Low Diversity Samples Research Articles

Shotgun Sequencing of Multi-Site Oral and Colonic Microbiota in Patients with AML: Site-Specific Patterns and Microbiota Decompartmentalization

Gut microbiota is a source of pathogens in immunocompromised patients. While most microbiota studies in patients with AML receiving induction chemotherapy have focused on the gut microbiota, the few on salivary microbiota suggest less severe dysbiosis. As the saliva does not have its own intrinsic microbiota and takes fractions of the microbiota from the different oral tissues it bathes, the extent to which the salivary microbiota reflects microbial changes in other oral sites is unknown. To fill this knowledge gap, we characterized the microbiota in longitudinal salivary, supragingival plaque, and fecal samples of AML patients receiving inpatient chemotherapy. 31 patients have been enrolled in this prospective single-center study. Baseline stool, saliva, and plaque samples were collected at baseline and repeated weekly until 5 weeks after starting chemotherapy or discharge from the hospital, whichever occurred first. Time was divided into 3 intervals: admission to day 10 of chemotherapy, days 11-20, and day 21 and later. 111 samples (stool: 39, saliva: 36, plaque: 36) from 16 patients have been sequenced to date. Samples underwent shotgun sequencing targeting 10M paired-end reads/sample. MetaPhlAn4 was used for taxonomic assignment. Alpha diversity was quantified by Shannon index and beta diversity by Bray-Curtis dissimilarity. 9 patients were male and 7 were female. Median age was 64 (range 34-82). 15 patients were newly diagnosed and 1 was relapsed/refractory. Chemotherapy regimens included CLAG-M (n=10), ATRA+ATO (n=3), Vyxeos (n=1), Venetoclax+Azacitidine (n=1), and intermediate-dose Ara-C (n=1). The 3 most frequently used antibacterial antibiotics were cefepime (n=14), IV vancomycin (n=12), and levofloxacin (n=11). Median hospitalization length was 19 days (range 6-31). Neutropenic fever occurred in 13 patients. Documented infections included bacteremia (n=8), skin/soft tissue (n=5), intestinal (n=4), urinary tract (n=4), and pneumonia (n=1). Four bacterial species isolated from blood cultures of patients with fever were detected by sequencing 1-2 weeks before bacteremia: Gemella Haemolysans in saliva, plaque, and stool, Streptococcus mitis in saliva and stool, Rothia mucilaginosa in saliva and plaque, and Citrobacter freundii in stool. Microbiota diversity from the 3 sites showed markedly different dynamics ( Fig. 1). Plaque microbiota diversity remained stable throughout, with levels as high as fecal microbiota in intervals 1 and 2, and higher than both fecal and salivary microbiota in interval 3. Salivary microbiota diversity was the lowest among sites in intervals 1 and 2 and experienced only a slight reduction in interval 3. In contrast, fecal microbiota diversity was high compared to the other sites in intervals 1 and 2 but dropped in interval 3 to the lowest among all sites. With all 3 intervals combined, salivary microbiota had lower diversity (median 1.9, range 0.04-3.9) than plaque (median 2.7, 0.2-4.3; P=0.01) and fecal microbiota (median 2.9, 0.3-3.7; P=0.048). The 3 sites largely segregated on hierarchical clustering using species abundances ( Fig. 2). However, microbiota in several low-diversity samples from different sites in the same patient coalesced: Veillonella parvula and R. mucilaginosa were detected in all 3 sites, V. dispar in stool and plaque, and S. parasanguinis in stool and saliva. Each overlap was present in 5 or more patients. This first shotgun sequencing analysis of longitudinal salivary, plaque, and fecal microbiota in AML patients offers several novel findings. First, plaque microbiota diversity is stable despite exposure to antibiotics, whereas fecal microbiota diversity is not. Second, while healthy adults show no overlap between oral and colonic microbiota, low-diversity fecal samples from AML patients contained species of their own oral microbiota, particularly at later time points. These data suggest ectopic colonization of the gut by oral flora. Future mechanistic studies of plaque biofilms may reveal community-protective mechanisms that sustain oral plaque microbial diversity and contribute oral flora to the gut. Mitigating gut dysbiosis in AML patients by manipulating plaque biofilms could be a novel approach to improve patient outcomes.

Abstract 6009: An end-to-end workflow for improved methylation detection

Abstract Introduction: DNA methylation at CpG nucleotide sites in eukaryotes is a key epigenetic mark that can help regulate gene expression. Specific changes in CpG methylation occur in many human cancers, making them a promising biomarker for early cancer detection. However, existing assays can be costly, lack specificity to regions of interest and often provide only semi-quantitative estimates of the methylation fraction. Here, we present a targeted methylome panel to decrease costs associated with next generation sequencing (NGS), methylation controls to calibrate quantitative assays and UMIs for accurate deduplication in low-diversity samples. Experimental Procedures: Genomic DNA (gDNA) was prepared for sequencing using the Twist Methylation Detection System consisting of enzymatic methylation conversion and hybrid capture using the Twist Human Methylome Panel. Twist’s synthetic CpG methylation level controls were spiked-in to gDNA and taken through the Twist Methylation Detection System to demonstrate their utility in calibrating methylation assays. Additionally, libraries were generated using cell free DNA (cfDNA) and either conventional or UMI-containing adapters to investigate the impact on quantitative detection and total unique coverage. Results: Using the Twist Human Methylome Panel at 150x raw coverage achieves uniform coverage with low off-bait Picard metrics. 6.59 million CpG sites were detected using a minimum depth of 10X. Target capture with the Human Methylome Panel allows for informative CpG calling of up to 82 samples on a single Illumina Novaseq S4 flowcell, compared to 3 samples per flowcell with a tradition whole-genome bisulfite sequencing (WGBS) assay. The Twist CpG methylation specific controls are constructed of 48 unique contrived sequences that contain a total of 8 different levels of methylation, ranging from 100% to 0%. Including these controls allows for quantitation of methylation levels in the experimental samples and qualification of the enzymatic conversion process. Conclusions: Our study leverages the Twist methylation detection portfolio to interrogate genome-wide methylation patterns for various applications. In combination, these tools can be used to design cost effective end-to-end assays. *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Lydia Bonar, Kristin Butcher, Michael Bocek, Holly Corbitt, Bryan Hoglund, Cibelle Nassif, Patrick Cherry, Derek Murphy, Jean Challacombe, Esteban Toro. An end-to-end workflow for improved methylation detection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6009.

VARGAN: variance enforcing network enhanced GAN

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we propose a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator’s loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.

Traces of SARS-CoV-2 RNA in Peripheral Blood Cells of Patients with COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third virus that caused coronavirus-related outbreaks over the past 20 years. The outbreak was first reported in December 2019 in Wuhan, China, but rapidly progressed into a pandemic of an unprecedented scale since the 1918 flu pandemic. Besides respiratory complications in patients with COVID-19, clinical characterization of severe infection cases showed several other comorbidities, including multiple organ failure, and septic shock. To better understand the systemic pathogenesis of COVID-19, we interrogated the virus's presence in the peripheral blood cells, which might provide a form of trafficking or hiding to the virus. By analyzing >2 billion sequence reads of high-throughput transcriptome sequence data from 180 samples of patients with active SARS-CoV-2 infection or healthy controls collected from 6 studies, we found evidence of traces of SARS-CoV-2 RNA in peripheral blood mononuclear cells in two samples from two independent studies. In contrast, the viral RNA was abundant in bronchoalveolar lavage specimens from the same patients. We also devised a “viral spike-to-actin” RNA normalization as a metric to compare across various samples and minimize errors caused by intersample variability in total human RNA abundance. Our observation suggests immune presentation and discounts the possibility of extensive viral infection of lymphocytes or monocytes.

Correction: Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform

[This corrects the article DOI: 10.1371/journal.pone.0120520.].

Composition and diversity of the preputial microbiota in healthy bulls

Characterization of microbial communities inhabiting the reproductive tracts of cattle may lead to a better comprehension of bovine physiology and reproductive health. To date, reported studies have utilized culture-independent 16S ribosomal RNA (rRNA) for the classification of microbiota in the vaginal tract of cows but no studies have looked at the microbiota of the prepuce or penis of the bull. The aim of this study was to elucidate the microbiota present on the epithelial surface of the penis and prepuce of the post-pubertal bull using 16S rRNA gene sequencing. Ninety-two healthy bulls of a variety of ages and breeding history, presented for routine breeding soundness examinations, were utilized in this investigation. Bacteria belonging to Firmicutes, Fusobacteria, Bacteroidetes, Proteobacteria, and Actinobacteria were identified in the prepuce. From all the bulls, two major community types were found, those with low or high bacterial species richness (up to 400 operational taxonomic units in one sample). There was no animal characteristic (breed or age) or management practice (feed type, antibiotic use, co-housing, breeding history) that was correlated with the bull penile microbial community composition. However, Bradyrhizobium was a distinguishing genus only found in the low diversity samples. The bull penile microbial community includes members of genera that are common in soil, cow vagina, respiratory tract, and feces. The baseline preputial microbial community in healthy bulls is described in the current study. This knowledge can be used later when investigating the interactions between disease and the male urogenital tract microbial community.

Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform.

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer’s, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.

LBOR01: ONE MILLION SAMPLES TYPED BY NGS – LESSONS LEARNED

Aim DKMS Life Science Lab adopted an NGS based HLA typing workflow in early 2013. Meanwhile more than a million samples have been successfully typed by NGS forming the basis for this review of the chances and pitfalls of such an approach. Methods Our NGS workflow is based on direct sequencing of PCR amplified exons on Illumina MiSeq instruments. Exons 2 and 3 of HLA-A, B, C, DRB1, DQB1 and DPB1 are amplified by PCR on Fluidigm Access Array chips. Recently the profile was extended to cover in addition ABO, Rhesus and CCR5. Amplicons of up to 768 samples are pooled for a sequencing run. Sequencing data is analysed by the in-house developed software neXtype. Results The rather simple workflow proved to be very robust. Loading of the Fluidigm chips is critical and, due to the low volumes, needs careful optimization and regular maintenance of the involved liquid handlers. PCR continuously yielded sufficient product for loading on the sequencers. The MiSeqs in general produce very high quality sequence data. Low diversity samples like amplicon products pose a particular challenge to the MiSeqs but are meanwhile handled well. However, care must be taken to keep the rate of very short PCR artefacts low as those may negatively impact the overall quality. For analysis of the data care must be taken to identify crossover PCR products which may resemble true HLA alleles. In addition, due to PCR artefacts the rate of error at a particular position might be significantly higher than indicated by the q -values. When taking this into account software like neXtype can provide highly automated and accurate results at a fraction of the time of Sanger data analysis. Given adequate software we identified imbalanced amplification as the main source of error. Rigorous quality control of every reagent batch with regard to even amplification is essential. In addition samples with DNA concentrations below 3 ng/μl should not be applied to 192 Fluidigm chips to avoid random loss of alleles. Conclusions Switching to NGS as the primary tool for HLA typing was highly beneficial for the lab. The NGS based workflow has proven superior to the previous Sanger based workflow in every aspect: Costs, hands on time, resolution, turnaround time, stability and scalability. We conclude that NGS technology has matured to a point where it is appropriate for everyday routine operation.

Capillary electrophoresis ribosomal RNA single-stranded conformation polymorphism: a new approach for characterization of low-diversity microbial communities

Capillary electrophoresis (CE) has been the principle system for nucleic acid analysis since the early 1990s due to its inherent advantages such as fast analysis time, high resolution and efficiency, minimal sample requirement, high detection sensitivity, and automation. In the past few decades, microbial community fingerprinting methods such as terminal restriction fragment length polymorphism and single-stranded conformation polymorphism (SSCP) have migrated to CE to utilize its advantages over conventional slab gel electrophoresis. Recently, a gel-based direct rRNA fingerprint method was demonstrated. Different from other existing microbial community characterization approaches, this novel approach is polymerase chain reaction free and capable of providing information on the relative abundance of rRNA from individual phylotypes in low-diversity samples. As a gel-based method, it has a long analysis time and relatively large reagent and sample requirements. Here, we addressed these limitations by transferring the RNA fingerprint approach to the CE platform. Analysis time significantly improved from 24 h to 60 min, and the use of a fluorescently labeled hybridization probe as the detection strategy decreased the sample requirement by ten-fold. The combination of fast analysis time, low sample requirement, and sensitive fluorescence detection makes CE-RNA-SSCP an appealing new approach for characterizing low-diversity microbial communities.

Direct rRNA Fingerprinting, a Novel Method to Profile Low Diversity Microbial Communities

In the past decade, an increasing number of methods in microbial ecology have been developed that address the questions of which microbes exist in the environment, what their roles are and, to some extent, what their abundance is. In the present paper, we propose and describe the proof of principle of a novel method for analysing shifts in microbial community composition that uses small RNA fragments directly derived from 16S rRNA. Community fingerprints are generated on the basis of sequence-dependent conformational differences of rRNA fragments. We applied this method to profile artificial and natural communities and to detect changes in community structure in enrichment cultures. This method constitutes a PCR-free alternative to microbial community characterisation and can provide information on the relative abundance of rRNA from individual phylotypes in low diversity samples.