Microbial Reads Research Articles

The Intratumor Bacterial and Fungal Microbiome Is Characterized by HPV, Smoking, and Alcohol Consumption in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) tumor phenotypes and clinical outcomes are significantly influenced by etiological agents, such as HPV infection, smoking, and alcohol consumption. Accordingly, the intratumor microbiome has been increasingly implicated in cancer progression and metastasis. However, few studies characterize the intratumor microbial landscape of HNSCC with respect to these etiological agents. In this study, we aimed to investigate the bacterial and fungal landscape of HNSCC in association with HPV infection, smoking, and alcohol consumption. RNA-sequencing data were extracted from The Cancer Genome Atlas (TCGA) regarding 449 tissue samples and 44 normal samples. Pathoscope 2.0 was used to extract the microbial reads. Microbe abundance was compared to clinical variables, oncogenic signatures, and immune-associated pathways. Our results demonstrated that a similar number of dysregulated microbes was overabundant in smokers and nonsmokers, while heavy drinkers were characterized by an underabundance of dysregulated microbes. Conversely, the majority of dysregulated microbes were overabundant in HPV+ tumor samples when compared to HPV- tumor samples. Moreover, we observed that many dysregulated microbes were associated with oncogenic and metastatic pathways, suggesting their roles in influencing carcinogenesis. These microbes provide insights regarding potential mechanisms for tumor pathogenesis and progression with respect to the three etiological agents.

Batch effects removal for microbiome data via conditional quantile regression

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

Microbial and human transcriptome in vaginal fluid at midgestation: Association with spontaneous preterm delivery.

BackgroundIntrauterine infection and inflammation caused by microbial transfer from the vagina are believed to be important factors causing spontaneous preterm delivery (PTD). Multiple studies have examined the relationship between the cervicovaginal microbiome and spontaneous PTD with divergent results. Most studies have applied a DNA‐based assessment, providing information on the microbial composition but not transcriptional activity. A transcriptomic approach was applied to investigate differences in the active vaginal microbiome and human transcriptome at midgestation between women delivering spontaneously preterm versus those delivering at term.MethodsVaginal swabs were collected in women with a singleton pregnancy at 18 + 0 to 20 + 6 gestational weeks. For each case of spontaneous PTD (delivery <37 + 0 weeks) two term controls were randomized (39 + 0 to 40 + 6 weeks). Vaginal specimens were subject to sequencing of both human and microbial RNA. Microbial reads were taxonomically classified using Kraken2 and RefSeq as a reference. Statistical analyses were performed using DESeq2. GSEA and HUMAnN3 were used for pathway analyses.ResultsWe found 17 human genes to be differentially expressed (false discovery rate, FDR < 0.05) in the preterm group (n = 48) compared to the term group (n = 96). Gene expression of kallikrein‐2 (KLK2), KLK3 and four isoforms of metallothioneins 1 (MT1s) was higher in the preterm group (FDR < 0.05). We found 11 individual bacterial species to be differentially expressed (FDR < 0.05), most with a low occurrence. No statistically significant differences in bacterial load, diversity or microbial community state types were found between the groups.ConclusionsIn our mainly white population, primarily bacterial species of low occurrence were differentially expressed at midgestation in women who delivered preterm versus at term. However, the expression of specific human transcripts including KLK2, KLK3 and several isoforms of MT1s was higher in preterm cases. This is of interest, because these genes may be involved in critical inflammatory pathways associated with spontaneous PTD.

Cancer type classification using plasma cell-free RNAs derived from human and microbes.

The utility of cell-free nucleic acids in monitoring cancer has been recognized by both scientists and clinicians. In addition to human transcripts, a fraction of cell-free nucleic acids in human plasma were proven to be derived from microbes and reported to have relevance to cancer. To obtain a better understanding of plasma cell-free RNAs (cfRNAs) in cancer patients, we profiled cfRNAs in ~300 plasma samples of 5 cancer types (colorectal cancer, stomach cancer, liver cancer, lung cancer, and esophageal cancer) and healthy donors (HDs) with RNA-seq. Microbe-derived cfRNAs were consistently detected by different computational methods when potential contaminations were carefully filtered. Clinically relevant signals were identified from human and microbial reads, and enriched Kyoto Encyclopedia of Genes and Genomes pathways of downregulated human genes and higher prevalence torque teno viruses both suggest that a fraction of cancer patients were immunosuppressed. Our data support the diagnostic value of human and microbe-derived plasma cfRNAs for cancer detection, as an area under the ROC curve of approximately 0.9 for distinguishing cancer patients from HDs was achieved. Moreover, human and microbial cfRNAs both have cancer type specificity, and combining two types of features could distinguish tumors of five different primary locations with an average recall of 60.4%. Compared to using human features alone, adding microbial features improved the average recall by approximately 8%. In summary, this work provides evidence for the clinical relevance of human and microbe-derived plasma cfRNAs and their potential utilities in cancer detection as well as the determination of tumor sites.

Comparison Analysis of Different DNA Extraction Methods on Suitability for Long-Read Metagenomic Nanopore Sequencing.

Metagenomic next-generation sequencing (mNGS) is a novel useful strategy that is increasingly used for pathogens detection in clinic. Some emerging mNGS technologies with long-read ability are useful to decrease sequencing time and increase diagnosed accuracy, which is of great significance in rapid pathogen diagnosis. Reliable DNA extraction is considered critical for the success of sequencing; hence, there is thus an urgent need of gentle DNA extraction method to get unbiased and more integrate DNA from all kinds of pathogens. In this study, we systematically compared three DNA extraction methods (enzymatic cell lysis based on MetaPolyzyme, mechanical cell lysis based on bead beating, and the control method without pre–cell lysis, respectively) by assessing DNA yield, integrity, and the microbial diversity based on long-read nanopore sequencing of urine samples with microbial infections. Compared with the control method, the enzymatic-based method increased the average length of microbial reads by a median of 2.1-fold [Inter Quartile Range (IQR), 1.7–2.5; maximum, 4.8) in 18 of the 20 samples and the mapped reads proportion of specific species by a median of 11.8-fold (Inter Quartile Range (IQR), 6.9–32.2; maximum, 79.27]. Moreover, it provided fully (20 of 20) consistent diagnosed results to the clinical culture and more representative microbial profiles (P < 0.05), which all strongly proves the excellent performance of enzymatic-based method in long-read mNGS–based pathogen identification and potential diseases diagnosis of microbiome related.

Abstract 3528: Influence of obesity-associated intra-tumor microbes on exacerbating cancer severity

Abstract Background: Despite there being a well-established connection between the gut microbiome and metabolic disease in humans, intra-tumor microbes and the mechanisms by which they regulate cell signaling, inflammation, and adipocyte growth to exacerbate disease severity in cancer patients also suffering from obesity remains largely unclear. In this study, we identified microbes to be distinctly abundant in cancer patients who are obese and correlated microbe abundance to patient survival, clinical variables, and immunological genes and pathways, in order to mechanistically explain how differential microbe abundance may influence clinical outcome. Methods: Microbial reads were aligned and extracted from raw whole-transcriptome RNA-sequencing data of cancer patient samples using Pathoscope 2.0 software. The Kruskal-Wallis test was used to correlate body mass index (BMI)-associated microbes to clinical variables. Reactome FIViz and Gene Set Enrichment Analysis were used to calculate pathway enrichment. Results: We identified specific microbes, including Pseudomonas fluorescens SBW25 and Enterobacter cloacae, and chemokine and interleukin-related genes to be potential determining factors of disease severity among cancer patients in BMI-associated groups. Gene set enrichment analysis revealed that microbes abundant in cancer tissue in obese patient samples, including Pseudomonas baetica in liver cancer patients, were significantly associated with the upregulation oncogenic, cell migration-related signaling pathways. Intra-tumor microbes from obese patient samples were also found to correlate with chemokine signaling and TFR2/NFkB-related genes, both of which have well-established roles in inflammatory activity. Conclusions: Our study significantly advances the understanding of the microbiome composition of the tumor microenvironment in patients who are obese and microbes’ relationship with clinical and immunologic variables, particularly inflammatory-related genes and pathways. We uncovered unknown mechanisms of the microbiome-immune interaction and obtained definitive data on microbiome dysbiosis in patients with obesity as a key determinant of severity of cancer, including microbe regulation of inflammasome activity. While deeper sequencing, more rigorous contamination correction, and in vitro and in vivo experiments are necessary to fully elucidate how microbe species can effectively act as therapeutic agents in probiotic and prebiotic therapies to reduce insulin resistance, inflammation, and glucose levels, our results are essential for guiding this future research. Citation Format: Aditi Gnanasekar, Neil Shende, Jaideep Chakladar, Wei T. Li, Lindsay M. Wong, Michael Karin, Weg M. Ongkeko. Influence of obesity-associated intra-tumor microbes on exacerbating cancer severity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3528.

AMAISE: a machine learning approach to index-free sequence enrichment

Metagenomics holds potential to improve clinical diagnostics of infectious diseases, but DNA from clinical specimens is often dominated by host-derived sequences. To address this, researchers employ host-depletion methods. Laboratory-based host-depletion methods, however, are costly in terms of time and effort, while computational host-depletion methods rely on memory-intensive reference index databases and struggle to accurately classify noisy sequence data. To solve these challenges, we propose an index-free tool, AMAISE (A Machine Learning Approach to Index-Free Sequence Enrichment). Applied to the task of separating host from microbial reads, AMAISE achieves over 98% accuracy. Applied prior to metagenomic classification, AMAISE results in a 14–18% decrease in memory usage compared to using metagenomic classification alone. Our results show that a reference-independent machine learning approach to host depletion allows for accurate and efficient sequence detection.

METAGENOMIC NEXT-GENERATION SEQUENCING DETECTS PATHOGENS IN ENDOPHTHALMITIS PATIENTS.

To investigate the utility of metagenomic next-generation sequencing (mNGS) in identifying the pathogens in endophthalmitis. In this prospective study, 36 cases of endophthalmitis were recruited. All patients received surgical treatment and intraocular drug lavage. The samples of vitreous or aqueous humor were extracted for mNGS and microbiological culture. The diagnostic performance of pathogens was compared between mNGS and culture. The positive rates of mNGS and culture were 88.89% (32/36) and 27.78% (10/36), respectively. There was a statistically significant difference between mNGS and culture (Chi-square = 27.657; P < 0.01). Staphylococcus epidermidis, Streptococcus pneumoniae, and Klebsiella pneumoniae were the most pathogenic bacteria in traumatic, postoperative, and endogenous endophthalmitis, respectively. The concordance of pathogen identified by mNGS and culture was 70% for culture-positive cases. Antibiotic resistance genes were identified in 9 cases. There was a marginal correlation between the final visual acuity and the microbial sequence read (Spearman correlation coefficient = 0.498; P = 0.05). Metagenomic next-generation sequencing has a higher positive rate of identifying pathogens in endophthalmitis than in culture. It can also provide information on antibiotic resistance and visual prognosis. However, caution must be taken when interpreting the results of mNGS because they may not be concordant with culture.

Longitudinal Patterns of Microbial Communities in the Water Diversion Rivers of South‐to‐North Water Diversion Project

AbstractWater diversion rivers are the fundamental transport lines in water diversion projects. The changeable hydrological and physicochemical conditions lead to significant changes in biogeographical characteristics of microbial communities within river catchments. This study gives an insight into longitudinal patterns of the composition and diversity characteristics of microbial communities in water diversion rivers in the East Route of the South‐to‐North Water Diversion Project using 16S rRNA sequencing. A total of 937 743 microbial sequence reads are obtained and classified into 9178 distinct operational taxonomic units, belonging to 48 phyla and 424 genera, respectively. Along the water diversion line, obvious distribution characteristics of microbial communities could be observed in Actinobacteria, Firmicutes, Exiguobacterium, CL500‐29_marine_group, Acinetobacter, and Cloacibacterium. The relative abundance of both Actinobacteria and CL500‐29_marine_group increases from the upper rivers in the northern region to the lower rivers in the southern region, while Firmicutes and Exiguobacterium show the opposite trend. Higher microbial diversity is found in the upper rivers of the water diversion line than in the lower rivers. Externally, redundant analysis indicated the spatial shift in microbial community is largely shaped by ammonium, total nitrogen, conductivity, and total phosphorus. Internally, longitudinal construction of the microbial community is mainly affected by species replacement.

Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling

BackgroundWith over 350,000 estimated deaths worldwide in 2018, prostate cancer (PCa) continues to be a major health concern and a significant cause of cancer-associated mortality among men. While cancer in general is considered a disease of the human genome, there is a growing body of evidence suggesting that changes to the healthy microbiota could play a vital role in cancer development, progression, and/or treatment outcome.MethodsUsing a metatranscriptomic approach, we annotated the microbial reads obtained from total RNA sequencing of 106 prostate tissue samples from 94 PCa patients (discovery cohort). We investigated microbial dysbiosis associated with PCa by systematically comparing the microbiomes between benign and malignant tissue samples, between less vs. more-aggressive PCa, and between patients who had biochemical recurrence as opposed to those who did not. We further performed differential gene expression and cell type enrichment analysis to explore the host transcriptomic and cellular responses to selected microbial genera. A public dataset (GSE115414) of total RNA sequencing reads from 24 prostate tissue samples (8 benign and 16 malignant) served as the validation cohort.ResultsWe observed decreased species diversity and significant under-representation of Staphylococcus saprophyticus and Vibrio parahaemolyticus, as well as significant over-abundance of Shewanella in malignant as compared to benign prostate tissue samples in both the discovery (p < 0.01) and validation (p < 0.05) cohorts. In addition, we identified Microbacterium species (p < 0.01) to be significantly over-abundant in pathologically advanced T3 tumors compared to T2 in the discovery cohort. Malignant samples having high vs. low Shewanella counts were associated with downregulated Toll-like receptor signaling pathways and decreased enrichment of dendritic cells. Malignant samples having low vs. high V. parahaemolyticus counts were enriched for olfactory transduction and drug metabolism pathways. Finally, malignant samples were enriched for M1 and M2 macrophages as compared to benign tissue samples.ConclusionsThe results from this exploratory study support the existence of an important biological link between the prostate microbiota and PCa development/progression. Our results highlight Shewanella, V. parahaemolyticus, and Microbacterium sp. as interesting candidates for further investigation of their association with PCa.

The Structure and Function of Gut Microbiomes of Two Species of Sea Urchins, Mesocentrotus nudus and Strongylocentrotus intermedius, in Japan

Sea urchin is an indicator of coastal environmental changes in the global warming era, and is also a model organism in developmental biology and evolution. Due to the depletion of wild resources, new aquaculture techniques for improving stocks have been well studied. The gut microbiome shapes various aspects of a host’s physiology. However, these microbiome structures and functions on sea urchins, particularly Mesocentrotus nudus and Strongylocentrotus intermedius which are important marine bioresources commonly found in Japan, have not been fully investigated yet. Using metagenomic approaches including meta16S and shotgun metagenome sequencings, the structures, functions, and dynamics of the gut microbiome of M. nudus and S. intermedius, related to both habitat environment and host growth, were studied. Firstly, a broad meta16S analysis revealed that at the family level, Psychromonadaceae and Flavobacteriaceae reads (38–71%) dominated in these sea urchins, which is a unique feature observed in species in Japan. Flavobacteriaceae reads were more abundant in individuals after rearing in an aquarium with circulating compared to one with running water. Campylobacteraceae and Vibrionaceae abundances increased in both kinds of laboratory-reared sea urchins in both types of experiments. 2-weeks feeding experiments of M. nudus and S. intermedius transplanted from the farm to laboratory revealed that these gut microbial structures were affected by diet rather than rearing environments and host species. Secondly, further meta16S analysis of microbial reads related to M. nudus growth revealed that at least four Amplicon Sequence Variant (ASV) affiliated to Saccharicrinis fermentans, which is known to be a nitrogen (N2) fixing bacterium, showed a significant positive correlation to the body weight and test diameter. Interestingly, gut microbiome comparisons using shotgun metagenome sequencing of individuals showing higher and lower growth rates revealed a significant abundance of “Nitrate and nitrite ammonification” genes in the higher-grown individuals under the circulating water rearing. These findings provide new insights on the structure-function relationship of sea urchin gut microbiomes beyond previously reported nitrogen fixation function in sea urchin in 1950s; we discovered a nitrate reduction function into ammonium for the growth promotion of sea urchin.

Targeting RNA with Next- and Third-Generation Sequencing Improves Pathogen Identification in Clinical Samples.

Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture‐based diagnosis in clinics is increasingly supplemented by metagenomic next‐generation‐sequencing (mNGS). Here, RNA/cDNA‐targeted sequencing (meta‐transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta‐transcriptomics using third‐generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple‐strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct‐RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA‐targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.

Combined nanopore adaptive sequencing and enzyme-based host depletion efficiently enriched microbial sequences and identified missing respiratory pathogens

BackgroundEnzyme-based host depletion significantly improves the sensitivity of clinical metagenomics. Recent studies found that real-time adaptive sequencing of DNA molecules was achieved using a nanopore sequencing machine, which enabled effective enrichment of microbial sequences. However, few studies have compared the enzyme-based host depletion and nanopore adaptive sequencing for microbial enrichment efficiency.ResultsTo compare the host depletion and microbial enrichment efficiency of enzyme-based and adaptive sequencing methods, the present study collected clinical samples from eight children with respiratory tract infections. The same respiratory samples were subjected to standard methods, adaptive sequencing methods, enzyme-based host depletion methods, and the combination of adaptive sequencing and enzyme-based host depletion methods. We compared the host depletion efficiency, microbial enrichment efficiency, and pathogenic microorganisms detected between the four methods. We found that adaptive sequencing, enzyme-based host depletion and the combined methods significantly enriched the microbial sequences and significantly increased the diversity of microorganisms (p value < 0.001 for each method compared to standard). The highest microbial enrichment efficiency was achieved using the combined method. Compared to the standard method, the combined method increased the microbial reads by a median of 113.41-fold (interquartile range 23.32–327.72, maximum 1812), and the number of genera by a median of 70-fold (interquartile range 56.75–86.75, maximum 164). The combined method detected 6 pathogens in 4 samples with a median read of 547, compared to 5 pathogens in 4 samples with a median read of 4 using the standard method.ConclusionThe combined method is an effective, easy-to-run method for enriching microbial sequences in clinical metagenomics from sputum and bronchoalveolar lavage fluid samples and may improve the sensitivity of clinical metagenomics for other host-derived clinical samples.

Abstract 1784: Absence of intratumor microbes induces methylation of tumor suppressors and cell cycle-related genes in papillary thyroid carcinoma

Abstract Background: Papillary thyroid carcinoma (PTC) is characterized by varied prognosis between gender and cancer subtype, but the cause for gender and subtype-based dissimilarities is unclear. Women are more frequently diagnosed with PTC, while men suffer more advanced-staged PTC. Additionally, tall cell and poorly differentiated variants are more aggressive than classical and follicular variants of PTC. While the intratumor microbiome has become increasingly implicated in cancer development, the microbial landscape of PTC is essentially uninvestigated. Past studies have shown how microbes within tumor tissue can contribute to or inhibit tumor growth by regulating gene expression and immune and cancer pathways. For example, a study published by Bahmani S. et. al. found that the absence of folate-producing Bifidobacterium and Lactobacillus was correlated with hypomethylation at regions of the p53 gene in colorectal cancer. We hypothesized that intratumor microbiome composition distinctly alters the immune landscape by preventing or inducing methylation of critical genes and may therefore predict cancer progression between PTC subtypes and between patient genders. Methods: Raw whole-transcriptome RNA-sequencing data for PTC and adjacent normal tissue was downloaded from The Cancer Genome Atlas, and microbial reads were extracted using Pathoscope 2.0 software. The Kruskal-Wallis test was performed to identify differentially abundant microbes between tumor and normal samples. Level 3 normalized DNA methylation 450k sequencing was downloaded from the GDC portal. We converted B-values to M-values and then performed the probe-wise differential methylation analysis on the matrix of M-values in limma to obtain t-statistics and p-values for each CpG site. The Kruskal-Wallis test was used to correlate microbe abundance to extent of methylation. R Results: Overall, PTC tumor tissue significantly lacked microbes that are populated in adjacent normal tissue. Tissue from different genders and subtypes was characterized by dysbiosis of specific microbe species, and microbes distinctly abundant in tall cell and male patient cohorts were correlated with greater methylation of tumor suppressors. The most significantly methylated sites occurred on chromosomes 1 and 17 and specifically at cell cycle related genes. Lower microbe abundance (Anabaena sp. K119, Trueperella pyogenes, Frankia sp.) in tumor tissue was predominantly correlated with greater extent of methylation at known tumor suppressor genes including NEURL1B, POLE, and SRCIN1. Conclusions: We identified microbes that are uniquely abundant in specific PTC types and are predominantly negatively correlated with methylation of tumor suppressors and cell cycle-related genes. Our results may provide a basis for developing specialized prebiotic and probiotic treatments for varied PTC tumors. Citation Format: Aditi Gnanasekar, Grant Castaneda, Anjali Iyangar, Shruti Magesh, Jaideep Chakladar, Wei T. Li, Lindsay M. Wong, Weg M. Ongkeko. Absence of intratumor microbes induces methylation of tumor suppressors and cell cycle-related genes in papillary thyroid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1784.

Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes.

Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study.

Detection of Anaplasma Phagocytophilum in Horses With Suspected Tick-Borne Disease in Northeastern United States by Metagenomic Sequencing.

Metagenomic sequencing of clinical diagnostic specimens has a potential for unbiased detection of infectious agents, diagnosis of polymicrobial infections and discovery of emerging pathogens. Herein, next generation sequencing (NGS)-based metagenomic approach was used to investigate the cause of illness in a subset of horses recruited for a tick-borne disease surveillance study during 2017–2019. Blood samples collected from 10 horses with suspected tick-borne infection and five apparently healthy horses were subjected to metagenomic analysis. Total genomic DNA extracted from the blood samples were enriched for microbial DNA and subjected to shotgun next generation sequencing using Nextera DNA Flex library preparation kit and V2 chemistry sequencing kit on the Illumina MiSeq sequencing platform. Overall, 0.4–0.6 million reads per sample were analyzed using Kraken metagenomic sequence classification program. The taxonomic classification of the reads indicated that bacterial genomes were overrepresented (0.5 to 1%) among the total microbial reads. Most of the bacterial reads (~91%) belonged to phyla Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, Cyanobacteria and Tenericutes in both groups. Importantly, 10–42.5% of Alphaproteobacterial reads in 5 of 10 animals with suspected tick-borne infection were identified as Anaplasma phagocytophilum. Of the 5 animals positive for A. phagocytophilum sequence reads, four animals tested A. phagocytophilum positive by PCR. Two animals with suspected tick-borne infection and A. phagocytophilum positive by PCR were found negative for any tick-borne microbial reads by metagenomic analysis. The present study demonstrates the usefulness of the NGS-based metagenomic analysis approach for the detection of blood-borne microbes.

First Description of the Composition and the Functional Capabilities of the Skin Microbial Community Accompanying Severe Scabies Infestation in Humans.

Epidemiological studies link Sarcoptes scabiei infection and impetigo. Scabies mites can promote Streptococcus pyogenes (Group A Streptococcus) and Staphylococcus aureus infections by breaching the skin barrier and excreting molecules that inhibit host innate immune responses. However, little is known about the composition and the function of the scabies-associated microbiota. Here, high-throughput whole-metagenome sequencing was used to explore the scabies-associated microbiome. Scabies mites including their immediate microenvironments were isolated from two patients with severe scabies in Northern Australia. Two ~45–50 million paired-end reads Illumina libraries were generated of which ~2 (5.1%) and 0.7 million (1.3%) microbial reads were filtered out by mapping to human (hg19) and mite draft genomes. Taxonomic profiling revealed a microbial community dominated by the phylum Firmicutes (A: 79% and B: 59%) and genera that comprise Streptococcus, Staphylococcus, Acinetobacter, and Corynebacterium. Assembly of the metagenome reads resulted in genome bins representing reference genomes of Acinetobacter baumannii, Streptococcus dysgalactiae (Group C/G), Proteus mirablis and Staphylococcus aureus. The contigs contained genes relevant to pathogenicity and antibiotics resistance. Confocal microscopy of a patient skin sample confirmed A. baumannii, Streptococci and S. aureus in scabies mite gut and faeces and the surrounding skin. The study provides fundamental evidence for the association of opportunistic pathogens with scabies infection.

A proof-of-concept study of an automated solution for clinical metagenomic next-generation sequencing.

Metagenomic next-generation sequencing (mNGS) has been utilized for diagnosing infectious diseases. It is a culture-free and hypothesis-free nucleic acid test for diagnosing all pathogens with known genomic sequences, including bacteria, fungi, viruses and parasites. While this technique greatly expands the clinical capacity of pathogen detection, it is a second-line choice due to lengthy procedures and microbial contaminations introduced from wet-lab processes. As a result, we aimed to reduce the hands-on time and exogenous contaminations in mNGS. We developed a device (NGSmaster) that automates the wet-lab workflow, including nucleic acid extraction, PCR-free library preparation and purification. It shortens the sample-to-results time to 16 and 18·5h for DNA and RNA sequencing respectively. We used it to test cultured bacteria for validation of the workflow and bioinformatic pipeline. We also compared PCR-free with PCR-based library prep and discovered no differences in microbial reads. Moreover we analysed results by automation and manual testing and found that automation can significantly reduce microbial contaminations. Finally, we tested artificial and clinical samples and showed mNGS results were concordant with traditional culture. NGSmaster can fulfil the microbiological diagnostic needs in a variety of sample types. This study opens up an opportunity of performing in-house mNGS to reduce turnaround time and workload, instead of transferring potentially contagious specimen to a third-party laboratory.

1201. Sarcoidosis Candidate Microbes Identified by Next Generation Sequencing

BackgroundSarcoidosis is an autoimmune disease characterized by granulomatous lung disease with very prominent mediastinal adenopathy. Acid-fast bacteria, fungi, and viruses have been considered as possible causes of sarcoidosis. We used next-generation or deep sequencing to characterize the microbial content of diseased mediastinal lymph nodes from 10 sarcoidosis patients compared to a set of 10 negative-controls.MethodsRNA was extracted from fixed paraffinized mediastinal lymph nodes (MLN) from 12 diseased specimens taken from 10 sarcoidosis patients and 2 positive control subjects (TB, MAI), and normal appearing MLN from 10 negative-control subjects (mostly cancer patients). The extracted RNA was sequenced on the Illumina 2500, yielding 125-bp paired-end reads. These reads were aligned to the human genome, human transcriptome, and a nonredundant panmicrobial database. Each experimental sample were compared against the set of 10 negative-controls using the false discovery rate method (q-value). Directed qPCR was performed on all the samples.Results100-153 million read-pairs were obtained from the 24 sequenced samples (12 sarcoidosis, 10 negative-control, 2 positive-control). Among these, 0.01-1.32% of the reads were microbial, with a trend towards fewer microbial reads in the sarcoidosis group compared to controls (means 66K vs. 457K, p=0.09). Mycobacterial sequence was significantly enriched (q< 0.05) in the MAI but not the TB sample compared to the negative-controls. Among the 12 sarcoidosis samples, sequence mappings were significantly enriched (q< 0.05) for the following genera: fungal, Magnaporthe (N=4 samples) and Debaromyces (1); bacteria, Odoribacter (1) and Granulicella (1); and viral, Roseolovirus (6) and Mardivirus (6). Further metagenomic analysis eliminated Magnaporthe as a candidate. qPCR confirmed the presence of Odoribacter in 2 specimens and Debaromyces in 1. Roseolovirus (HHV6) could not be detected by qPCR in any of the samples.ConclusionWe conclude that sequencing is a feasible method for identifying candidate microbes that might trigger sarcoidosis in human subjects. Further research is required to establish or refute the pathogenicity of these organisms in patients with sarcoidosis.Disclosures All Authors: No reported disclosures

Evaluation of methods for the reduction of contaminating host reads when performing shotgun metagenomic sequencing of the milk microbiome

Shotgun metagenomic sequencing is a valuable tool for the taxonomic and functional profiling of microbial communities. However, this approach is challenging in samples, such as milk, where a low microbial abundance, combined with high levels of host DNA, result in inefficient and uneconomical sequencing. Here we evaluate approaches to deplete host DNA or enrich microbial DNA prior to sequencing using three commercially available kits. We compared the percentage of microbial reads obtained from each kit after shotgun metagenomic sequencing. Using bovine and human milk samples, we determined that host depletion with the MolYsis complete5 kit significantly improved microbial sequencing depth compared to other approaches tested. Importantly, no biases were introduced. Additionally, the increased microbial sequencing depth allowed for further characterization of the microbiome through the generation of metagenome-assembled genomes (MAGs). Furthermore, with the use of a mock community, we compared three common classifiers and determined that Kraken2 was the optimal classifier for these samples. This evaluation shows that microbiome analysis can be performed on both bovine and human milk samples at a much greater resolution without the need for more expensive deep-sequencing approaches.