This work was supported by National Science Foundation grants to A.S. (DEB #1427772, 1745411, 1750037).

1. Tick Collection and Surface Sterilization
<ol>
	<li>Collect ticks by dragging a 1 m2 white cloth over a tick-associated habitat, removing ticks attached to host species, or rearing ticks in the lab15,16. Use fine forceps to manipulate ticks and store them at -80 &#176;C.</li>
	<li>Place ticks in the individual PCR tubes and remove surface contaminants by vortexing for 15 s successively with 500 &#956;L of hydrogen peroxide (H2O2), 70% ethanol, and ddH2O.</li>
	<li>Place the ticks in a new PCR tube and allow them to air-dry.</li>
	<li>In this tube, mechanically disrupt tick tissues by crushing the ticks with a mortar and pestle (used in this study), using small beads in a bead beater, or cutting the tick apart with a scalpel.</li>
</ol>
2. DNA Purification
<ol>
	<li>Purify DNA from individual ticks, following the instructions provided in a commercially available DNA extraction kit. Elute for a final volume of 100 &#956;L. 
	NOTE: Refer to Figure 1 for an overview of steps 2.2-2.8. Alternative extraction methods include phenol-chloroform extraction17,18, ethanol precipitation19, or extraction with a chelating material20,21.</li>
	<li>Confirm successful DNA purification using a fluorometer or spectrophotometer. For fluorometry, use 10 &#956;L of DNA template in a 190 &#956;L double-stranded DNA assay. The expected yield is 0.1-1.0 ng/&#956;L22. For spectrophotometry, use 1 &#956;L of DNA template in a nucleic acid quantification. The expected A260/280 ratio is approximately 1.823.</li>
	<li>If not proceeding immediately to amplification, store the samples at -20 &#176;C.</li>
</ol>
3. 16S rRNA Gene Amplification
<ol>
	<li>Set up the amplicon PCR in a 27.5 &#956;L reaction containing: 5 &#956;L of each primer at 1 &#956;M; 12.5 &#956;L of commercially available PCR mix; and 5 &#956;L of DNA extracted from individual ticks at a 5 ng/&#956;L concentration. 
	NOTE: Use the following primers for amplification of the hypervariable V3-V4 region of the 16S rRNA gene13: 
	5&#39;--TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG--3&#39;, 
	5&#39;--GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC--3&#39;</li>
	<li>Move tubes to a thermocycler programmed for: an initial denaturation at 95 &#176;C for 3 min; 25 cycles of 1) 95 &#176;C for 30 s, 2) 57 &#176;C for 30 s, 3) 72 &#176;C for 30 s; a final extension at 72 &#176;C for 5 min; and a final hold at 10 &#176;C.</li>
	<li>Once the PCR is done, visualize the PCR product by loading 4-6 &#956;L/sample on a 1.5% agarose gel. Look for a band at 460 bp to confirm amplification. 
	NOTE: Amplicon PCR product may not be visible on the gel if the sample concentration is too low (&#60; 10 ng). Primer dimer presence is common in low-concentration samples. If other non-target bands are present, adjust the annealing temperature or decrease the number of cycles.</li>
	<li>If not proceeding immediately to purification, store the samples at 4 &#176;C.</li>
</ol>
4. 16S Amplicon Purification
<ol>
	<li>Using a new PCR tube for each sample, combine the PCR product with ddH2O to obtain 60 &#956;L total. For samples with low DNA concentrations, perform amplicon PCR in triplicate to reduce amplification bias and pool the samples to concentrate.</li>
	<li>Bring paramagnetic beads to room temperature and vortex them well before use.</li>
	<li>Add 48 &#956;L of paramagnetic beads to 60 &#956;L of the sample and incubate for 5 min. Place the tubes on a magnetic rack for 5 min until solution becomes clear. Remove the supernatant. 
	NOTE: This bead concentration targets the removal of primer dimers (&#60; 60 bp). If needed, adjust the bead concentration to remove other non-specific banding.</li>
	<li>With tubes on magnetic rack, add 500 &#956;L of freshly-prepared 80% EtOH. Immediately after adding EtOH to all tubes, pipette out the liquid. Do not remove the beads. Repeat the EtOH addition and removal one more time.</li>
	<li>Air-dry the samples to remove excess EtOH by leaving the tubes open on the magnetic rack for 5 min, or until small cracks are visible in the beads.</li>
	<li>Add 20 &#956;L of TE buffer and incubate the samples off the magnet, at room temperature, for 5-10 min.</li>
	<li>Place the tubes back on the magnet. Once the beads and liquid are separated, transfer the supernatant to a fresh tube to obtain the cleaned PCR product.</li>
	<li>(Optional) Visualize the product to confirm successful amplicon purification by loading 4-6 &#956;L/sample on a 1.5% gel. The 460 bp band should be visible, and there should be no primer dimer.</li>
	<li>If not proceeding immediately to index PCR, store the samples 4 &#176;C.</li>
</ol>
5. Sample Barcoding and Purification
<ol>
	<li>Assign unique primer combinations to each sample by selecting either forward or reverse primers, or both, from a commercially available library index kit. 
	NOTE: Uniquely labeling samples allows for differentiation after sequencing. Kits typically provide enough primers to sequence 96-384 samples.</li>
	<li>Attach the dual primers, or indices, to samples by performing PCR in a 25 &#956;L reaction containing 2.5 &#956;L of each primer (N7xx and S5xx), 12.5 &#956;L of commercially available PCR master mix, 5 &#956;L of ddH2O, and 2.5 &#956;L of cleaned amplicon product.</li>
	<li>Move the tubes to a thermocycler programmed for: an initial denaturation at 95 &#176;C for 3 min; 8-14 cycles of 1) 95 &#176;C for 30 s, 2) 55 &#176;C for 30 s, 3) 72 &#176;C for 30 s; a final extension at 72 &#176;C for 5 min; and a final hold at 10 &#176;C.</li>
	<li>Once the PCR is done, visualize the PCR product by loading 4-6 &#956;L/sample on a 1.5% agarose gel. Look for a band at 550 bp to confirm amplification. 
	NOTE: Use the visualization results to inform index PCR cycling conditions. Lower the cycle count to mitigate non-specific binding or increase the cycle count to obtain visible bands for each sample.</li>
	<li>Repeat the clean-up procedure listed in step 4. To avoid dilution during clean-up, perform index PCR in duplicate and pool the product here.</li>
	<li>If not proceeding immediately to library quantification and normalization, store the samples at 4 &#176;C.</li>
</ol>
6. Library Quantification and Normalization
<ol>
	<li>Estimate the concentration of each purified, barcoded product from step 5.5 using a fluorometer or spectrophotometer (see step 2.2). Dilute the samples in TE buffer to obtain concentrations of approximately 1 pM. 
	NOTE: Library quantification is necessary to achieve the sample loading concentration recommended for the sequencing platform. Library quantification is achieved through qPCR, but estimating the sample concentration prior to qPCR saves reagents and time. The 1 pM concentration is recommended to maximize the accuracy of library quantification, as this is the mean concentration of the qPCR standards provided in the quantification kit24.</li>
	<li>Perform qPCR in a 10 &#956;L reaction containing 6.0 &#956;L of qPCR master mix (from library quantification kit), 2.0 &#956;L of ddH2O, and 2.0 &#956;L of the sample or standard. Run each sample and standard in triplicate for greater accuracy and precision. Run each sample at three or more dilution levels (e.g., 0.1 pm, 1 pm, and 10 pm for estimated starting concentrations) to ensure accurate quantification. 
	NOTE: Detailed information about the provided primers and standards will vary based on the quantification kit used and are available in the products technical data sheet. Refer to the Table of Materials for the quantification kit used in this study.</li>
	<li>Move the qPCR plate or tubes to a real-time PCR instrument programmed for: an initial denaturation of 95 &#176;C for 5 min; 35 cycles of 1) 95 &#176;C for 30 s and 2) 60 &#176;C for 45 s; and a dissociation step of 1) 95 &#176;C for 15 s, 2) 60 &#176;C for 30 s, and 3) 95 &#176;C for 15 s.</li>
	<li>Calculate the average starting concentration for each sample, using the quantification values obtained from the qPCR results and the sample dilution levels used. 
	NOTE: For example, an average concentration of 2 pM for a sample diluted 1:100,000 yields an original starting concentration of 200 nM. If none of the dilution levels for a given sample falls within the range of the standard curves, quantification results may not be accurate; in which case, adjust the dilution levels and re-perform qPCR.</li>
	<li>Dilute each purified, barcoded sample to 4 nM in TE buffer, based on the average concentrations calculated in step 7.5. 
	NOTE: For example, for an average sample concentration of 200 nM, dilute the sample 1:50 in TE buffer to achieve a 4 nM concentration. The precise sample concentration will vary based on the sequencing system and reagent kit used. Refer to the sequencing system user guide for the recommended concentration.</li>
	<li>Create the combined library by adding equal volumes (typically 5-10 &#956;L) of all individual libraries into a single tube. 
	NOTE: As all samples should be at a 4 nM concentration, adding equal volumes achieves an equal concentration of all samples in the pooled library.</li>
	<li>Repeat steps 6.2-6.4 on the combined library to confirm the 4 nM concentration.</li>
	<li>Calculate the combined library concentration and dilute or re-constitute the final, pooled library using Tris buffer as necessary to achieve 4 nM.</li>
	<li>If not proceeding immediately to sample loading, store the samples at -20 &#176;C. Perform the sequencing run shortly after quantification to minimize loss or changes to DNA concentration during storage.</li>
</ol>
7. Library Denaturation and Dilution, and Sequencing Run (perform on the same day)
<ol>
	<li>Denature the 4 nM combined library from step 6.9 with NaOH. 
	NOTE: These final library preparation steps vary for each sequencing system. Refer to the sequencing system user guide for more detailed and updated protocols. New users will likely need to be trained on the usage of the sequencing platform or may send their libraries to a core sequencing facility.</li>
	<li>Dilute the denatured library to the desired loading concentration using the buffer provided in the sequencing reagent kit (Table of Materials). 
	NOTE: Optimal loading concentrations vary by sequencing system and typically range from 1-250 pM25.</li>
	<li>Denature and dilute the sequencing control. 
	NOTE: Adding a sequencing control corrects for sequencing issues arising from low diversity libraries. Denature the sequencing control using NaOH and dilute to the same concentration as the library.</li>
	<li>Combine the library and sequencing control. 
	NOTE: The ratio of sequencing control to combined library will depend on the library diversity and sequencing system used, but it is typically 1:126.</li>
	<li>Load the combined library and sequencing control mixture onto the sequencing flow cell.</li>
</ol>
8. Amplicon Sequence Analysis
<ol>
	<li>Assess the overall success of the run by examining the run metrics on the cloud computing environment corresponding to the sequencing system used. 
	NOTE: The run metrics, such as number of sequencing reads, will vary based on sequencing platform and reagent kit used. Target values for common sequencing systems are listed in Table 1.</li>
	<li>Download the raw sequencing data and desired bioinformatics open source software, Quantitative Insights into Microbial Ecology (QIIME)27 or Mothur28. 
	NOTE: Both QIIME and Mothur are open-source and free to download. Detailed instructions on using these programs can be found online and are sufficiently detailed for first-time users. The steps below provide a broad overview of a bioinformatics pipeline conducted in QIIME. Familiarity with python is not necessary but will facilitate the implementation of the following scripts</li>
	<li>Create and validate the mapping file using the &#34;validate_mapping_file.py&#34; script in QIIME. 
	NOTE: The mapping file contains all the metadata necessary to perform data analysis, including sample ID, amplicon primer sequences, and sample description. The validation step checks that all necessary data have been entered in the proper format.</li>
	<li>Demultiplex and filter sequences using the &#34;split_libraries.py&#34; script (Figure 1). 
	NOTE: This script assigns barcoded reads to samples based on the index primer combinations and sample IDs input in the mapping file. It also performs several quality filtering steps to control for sequencing error based on user-defined cut-offs for minimum quality score, sequence lengths, and end-trimming.</li>
	<li>Assign operational taxonomic units (OTUs) to sequences using the &#34;pick_open_references_otus.py&#34; script. 
	NOTE: In this step, sequences are clustered against a reference sequence collection based on a threshold of identity (typically 97%). Reads which do not match the reference sequence collection are clustered against one another. De novo and closed-reference OTU picking options are also available, but open-reference picking is recommended by QIIME developers.</li>
	<li>Normalize the OTU table using the &#34;alpha_rarefaction.py&#34; or &#34;normalize_table.py&#34; script. 
	NOTE: This step corrects for variation in column sums, or total sequence reads per sample, that result from modern sequencing technologies. Normalization can be performed using traditional rarefaction, or through alternative methods such as cumulative sum scaling.</li>
	<li>Perform several alpha-diversity, beta-diversity, and taxonomic composition diversity analyses at once using the &#34;core_diversity_analysis.py&#34; script. 
	NOTE: Alternatively, these analyses can be run separately using the individual scripts (e.g., &#34;alpha_diversity.py&#34;).</li>
</ol>

The authors have nothing to disclose.

Next-generation sequencing of 16S rRNA has become a standard approach for microbial identification and enabled the study of how vector microbiomes affect pathogen transmission. The protocol outlined here details the use of this method to investigate microbial community assembly in I. pacificus, a vector species for Lyme disease; however, it can easily be applied to study other tick species or arthropod vector species.
Indeed, 16S rRNA sequencing for microbiome analysis has been used b...

The resurgence and spread of vector-borne diseases in recent decades pose a serious threat to global human and wildlife health. Effective vaccines are lacking for a majority of these diseases, and control efforts are hindered by the complex biological nature of vectors and vector-host interactions. Understanding the role of microbial interactions within a vector in pathogen transmission can allow for the development of novel strategies which circumvent these challenges. In particular, interactions between vector-associated microbial commensals, symbionts, and pathogens, referred to as the microbiome, may have important consequences for pathogen transmission. Overwhelming evidence now supports this assertion, with examples demonstrating a link between the vector microbiome and competence for diseases such as malaria, Zika virus, and Lyme disease1,2,3. However, translating these findings into strategies for disease control requires a far more detailed understanding of the structure, function, and origin of vector microbiomes. Identification and characterization of the vector microbial community under varying ecological and experimental conditions constitute an important path forward in this field.
A procedure for identifying the microbial residents of a pathogen vector is provided here by utilizing the Western black-legged tick, Ixodes pacificus, a vector species of the Lyme disease pathogen Borrelia burgdorferi. While ticks harbor more types of human pathogens than any other arthropod, relatively little is known about the biology and community ecology of tick microbiomes4. It is evident that ticks harbor a diverse array of viruses, bacteria, fungi, and protozoans which include commensals, endosymbionts, and transient microbial residents5,4. Prior work has demonstrated strong variations in Ixodes microbiomes associated with geography, species, sex, life stage, and blood meal source6,7,8. However, the mechanisms underlying this variation remain unknown and warrant more detailed investigations of the origin and assembly of these microbial communities. Ticks can acquire microbes through vertical transmission, contact with hosts, and uptake from the environment through the spiracles, mouth, and anal pore9. Understanding the factors shaping the initial formation and development of the tick microbiome, specifically the relative contribution of vertical and environmental transmission, is important for understanding the natural patterns and variations in tick microbiome diversity and how these communities interact during pathogen transmission, with possible applications to disease or vector control.
Powerful molecular techniques, such as next-generation sequencing, now exist for identifying microbial communities and can be employed to characterize vector microbiomes under diverse environmental or experimental conditions. Prior to the advent of these high-throughput sequencing approaches, the identification of microbes relied predominantly on microscopy and culture. While microscopy is a rapid and easy technique, morphological methods for identifying microbes are inherently subjective and coarse and limited by low sensitivity and detection10. Culture-based methods are broadly used for microbial identification and can be used to determine susceptibility of microbes to drug treatments11. However, this method also suffers from low sensitivity, as it has been estimated that fewer than 2% of environmental microbes can be cultured in a laboratory setting12. Histological staining approaches have also been employed to detect and localize specific microbes within vectors, enable investigations of various taxa distributions within the tick, and study hypotheses about microbial interactions. However, prior knowledge of microbial identity is required for selecting the appropriate stains, making this approach ill-suited for microbial characterization and identification. Furthermore, histological staining is a highly time-intensive, laborious process and does not scale well for large sample sizes. Traditional molecular approaches such as Sanger sequencing are similarly limited in their sensitivity and detection of diverse microbial communities.
Next-generation sequencing allows for the rapid identification of microbes from a large number of samples. The presence of standard marker genes and reference databases further enables enhanced taxonomic resolution, often to the genus or species level. Small subunit ribosomal RNAs are frequently used to achieve this goal, with 16S rRNA being the most common due to the presence of conserved and variable regions within the gene, allowing for the creation of universal primers with unique amplicons for each bacterial species13,14. This report details a procedure for identifying taxa in the tick microbiome through 16S rRNA next-generation sequencing. In particular, this protocol emphasizes the steps involved in preparing samples for sequencing. More generalized details on the sequencing and bioinformatics steps are provided, as there are a variety of sequencing platforms and analysis programs currently available, each with extensive existing documentation. The overall feasibility of this next-generation sequencing approach is demonstrated by applying it to an investigation of microbial community assembly within a key disease vector.

<table><tbody><tr><td>Item</td><td>Name of Material/Equipment</td><td>Company</td><td>Catalog #</td></tr><tr><td>1</td><td>DNeasy Blood &amp;amp; Tissue Kit</td><td>Qiagen</td><td>69504</td></tr><tr><td>2</td><td>Qubit 4 Fluorometer</td><td>ThermoFisher Scientific</td><td>Q3326</td></tr><tr><td>3</td><td>NanoDrop 8000 Spectrophotometer</td><td>ThermoFisher Scientific</td><td>ND-8000-GL</td></tr><tr><td>4</td><td>2x KAPA HiFi HotStart ReadyMix</td><td>Kapa Biosystems</td><td>KK2501</td></tr><tr><td>5</td><td>AMPure XP beads</td><td>Agen Court</td><td>A63880&amp;nbsp;</td></tr><tr><td>6</td><td>Magnetic Rack</td><td>ThermoFisher Scientific</td><td>MR02</td></tr><tr><td>6</td><td>TE buffer</td><td>Teknova</td><td>T0223</td></tr><tr><td>7</td><td>Nextera Index Kit</td><td>Illumina</td><td>FC-121-1011</td></tr><tr><td>8</td><td>KAPA Library Quantification Kit</td><td>Roche</td><td>KK4824</td></tr><tr><td>9</td><td>MiSeq System</td><td>Illumina</td><td>SY-410-1003</td></tr><tr><td>10</td><td>MiSeq Reagent Kit v3&amp;nbsp;</td><td>Illumina</td><td>MS-102-3001</td></tr><tr><td>11</td><td>10 mM Tris-HCl with 0.1% Tween 20</td><td>Teknova</td><td>T7724</td></tr></tbody></table>

tick microbiome characterization by next-generation 16s rrna amplicon sequencing

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective and widely available vaccines are lacking for a majority of these diseases, necessitating the development of novel disease mitigation strategies. To this end, a promising avenue of disease control involves targeting the vector microbiome, the community of microbes inhabiting the vector. The vector microbiome plays a pivotal role in pathogen dynamics, and manipulations of the microbiome have led to reduced vector abundance or pathogen transmission for a handful of vector-borne diseases. However, translating these findings into disease control applications requires a thorough understanding of vector microbial ecology, historically limited by insufficient technology in this field. The advent of next-generation sequencing approaches has enabled rapid, highly parallel sequencing of diverse microbial communities. Targeting the highly-conserved 16S rRNA gene has facilitated characterizations of microbes present within vectors under varying ecological and experimental conditions. This technique involves amplification of the 16S rRNA gene, sample barcoding via PCR, loading samples onto a flow cell for sequencing, and bioinformatics approaches to match sequence data with phylogenetic information. Species or genus-level identification for a high number of replicates can typically be achieved through this approach, thus circumventing challenges of low detection, resolution, and output from traditional culturing, microscopy, or histological staining techniques. Therefore, this method is well-suited for characterizing vector microbes under diverse conditions but cannot currently provide information on microbial function, location within the vector, or response to antibiotic treatment. Overall, 16S next-generation sequencing is a powerful technique for better understanding the identity and role of vector microbes in disease dynamics.

A total of 42 ticks from three separate egg clutches and two environmental exposure periods, 0 and 2 weeks in soil, were processed for microbiome sequencing. Each treatment group, considered to be a single clutch and exposure time, contained 6-8 replicate tick samples. These processed tick extracts were loaded onto a next-generation sequencer and yielded 12,885,713 paired-end reads passing filter. Included in this run were 3 negative controls from the extraction step, yielding a total of ...

Watch this Scientific Journal Video about Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing at JoVE.com

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective ...

tick-microbiome-characterization-next-generation-16s-rrna-amplicon

Research

JoVE Journal

Biology

12.8K Views.  San Francisco State University. Hier presenteren we een volgende-generatie sequencing-protocol voor het 16S rRNA rangschikken waarmee identificatie en karakterisering van microbiële gemeenschappen binnen de vectoren. Deze methode houdt het DNA-extractie, versterking en barcoding van monsters door middel van PCR, rangschikking op een stroom-cel, en bio-informatica aan sequencedata tot fylogenetische informatie.

Video: Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Genetics

A Method for Targeted 16S Sequencing of Human Milk Samples

Studies of microbial communities have become widespread with the development of relatively inexpensive, rapid, and high throughput sequencing. However, as with all these technologies, reproducible results depend on a laboratory workflow that incorporates appropriate precautions and controls. This is particularly important with low-biomass samples where contaminating bacterial DNA can generate misleading results. This article details a semi-automated workflow to identify microbes from human breast milk samples using targeted sequencing of the 16S ribosomal RNA (rRNA) V4 region on a low- to mid-throughput scale. The protocol describes sample preparation from whole milk including: sample lysis, nucleic acid extraction, amplification of the V4 region of the 16S rRNA gene, and library preparation with quality control measures. Importantly, the protocol and discussion consider issues that are salient to the preparation and analysis of low-biomass samples including appropriate positive and negative controls, PCR inhibitor removal, sample contamination by environmental, reagent, or experimental sources, and experimental best practices designed to ensure reproducibility. While the protocol as described is specific to human milk samples, it is adaptable to numerous low- and high-biomass sample types, including samples collected on swabs, frozen neat, or stabilized in a preservation buffer.

A semi-automated workflow is presented for targeted sequencing of 16S rRNA from human milk and other low-biomass sample types.

Studies of microbial communities have become widespread with the development of relatively inexpensive, rapid, and high throughput sequencing. However, as ...

Immunology and Infection

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the subcellular distribution of a specific tagged fluorescent marker in the secretory pathway. Arabidopsis is a powerful biological model for genetic studies because of its genome size, generation time, and conservation of molecular mechanisms among kingdoms. The array genotyping as an approach to map the mutation in alternative to the traditional method based on molecular markers is advantageous because it is relatively faster and may allow the mapping of several mutants in a really short time frame. This method allows the identification of proteins that can influence the integrity of any organelle in plants. Here, as an example, we propose a screen to map genes important for the integrity of the endoplasmic reticulum (ER). Our approach, however, can be easily extended to other plant cell organelles (for example see1,2), and thus represents an important step toward understanding the molecular basis governing other subcellular structures.

A fundamental quest in cell biology is to define the mechanisms that underlie the identity of the organelles that make eukaryotic cells. Here we propose a method to identify the genes responsible for the morphological and functional integrity of plant organelles using fluorescence microscopy and next-generation sequencing tools.

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

Targeted DNA Methylation Analysis by Next-generation Sequencing

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of particular interest for epigenetic studies as it has been demonstrated to be both a long lasting and a dynamic regulator of gene expression. Efforts to examine epigenetic changes in health and disease have been hindered by the lack of high-throughput, quantitatively accurate methods. With the advent and popularization of next-generation sequencing (NGS) technologies, these tools are now being applied to epigenomics in addition to existing genomic and transcriptomic methodologies. For epigenetic investigations of cytosine methylation where regions of interest, such as specific gene promoters or CpG islands, have been identified and there is a need to examine significant numbers of samples with high quantitative accuracy, we have developed a method called Bisulfite Amplicon Sequencing (BSAS). This method combines bisulfite conversion with targeted amplification of regions of interest, transposome-mediated library construction and benchtop NGS. BSAS offers a rapid and efficient method for analysis of up to 10 kb of targeted regions in up to 96 samples at a time that can be performed by most research groups with basic molecular biology skills. The results provide absolute quantitation of cytosine methylation with base specificity. BSAS can be applied to any genomic region from any DNA source. This method is useful for hypothesis testing studies of target regions of interest as well as confirmation of regions identified in genome-wide methylation analyses such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing, and methylated DNA immunoprecipitation sequencing.

Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of ...

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of diverse libraries of retroviral integration sites using ligation-mediated PCR (LM-PCR) amplification and next-generation sequencing (NGS), (2) mapping the genomic location of each virus-host junction using BEDTools, and (3) analyzing the data for statistical relevance. Genomic DNA extracted from infected cells is fragmented by digestion with restriction enzymes or by sonication. After suitable DNA end-repair, double-stranded linkers are ligated onto the DNA ends, and semi-nested PCR is conducted using primers complementary to both the long terminal repeat (LTR) end of the virus and the ligated linker DNA. The PCR primers carry sequences required for DNA clustering during NGS, negating the requirement for separate adapter ligation. Quality control (QC) is conducted to assess DNA fragment size distribution and adapter DNA incorporation prior to NGS. Sequence output files are filtered for LTR-containing reads, and the sequences defining the LTR and the linker are cropped away. Trimmed host cell sequences are mapped to a reference genome using BLAT and are filtered for minimally 97% identity to a unique point in the reference genome. Unique integration sites are scrutinized for adjacent nucleotide (nt) sequence and distribution relative to various genomic features. Using this protocol, integration site libraries of high complexity can be constructed from genomic DNA in three days. The entire protocol that encompasses exogenous viral infection of susceptible tissue culture cells to integration site analysis can therefore be conducted in approximately one to two weeks. Recent applications of this technology pertain to longitudinal analysis of integration sites from HIV-infected patients.

We describe a protocol for amplifying retroviral integration sites from the genomic DNA of infected cells, sequencing the amplified virus-host junctions, and then mapping these sequences to a reference genome. We also describe techniques to quantify the distribution of integration sites relative to various genomic annotations using BEDTools.

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of ...

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing technologies have allowed for the rapid and efficient characterization of bacterial communities using 16S rRNA gene sequencing from a variety of sources. Although readily available tools for 16S rRNA sequence analysis have standardized computational workflows, sample processing for DNA extraction remains a continued source of variability across studies. Here we describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs. We also delineate downstream methods for 16S rRNA gene sequencing, including generation of sequencing libraries, data quality control, and sequence analysis. The workflow can accommodate multiple samples types, including stool and swabs collected from a variety of anatomical locations and host species. Additionally, recovered DNA and RNA can be separated and used for other applications, including whole genome sequencing or RNA-seq. The method described allows for a common processing approach for multiple sample types and accommodates downstream analysis of genomic, metagenomic and transcriptional information.

We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

16S rRNA Sequencing: A PCR-based Technique to Identify Bacterial Species

Source: Ewa Bukowska-Faniband1, Tilde Andersson1, Rolf Lood1 
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, Lund University, 221 00 Lund, Sweden
Planet Earth is a habitat for millions of bacterial species, each of which has specific characteristics. Identification of bacterial species is widely used in microbial ecology to determine biodiversity of environmental samples and medical microbiology to diagnose infected patients. Bacteria can be classified using conventional microbiology methods, such as microscopy, growth on specific media, biochemical and serological tests, and antibiotic sensitivity assays. In recent decades, molecular microbiology methods have revolutionized bacterial identification. A popular method is 16S ribosomal RNA (rRNA) gene sequencing. This method is not only faster and more accurate than conventional methods, but also allows identification of strains that are difficult to grow in laboratory conditions. Furthermore, differentiation of strains at the molecular level enables discrimination between phenotypically identical bacteria (1-4).
16S rRNA joins with a complex of 19 proteins to form a 30S subunit of the bacterial ribosome (5). It is encoded by the 16S rRNA gene, which is present and highly conserved in all bacteria due to its essential function in ribosome assembly; however, it also contains variable regions which may serve as fingerprints for particular species. These features have made the 16S rRNA gene an ideal genetic fragment to be used in identification, comparison, and phylogenetic classification of bacteria (6).
16S rRNA gene sequencing is based on the polymerase chain reaction (PCR) (7-8) followed by DNA sequencing (9). PCR is a molecular biology method used to amplify specific fragments of DNA through a series of cycles that include:
i) Denaturation of a double stranded DNA template 
ii) Annealing of primers (short oligonucleotides) that are complementary to the template 
iii) Extension of primers by the DNA polymerase enzyme, which synthesizes a new DNA strand
A schematic overview of the method is shown in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/10510/10510fig1.jpg" /> 
Figure 1: Schematic overview of the PCR reaction. <a href="https://www.jove.com/files/ftp_upload/10510/10510fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
There are several factors that are important for a successful PCR reaction, one of which is quality of the DNA template. Isolation of chromosomal DNA from bacteria can be performed using standard protocols or commercial kits. Special care should be taken to obtain DNA that is free of contaminants that can inhibit the PCR reaction.
Conserved regions of the 16S rRNA gene permit the design of universal primer pairs (one forward and one reverse) that can bind to and amplify the target region in any bacterial species. The target region can vary in size. While some primer pairs can amplify most of the 16S rRNA gene, others amplify only parts of it. Examples of commonly used primers are shown in Table 1 and their binding sites are depicted in Figure 2.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Primer name</td>
			<td>Sequence (5&#39;&#8594;3&#39;)</td>
			<td>Forward/ reverse</td>
			<td>Reference</td>
		</tr>
		<tr>
			<td>8F b)</td>
			<td>AGAGTTTGATCCTGGCTCAG</td>
			<td>forward</td>
			<td>-1</td>
		</tr>
		<tr>
			<td>27F</td>
			<td>AGAGTTTGATCMTGGCTCAG</td>
			<td>forward</td>
			<td>-10</td>
		</tr>
		<tr>
			<td>515F</td>
			<td>GTGCCAGCMGCCGCGGTAA</td>
			<td>forward</td>
			<td>-11</td>
		</tr>
		<tr>
			<td>911R</td>
			<td>GCCCCCGTCAATTCMTTTGA</td>
			<td>reverse</td>
			<td>-12</td>
		</tr>
		<tr>
			<td>1391R</td>
			<td>GACGGGCGGTGTGTRCA</td>
			<td>reverse</td>
			<td>-11</td>
		</tr>
		<tr>
			<td>1492R</td>
			<td>GGTTACCTTGTTACGACTT</td>
			<td>reverse</td>
			<td>-11</td>
		</tr>
	</tbody>
</table>
Table 1: Examples of standard oligonucleotides used in amplification of 16S rRNA genes a). 
a) The expected lengths of the PCR product generated using the different primer combinations can be estimated by calculating the distance between the binding sites for the forward and the reverse primer (see Figure 2), e.g. the size of the PCR product using primer pair 8F-1492R is ~1500 bp, and for primer pair 27F-911R ~900 bp. 
b) also known as fD1
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/10510/10510fig2.jpg" /> 
Figure 2: Representative figure of the 16S rRNA sequence and the primer-binding sites. Conserved regions are colored in grey and variable regions are filled with diagonal lines. To allow for the highest resolution, primer 8F and 1492R (name based on location on rRNA sequence) are used to amplify the whole sequence, allowing for the sequencing of several variable regions of the gene. <a href="https://www.jove.com/files/ftp_upload/10510/10510fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Cycling conditions for PCR (i.e. the temperature and time required for the DNA to be denatured, annealed with primers, and synthesized) are dependent on the type of polymerase that is used and the properties of the primers. It is recommended to follow the manufacturer&#39;s guidelines for a particular polymerase.
After the PCR program is completed, the products are analyzed by agarose gel electrophoresis. A successful PCR yields a single band of expected size. The product must be purified prior to sequencing to remove residual primers, deoxyribonucleotides, polymerase, and buffer which were present in the PCR reaction. The purified DNA fragments are usually sent for sequencing to commercial sequencing services; however, some institutions perform DNA sequencing at their own core facilities.
The DNA sequence is automatically generated from a DNA chromatogram by a computer and must be carefully checked for quality, as manual editing is sometimes needed. Following this step, the gene sequence is compared with sequences deposited in the 16S rRNA database. The regions of similarity are identified, and the most similar sequences are delivered.

16S rRNA Sequencing: Identifying Bacterial Species by PCR

Source: Ewa Bukowska-Faniband1, Tilde Andersson1, Rolf Lood1
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, ...

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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