which we determined by using Sanger sequencing samples from patients enrolled in therapeutic efficacy studies from 9 sub-Saharan countries during 2014-2018. Of the 2,865 samples successfully sequenced before treatment (day of enrollment) and on the day of treatment failure, 29 (1.0%) samples contained 11 unique nonsynonymous mutations and 83 (2.9%) samples contained 27 unique synonymous mutations. Two samples from Kenya contained the S522C mutation, which has been associated with delayed parasite clearance; however, no samples contained validated or candidate artemisinin-resistance mutations.

Using the Plasmodium mitochondrial genome for classifying mixed-species infections and inferring the geographical origin of P. falciparum parasites imported to the U.S.

The ability to identify mixed-species infections and track the origin of Plasmodium parasites can further enhance the development of treatment and prevention recommendations as well as outbreak investigations. Here, we explore the utility of using the full Plasmodium mitochondrial genome to classify Plasmodium species, detect mixed infections, and infer the geographical origin of imported P. falciparum parasites to the United States (U.S.). Using the recently developed standardized, high-throughput Malaria Resistance Surveillance (MaRS) protocol, the full Plasmodium mitochondrial genomes of 265 malaria cases imported to the U.S. from 2014-2017 were sequenced and analyzed. P. falciparum infections were found in 94.7% (251/265) of samples. Five percent (14/265) of samples were identified as mixed- Plasmodium species or non-P. falciparum, including P. vivax, P. malariae, P. ovale curtisi, and P. ovale wallikeri. P. falciparum mitochondrial haplotypes analysis revealed greater than eighteen percent of samples to have at least two P. falciparum mitochondrial genome haplotypes, indicating either heteroplasmy or multi-clonal infections. Maximum-likelihood phylogenies of 912 P. falciparum mitochondrial genomes with known country origin were used to infer the geographical origin of thirteen samples from persons with unknown travel histories as: Africa (country unspecified) (n = 10), Ghana (n = 1), Southeast Asia (n = 1), and the Philippines (n = 1). We demonstrate the utility and current limitations of using the Plasmodium mitochondrial genome to classify samples with mixed-infections and infer the geographical origin of imported P. falciparum malaria cases to the U.S. with unknown travel history.

Forensic human identification with targeted microbiome markers using nearest neighbor classification.

From the perspective of forensics genetics, the human microbiome is a rich, relatively untapped resource for human identity testing. Since it varies within and among people, and perhaps temporally, the potential forensic applications of the use of the microbiome can exceed that of human identification. However, the same inherent variability in microbial distributions may pose a substantial barrier to forming predictions on an individual as the source of the microbial sample unless stable signatures of the microbiome are identified and targeted. One of the more commonly adopted strategies for microbial human identification relies on quantifying which taxa are present and their respective abundance levels. It remains an open question if such microbial signatures are more individualizing than estimates of the degree of genetic relatedness between microbial samples. This study attempts to address this question by contrasting two prediction strategies. The first approach uses phylogenetic distance to predict the host individual; thus it operates under the premise that microbes within individuals are more closely related than microbes between/among individuals. The second approach uses population genetic measures of diversity at clade-specific markers, serving as a fine-grained assessment of microbial composition and quantification. Both assessments were performed using targeted sequencing of 286 markers from 22 microbial taxa sampled in 51 individuals across three body sites measured in triplicate. Nearest neighbor and reverse nearest neighbor classifiers were constructed based on the pooled data and yielded 71% and 78% accuracy, respectively, when diversity was considered, and performed significantly worse when a phylogenetic distance was used (54% and 63% accuracy, respectively). However, empirical estimates of classification accuracy were 100% when conditioned on a maximum nearest neighbor distance when diversity was used, while identification based on a phylogenetic distance failed to reach saturation. These findings suggest that microbial strain composition is more individualizing than that of a phylogeny, perhaps indicating that microbial composition may be more individualizing than recent common ancestry. One inference that may be drawn from these findings is that host-environment interactions may maintain the targeted microbial profile and that this maintenance may not necessarily be repopulated by intra-individual microbial strains.

Targeted sequencing of clade-specific markers from skin microbiomes for forensic human identification.

The human skin microbiome is comprised of diverse communities of bacterial, eukaryotic, and viral taxa and contributes millions of additional genes to the repertoire of human genes, affecting human metabolism and immune response. Numerous genetic and environmental factors influence the microbiome composition and as such contribute to individual-specific microbial signatures which may be exploited for forensic applications. Previous studies have demonstrated the potential to associate skin microbial profiles collected from touched items to their individual owner, mainly using unsupervised methods from samples collected over short time intervals. Those studies utilize either targeted 16S rRNA or shotgun metagenomic sequencing to characterize skin microbiomes; however, these approaches have limited species and strain resolution and susceptibility to stochastic effects, respectively. Clade-specific markers from the skin microbiome, using supervised learning, can predict individual identity using skin microbiomes from their respective donors with high accuracy. In this study the hidSkinPlex is presented, a novel targeted sequencing method using skin microbiome markers developed for human identification. The hidSkinPlex (comprised of 286 bacterial (and phage) family-, genus-, species-, and subspecies-level markers), initially was evaluated on three bacterial control samples represented in the panel (i.e., Propionibacterium acnes, Propionibacterium granulosum, and Rothia dentocariosa) to assess the performance of the multiplex. The hidSkinPlex was further evaluated for prediction purposes. The hidSkinPlex markers were used to attribute skin microbiomes collected from eight individuals from three body sites (i.e., foot (Fb), hand (Hp) and manubrium (Mb)) to their host donor. Supervised learning, specifically regularized multinomial logistic regression and 1-nearest-neighbor classification were used to classify skin microbiomes to their hosts with up to 92% (Fb), 96% (Mb), and 100% (Hp) accuracy. All samples (n=72) regardless of body site origin were correctly classified with up to 94% accuracy, and body site origin could be predicted with up to 86% accuracy. Finally, human short tandem repeat and single-nucleotide polymorphism profiles were generated from skin swab extracts from a single subject to highlight the potential to use microbiome profiling in conjunction with low-biomass samples. The hidSkinPlex is a novel targeted enrichment approach to profile skin microbiomes for human forensic identification purposes and provides a method to further characterize the utility of skin microflora for human identification in future studies, such as the stability and diversity of the personal skin microbiome.

Forensic Human Identification Using Skin Microbiomes.

The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification.IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

Increasing the reach of forensic genetics with massively parallel sequencing.

The field of forensic genetics has made great strides in the analysis of biological evidence related to criminal and civil matters. More so, the discipline has set a standard of performance and quality in the forensic sciences. The advent of massively parallel sequencing will allow the field to expand its capabilities substantially. This review describes the salient features of massively parallel sequencing and how it can impact forensic genetics. The features of this technology offer increased number and types of genetic markers that can be analyzed, higher throughput of samples, and the capability of targeting different organisms, all by one unifying methodology. While there are many applications, three are described where massively parallel sequencing will have immediate impact: molecular autopsy, microbial forensics and differentiation of monozygotic twins. The intent of this review is to expose the forensic science community to the potential enhancements that have or are soon to arrive and demonstrate the continued expansion the field of forensic genetics and its service in the investigation of legal matters.

A Comparison and Integration of MiSeq and MinION Platforms for Sequencing Single Source and Mixed Mitochondrial Genomes.

Single source and multiple donor (mixed) samples of human mitochondrial DNA were analyzed and compared using the MinION and the MiSeq platforms. A generalized variant detection strategy was employed to provide a cursory framework for evaluating the reliability and accuracy of mitochondrial sequences produced by the MinION. The feasibility of long-read phasing was investigated to establish its efficacy in quantitatively distinguishing and deconvolving individuals in a mixture. Finally, a proof-of-concept was demonstrated by integrating both platforms in a hybrid assembly that leverages solely mixture data to accurately reconstruct full mitochondrial genomes.

Expansion of Microbial Forensics.

Microbial forensics has been defined as the discipline of applying scientific methods to the analysis of evidence related to bioterrorism, biocrimes, hoaxes, or the accidental release of a biological agent or toxin for attribution purposes. Over the past 15 years, technology, particularly massively parallel sequencing, and bioinformatics advances now allow the characterization of microorganisms for a variety of human forensic applications, such as human identification, body fluid characterization, postmortem interval estimation, and biocrimes involving tracking of infectious agents. Thus, microbial forensics should be more broadly described as the discipline of applying scientific methods to the analysis of microbial evidence in criminal and civil cases for investigative purposes.

Evaluation of the Illumina(®) Beta Version ForenSeq™ DNA Signature Prep Kit for use in genetic profiling.

While capillary electrophoresis-based technologies have been the mainstay for human identity typing applications, there are limitations with this methodology's resolution, scalability, and throughput. Massively parallel sequencing (MPS) offers the capability to multiplex multiple types of forensically-relevant markers and multiple samples together in one run all at an overall lower cost per nucleotide than traditional capillary electrophoresis-based methods; thus, addressing some of these limitations. MPS also is poised to expand forensic typing capabilities by providing new strategies for mixture deconvolution with the identification of intra-STR allele sequence variants and the potential to generate new types of investigative leads with an increase in the overall number and types of genetic markers being analyzed. The beta version of the Illumina ForenSeq DNA Signature Prep Kit is a MPS library preparation method with a streamlined workflow that allows for targeted amplification and sequencing of 63 STRs and 95 identity SNPs, with the option to include an additional 56 ancestry SNPs and 22 phenotypic SNPs depending on the primer mix chosen for amplification, on the MiSeq desktop sequencer (Illumina). This study was divided into a series of experiments that evaluated reliability, sensitivity of detection, mixture analysis, concordance, and the ability to analyze challenged samples. Genotype accuracy, depth of coverage, and allele balance were used as informative metrics for the quality of the data produced. The ForenSeq DNA Signature Prep Kit produced reliable, reproducible results and obtained full profiles with DNA input amounts of 1ng. Data were found to be concordant with current capillary electrophoresis methods, and mixtures at a 1:19 ratio were resolved accurately. Data from the challenged samples showed concordant results with current DNA typing methods with markers in common and minimal allele drop out from the large number of markers typed on these samples. This set of experiments indicates the beta version of the ForenSeq DNA Signature Prep Kit is a valid tool for forensic DNA typing and warrants full validation studies of this MPS technology.

Correcting Inconsistencies and Errors in Bacterial Genome Metadata Using an Automated Curation Tool in Excel (AutoCurE).

Whole-genome data are invaluable for large-scale comparative genomic studies. Current sequencing technologies have made it feasible to sequence entire bacterial genomes with relative ease and time with a substantially reduced cost per nucleotide, hence cost per genome. More than 3,000 bacterial genomes have been sequenced and are available at the finished status. Publically available genomes can be readily downloaded; however, there are challenges to verify the specific supporting data contained within the download and to identify errors and inconsistencies that may be present within the organizational data content and metadata. AutoCurE, an automated tool for bacterial genome database curation in Excel, was developed to facilitate local database curation of supporting data that accompany downloaded genomes from the National Center for Biotechnology Information. AutoCurE provides an automated approach to curate local genomic databases by flagging inconsistencies or errors by comparing the downloaded supporting data to the genome reports to verify genome name, RefSeq accession numbers, the presence of archaea, BioProject/UIDs, and sequence file descriptions. Flags are generated for nine metadata fields if there are inconsistencies between the downloaded genomes and genomes reports and if erroneous or missing data are evident. AutoCurE is an easy-to-use tool for local database curation for large-scale genome data prior to downstream analyses.

Validation of high throughput sequencing and microbial forensics applications.

High throughput sequencing (HTS) generates large amounts of high quality sequence data for microbial genomics. The value of HTS for microbial forensics is the speed at which evidence can be collected and the power to characterize microbial-related evidence to solve biocrimes and bioterrorist events. As HTS technologies continue to improve, they provide increasingly powerful sets of tools to support the entire field of microbial forensics. Accurate, credible results allow analysis and interpretation, significantly influencing the course and/or focus of an investigation, and can impact the response of the government to an attack having individual, political, economic or military consequences. Interpretation of the results of microbial forensic analyses relies on understanding the performance and limitations of HTS methods, including analytical processes, assays and data interpretation. The utility of HTS must be defined carefully within established operating conditions and tolerances. Validation is essential in the development and implementation of microbial forensics methods used for formulating investigative leads attribution. HTS strategies vary, requiring guiding principles for HTS system validation. Three initial aspects of HTS, irrespective of chemistry, instrumentation or software are: 1) sample preparation, 2) sequencing, and 3) data analysis. Criteria that should be considered for HTS validation for microbial forensics are presented here. Validation should be defined in terms of specific application and the criteria described here comprise a foundation for investigators to establish, validate and implement HTS as a tool in microbial forensics, enhancing public safety and national security.

A high volume extraction and purification method for recovering DNA from human bone.

DNA recovery, purity and overall extraction efficiency of a protocol employing a novel silica-based column, Hi-Flow(®) (Generon Ltd., Maidenhead, UK), were compared with that of a standard organic DNA extraction methodology. The quantities of DNA recovered by each method were compared by real-time PCR and quality of DNA by STR typing using the PowerPlex(®) ESI 17 Pro System (Promega Corporation, Madison, WI) on DNA from 10 human bone samples. Overall, the Hi-Flow method recovered comparable quantities of DNA ranging from 0.8ng±1 to 900ng±159 of DNA compared with the organic method ranging from 0.5ng±0.9 to 855ng±156 of DNA. Complete profiles (17/17 loci tested) were obtained for at least one of three replicates for 3/10 samples using the Hi-Flow method and from 2/10 samples with the organic method. All remaining bone samples yielded partial profiles for all replicates with both methods. Compared with a standard organic DNA isolation method, the results indicated that the Hi-Flow method provided equal or improved recovery and quality of DNA without the harmful effects of organic extraction. Moreover, larger extraction volumes (up to 20mL) can be employed with the Hi-Flow method which enabled more bone sample to be extracted at one time.