SARS-CoV-2 infection in free-ranging white-tailed deer.

Humans have infected a wide range of animals with SARS-CoV-21-5, but the establishment of a new natural animal reservoir has not been observed. Here we document that free-ranging white-tailed deer (Odocoileus virginianus) are highly susceptible to infection with SARS-CoV-2, are exposed to multiple SARS-CoV-2 variants from humans and are capable of sustaining transmission in nature. Using real-time PCR with reverse transcription, we detected SARS-CoV-2 in more than one-third (129 out of 360, 35.8%) of nasal swabs obtained from O. virginianus in northeast Ohio in the USA during January to March 2021. Deer in six locations were infected with three SARS-CoV-2 lineages (B.1.2, B.1.582 and B.1.596). The B.1.2 viruses, dominant in humans in Ohio at the time, infected deer in four locations. We detected probable deer-to-deer transmission of B.1.2, B.1.582 and B.1.596 viruses, enabling the virus to acquire amino acid substitutions in the spike protein (including the receptor-binding domain) and ORF1 that are observed infrequently in humans. No spillback to humans was observed, but these findings demonstrate that SARS-CoV-2 viruses have been transmitted in wildlife in the USA, potentially opening new pathways for evolution. There is an urgent need to establish comprehensive 'One Health' programmes to monitor the environment, deer and other wildlife hosts globally.

The emergence of SARS-CoV-2 lineages and associated saliva antibody responses among asymptomatic individuals in a large university community.

SARS-CoV-2 (CoV2) infected, asymptomatic individuals are an important contributor to COVID transmission. CoV2-specific immunoglobulin (Ig)-as generated by the immune system following infection or vaccination-has helped limit CoV2 transmission from asymptomatic individuals to susceptible populations (e.g. elderly). Here, we describe the relationships between COVID incidence and CoV2 lineage, viral load, saliva Ig levels (CoV2-specific IgM, IgA and IgG), and ACE2 binding inhibition capacity in asymptomatic individuals between January 2021 and May 2022. These data were generated as part of a large university COVID monitoring program in Ohio, United States of America, and demonstrate that COVID incidence among asymptomatic individuals occurred in waves which mirrored those in surrounding regions, with saliva CoV2 viral loads becoming progressively higher in our community until vaccine mandates were established. Among the unvaccinated, infection with each CoV2 lineage (pre-Omicron) resulted in saliva Spike-specific IgM, IgA, and IgG responses, the latter increasing significantly post-infection and being more pronounced than N-specific IgG responses. Vaccination resulted in significantly higher Spike-specific IgG levels compared to unvaccinated infected individuals, and uninfected vaccinees' saliva was more capable of inhibiting Spike function. Vaccinees with breakthrough Delta infections had Spike-specific IgG levels comparable to those of uninfected vaccinees; however, their ability to inhibit Spike binding was diminished. These data are consistent with COVID vaccines having achieved hoped-for effects in our community, including the generation of mucosal antibodies that inhibit Spike and lower community viral loads, and suggest breakthrough Delta infections were not due to an absence of vaccine-elicited Ig, but instead limited Spike binding activity in the face of high community viral loads.

Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health.

CONTEXT: Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community. PROGRAM: Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020. IMPLEMENTATION: The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard. EVALUATION: This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring. DISCUSSION: Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

MixviR: an R Package for Exploring Variation Associated with Genomic Sequence Data from Environmental SARS-CoV-2 and Other Mixed Microbial Samples.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19) pandemic has highlighted an important role for efficient surveillance of microbial pathogens. High-throughput sequencing technologies provide valuable surveillance tools, offering opportunities to conduct high-resolution monitoring from diverse sample types, including from environmental sources. However, given their large size and potential to contain mixtures of lineages within samples, such genomic data sets can present challenges for analyzing the data and communicating results with diverse stakeholders. Here, we report MixviR, an R package for exploring, analyzing, and visualizing genomic data from potentially mixed samples of a target microbial group. MixviR characterizes variation at both the nucleotide and amino acid levels and offers the RShiny interactive dashboard for exploring data. We demonstrate MixviR's utility with validation studies using mixtures of known lineages from both SARS-CoV-2 and Mycobacterium tuberculosis and with a case study analyzing lineages of SARS-CoV-2 in wastewater samples over time at a sampling location in Ohio, USA. IMPORTANCE High-throughput sequencing technologies hold great potential for contributing to genomic-based surveillance of microbial diversity from environmental samples. However, the size of the data sets, along with the potential for environmental samples to contain multiple evolutionary lineages of interest, present challenges around analyzing and effectively communicating inferences from these data sets. The software described here provides a novel and valuable tool for exploring such data. Though originally designed and used for monitoring SARS-CoV-2 lineages in wastewater, it can also be applied to analyses of genomic diversity in other microbial groups.

Paternity testing using massively parallel sequencing and the PowerSeq™ AUTO/Y system for short tandem repeat sequencing.

Massively parallel sequencing (MPS) is gaining attention as a new technology for routine forensic casework, including paternity testing. Recently released MPS multiplex panels provide many more loci compared to CE methods, plus provide sequence-based alleles that together improve the statistical power of the genetic testing. Here, an MPS system (PowerSeq™ AUTO/Y) was applied for STR sequencing in the study of first-degree STR sequence allele inheritance from families in Southern Brazil. In 29 trios (mother-child-father) analyzed, the paternity index values generally increased when data from sequence-based analysis were used in comparison to length-based data. Further, allele inconsistencies (e.g., single repeat mutation events) between child and parents could be resolved with MPS by assessing the core repeat and flanking region sequences. Lastly, the sequence information allowed for identification of isoalleles (alleles of the same size, but different sequence) to determine specific paternal and maternal inheritances. The results from this study showed advantages of implementing sequence-based analysis, MPS, in paternity testing with improved statistical calculations and a greater resolution for the trios/families tested.

Oil Biodegradation and Oil-Degrading Microbial Populations in Marsh Sediments Impacted by Oil from the Deepwater Horizon Well Blowout.

To study hydrocarbon biodegradation in marsh sediments impacted by Macondo oil from the Deepwater Horizon well blowout, we collected sediment cores 18-36 months after the accident at the marshes in Bay Jimmy (Upper Barataria Bay), Louisiana, United States. The highest concentrations of oil were found in the top 2 cm of sediment nearest the waterline at the shorelines known to have been heavily oiled. Although petroleum hydrocarbons were detectable, Macondo oil could not be identified below 8 cm in 19 of the 20 surveyed sites. At the one site where oil was detected below 8 cm, concentrations were low. Residual Macondo oil was already highly weathered at the start of the study, and the concentrations of individual saturated hydrocarbons and polycyclic aromatic hydrocarbons continued to decrease over the course of the study due to biodegradation. Desulfococcus oleovorans, Marinobacter hydrocarbonoclasticus, Mycobacterium vanbaalenii, and related mycobacteria were the most abundant oil-degrading microorganisms detected in the top 2 cm at the oiled sites. Relative populations of these taxa declined as oil concentrations declined. The diversity of the microbial community was low at heavily oiled sites compared to that of the unoiled reference sites. As oil concentrations decreased over time, microbial diversity increased and approached the diversity levels of the reference sites. These trends show that the oil continues to be biodegraded, and microbial diversity continues to increase, indicating ongoing overall ecological recovery.

Next-generation sequencing approach to epigenetic-based tissue source attribution.

The ability to determine the tissue source of biological materials from evidence samples can be highly informative for interpreting forensic data. In this study, a previously published CE-based method to probe locus-specific DNA methylation was modified to accommodate detection using next-generation sequencing (NGS) to perform tissue source attribution. DNA samples (1 ng) from each of four different tissue types were digested with the methylation sensitive restriction endonuclease Hha1 and PCR was used to amplify an optimized subset of ten methylated loci, including positive and negative control loci. The products were prepared as NGS libraries, pooled in a multiplex assay with sample-specific barcodes, sequenced with an Illumina MiSeq, and analyzed using a k-Nearest Neighbor algorithm. With this initial effort a concordance rate of 15/16 was demonstrated from samples of varying types: semen, saliva, skin epidermis, and blood. This method also was designed to be compatible with the workflows published to date for NGS of STRs. Thus, the methylation approach described here is highly accurate and upon further validation and testing may be potentially used in practice as a confirmatory test in conjunction with other NGS protocols used in forensic laboratories.

Live tracking of a plant pathogen outbreak reveals rapid and successive, multidecade plasmid reduction.

Quickly understanding the genomic changes that lead to pathogen emergence is necessary to launch mitigation efforts and reduce harm. In this study, we tracked in real time a 2022 bacterial plant disease outbreak in U.S. geraniums (Pelargonium × hortorum) caused by Xhp2022, a novel lineage of Xanthomonas hortorum. Genomes from 31 Xhp2022 isolates from seven states showed limited chromosomal variation and all contained a single plasmid (p93). Time tree and single nucleotide polymorphism whole-genome analysis estimated that Xhp2022 emerged within the last decade. The phylogenomic analysis determined that p93 resulted from the cointegration of three plasmids (p31, p45, and p66) found sporadically across isolates from previous outbreaks. Although p93 had a 49 kb nucleotide reduction, it retained putative fitness genes, which became predominant in the 2022 outbreak. Overall, we demonstrated, through rapid whole-genome sequencing and analysis, a recent, traceable event of genome reduction for niche adaptation typically observed over millennia in obligate and fastidious pathogens.IMPORTANCEThe geranium industry, valued at $4 million annually, faces an ongoing Xanthomonas hortorum pv. pelargonii (Xhp) pathogen outbreak. To track and describe the outbreak, we compared the genome structure across historical and globally distributed isolates. Our research revealed Xhp population has not had chromosome rearrangements since 1974 and has three distinct plasmids. In 2012, we found all three plasmids in individual Xhp isolates. However, in 2022, the three plasmids co-integrated into one plasmid named p93. p93 retained putative fitness genes but lost extraneous genomic material. Our findings show that the 2022 strain group of the bacterial plant pathogen Xanthomonas hortorum underwent a plasmid reduction. We also observed several Xanthomonas species from different years, hosts, and continents have similar plasmids to p93, possibly due to shared agricultural settings. We noticed parallels between genome efficiency and reduction that we see across millennia with obligate parasites with increased niche specificity.

Identification of SARS-CoV-2 variants in indoor dust.

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings.

Metagenomics for Pathogen Detection During a Mass Mortality Event in Songbirds.

Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria forming the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes, including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events.

Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer.

While SARS-CoV-2 has sporadically infected a wide range of animal species worldwide1, the virus has been repeatedly and frequently detected in white-tailed deer in North America2â€"7. The zoonotic origins of this pandemic virus highlight the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, which disseminated across hundreds of kilometers. We discovered that alpha and delta variants evolved in white-tailed deer at three-times the rate observed in humans. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only faster in white-tailed deer but driven by different mutational biases and selection pressures. White-tailed deer are not just short-term recipients of human viral diversity but serve as reservoirs for alpha and other variants to evolve in new directions after going extinct in humans. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal model experiments using viruses isolated from white-tailed deer. Still, SARS-CoV-2 viruses have transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.

Lack of SARS-CoV-2 Viral RNA Detection among a Convenience Sampling of Ohio Wildlife, Companion, and Agricultural Animals, 2020-2021.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in humans in late 2019 and spread rapidly, becoming a global pandemic. A zoonotic spillover event from animal to human was identified as the presumed origin. Subsequently, reports began emerging regarding spillback events resulting in SARS-CoV-2 infections in multiple animal species. These events highlighted critical links between animal and human health while also raising concerns about the development of new reservoir hosts and potential viral mutations that could alter the virulence and transmission or evade immune responses. Characterizing susceptibility, prevalence, and transmission between animal species became a priority to help protect animal and human health. In this study, we coalesced a large team of investigators and community partners to surveil for SARS-CoV-2 in domestic and free-ranging animals around Ohio between May 2020 and August 2021. We focused on species with known or predicted susceptibility to SARS-CoV-2 infection, highly congregated or medically compromised animals (e.g., shelters, barns, veterinary hospitals), and animals that had frequent contact with humans (e.g., pets, agricultural animals, zoo animals, or animals in wildlife hospitals). This included free-ranging deer (n = 76 individuals), free-ranging mink (n = 57), multiple species of bats (n = 59), and other wildlife in addition to domestic cats (n = 275) and pigs (n = 184). In total, we tested 792 individual animals (34 species) via rRT-PCR for SARS-CoV-2 RNA. SARS-CoV-2 viral RNA was not detected in any of the tested animals despite a major peak in human SARS-CoV-2 cases that occurred in Ohio subsequent to the peak of animal samplings. Importantly, we did not test for SARS-CoV-2 antibodies in this study, which limited our ability to assess exposure. While the results of this study were negative, the surveillance effort was critical and remains key to understanding, predicting, and preventing the re-emergence of SARS-CoV-2 in humans or animals.

Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer.

The zoonotic origin of the COVID-19 pandemic virus highlights the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected that SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, disseminating across hundreds of kilometers. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only three-times faster in white-tailed deer compared to the rate observed in humans but also driven by different mutational biases and selection pressures. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal models using white-tailed deer origin viruses. Still, SARS-CoV-2 has transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.

Evidence of early B-cell dysregulation in simian immunodeficiency virus infection: rapid depletion of naïve and memory B-cell subsets with delayed reconstitution of the naïve B-cell population.

Despite eliciting a robust antibody response in humans, several studies in human immunodeficiency virus (HIV)-infected patients have demonstrated the presence of B-cell deficiencies during the chronic stage of infection. While several explanations for the HIV-induced B-cell deficit have been proposed, a clear mechanistic understanding of this loss of B-cell functionality is not known. This study utilizes simian immunodeficiency virus (SIV) infection of rhesus macaques to assess B-cell population dynamics beginning at the acute phase and continuing through the chronic phase of infection. Flow cytometric assessment demonstrated a significant early depletion of both naïve and memory B-cell subsets in the peripheral blood, with differential kinetics for recovery of these populations. Furthermore, the altered numbers of naïve and memory B-cell subsets in these animals corresponded with increased B-cell activation and altered proliferation profiles during the acute phase of infection. Finally, all animals produced high titers of antibody, demonstrating that the measurement of virus-specific antibody responses was not an accurate reflection of alterations in the B-cell compartment. These data indicate that dynamic B-cell population changes in SIV-infected macaques arise very early after infection at the precise time when an effective adaptive immune response is needed.

Global Patterns and Climatic Controls of Dust-Associated Microbial Communities.

The ubiquity and long-range transport of the microorganisms inhabiting dust can pose a serious risk to human, animal, and plant health. The well-recognized importance of dust-associated microorganisms contrasts starkly with our limited understanding of the factors determining the variation in the composition of these communities at the global scale. Here, we provide the first insight into the global determinants of dust-associated microorganisms by quantifying the environmental factors shaping bacterial and fungal community composition in 467 outdoor settled dust samples collected from 33 countries and 6 continents. Our results show that the global variation in dust-associated bacterial and fungal community composition was, to some degree, predictable from mean annual precipitation and temperature. Notably, our results show that the fungal genera Alternaria and Aspergillus, which contain many species that can serve as triggers of allergenic disease in humans and as plant pathogens, were more abundant in drier regions. Collectively, these results highlight the key influence of climate on the global distribution of dust-associated microorganisms and provide the baseline information needed to build a more comprehensive understanding of how microbial exposures vary across the globe and in response to climate change. IMPORTANCE A broad diversity of microorganisms can be found in dust, with some of these microorganisms capable of causing allergenic disease in human via inhalation or affecting plant health by acting as plant pathogens. However, the spatial variation in dust microbiomes and the environmental factors associated with this variation have not been comprehensively assessed at the global scale. Here, we investigated the bacteria and fungi found in outdoor settled dust samples spanning 33 countries and 6 continents. Our results show that dust-associated bacteria and fungi exhibit climate-driven variability in community composition at the global scale. Our results call for the development of strategies to predict the geographic distribution of dust-associated microorganisms and to identify the potential associations between microbial exposures and the health of humans, animals, and plants.

SARS-CoV-2 infection in free-ranging white-tailed deer ( Odocoileus virginianus ).

Human-to-animal spillover of SARS-CoV-2 virus has occurred in a wide range of animals, but thus far, the establishment of a new natural animal reservoir has not been detected. Here, we detected SARS-CoV-2 virus using rRT-PCR in 129 out of 360 (35.8%) free-ranging white-tailed deer ( Odocoileus virginianus ) from northeast Ohio (USA) sampled between January-March 2021. Deer in 6 locations were infected with at least 3 lineages of SARS-CoV-2 (B.1.2, B.1.596, B.1.582). The B.1.2 viruses, dominant in Ohio at the time, spilled over multiple times into deer populations in different locations. Deer-to-deer transmission may have occurred in three locations. The establishment of a natural reservoir of SARS-CoV-2 in white-tailed deer could facilitate divergent evolutionary trajectories and future spillback to humans, further complicating long-term COVID-19 control strategies. ONE-SENTENCE SUMMARY: A significant proportion of SARS-CoV-2 infection in free-ranging US white-tailed deer reveals a potential new reservoir.

Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance.

The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.

Sequence-based autosomal STR characterization in four US populations using PowerSeq™ Auto/Y system.

The forensic science community is poised to utilize modern advances in massively parallel sequencing (MPS) technologies to better characterize biological samples with higher resolution. A critical component towards the advancement of forensic DNA analysis with these technologies is a comprehensive understanding of the diversity and population distribution of sequence-based short tandem repeat (STR) alleles. Here we analyzed 786 samples of individuals from different population groups, including four of the mostly commonly encountered in forensic casework in the USA. DNA samples were amplified with the PowerSeq™ Auto/Y System Prototype Kit (Promega Corp.), and sequencing was performed on an Illumina® MiSeq instrument. Sequence data were analyzed using a bioinformatics processing tool, Altius. For additional data analysis and profile comparison, capillary electrophoresis (CE) size-based STR genotypes were generated for a subset of individuals, and where possible, also with a second commercially available MPS STR assay. Autosomal STR loci were analyzed and frequencies were calculated based on sequence composition. Also, population genetics studies were performed, with Hardy-Weinberg equilibrium, polymorphic information content (PIC), and observed and expected heterozygosity all assessed. Overall, sequence-based allelic variants of the repeat region were observed in 20 out of 22 different STR loci commonly used in forensic DNA genotyping, with the highest number of sequence variation observed at locus D12S391. The highest increase in allelic diversity and in PIC through sequence-based genotyping was observed at loci D3S1358 and D8S1179. Such detailed sequence analysis, as the one performed in the present study, is important to help understand the diversity of sequence-based STR alleles across different populations and to demonstrate how such allelic variation can improve statistics used for forensic casework.

Genetic analysis of Southern Brazil subjects using the PowerSeq™ AUTO/Y system for short tandem repeat sequencing.

With the advent of Next-Generation Sequencing technology, sequencing of short tandem repeats (STRs) allows for a more detailed analysis when compared to size-based fragment methods (capillary electrophoresis-CE). The implementation of high-throughput sequencing can help uncover deeper genetic diversities of different populations. Subjects from the South region of Brazil present a particular and more homogeneous ancestry background when compared to other regions of the country. Both autosomal and Y- STRs have been analyzed in these individuals; however, all analyses published to date encompass data from CE-based fragment analysis. In this study, a genetic analysis of 59 individuals from Southern Brazil was performed on STR sequences. Forensically relevant STRs were PCR-enriched using a prototype of the PowerSeq™ AUTO/Y system (Promega Corp.). Next-generation sequencing was performed on an Illumina MiSeq instrument. The raw data (FASTQ files) were processed using a custom designed sequence processing tool, Altius. Isoalleles, which are sequence-based allelic variants that do not differ in length, were observed in nine autosomal and in six Y- STRs from the core global forensic marker set. The number of distinctive alleles based on sequence was higher when compared to those based on length, 37.3% higher in autosomal STRs and 13.8% higher in Y-STRs. The most polymorphic autosomal locus was D12S391, which presented 38 different sequence-based alleles. Among the loci in the Y chromosome, DYS389II presented the highest number of isoalleles. In comparison to CE analysis, Observed and Expected Heterozygosity, Polymorphic Information Content (PIC) and Genetic Diversity also presented higher values when the alleles were analyzed based on their sequence. For autosomal loci, Polymorphic Information Content (PIC) was 2.6% higher for sequence-based data. Diversity was 9.3% and 6.5% higher for autosomal and Y markers, respectively. In the analysis of the repeat structures for the STR loci, a new allele variant was found for allele 18 in the vWA locus. The STR flanking regions were also further investigated and sixteen variations were observed at nine autosomal STR loci and one Y-STR locus. The results obtained in this study demonstrate the importance of genetic analysis based on sequencing and highlight the diversity of the South Brazilian population when characterized by STR sequencing.

Secure and robust cloud computing for high-throughput forensic microsatellite sequence analysis and databasing.

Next-generation Sequencing (NGS) is a rapidly evolving technology with demonstrated benefits for forensic genetic applications, and the strategies to analyze and manage the massive NGS datasets are currently in development. Here, the computing, data storage, connectivity, and security resources of the Cloud were evaluated as a model for forensic laboratory systems that produce NGS data. A complete front-to-end Cloud system was developed to upload, process, and interpret raw NGS data using a web browser dashboard. The system was extensible, demonstrating analysis capabilities of autosomal and Y-STRs from a variety of NGS instrumentation (Illumina MiniSeq and MiSeq, and Oxford Nanopore MinION). NGS data for STRs were concordant with standard reference materials previously characterized with capillary electrophoresis and Sanger sequencing. The computing power of the Cloud was implemented with on-demand auto-scaling to allow multiple file analysis in tandem. The system was designed to store resulting data in a relational database, amenable to downstream sample interpretations and databasing applications following the most recent guidelines in nomenclature for sequenced alleles. Lastly, a multi-layered Cloud security architecture was tested and showed that industry standards for securing data and computing resources were readily applied to the NGS system without disadvantageous effects for bioinformatic analysis, connectivity or data storage/retrieval. The results of this study demonstrate the feasibility of using Cloud-based systems for secured NGS data analysis, storage, databasing, and multi-user distributed connectivity.