Authors would like to thank Andrew Bird for the silage samples and Audrey Farbos of the Exeter Sequencing Service for her assistance in preparing DNA sequencing libraries. Exeter Sequencing Service and Computational core facilities at the University of Exeter. Medical Research Council Clinical Infrastructure award (MR/M008924/1). Wellcome Trust Institutional Strategic Support Fund (WT097835MF), Wellcome Trust Multi User Equipment Award (WT101650MA) and BBSRC LOLA award (BB/K003240/1).

1. Site Location
<ol>
	<li>Collect the silage sample from an appropriate site such as a farm. Here, the farm was located in Ballydulea, Co. Cork, Ireland (51&#176;51&#39;58.4&#34;N 8&#176;16&#39;48.7&#34;W).</li>
</ol>
2. DNA Extraction
NOTE: DNA extraction was performed using a commercial kit following the manufacturer&#39;s instructions. A negative control, which contained no sample, was used throughout the library preparation method.
<ol>
	<li>Add 100 - 400 mg of sample to 978 &#181;L sodium phosphate buffer and 122 &#181;L soil lysis buffer in the supplied lysis tubes.</li>
	<li>Homogenize samples by placing the lysis tubes into the homogenizer for 40 s at a speed of 6.0 m/s.</li>
	<li>Centrifuge lysates at 14,000 x g for 15 min and transfer the supernatant to a clean micro-centrifuge tube containing 250 &#181;L of Protein Precipitate Solution (PPS). Mix the solution by inverting 10 times and centrifuge at 14,000 x g for 5 min.</li>
	<li>Add the supernatant to 1 mL DNA binding matrix in a clean 15 mL centrifuge tube. Mix the solution by inverting the tube constantly for 3 min. Allow the mixture to settle for 3 min, then discard 500 &#181;L of supernatant. Mix the remaining supernatant.</li>
	<li>Transfer 600 &#181;L of the suspension to a spin filter and centrifuge at 14,000 x g for 1 min. Discard the filtrate and repeat the process with the remaining suspension.</li>
	<li>Add 500 &#181;L of wash buffer to the DNA binding matrix within the spin filter, mix by pipetting, then centrifuge at 14,000 x g for 1 min.</li>
	<li>Discard the filtrate and centrifuge the spin filter again at 14,000 x g for 2 min to ensure all wash buffer is removed. Dry the spin filter at 23 &#730;C for 5 min.</li>
	<li>Pre-warm (70 &#730;C) the DNase-free water (DES) and re-suspend the DNA binding matrix in 100 &#181;L of DES within the spin filter. Transfer the spin filter to a clean 1.5 mL micro-centrifuge tube and centrifuge at 14,000 x g for 1 min to elute DNA. Store the purified DNA at -20 &#730;C until further analysis is performed.</li>
</ol>
3. DNA Purification Using DNA Purification Beads
NOTE: Prior to metagenomic library preparation the extracted DNA was purified using purification beads to ensure a pure DNA sample was obtained.
<ol>
	<li>Incubate the beads at 23 &#730;C for 30 min before use. Add 2 volumes of beads to the DNA sample and incubate the solution at 23 &#730;C for 5 min.</li>
	<li>Place the samples onto a separation magnet for 5 min and then discard the supernatant. Wash the beads twice with 200 &#181;L fresh 80% ethanol (EtOH). Air dry the beads for 10 min.</li>
	<li>Remove the samples from the separation magnet and add 50 &#181;L of elution buffer (EB), mix by pipetting.</li>
	<li>Incubate the suspension at 23 &#730;C for 5 min, after which place the samples back onto the separation magnet for 3 min.</li>
	<li>Transfer the supernatant, which contains the DNA, to a clean tube. Discard the beads.</li>
	<li>Quantify the purified DNA as per section four.</li>
</ol>
4. Quantification of Purified DNA
NOTE: Purified DNA was quantified using a fluorometer and double-stranded (dsDNA) High Sensitivity (HS) assay kit following the manufacturer&#39;s instructions.
<ol>
	<li>Prepare a working solution using 199:1 ratio of buffer to reagent.</li>
	<li>Add 10 &#181;L of each DNA standard to 190 &#181;L of working solution.</li>
	<li>Add 10 &#181;L of purified DNA to 190 &#181;L of working solution. The final volume should be 200 &#181;L. Incubate standard and DNA samples at 23 &#730;C for 2 min.</li>
	<li>Analyze standards before the DNA samples on the fluorometer using the on-screen instructions.</li>
</ol>
5. Shotgun Sequencing Library Preparation
NOTE: The shotgun sequencing library was prepared using a commercial library preparation kit using the manufacturer&#39;s instructions.
<ol>
	<li>Dilute the DNA samples to 0.2 ng/&#181;L using EB. Any sample which is already below this concentration, i.e. the negative control, is left at its current concentration.</li>
	<li>Mix 5 &#181;L of the purified DNA with 10 &#181;L buffer and 5 &#181;L enzyme mix. Incubate samples at 55 &#730;C for 5 min.</li>
	<li>Add 5 &#181;L of neutralizing buffer and incubate the solution at 23 &#730;C for 5 min.</li>
	<li>Add 5 &#181;L of each of the sample specific sequencing indices and 15 &#181;L of PCR master mix.</li>
	<li>In a thermocycler, incubate the samples at 72 &#730;C for 3 min, 95 &#730;C for 30 s, before 12 cycles of 95 &#730;C for 10 s, 55 &#730;C for 30 s and 72 &#730;C for 30 s. Incubate samples finally at 72 &#730;C for 5 min.</li>
	<li>Purify the prepared DNA using the bead purification as before but with a final elution of 30 &#181;L of EB.</li>
</ol>
6. Library Quantity and Quality Check
NOTE: The quantity and quality of the prepared libraries were assessed using a commercial kit and instrumentation.
<ol>
	<li>Incubate the kit components at 23 &#730;C for 30 min prior to use.</li>
	<li>Add 2 &#181;L of DNA to 2 &#181;L of buffer and vortex for 1 min at 2,000 rpm.</li>
	<li>Spin down the sample to ensure it is at the bottom of the tube.</li>
	<li>Insert the sample tubes, analysis tape and tips into the instrument, and perform analysis as directed by the software.</li>
</ol>
7. DNA Sequencing
<ol>
	<li>Transfer the prepared and quantified DNA sequencing libraries samples to a sequencing service and sequence using 300 bp paired end sequencing 45.</li>
</ol>
8. Analysis of Raw Sequence Data
NOTE: The commands for each program using a Linux operating system are shown below the protocol step. The pipeline used for sequence data analysis is shown in Figure 1. The programs are to be installed by the user prior to analysis. This process should be performed individually for each sample.
<ol>
	<li>Analyze and visualize DNA sequence data using FastQC 46 by typing in to the command line /path-to-file/fastqc, followed by the forward and reverse raw reads raw_read1.fastq raw_read2.fastq.</li>
	<li>Specify an output folder by typing -o output_fastqc and the file format of the raw read files by -f fastq.</li>
	<li>View the output file (Figure 2). 
	path-to-file/fastqc raw_read1.fastq raw_read2.fastq -o output_directory -f fastq.</li>
</ol>
9. Quality Control Trimming and Filtering Sequence Data
<ol>
	<li>Run the trimming program, Trimmomatic 28 by typing into the command line java -jar /path-to-file/ trimmomatic-0.35.jar.</li>
	<li>Specify the files are paired end files by typing &#39;PE&#39;. State that 16 central processing units (CPUs) should be used by the program by typing -threads 16.</li>
	<li>List the two files to QC check by typing the names of the raw forward and reverse reads. The prefix of the output files is determined by typing -baseout silage.</li>
	<li>Define the options for the program by typing ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 CROP:200 HEADCROP:15 MINLEN:36.</li>
	<li>Once complete, analyze the trimmed sequences using FastQC as before and compare the output to the raw sequence data to ensure trimming has been performed successfully. 
	NOTE: The software tool, Trimmomatic, trimmed reads further by removing leading low quality or N bases (below quality 3), removing trailing low quality or N bases (below quality 3) and scanning each read with a 4-base wide sliding window. The parameters were set for cutting when the average quality per base drops below 20 and then to drop any reads below 36 bases long. Finally, 15 bases were cropped from the head of each read and reads were cropped to keep 200 bases from the start of the read. This final step was performed to overcome some quality issues when sequencing long (&#62; 200 bp) reads. These can be adjusted for specific samples 28. 
	java -jar /path-to-file/trimmomatic-0.35.jar PE -threads 16 raw_read1.fastq raw_read2.fastq -baseout silage ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 CROP:200 HEADCROP:15 MINLEN:36</li>
</ol>
10. Metagenome Assembly
<ol>
	<li>Merge the unpaired, trimmed reads by typing cat followed by the unpaired reads; silage_read1_unpaired.fastq silage_read2_unpaired.fastq. Write the files to a new file by typing &#62; silage_merged_unpaired.fastq 
	cat silage_read1_unpaired.fastq silage_read2_unpaired.fastq &#62; silage_merged_unpaired.fastq</li>
	<li>To de novo assemble the sequenced DNA, use SPAdes (St. Petersburg genome assembler) 30 by typing /path-to-file/spades.py. Specify that 16 CPUs are to be used by typing -t 16 and that the metagenomic parameter should applied by typing --meta.</li>
	<li>Identify the trimmed forward reads using -1 silage_read1_paired.fastq and the reverse reads by -2 silage_read2_paired.fastq. The merged unpaired reads are specified by -s silage_merged_unpaired.fastq.</li>
	<li>Define the output folder by typing -o silage_spades. 
	path-to-file/spades.py -t 16 --meta -1 silage_read1_paired.fastq -2 silage_read2_paired.fastq -s silage_merged_unpaired.fastq -o silage_spades</li>
</ol>
11. Paired-end Read Overlap
<ol>
	<li>Merge pairs of DNA sequence reads using FLASH (Fast Length Adjustment of Short Reads) 29 by typing into command line /path-to-file/flash. Specify that 16 CPUs should be used by using -t 16 and the output prefix by typing -o silage.</li>
	<li>Identify trimmed reads by typing silage_trimmed_R1.fastq silage_trimmed_R2.fastq 
	path-to-file/flash -t 16 -o FLASHed silage_read1_paired.fastq silage_read2_paired.fastq</li>
</ol>
12. Taxonomic Classification
<ol>
	<li>Type /path-to-file/kraken and specify the database by typing --db /path-to-file/standard.</li>
	<li>Define that 16 CPUs should be used by typing --threads 16 and identify an output folder by using --output FLASHed_silage_extendedFrags_kraken.txt. Type the input file name; FLASHed_silage.extendedFrags.fastq 
	path-to-file/kraken --db standard --thread 16 --output FLASHed_silage_extendedFrags_kraken.txt FLASHed_silage.extendedFrags.fastq 
	NOTE: Classification of the assembled DNA sequence scaffolds using Kraken 7 was completed against the most recent, standard Kraken database that contained all available Prokaryote genome sequences.</li>
	<li>Transfer columns 2 and 3 from the output file and to a new file by typing cut -f2,3 FLASHed_silage_extendedFrags_kraken.txt &#62; FLASHed_silage_extendedFrags_kraken.int</li>
	<li>Open the output file in web browser. 
	cut -f2,3 FLASHed_silage_extendedFrags_kraken.txt &#62; FLASHed_silage_extendedFrags_kraken.int</li>
	<li>Import the new file into Krona 12 by typing ktImportTaxonomy. Specify the input file by typing FLASHed_silage_extendedFrags_kraken.int. Identify the output file by typing -o FLASHed_silage_extendedFrags_kraken.out.html. 
	path-to-file/ktImportTaxonomy FLASHed_silage_extendedFrags_kraken.int -o FLASHed_silage_extendedFrags_kraken.out.html</li>
</ol>
13. Functional Annotation
<ol>
	<li>Go to the MG-RAST 47 website, http://metagenomics.anl.gov/. Register as a new user if required. After logging in, Click on the &#8220;Upload&#8221; button. Upload the assembled scaffolds from Step 10.</li>
	<li>Once the files have uploaded, click on &#34;Submit&#34; and follow the instructions and await the completion of analysis.</li>
	<li>After the analysis is complete, view the link sent via email from MG-RAST, or alternatively, click on &#34;Progress&#34;. There is a list of completed jobs. Click on the relevant job id and then on the link to the &#34;download page&#34;.</li>
	<li>On the download page, under the heading &#34;Protein Clustering 90%&#34;, click on the protein button to download the predicted protein file, 550.cluster.aa90.faa.</li>
	<li>To classify the proteins as putatively belonging to a particular CAZy enzyme class, compare the downloaded proteins to the CAZy database 48. Download the Carbohydrate-Active enZYmes Database (CAZy) from files are: AA.zip, CE.zip, GH.zip, GT.zip and PL.zip. These files represent the following enzyme classes respectively: Auxiliary Activities (AA), Carbohydrate Esterases (CE), Glycoside Hydrolases (GH), Glycosyl Transferases (GT) and Polysaccharide Lyases (PL).</li>
	<li>Unzip the database files and annotate the proteins by determining the protein similarity to the CAZy database proteins using the USEARCH UBLAST algorithm 49. To use a bash loop (for i in *.txt) to iterate through the 5 database .txt files type &#34;for i in *.txt; do&#34;.</li>
	<li>Run USEARCH by typing /path-to-file/usearch8 with the parameter -ublast in order to use the ublast algorithm. Then type in the name of the protein sequence file downloaded from MG-RAST, &#34;mgmXXXXXX.3.550.cluster.aa90.faa&#34;.</li>
	<li>To indicate the database file to be used type &#34;-db $i&#34; and to specify the E-value threshold at 1e-5, type &#34;-evalue 1e-5&#34;.</li>
	<li>To terminate the search after the discovery of a target sequence and therefore classifying that protein sequence as belonging to the target enzyme class, e.g. GH, type &#34;-masaccepts 1&#34;.</li>
	<li>To define that 16 CPUs should be used type &#34;-threads 16&#34; and to specify the format of the output file as atab-separated text type &#34;-blast6out&#34;. To identify the output file type &#34;$i.ublast&#34;. To terminate the bash loop, type &#34;; done&#34; 
	for i in *.txt; 
	do /path-to-file/usearch8 -ublast ../mgmXXXXXX.3.550.cluster.aa90.faa -db $i -evalue 1e-5 -maxaccepts 1 -threads 16 -blast6out $i.ublast; 
	done</li>
</ol>
14. Visualizing CAZy Annotation
<ol>
	<li>To visualize the output from the CAZy annotation as a Venn diagram, generate protein ID lists for each enzyme class using a bash loop. Type &#34;for i in *.ublast; do&#34;.</li>
	<li>To transfer column 1 from the output file and to a new file, type &#34;cat $i | cut -f 1 &#62;$i.list&#34;.</li>
	<li>Terminate the loop and type &#34;; done&#34;.</li>
	<li>Open the .list files in a text editor. Go to the website , select the number of sets as 5 and paste the content of each list file in a separate box. Download the resulting diagram as a .SVG file. 
	for i in *.ublast; 
	do cat $i | cut -f 1 &#62;$i.list; 
	done</li>
</ol>

The authors have nothing to disclose.

While an in silico analysis can give an excellent insight to the microbial communities that are present within environmental samples, it is critical that the taxonomic classifications demonstrated be performed in association with relevant controls and that a suitable depth of sequencing has been achieved to capture the entire population present 51.
With any computational analysis, there are many routes to achieve a similar goal. The methods that we have used in this study are examples of suitable and straightforward methods, that have been brought together to achieve a range of analyses on the silage microbiome. A variety and an ever-increasing number of bioinformatics tools and techniques are available to analyze metagenomic data, for instance Phylosift 8 and MetaPhlAn2 52, and these should be evaluated prior to the investigation for their relevance to the sample and the analysis required 53. Metagenomic analysis methods are limited by the databases for available for classification, sequencing depth and the quality of sequencing.
The bioinformatic processing demonstrated here was performed on a local, high powered machine; however cloud-based systems are also available. These cloud-based services allow for the rental of the necessary computational power without having the high-cost investment of a suitable powerful local workstation. A potential application of this method would be to assess silage before its use in agriculture to ensure that no potentially harmful bacteria are present therefore preventing them from entering the food chain.

Metagenomics is the direct analysis of DNA purified from biological communities found within environmental samples 1 and was originally used to detect unculturable bacteria found in sediments 2. Metagenomics has been widely used for a number of applications, such as identifying the human microbiome 3, classifying microbial populations within the ocean 4 and even for the analysis of the bacterial communities that develop on coffee machines 5. The introduction of next generation sequencing technologies resulted in greater sequencing throughput and output. Consequently, DNA sequencing has become more economical 6 and the depth of sequencing that can be performed has greatly increased, enabling metagenomics to become a powerful, analytical tool.
&#34;Front-end&#34; enhancements in the practical, molecular aspect of metagenomic sequencing have driven the growth of the in silico bioinformatics tools available for the taxonomic classification 7-9, functional annotation 10,11 and visual representation 12,13 of DNA sequence data. The increasing number of available, sequenced prokaryotic and eukaryotic 14 genomes allows further accuracy in the classification of microbial communities, which are invariably performed against a &#34;back-end&#34; reference database of sequenced genomes 15. Two main approaches can be adopted for metagenomic analysis.
The more conventional method is analysis of the 16S rRNA gene coding region of bacterial genome. The 16S rRNA is highly conserved between prokaryote species but exhibits nine hyper-variable regions (V1 - V9) which can be exploited for species identification 16. The introduction of longer sequencing (&#8804; 300 bp paired end) allowed for the analysis of DNA sequences spanning two hyper-variable regions, in particular the V3 - V4 region 17. Advances in other sequencing technologies, such as Oxford Nanopore 18 and PacBIO 19, do allow the entire 16S rRNA gene to be sequenced contiguously.
While 16S rDNA based libraries provide a targeted approach to species identification and enable the detection of low copy number DNA that naturally occurs within purified samples, shotgun sequencing libraries allow for the detection of species that may contain DNA regions that are either not amplifiable by the 16S rRNA marker primer sequences used, or because the differences between the template sequence and the amplifying primer sequence are too great 20,21. Furthermore, although DNA polymerases have a high fidelity of DNA replication, base errors can nonetheless occur during PCR amplification and these incorporated errors can result in incorrect classification of originating species 22. Biases in the PCR amplification of template sequences can also occur; sequences of DNA with a high GC content can be under represented in the final amplicon pool 23 and similarly unnatural base modifications, such as thymine glycol, can halt DNA polymerases causing failures in the amplification of DNA sequences 24. In contrast, a shotgun sequencing DNA library is a DNA library that has been prepared by using all of the purified DNA that has been extracted from a sample and subsequently fragmented into shorter DNA chain lengths prior to preparation for sequencing. Taxonomic classification of DNA sequences generated by shotgun sequencing is more accurate when compared to 16S rRNA amplicon sequencing 25, although the financial cost required to reach a reliable sequencing depth is greater than that of amplicon sequencing 26. The major benefit of shotgun sequencing metagenomics is that sequenced regions of the various genomes in the sample are available for gene prospecting once they have been has been taxonomically classified 27.
Metagenomic sequence data is analyzed by an ever-increasing range of bioinformatic tools. These tools are able to perform a wide variety of applications, for example, quality control analysis of the raw sequence data 28, overlapping of paired end reads 29, de novo assembly of sequence reads to contigs and scaffolds 30,31, taxonomic classification and visualization of sequence reads and assembled sequences 7,12,32,33 and the functional annotation of assembled sequences 34,35.
Silage, produced by farmers throughout the world from fermented cereals such as maize (Zea mays), is predominately used as cattle feed. Silage is treated with the bacterium Lactobacillus sp. to aid fermentation 36 but to date, there is limited knowledge of the other microbial populations found in silage. The fermentation process can lead to undesirable and potentially harmful micro-organisms becoming prevalent within the silage 37. In addition to yeasts and molds, bacteria are particularly adaptable to the anaerobic environment in fermenting silage and are more frequently associated with diseases in livestock rather than the degradation of the silage 38. Butyric acid bacteria can be inadvertently added from soil remains when filling the silage silos and are able to convert the lactic acid, a product of anaerobic digestion, to butyric acid, thus increasing the pH of the silage 39. This increase in pH can lead to an upsurge in spoilage bacteria that would normally be unable to sustain growth under optimum silage fermentation conditions 38. Clostridium spp., Listeria spp. and Bacillus spp. are of particular concern, especially in silage for dairy cattle feed, as bacterial spores that have survived the gastrointestinal tract 40 can enter the food-chain, lead to food spoilage and, in rare cases, to animal and human fatalities 37,39,41-44. Moreover, while it is difficult to estimate the exact economic impact of veterinary treatment and livestock loss caused by silage spoilage, it is likely to be detrimental to a farm if an outbreak was to occur.
It is hypothesized that by using a metagenomic approach we can classify the microbial populations that are present in silage samples and furthermore identify microbial communities associated with silage spoilage that would, in turn, potentially have a detrimental effect on the livestock, enabling remedial action to be taken before the silage is to be used as a food source.

<table><tbody><tr><td>FastDNA SPIN Kit for Soil</td><td>MP Bio</td><td>116560200</td><td>DNA Extraction</td></tr><tr><td>DNA FastPrep</td><td>MP Bio</td><td>116004500</td><td>DNA Extraction</td></tr><tr><td>Agencourt AMPure XP beads</td><td>Beckman Coulter</td><td>A63880</td><td>DNA Purification</td></tr><tr><td>Elution Buffer</td><td>Qiagen</td><td>19806</td><td>DNA Purification</td></tr><tr><td>Qubit Fluorometer</td><td>Thermo Fisher</td><td>Q33216</td><td>DNA Quantification</td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Thermo Fisher</td><td>Q32854</td><td>DNA Quantification</td></tr><tr><td>Nextera XT DNA Library Prep Kit</td><td>Illumina</td><td>FC-131-1024</td><td>Library Preparation</td></tr><tr><td>Nextera XT Index Kit</td><td>Illumina</td><td>FC-131-1001</td><td>Library Preparation</td></tr><tr><td>TapeStation 2200</td><td>Agilent</td><td>G2964AA</td><td>DNA Quantification</td></tr><tr><td>HS D100 ScreenTape</td><td>Agilent</td><td>5067-5584</td><td>DNA Quantification</td></tr><tr><td>HS D100 ScreenTape Reagents</td><td>Agilent</td><td>5067-5585</td><td>DNA Quantification</td></tr><tr><td>TapeStation Tips</td><td>Agilent</td><td>5067-5153</td><td>DNA Quantification</td></tr><tr><td>TapeStation Tubes</td><td>Agilent</td><td>401428 and 401425</td><td>DNA Quantification</td></tr><tr><td>HiSeq 2500</td><td>Illumina</td><td></td><td>DNA Sequencing - provided by a sequencing service</td></tr><tr><td>High Power Analysis Workstation</td><td>Various</td><td></td><td>Local or cloud based, user preferred system</td></tr></tbody></table>

metagenomic analysis of silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Prior to bioinformatic processing, raw sequence reads were trimmed and adapters were removed using Trimmomatic software 28. After the trimming and filtering step, the number of reads was reduced to 50% of the sequence reads (Table 1). The average base phred score was &#62;30 after quality control (Figure 2).
Pairs of DNA sequences which had overlapping regions were merged using FLASH software 29 to generate single longer reads, non-overlapping reads were kept in a separate file. 45.47% reads (105,343) combined successfully. Following the overlapping of reads using FLASH of reads, the resulting extended fragments underwent bacterial taxonomic classification using Kraken software 7 and were subsequently visualized with Krona software (Figure 3).
The majority of the bacterial species present in the silage metagenome are found within 4 prokaryotic phyla: Firmicutes (34%), Actinobacteria (28%), Proteobacteria (27%) and Bacteroidetes (7%). The distribution of classes present within these phyla can be seen in Figure 4. The most abundant species in the metagenome were Lactobacillus spp. (24%; Firmicutes), Corynebacterium spp. (8%; Actinobacteria), Propionibacterium spp. (3%; Actinobacteria) and Prevotella spp. (3%; Bacteroidetes). Species important to animal health and implicated in disease were also observed; Clostridium spp. (1%) Bacillus spp. (0.6%), Listeria spp. (0.2%) were predicted to be present in the silage sample.
Functional annotation was performed on assembled reads. The metagenome was assembled using the SPAdes assembler 30 using the trimmed and filtered paired-end and unpaired reads generating 92,284 scaffolds. In order to identify cellulases, proteins were predicted using MG-RAST and annotated using the Carbohydrate-Active enZYmes Database (CAZy). Of the 97,562 predicted proteins, 6357 were annotated as a putative carbohydrate-active enzyme in one of the five enzymes classes that make up the CAZy database (Figure 5). Results were visualized as a Venn diagram using InteractiVenn software 50 showing the distribution of protein annotations including those containing more than one CAZy enzyme class annotation. Of these, 3861 were predicted to have glycoside hydrolase activity and will be further characterized in the laboratory to confirm function.
<img alt="Figure 1" src="/files/ftp_upload/54936/54936fig1.jpg" /> 
Figure 1: Bioinformatic Metagenomics Pipeline for the Analysis of Silage. Two main approaches were used to investigate the microbiome of silage, taxonomic classification and functional annotation. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54936/54936fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54936/54936fig2.jpg" /> 
Figure 2: Sequence Quality Per-base Before and After Trimming and Adapter Removal. The per-base sequence quality plot from FASTQC shows the average phred score across the length of the sequence reads pre- and post- quality control. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54936/54936fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54936/54936fig3.jpg" /> 
Figure 3: Taxonomic Classification of the Bacterial Microbiome of Solid Silage. Classification of trimmed and overlapping sequence reads from FLASH was performed using Kraken 7 and subsequently visualized with krona. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54936/54936fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54936/54936fig4.jpg" /> 
Figure 4: Taxonomic Class Distribution of the 4 Most Abundant Phyla in the Bacterial Microbiome of Solid Silage. The percentage of each class of bacteria within the four most abundant phyla. Firmicutes: Clostridia (red) and Bacilli (dark blue); Proteobacteria: delta/epsilon (pink), alpha (pale blue), gamma (orange) and beta (turquoise); Bacteroidetes: Flavobacteriia (dark blue) and Bacteroidia (pale green); Actinobacteria: Coriobacteriia (dark purple) and other Actinobacteria (dark green). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54936/54936fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54936/54936fig5.jpg" /> 
Figure 5: CAZy Annotation of the Predicted Proteome in the Solid Silage Microbiome. Venn diagram showing the distribution of the five enzyme classes of CAZy annotations in the predicted proteome of solid silage microbiome. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54936/54936fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td># Raw reads</td>
			<td rowspan="2"># Filtered reads (paired)</td>
			<td># Filtered reads</td>
			<td rowspan="2"># FLASHed reads</td>
		</tr>
		<tr>
			<td>(paired)</td>
			<td>(unpaired)</td>
		</tr>
		<tr>
			<td>2,374,949 x2</td>
			<td>231,679 x2</td>
			<td>1,892,534</td>
			<td>105,343</td>
		</tr>
	</tbody>
</table>
Table 1: Summary Table of Sequencing Reads.

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Metagenomic Analysis of Silage

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

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Genetics

18.1K Views.  University of Exeter. Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Video: Metagenomic Analysis of Silage

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered &#34;bee-safe,&#34; there is mounting evidence that fungicide residues in pollen are associated with bee declines (for both honey and bumble bee species). While the mechanisms remain relatively unknown, researchers have speculated that bee-microbe symbioses are involved. Microbes play a pivotal role in the preservation and/or processing of pollen, which serves as nutrition for larval bees. By altering the microbial community, it is likely that fungicides disrupt these microbe-mediated services, and thereby compromise bee health. This manuscript describes the protocols used to investigate the indirect mechanism(s) by which fungicides may be causing colony decline. Cage experiments exposing bees to fungicide-treated flowers have already provided the first evidence that fungicides cause profound colony losses in a native bumble bee (Bombus impatiens). Using field-relevant doses of fungicides, a series of experiments have been developed to provide a finer description of microbial community dynamics of fungicide-exposed pollen. Shifts in the structural composition of fungal and bacterial assemblages within the pollen microbiome are investigated by next-generation sequencing and metagenomic analysis. Experiments developed herein have been designed to provide a mechanistic understanding of how fungicides affect the microbiome of pollen-provisions. Ultimately, these findings should shed light on the indirect pathway through which fungicides may be causing colony declines.

Microbial consortia within bumble bee hives enrich and preserve pollen for bee larvae. Using next generation sequencing, along with laboratory and field-based experiments, this manuscript describes protocols used to test the hypothesis that fungicide residues alter the pollen microbiome, and colony demographics, ultimately leading to colony loss.

Growers often use fungicide sprays during bloom to protect crops against disease, which exposes bees to fungicide residues. Although considered ...

Environment

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 ...

Nanopore DNA Sequencing for Metagenomic Soil Analysis

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA sequences identified using computer software. Nanopore DNA sequencing is a flexible technique that allows for rapid microbial genome sequencing to identify bacterial and viral species, to characterize bacterial strains, and to detect genetic mutations that confer resistance to antibiotics. The advantages of nanopore sequencing (NS) for life sciences include its low complexity, reduced cost, and rapid real-time sequencing of purified genomic DNA, PCR amplicons, cDNA samples, or RNA. NS is an example of "strand sequencing" which involves sequencing DNA by guiding a single stranded DNA molecule through a nanopore that is inserted into a synthetic polymer membrane. The membrane has an electrical current applied across it, so as the individual bases pass through the nanopore the electrical current is disrupted to varying degrees by the four nucleotide bases. The identification of each nucleotide occurs by detecting the characteristic modulation of the electrical current by the different bases as they pass through the nanopore. The NS system consists of a handheld, USB powered portable device and a disposable flow cell that contains a nanopore array. The portable device plugs into a standard laptop computer that reads and records the DNA sequence using computer software.

Nanopore technology for sequencing biomolecules has wide applications in the life sciences, including identification of pathogens, food safety monitoring, genomic analysis, metagenomic environmental monitoring, and characterization of bacterial antibiotic resistance. In this article, the procedure for metagenomic soil DNA sequencing for species identification using the nanopore sequencing technology is demonstrated.

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil and gas. This process revolutionized the U.S. energy industry, as it gives access to resources that were previously unobtainable and now produces two-thirds of the total natural gas in the United States. Although fracking has had a positive impact on the U.S. economy, several studies have highlighted its detrimental environmental effects. Of particular concern is the effect of fracking on headwater streams, which are especially important due to their disproportionately large impact on the health of the entire watershed. The bacteria within those streams can be used as indicators of stream health, as the bacteria present and their abundance in a disturbed stream would be expected to differ from those in an otherwise comparable but undisturbed stream. Therefore, this protocol aims to use the bacterial community to determine if streams have been impacted by fracking. To this end, sediment, and water samples, from streams near fracking (potentially impacted) and upstream or in a different watershed of fracking activity (unimpacted) must be collected. Those samples are then subjected to nucleic acid extraction, library preparation, and sequencing to investigate microbial community composition. Correlational analysis and machine learning models can subsequently be employed to identify which features are explanative of variation in the community, as well as identification of predictive biomarkers for fracking&#39;s impact. These methods can reveal a variety of differences in the microbial communities among headwater streams, based on the proximity to fracking, and serve as a foundation for future investigations on the environmental impact of fracking activities.

Here, we present a protocol to investigate the impacts of hydraulic fracturing on nearby streams by analyzing their water and sediment microbial communities.

Hydraulic fracturing (HF), commonly called &#34;fracking&#34;, uses a mixture of high-pressure water, sand, and chemicals to fracture rocks, releasing oil ...

Derivatization and Separation of Barley Flour Metabolites into Four Fractions

Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry

Climate change increases drought risk to agriculture and impacts both food nutrient content and overall food security. Metabolomics is one way to observe and quantify the impacts of drought on grain and other agricultural products. The identified metabolites may allow for the identification of the biochemical response that allows the plant to tolerate stressful environments. The methodology presented herein allowed for the total metabolomic analysis of barley flour using gas chromatography/mass spectrometry (GC/MS). 
Barley flour metabolite extracts were fractionated into four fractions based on polarity. To allow for analysis by GC/MS, metabolites were derivatized to increase volatility and metabolite separation: fatty acids esters were derivatized into fatty acid methyl esters; sugars were oximated into their straight chain form; and metabolites with hydroxyl groups were converted to their corresponding silyl ethers. The derivatized samples were injected into the GC/MS and the generated mass spectra were used for metabolite identification by comparing the generated spectra to the National Institute of Standards and Technology (NIST) Tandem Mass Spectra library. The method described here can also be used to examine the total metabolome for other plants, furthering our understanding of the biochemical responses of stressed plants.

A method for metabolomic analysis of barley is presented. The method entails fractionation and derivatization of metabolites and analysis thereof by gas chromatography/mass spectrometry (GC/MS). Metabolomic analysis can be used to determine the effect of intercropping and drought stress on the grain.

Climate change increases drought risk to agriculture and impacts both food nutrient content and overall food security. Metabolomics is one way to observe ...

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent. In the experiment, yeast is grown for 48 hours in 1/2X YPD broth containing 3X glucose. The culture is split into a control and treated group. The experiment culture is treated with 0.5 mM H2O2 in Hanks Buffered Saline (HBSS) for 1 hour. The control culture is treated with HBSS only. Total RNA is extracted from both cultures and is converted to a biotin-labeled cRNA product through a multistep process. The final synthesis product is taken back to the UVM Microarray Core Facility and hybridized to the Affymetrix yeast GeneChips. The resulting gene expression data are uploaded into bioinformatics data analysis software.

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent.

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen ...

Biology

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and microbial communities, a comprehensive workflow for DNA and RNA extraction was developed. The workflow concurrently generates viral and microbial metagenomes, as well as metatranscriptomes, from a single sample for next-generation sequencing. The coupling of these approaches provides an overview of both the taxonomical characteristics and the community encoded functions. The presented methods use Cystic Fibrosis (CF) sputum, a problematic sample type, because it is exceptionally viscous and contains high amount of mucins, free neutrophil DNA, and other unknown contaminants. The protocols described here target these problems and successfully recover viral and microbial DNA with minimal human DNA contamination. To complement the metagenomics studies, a metatranscriptomics protocol was optimized to recover both microbial and host mRNA that contains relatively few ribosomal RNA (rRNA) sequences. An overview of the data characteristics is presented to serve as a reference for assessing the success of the methods. Additional CF sputum samples were also collected to (i) evaluate the consistency of the microbiome profiles across seven consecutive days within a single patient, and (ii) compare the consistency of metagenomic approach to a 16S ribosomal RNA gene-based sequencing. The results showed that daily fluctuation of microbial profiles without antibiotic perturbation was minimal and the taxonomy profiles of the common CF-associated bacteria were highly similar between the 16S rDNA libraries and metagenomes generated from the hypotonic lysis (HL)-derived DNA. However, the differences between 16S rDNA taxonomical profiles generated from total DNA and HL-derived DNA suggest that hypotonic lysis and the washing steps benefit in not only removing the human-derived DNA, but also microbial-derived extracellular DNA that may misrepresent the actual microbial profiles.

Using the cystic fibrosis airway as an example, the manuscript presents a comprehensive workflow comprising a combination of metagenomic and metatranscriptomic approaches to characterize the microbial and viral communities in animal-associated samples.

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and ...

A Hybrid DNA Extraction Method for the Qualitative and Quantitative Assessment of Bacterial Communities from Poultry Production Samples

The efficacy of DNA extraction protocols can be highly dependent upon both the type of sample being investigated and the types of downstream analyses performed. Considering that the use of new bacterial community analysis techniques (e.g., microbiomics, metagenomics) is becoming more prevalent in the agricultural and environmental sciences and many environmental samples within these disciplines can be physiochemically and microbiologically unique (e.g., fecal and litter/bedding samples from the poultry production spectrum), appropriate and effective DNA extraction methods need to be carefully chosen. Therefore, a novel semi-automated hybrid DNA extraction method was developed specifically for use with environmental poultry production samples. This method is a combination of the two major types of DNA extraction: mechanical and enzymatic. A two-step intense mechanical homogenization step (using bead-beating specifically formulated for environmental samples) was added to the beginning of the &#8220;gold standard&#8221; enzymatic DNA extraction method for fecal samples to enhance the removal of bacteria and DNA from the sample matrix and improve the recovery of Gram-positive bacterial community members. Once the enzymatic extraction portion of the hybrid method was initiated, the remaining purification process was automated using a robotic workstation to increase sample throughput and decrease sample processing error. In comparison to the strict mechanical and enzymatic DNA extraction methods, this novel hybrid method provided the best overall combined performance when considering quantitative (using 16S rRNA qPCR) and qualitative (using microbiomics) estimates of the total bacterial communities when processing poultry feces and litter samples.

A novel semi-automated hybrid DNA extraction method for use with environmental poultry production samples was developed and demonstrated improvements over a common mechanical and enzymatic extraction method in terms of the quantitative and qualitative estimates of the total bacterial communities.

The efficacy of DNA extraction protocols can be highly dependent upon both the type of sample being investigated and the types of downstream analyses ...

Quasi-metagenomic Analysis of Salmonella from Food and Environmental Samples

Quasi-metagenomics sequencing refers to the sequencing-based analysis of modified microbiomes of food and environmental samples. In this protocol, microbiome modification is designed to concentrate genomic DNA of a target foodborne pathogen contaminant to facilitate the detection and subtyping of the pathogen in a single workflow. Here, we explain and demonstrate the sample preparation steps for the quasi-metagenomics analysis of Salmonella enterica from representative food and environmental samples including alfalfa sprouts, ground black pepper, ground beef, chicken breast and environmental swabs. Samples are first subjected to the culture enrichment of Salmonella for a shortened and adjustable duration (4&#8211;24 h). Salmonella cells are then selectively captured from the enrichment culture by immunomagnetic separation (IMS). Finally, multiple displacement amplification (MDA) is performed to amplify DNA from IMS-captured cells. The DNA output of this protocol can be sequenced by high throughput sequencing platforms. An optional quantitative PCR analysis can be performed to replace sequencing for Salmonella detection or assess the concentration of Salmonella DNA before sequencing.

Quasi-metagenomic Analysis of Salmonella from Food and Environmental Samples

Here, we present a protocol to prepare DNA samples from food and environmental microbiomes for concerted detection and subtyping of Salmonella through quasimetagenomic sequencing. The combined use of culture enrichment, immunomagnetic separation (IMS), and multiple displacement amplification (MDA) allows effective concentration of Salmonella genomic DNA from food and environmental samples.&#160;

Metagenomic Analysis of Silage

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