DengueSeq: a pan-serotype whole genome amplicon sequencing protocol for dengue virus.

BACKGROUND: The increasing burden of dengue virus on public health due to more explosive and frequent outbreaks highlights the need for improved surveillance and control. Genomic surveillance of dengue virus not only provides important insights into the emergence and spread of genetically diverse serotypes and genotypes, but it is also critical to monitor the effectiveness of newly implemented control strategies. Here, we present DengueSeq, an amplicon sequencing protocol, which enables whole-genome sequencing of all four dengue virus serotypes. RESULTS: We developed primer schemes for the four dengue virus serotypes, which can be combined into a pan-serotype approach. We validated both approaches using genetically diverse virus stocks and clinical specimens that contained a range of virus copies. High genome coverage (>95%) was achieved for all genotypes, except DENV2 (genotype VI) and DENV 4 (genotype IV) sylvatics, with similar performance of the serotype-specific and pan-serotype approaches. The limit of detection to reach 70% coverage was 10-100 RNA copies/µL for all four serotypes, which is similar to other commonly used primer schemes. DengueSeq facilitates the sequencing of samples without known serotypes, allows the detection of multiple serotypes in the same sample, and can be used with a variety of library prep kits and sequencing instruments. CONCLUSIONS: DengueSeq was systematically evaluated with virus stocks and clinical specimens spanning the genetic diversity within each of the four dengue virus serotypes. The primer schemes can be plugged into existing amplicon sequencing workflows to facilitate the global need for expanded dengue virus genomic surveillance.

Concurrent Outbreaks of Hepatitis A, Invasive Meningococcal Disease, and Mpox, Florida, USA, 2021-2022.

In 2022, concurrent outbreaks of hepatitis A, invasive meningococcal disease (IMD), and mpox were identified in Florida, USA, primarily among men who have sex with men. The hepatitis A outbreak (153 cases) was associated with hepatitis A virus genotype IA. The IMD outbreak (44 cases) was associated with Neisseria meningitidis serogroup C, sequence type 11, clonal complex 11. The mpox outbreak in Florida (2,845 cases) was part of a global epidemic. The hepatitis A and IMD outbreaks were concentrated in Central Florida and peaked during March--June, whereas mpox cases were more heavily concentrated in South Florida and had peak incidence in August. HIV infection was more common (52%) among mpox cases than among hepatitis A (21%) or IMD (34%) cases. Where feasible, vaccination against hepatitis A, meningococcal disease, and mpox should be encouraged among at-risk groups and offered along with program services that target those groups.

Detection of resistance to macrolides and fluoroquinolones in Mycoplasma genitalium by targeted next-generation sequencing.

Mycoplasma genitalium is fastidious to culture, and its detection in human clinical specimens relies mainly on molecular methods. Phenotypic determination of antibiotic susceptibility for this bacterium is not a timely or feasible option for most clinical laboratories. This study sought to determine whether next-generation sequencing technologies can effectively be employed in determining genetic mutations associated with drug resistance in M. genitalium samples collected in Aptima Hologic tubes and possibly integrating them into viable workflows in public health laboratories. Following analysis by a custom-designed bioinformatics pipeline, at least one mutation/sample has been identified in 94/98 specimens in at least one of seven loci (macrolides: rrl, rplD, rplV; fluoroquinolones: parC, parE, gyrA, gyrB) described previously to be connected to antibiotic resistance. This method identified a total of 469 single nucleotide polymorphisms (SNPs) (452 mutations): 134 of 23S rRNA SNPs and 318 amino acid mutations: 114 substitutions and 204 synonymous; the turnaround time (sample to analyzed sequence) was typically 3 days. The assays and workflows described in this work demonstrated that the determination of a drug resistance profile for macrolides and fluoroquinolones of M. genitalium samples by using next-generation sequencing in clinical samples is a feasible approach that can be implemented in clinical laboratories, following thorough and extensive validation studies.IMPORTANCEThe mechanisms of drug resistance in Mycoplasma genitalium are complex and involve several genetic loci. The molecular methods for accurately characterizing resistance to fluoroquinolones and macrolides in this organism are often not available or approved for patient use and do not cover all genetic determinants. To this end, we propose a next-generation sequencing-based method with a turnaround time of 3 days that includes the investigation of all drug resistance loci of M. genitalium. Following adaptation, validation, and verification for routine clinical use, assays based on this method may yield molecular results that can be used to guide proper treatment regimens and for surveillance of drug resistance in the general population.

DengueSeq: A pan-serotype whole genome amplicon sequencing protocol for dengue virus.

Background: The increasing burden of dengue virus on public health due to more explosive and frequent outbreaks highlights the need for improved surveillance and control. Genomic surveillance of dengue virus not only provides important insights into the emergence and spread of genetically diverse serotypes and genotypes, but it is also critical to monitor the effectiveness of newly implemented control strategies. Here, we present DengueSeq, an amplicon sequencing protocol, which enables whole-genome sequencing of all four dengue virus serotypes. Results: We developed primer schemes for the four dengue virus serotypes, which can be combined into a pan-serotype approach. We validated both approaches using genetically diverse virus stocks and clinical specimens that contained a range of virus copies. High genome coverage (>95%) was achieved for all genotypes, except DENV2 (genotype VI) and DENV 4 (genotype IV) sylvatics, with similar performance of the serotype-specific and pan-serotype approaches. The limit of detection to reach 70% coverage was 101-102 RNA copies/µL for all four serotypes, which is similar to other commonly used primer schemes. DengueSeq facilitates the sequencing of samples without known serotypes, allows the detection of multiple serotypes in the same sample, and can be used with a variety of library prep kits and sequencing instruments. Conclusions: DengueSeq was systematically evaluated with virus stocks and clinical specimens spanning the genetic diversity within each of the four dengue virus serotypes. The primer schemes can be plugged into existing amplicon sequencing workflows to facilitate the global need for expanded dengue virus genomic surveillance.

Containment of a Verona Integron-Encoded Metallo-Beta-Lactamase-Producing Pseudomonas aeruginosa Outbreak Associated With an Acute Care Hospital Sink-Tennessee, 2018-2020.

Background: Contaminated healthcare facility wastewater plumbing is recognized as a source of carbapenemase-producing organism transmission. In August 2019, the Tennessee Department of Health (TDH) identified a patient colonized with Verona integron-encoded metallo-beta-lactamase-producing carbapenem-resistant Pseudomonas aeruginosa (VIM-CRPA). A record review revealed that 33% (4 of 12) of all reported patients in Tennessee with VIM had history of prior admission to acute care hospital (ACH) A intensive care unit (ICU) Room X, prompting further investigation. Methods: A case was defined as polymerase chain reaction detection of blaVIM in a patient with prior admission to ACH A from November 2017 to November 2020. The TDH performed point prevalence surveys, discharge screening, onsite observations, and environmental testing at ACH A. The VIM-CRPA isolates underwent whole-genome sequencing (WGS). Results: In a screening of 44% (n = 11) of 25 patients admitted to Room X between January and June 2020, we identified 36% (n = 4) colonized with VIM-CRPA, resulting in 8 cases associated with Room X from March 2018 to June 2020. No additional cases were identified in 2 point-prevalence surveys of the ACH A ICU. Samples from the bathroom and handwashing sink drains in Room X grew VIM-CRPA; all available case and environmental isolates were found to be ST253 harboring blaVIM-1 and to be closely related by WGS. Transmission ended after implementation of intensive water management and infection control interventions. Conclusions: A single ICU room's contaminated drains were associated with 8 VIM-CRPA cases over a 2-year period. This outbreak highlights the need to include wastewater plumbing in hospital water management plans to mitigate the risk of transmission of antibiotic-resistant organisms to patients.

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.

Heavy Rainfall, Sewer Overflows, and Salmonellosis in Black Skimmers (Rynchops niger).

Extreme weather events, particularly heavy rainfall, are occurring at greater frequency with climate change. Although adverse human health effects from heavy rainfall are often publicized, impacts to free-ranging wildlife populations are less well known. We first summarize documented associations of heavy rainfall on wildlife health. We then report a novel investigation of a salmonellosis outbreak in a colony of black skimmers (Rynchops niger) in Florida, USA. During June-September 2016, heavy rainfall resulted in the discharge of millions of gallons of untreated wastewater into the Tampa Bay system, contaminating the water body, where adult skimmers foraged. At least 48 fledglings died, comprising 39% of the colony's nesting season's offspring. Of eight examined deceased birds from the colony, six had a systemic salmonellosis infection. Isolates were identified as Salmonella enterica serotype Typhimurium. Their pulsed-field gel electrophoresis patterns were identical to each other and matched those from several human Salmonella sp. infections. Differences among whole-genome sequences were negligible. These findings and the outbreak's epidemic curve suggest propagated transmission occurred within the colony. A multidisciplinary and One Health approach is recommended to mitigate any adverse effects of climate change-driven stochastic events, especially when they place already imperiled wildlife at further risk.

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.

Targeted deep amplicon sequencing of antimalarial resistance markers in Plasmodium falciparum isolates from Cameroon.

BACKGROUND: Recent studies showed the first emergence of the R561H artemisinin-associated resistance marker in Africa, which highlights the importance of continued molecular surveillance to assess the selection and spread of this and other drug resistance markers in the region. METHOD: In this study, we used targeted amplicon deep sequencing of 116 isolates collected in two areas of Cameroon to genotype the major drug resistance genes, k13, crt, mdr1, dhfr, and dhps, and the cytochrome b gene (cytb) in Plasmodium falciparum. RESULTS: No confirmed or associated artemisinin resistance markers were observed in Pfk13. In comparison, both major and minor alleles associated with drug resistance were found in Pfcrt, Pfmdr1, Pfdhfr, and Pfdhps. Notably, a high frequency of other nonsynonymous mutations was observed across all the genes, except for Pfcytb, suggesting continued selection pressure. CONCLUSIONS: The results from this study supported the continued use of artemisinin-based combination therapy and administration of sulfadoxine-pyrimethamine for intermittent preventive therapy in pregnant women, and for seasonal chemoprevention in these study sites in Cameroon.

Targeted deep amplicon sequencing of kelch 13 and cytochrome b in Plasmodium falciparum isolates from an endemic African country using the Malaria Resistance Surveillance (MaRS) protocol.

BACKGROUND: Routine molecular surveillance for imported drug-resistant malaria parasites to the USA and European Union is an important public health activity. The obtained molecular data are used to help keep chemoprophylaxis and treatment guidelines up to date for persons traveling to malaria endemic countries. Recent advances in next-generation sequencing (NGS) technologies provide a new and effective way of tracking malaria drug-resistant parasites. METHODS: As part of a technology transfer arrangement between the CDC Malaria Branch and the Istituto Superiore di Sanità (ISS), Rome, Italy, the recently described Malaria Resistance Surveillance (MaRS) protocol was used to genotype 148 Plasmodium falciparum isolates from Eritrea for kelch 13 (k13) and cytochrome b (cytb) genes, molecular markers associated with resistance to artemisinin (ART) and atovaquone/proguanil (AP), respectively. RESULTS: Spanning the full-length k13 gene, seven non-synonymous single nucleotide polymorphisms (SNPs) were found (K189N, K189T, E208K, D281V, E401Q, R622I and T535M), of which none have been associated with artemisinin resistance. No mutations were found in cytochrome b. CONCLUSION: All patients successfully genotyped carried parasites susceptible to ART and AP treatment. Future studies between CDC Malaria Branch and ISS are planned to expand the MaRS system, including data sharing, in an effort to maintain up to date treatment guidelines for travelers to malaria endemic countries.

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.

Next-Generation Sequencing and Bioinformatics Protocol for Malaria Drug Resistance Marker Surveillance.

The recent advances in next-generation sequencing technologies provide a new and effective way of tracking malaria drug-resistant parasites. To take advantage of this technology, an end-to-end Illumina targeted amplicon deep sequencing (TADS) and bioinformatics pipeline for molecular surveillance of drug resistance in P. falciparum, called malaria resistance surveillance (MaRS), was developed. TADS relies on PCR enriching genomic regions, specifically target genes of interest, prior to deep sequencing. MaRS enables researchers to simultaneously collect data on allele frequencies of multiple full-length P. falciparum drug resistance genes (crt, mdr1, k13, dhfr, dhps, and the cytochrome b gene), as well as the mitochondrial genome. Information is captured at the individual patient level for both known and potential new single nucleotide polymorphisms associated with drug resistance. The MaRS pipeline was validated using 245 imported malaria cases that were reported to the Centers for Disease Control and Prevention (CDC). The chloroquine resistance crt CVIET genotype (mutations underlined) was observed in 42% of samples, the highly pyrimethamine-resistant dhpsIRN triple mutant in 92% of samples, and the sulfadoxine resistance dhps mutation SGEAA in 26% of samples. The mdr1 NFSND genotype was found in 40% of samples. With the exception of two cases imported from Cambodia, no artemisinin resistance k13 alleles were identified, and 99% of patients carried parasites susceptible to atovaquone-proguanil. Our goal is to implement MaRS at the CDC for routine surveillance of imported malaria cases in the United States and to aid in the adoption of this system at participating state public health laboratories, as well as by global partners.

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.