Three Reagents for in-Solution Enrichment of Ancient Human DNA at More than a Million SNPs

In-solution enrichment for hundreds of thousands of single nucleotide polymorphisms (SNPs) has been the source of >70% of all genome-scale ancient human DNA data published to date. This approach has made it possible to generate data for one to two orders of magnitude lower cost than random shotgun sequencing, making it economical to study ancient samples with low proportions of human DNA, and increasing the rate of conversion of sampled remains into working data thereby facilitating ethical stewardship of human remains. So far, nearly all ancient DNA data obtained using in-solution enrichment has been generated using a set of bait sequences targeting about 1.24 million SNPs (the 1240k reagent). These sequences were published in 2015, but synthesis of the reagent has been cost-effective for only a few laboratories. In 2021, two companies made available reagents that target the same core set of SNPs along with supplementary content. Here, we test the properties of the three reagents on a common set of 27 ancient DNA libraries across a range of richness of DNA content and percentages of human molecules. All three reagents are highly effective at enriching many hundreds of thousands of SNPs. For all three reagents and a wide range of conditions, one round of enrichment produces data that is as useful as two rounds when tens of millions of sequences are read out as is typical for such experiments. In our testing, the Twist Ancient DNA reagent produces the highest coverages, greatest uniformity on targeted positions, and almost no bias toward enriching one allele more than another relative to shotgun sequencing. Allelic bias in 1240k enrichment has made it challenging to carry out joint analysis of these data with shotgun data, creating a situation where the ancient DNA community has been publishing two important bodes of data that cannot easily be co-analyzed by population genetic methods. To address this challenge, we introduce a subset of hundreds of thousands of SNPs for which 1240k data can be effectively co-analyzed with all other major data types.

Culture-Dependent and Metagenomic Profiling of Eukaryal Diversity in Petroleum Hydrocarbon-Polluted Soil from B-Dere, Gokana, Rivers State, Nigeria

B-Dere is one of the communities in Ogoniland and a major oil producing area in Rivers State where oil exploration and production activities commenced 50 years ago and is now characterized by oil fields and installations that have remained dormant for several decades. Past spills, lack of maintenance, oil trapping and damage to oil infrastructures have been common sight in this region and the environment has been without remediation over the years. B-Dere community has suffered surface water pollution throughout its creeks with massive hydrocarbons for years without remediation. The aim of this study was to determine the Culture- dependent and Metagenomic studies of fungal diversity in petroleum polluted soils in B-Dere community in Gokana LGA of Rivers State, Nigeria. This is to profile fungal communities through next-generation techniques by shotgun sequencing of total DNA isolates directly from the oil polluted environment. Soil samples were collected aseptically with hand auger at a depths of 0-15 and 15-30 cm and made up to a composite sample and transported to the laboratory for analysis using standard microbiological methods for culture- dependent analysis while the Metagenomic studies was carried out at the Microbial Insights, Incorporated; United State of America. In this study next-generation sequencing techniques by shotgun sequencing of total DNA methods were used for identification of fungal isolates from the crude oil polluted soils in B-Dere. Deoxyribonucleic acid (DNA) extraction from crude oil polluted soil samples was performed using ZymoBiomics DNA extraction kits (Zymo Research, Inc; USA). DNA sequencing was performed by the next generation sequencing technique to determine the nucleotide sequence of all eukaryal community structure present in the polluted soil sample using ITS region. Results of the culture-dependent technique showed that only two fungal genera namely Penicillium sp and Aspergillus sp were isolated and identified while the soil was mainly dominated by the genera Penicillium (73.33%), followed by the Rhodotorula (6.54%), Dactylellina(5.09%), Kalmanozyma(2.56%), Fereydounia(1.89%), Xerochrysium(1.36%), Arthrobotrys (1.14%) and Diutina (0.77%) by the metagenomic analysis. However the three major groups were classified as Ascomycota, Basidiomycota and Mucoromycota with Ascomycota having the highest taxonomic reads of 86.76%. However, a total of 60 eukaryal species were identified, in the metagenomic study. In conclusion, these fungal strains can be used in bioremediation process and oil pollution reduction in soil ecosystems because of their high activity in aliphatic hydrocarbon degradation and cell surface hydrophobicity. The next-generation techniques by shotgun sequencing assays appear to be suitable alternatives for rapid identification of the above mentioned fungal isolates.

A two-step metagenomics approach for prey identification from the blood meals of common vampire bats (Desmodus rotundus)

ABSTRACTThe feeding behaviour of the sanguivorous common vampire bat (Desmodus rotundus) facilitates the transmission of pathogens that can impact both human and animal health. To formulate effective strategies in controlling the spread of diseases, there is a need to obtain information on which animals they feed on. One DNA-based approach, shotgun sequencing, can be used to obtain such information. Even though it is costly, shotgun sequencing can be used to simultaneously retrieve prey and vampire bat mitochondrial DNA for population studies within one round of sequencing. However, due to the challenges of analysing shotgun sequenced metagenomic data such as false negatives/positives and typically low proportion of reads mapped to diet items, shotgun sequencing has not been used for the identification of prey from common vampire bat blood meals. To overcome these challenges and generate longer mitochondrial contigs which could be useful for prey population studies, we shotgun sequenced common vampire bat blood meal samples (n=8) and utilised a two-step metagenomic approach based on combining existing bioinformatic workflows (alignment and de novo mtDNA assembly) to identify prey. Further, we validated our results to detections made through metabarcoding. We accurately identified the common vampire bats’ prey in seven out of eight samples without any false positives. We also generated prey mitochondrial contig lengths between 138bp to 3231bp (mean=985bp, SD=981bp). As we develop more computationally efficient bioinformatics pipelines and reduce sequencing costs, we can expect an uptake in metagenomics dietary studies in the near future.

Evaluation of urethrotropic clade meningococcal infection by urine metagenomic shotgun sequencing.

Background Urethral infections caused by an emerging nongroupable (NG) urethrotropic clade of Neisseria meningitidis (Nm) were first reported in the United States in 2015 (the “U.S. NmNG urethritis clade”). Here we evaluate for the presence of other urethral pathogens in men with U.S. NmNG urethritis clade infection. Methods: We evaluated 129 urine specimens collected from men at a sexual health clinic, including 33 from patients with culture-confirmed or suspected urethral Nm infection and 96 specimens in which nucleic acid amplification test detected Neisseria gonorrhoeae (Ng), Chlamydia trachomatis (Ct), both pathogens, or neither pathogen. Nm was detected first by real-time PCR, followed by metagenomic shotgun sequencing of 91 specimens to identify coinfections. Nm genomes were sequenced following selective whole genome amplification when possible. Results: Metagenomic sequencing detected Nm in 16 of 17 specimens from culture-confirmed Nm cases, with no coinfection by other conventional urethral pathogens. Metagenomic sequencing also detected Nm in three Ct positive specimens, one specimen positive for both Ng and Ct, and nine specimens with negative Ng and Ct results, eight of which had suspected Neisseria infections. Nm from culture-confirmed Nm cases belonged to the U.S. NmNG urethritis clade, while Nm identified in other specimens belonged to multiple clonal complexes. Additional urethral pathogens were predominant in non-Nm specimens, including Ng, Ct, Mycoplasma genitalium , Ureaplasma urealyticum , and herpes simplex virus type-2. Conclusions: Coinfection with other conventional urethral pathogens is rare in men with culture-confirmed U.S. NmNG urethritis clade infection and points to the strong association of this clade with disease.

Assessing saliva microbiome collection and processing methods

The oral microbiome has been connected with lung health and may be of significance in the progression of SARS-CoV-2 infection. Saliva-based SARS-CoV-2 tests provide the opportunity to leverage stored samples for assessing the oral microbiome. However, these collection kits have not been tested for their accuracy in measuring the oral microbiome. Saliva is highly enriched with human DNA and reducing it prior to shotgun sequencing may increase the depth of bacterial reads. We examined both the effect of saliva collection method and sequence processing on measurement of microbiome depth and diversity by 16S rRNA gene amplicon and shotgun metagenomics. We collected 56 samples from 22 subjects. Each subject provided saliva samples with and without preservative, and a subset provided a second set of samples the following day. 16S rRNA gene (V4) sequencing was performed on all samples, and shotgun metagenomics was performed on a subset of samples collected with preservative with and without human DNA depletion before sequencing. We observed that the beta diversity distances within subjects over time was smaller than between unrelated subjects, and distances within subjects were smaller in samples collected with preservative. Samples collected with preservative had higher alpha diversity measuring both richness and evenness. Human DNA depletion before extraction and shotgun sequencing yielded higher total and relative reads mapping to bacterial sequences. We conclude that collecting saliva with preservative may provide more consistent measures of the oral microbiome and depleting human DNA increases yield of bacterial sequences.

Fibrin(ogen) Promotes Immune Cell Infiltration, Dysbiosis and ROS Production in Experimental Colitis

Elevated stool fibrinogen has recently been shown to predict disease course in ulcerative colitis (UC), suggesting that fibrin(ogen) in the colitis microenvironment promotes UC pathogenesis. This conclusion is consistent with previous studies from our laboratory showing that fibrin(ogen)/leukocyte interactions mediated by the integrin receptor α Mβ 2 promote experimental colitis and colitis-associated cancer, but the mechanisms are not well defined. To delineate the mechanism coupling fibrinogen to colitis pathogenesis, we induced colitis in Fib WT and mice carrying a mutant fibrinogen lacking the α Mβ 2 binding motif (Fibg 390-396A) with Dextran Sulfate Sodium (DSS). We performed flow cytometric, protein analyses of colons and fecal microbiome and metabolomics analyses after DSS exposure. Five days after DSS challenge, a timepoint prior to significant epithelial damage, we observed significantly diminished infiltration of natural killer cells, T cells, dendritic cells, macrophages and neutrophils in colons harvested from DSS-challenged Fibg 390-396A mice relative to Fib WT mice. We also observed significantly diminished proinflammatory cytokine production by NK cells, macrophages and dendritic cells isolated from DSS-challenged Fibg 390-396A colons. It is well-established that the microbiome composition is a major determinant of colitis in humans and mice. One mechanism by which microbiome contents alter the course of colitis is by the elaboration of certain fecal metabolites. NMR-based fecal metabolomics analyses demonstrated no significant changes in short-chain fatty acids (acetate, propionate, and butyrate) and various amino acids (valine, proline, alanine) however we found significantly less uracil in fecal from Fibg 390-296A relative to Fib WT mice following DSS exposure. Uracil is a key ligand for Duox2 (dual NADPH oxidase), a gut epithelial specific enzyme, that drives reactive oxygen species production by gut epithelial in response to dysbiosis. Notably, Duox2 is the highest induced gene in inflammatory bowel disease (IBD) and has been identified as a risk gene. Our molecular analyses showed no difference in colonic Duox2 expression between genotypes at baseline, but Duox2 expression significantly increased in Fib WT relative to Fibg 390-296A after just 5 days of DSS exposure. Further western blot analyses revealed that Duox2 expression only in the colonic epithelial cells and not in the lamina propria cells. To determine if fibrinogen cause dysbiosis in DSS-induced colitis model, we performed shotgun sequencing on fecal samples from Fib WT and Fibg 390-396A mice at baseline and after 7 days of DSS challenge. Interesting shotgun sequencing analyses revealed major fibrin genotype dependent significant differences in the microbiomes of Fib WT and Fibg 390-396A mice at baseline as well as following DSS challenge. Our studies are the first to show that fibrin(ogen) is a major determinant of the gut-microbiome in the context of experimental colitis. Altogether, these studies demonstrate that fibrin(ogen) in the colitis microenvironment promotes the infiltration and activation of multiple leukocyte subsets that drive colitis pathogenesis. These results also suggest that fibrin(ogen) promotes colitis-associated dysbiosis and Duox2 expression, leading to ROS production that contributes to colitis pathogenesis and possibley tumorigenesis. Overall, these studies suggest that fibrin(ogen)-α Mβ 2 interactions represent an attractive therapeutic target for IBD without incurring the potential bleeding risks associated with anticoagulants or other modalities targeting fibrin deposition. Disclosures No relevant conflicts of interest to declare.

GBSmode: a pipeline for haplotype-aware analysis of genotyping-by-sequencing data

BACKGROUND: Genotyping-by-sequencing (GBS) has revolutionised molecular genetic analysis. It enables simultaneous genotyping of thousands of DNA markers in the genome of any species. In contrast to whole-genome shotgun sequencing, GBS exploits a restriction enzyme to reduce genome complexity and directs the sequencing to begin at fixed digestion sites. However, currently used tools for the analysis of GBS data, such as SAMtools, often neglect the fundamental technical differences between GBS and shotgun sequencing. RESULTS: Here we present GBSmode, a dedicated pipeline to call DNA sequence variants using whole-read information from GBS data. It removes false positives by incorporating biological features such as the ploidy level and the number of possible alleles in the population under investigation. Comparison of GBSmode with SAMtools in an F2 population of rice (Oryza sativa L.) showed both identified a similar number of polymorphisms (13,449 and 14,445, respectively) with a high overlap (8,143). However, differences were found in the number of read misalignments (8.0% and 14.3% for GBSmode and SAMtools, respectively) and genotyping errors (5.0% and 8.3% for GBSmode and SAMtools, respectively). Further tests in a bi-parental F1 population of cassava (Manihot esculenta Crantz) showed GBSmode found 31,489 polymorphic loci, whereas the number was higher with SAMtools (43,860). However, this difference was mainly attributable to GBSmode rejecting 11,695 loci that were biologically not possible. CONCLUSIONS: This study shows that GBSmode is a versatile tool for the analysis of GBS data. Moreover, GBSmode was able to reduce genotyping errors arising from read misalignments by combining haplotype data with biological information. Whilst other tools may find more markers, GBSmode is designed for accuracy.

Identification of sample mix-ups and mixtures in microbiome data in Diversity Outbred mice

In a Diversity Outbred mouse project with genotype data on 500 mice, including 297 with microbiome data, we identified three sets of sample mix-ups (two pairs and one trio) as well as at least 15 microbiome samples that appear to be mixtures of pairs of mice. The microbiome data consisted of shotgun sequencing reads from fecal DNA, used to characterize the gut microbial communities present in these mice. These sequence reads included sufficient reads derived from the host mouse to identify the individual. A number of microbiome samples appeared to contain a mixture of DNA from two mice. We describe a method for identifying sample mix-ups in such microbiome data, as well as a method for evaluating sample mixtures in this context.

Comparison of 16S and whole genome dog microbiomes using machine learning

Background Recent advances in sequencing technologies have driven studies identifying the microbiome as a key regulator of overall health and disease in the host. Both 16S amplicon and whole genome shotgun sequencing technologies are currently being used to investigate this relationship, however, the choice of sequencing technology often depends on the nature and experimental design of the study. In principle, the outputs rendered by analysis pipelines are heavily influenced by the data used as input; it is then important to consider that the genomic features produced by different sequencing technologies may emphasize different results. Results In this work, we use public 16S amplicon and whole genome shotgun sequencing (WGS) data from the same dogs to investigate the relationship between sequencing technology and the captured gut metagenomic landscape in dogs. In our analyses, we compare the taxonomic resolution at the species and phyla levels and benchmark 12 classification algorithms in their ability to accurately identify host phenotype using only taxonomic relative abundance information from 16S and WGS datasets with identical study designs. Our best performing model, a random forest trained by the WGS dataset, identified a species (Bacteroides coprocola) that predominantly contributes to the abundance of leuB, a gene involved in branched chain amino acid biosynthesis; a risk factor for glucose intolerance, insulin resistance, and type 2 diabetes. This trend was not conserved when we trained the model using 16S sequencing profiles from the same dogs. Conclusions Our results indicate that WGS sequencing of dog microbiomes detects a greater taxonomic diversity than 16S sequencing of the same dogs at the species level and with respect to four gut-enriched phyla levels. This difference in detection does not significantly impact the performance metrics of machine learning algorithms after down-sampling. Although the important features extracted from our best performing model are not conserved between the two technologies, the important features extracted from either instance indicate the utility of machine learning algorithms in identifying biologically meaningful relationships between the host and microbiome community members. In conclusion, this work provides the first systematic machine learning comparison of dog 16S and WGS microbiomes derived from identical study designs.

Molecular Characterization of Microbiota in Cerebrospinal Fluid From Patients With CSF Shunt Infections Using Whole Genome Amplification Followed by Shotgun Sequencing

Understanding the etiology of cerebrospinal fluid (CSF) shunt infections and reinfections requires detailed characterization of associated microorganisms. Traditionally, identification of bacteria present in the CSF has relied on culture methods, but recent studies have used high throughput sequencing of 16S rRNA genes. Here we evaluated the method of shotgun DNA sequencing for its potential to provide additional genomic information. CSF samples were collected from 3 patients near the beginning and end of each of 2 infection episodes. Extracted total DNA was sequenced by: (1) whole genome amplification followed by shotgun sequencing (WGA) and (2) high-throughput sequencing of the 16S rRNA V4 region (16S). Taxonomic assignments of sequences from WGA and 16S were compared with one another and with conventional microbiological cultures. While classification of bacteria was consistent among the 3 approaches, WGA provided additional insights into sample microbiological composition, such as showing relative abundances of microbial versus human DNA, identifying samples of questionable quality, and detecting significant viral load in some samples. One sample yielded sufficient non-human reads to allow assembly of a high-quality Staphylococcus epidermidis genome, denoted CLIMB1, which we characterized in terms of its MLST profile, gene complement (including putative antimicrobial resistance genes), and similarity to other annotated S. epidermidis genomes. Our results demonstrate that WGA directly applied to CSF is a valuable tool for the identification and genomic characterization of dominant microorganisms in CSF shunt infections, which can facilitate molecular approaches for the development of better diagnostic and treatment methods.