Last year, one of my former research groups at Montana State University was awarded a USDA NIFA Foundational program grant, and I am a sub-award PI on that grant. We’ll be working together to investigate the effect of diversified farming systems – such as those that use cover crops, rotations, or integrate livestock grazing into field management – on crop production and soil bacterial communities: “Diversifying cropping systems through cover crops and targeted grazing: impacts on plant-microbe-insect interactions, yield and economic returns.”
The first soil samples were collected in Montana this summer, and I have been processing them for the past few weeks. I am using the opportunity to train a master’s student on microbiology and molecular genetics lab work.
Tindall Ouverson started this fall as a master’s student at MSU, working with Fabian Menalled and Tim Seipel in Bozeman, MT. She’s an environmental and soil scientist, and this is her first time working with microbes. She was here in Eugene for just a few days to learn everything needed for sequencing: DNA extraction, polymerase chain reaction, gel electrophoresis and visualization, DNA cleanup using magnetic beads, quantification, and pooling. Despite not having experience in microbiology or molecular biology, Tindall showed a real aptitude and picked up the techniques faster than I expected!
Once the sequences are generated, I’ll be (remotely) training Tindall on DNA sequence analysis. I’ll also be serving as one of her thesis committee members! Tindall will be the first of (hopefully) many cross-trained graduate students between myself and collaborators at MSU.
Zinc is an important mineral in your diet; it’s required by many of your enzymes and having too much or too little can cause health problems. We know quite a bit about how important zinc is to sheep, in particular for their growth, immune system, and fertility. We also know that organically- versus inorganically-sourced zinc differs in its bio-availability, or how easy it is for cells to access and use it. Surprisingly, we know nothing about how different zinc formulations might affect gut microbiota, despite the knowledge that microorganisms may also need zinc.
This collaborative study was led by Dr. Whit Stewart and his then-graduate student, Chad Page, while they were at Montana State University (they are now both at the University of Wyoming). Chad’s work focused on how different sources of zinc affected sheep growth and performance (previously presented, publication forthcoming), and I put together this companion paper examining the effects on rumen bacteria.
The pre-print is available now for Journal of Animal Science members, and the finished proof should be available soon. JAS is the main publication for the American Society of Animal Science, and one of the flagship journals in the field.
Zinc amino acid supplementation alters yearling ram rumen bacterial communities but zinc sulfate supplementation does not.
Ishaq, S.L., Page, C.M., Yeoman, C.J., Murphy, T.W., Van Emon, M.L., Stewart, W.C. 2018. Journal of Animal Science. Accepted.Article.
Despite the body of research into Zn for human and animal health and productivity, very little work has been done to discern whether this benefit is exerted solely on the host organism, or whether there is some effect of dietary Zn upon the gastrointestinal microbiota, particularly in ruminants. We hypothesized that 1) supplementation with Zn would alter the rumen bacterial community in yearling rams, but that 2) supplementation with either inorganically-sourced ZnSO4, or a chelated Zn amino acid complex, which was more bioavailable, would affect the rumen bacterial community differently. Sixteen purebred Targhee yearling rams were utilized in an 84 d completely-randomized design, and allocated to one of three pelleted dietary treatments: control diet without fortified Zn (~1 x NRC), a diet fortified with a Zn amino acid complex (~2 x NRC), and a diet fortified with ZnSO4 (~2 x NRC). Rumen bacterial community was assessed using Illumina MiSeq of the V4-V6 region of the 16S rRNA gene. One hundred and eleven OTUs were found with > 1% abundance across all samples. The genera Prevotella, Solobacterium, Ruminococcus, Butyrivibrio, Olsenella, Atopobium, and the candidate genus Saccharimonas were abundant in all samples. Total rumen bacterial evenness and diversity in rams were reduced by supplementation with a Zn-amino-acid complex, but not in rams supplemented with an equal concentration of ZnSO4, likely due to differences in bioavailability between organic and inorganically-sourced supplement formulations. A number of bacterial genera were altered by Zn supplementation, but only the phylum Tenericutes was significantly reduced by ZnSO4 supplementation, suggesting that either Zn supplementation formulation could be utilized without causing a high-level shift in the rumen bacterial community which could have negative consequences for digestion and animal health.
Sequence data contamination from biological or digital sources can obscure true results and falsely raise one’s hopes. Contamination is a persist issue in microbial ecology, and each experiment faces unique challenges from a myriad of sources, which I have previously discussed. In microbiology, those microscopic stowaways and spurious sequencing errors can be difficult to identify as non-sample contaminants, and collectively they can create large-scale changes to what you think a microbial community looks like.
Samples from large studies are often processed in batches based on how many samples can be processed by certain laboratory equipment, and if these span multiple bottles of reagents, or water-filtration systems, each batch might end up with a unique contamination profile. If your samples are not randomized between batches, and each batch ends up representing a specific time point or a treatment from your experiment, these batch effects can be mistaken for a treatment effect (a.k.a. a false positive).
“The times were statistically greater than prior time periods, while simultaneously being statistically lesser to prior times, according to longitudinal analysis.”
Over the past year, I analyzed a particularly complex bacterial 16S rRNA gene sequence data set, comprising nearly 600 home dust samples, and about 90 controls. Samples were collected from three climate regions in Oregon, over a span of one year, in which homes were sampled before and approximately six weeks after a home-specific weatherization improvement (treatment homes) or simply six weeks later in (comparison) homes which were eligible for weatherization but did not receive it. As these samples were collected over a span of a year, they were extracted with two different sequencing kits and multiple DNA extraction batches, although all within a short time after collection. The extracted DNA was spread across two sequence runs to allow for data processing to begin on cohort 1, while we waited for cohort 2 homes to be weatherized. Thus, there were a lot of opportunities to introduce technical error or biological contamination that could be conflated with treatment effects.
On top of this, each home was unique, with it’s own human and animal occupants, architectural and interior design, plants, compost, and quirks, and we didn’t ask homeowners to modify their behavior in any way. This was important, as it meant each of the homes – and their microbiomes – are somewhat unique. Therefore I didn’t want to remove sequences which might be contaminants on the basis of low abundance and risk removing microbial community members which were specific to that home. After the typical quality assurance steps to curate and process the data, which can be found on GitHub as an R script of a DADA2 package workflow, I needed to decide what to do with the negative controls.
Because sequencing is expensive, most of the time there is only one negative control included in sequencing library preparation, if that. The negative control is a blank sample – just water, or an unused swab – which does not intentionally contain cells or nucleic acids. Thus anything you find there will have come from contamination. The negative control can be used to normalize the relative abundance numbers – if you find 1,000 sequences in the negative control, which is supposed to have no DNA in it, then you might only continue looking at samples with a certain amount higher than 1,000 sequences. This risks throwing out valid sequences that happen to be rare. Alternatively, you can try to identify the contaminants and remove whole taxa from your data set, risking the complete removal of valid taxa.
I had three types of negative controls: sterile DNA swabs which were processed to check for biological contamination in collection materials, kit controls where a blank extraction was run for each batch of extractions to test for biological contamination in extraction reagents, and PCR negative controls to check for DNA contamination of PCR reagents. In total, 90 control samples were sequenced, giving me unprecedented resolution to deal with contamination. Looking at the total number of sequences before and after my quality-analysis processing, I can see that the number of sequences in my negative controls reduces dramatically; they were low-quality in some way and might be sequencing artifacts. But, an unsatisfactory number remain after QA filtering; these are high-quality and likely come from microbial contamination.
I wasn’t sure how I wanted to deal with each type of control. I came up with three approaches, and then looked at unweighted, non-rarefied ordination plots (PCoA) to watch how my axes changed based on important components (factors). What follows is a narrative summarize of what I did, but I included the R script of my phyloseq package workflow and workaround on GitHub.
“In microbial ecology, preprints are posted on late November nights. The foreboding atmosphere of conflated factors makes everyone uneasy.”
Ordination plots visualize lots of complex communities together. In both ordination figures below, each point on the graph represents a dust sample from one house. They are clustered by community distance: those closer together on the plot have a more similar community than points which are further away from each other. The points are shaped by the location of the samples, including Bend, Eugene, Portland, along with a few pilot samples labeled “Out”, and negative controls which have no location (not pictured but listed as NA). The points are colored by DNA extraction b
In Figure 1, the primary axis (axis 1) shows a clear clustering of samples by DNA extraction batch, but this is also mixed with geographic location, and as it turns out – date of collection and sequencing run. We know from other studies that geographic location, date of collection, and sequencing batch can all affect the microbial community.
Approach 1: Subtraction + outright removal
This approach subsets my data into DNA extraction batches, and then uses the number of sequences found in the negative controls to subtract out sequences from my dust samples. This assumes that if a particular sequence showed up 10 times in my negative control, but 50 times in my dust samples, that only 40 of those in my dust sample were real. For each of my DNA extraction batch negative control samples, I obtained the sum of each potential contaminant that I found there, and then subtracted those sums from the same sequence columns in my dust samples.
Approach 1 was alright, but there was still an effect of DNA extraction batch (indicated by color scale) that was stronger than location or treatment (not included on this graph). This approach is also more pertinent for working with OTUs, or situations where you wouldn’t want to remove the whole OTU, just subtract out a certain number sequences from specific columns. There is currently no way to do that just from phyloseq, so I made a work-around (see the GitHub page). However, using DADA2 gives you Sequence Variants, which are more precise and I found it’s better to remove them with approach 3.
Approach 2: Total Removal
This approach removes any contaminant sequences that is found in ANY of the negative controls from ALL the house samples, regardless of which negative control was for which extraction batch. This approach assumes that if it a sequence was found as a contaminant in a negative control somewhere, that it is a contaminant everywhere.
Once again, approach 2 was alright, and now that primary axis (axis 1) of potential batch effect is now my secondary axis; so there is still an effect of DNA extraction batch (indicated by color scale) but it is weaker. When I recolor by different variables, there is much more clustering by Treatment than by any batch effects. However, that second axis is also one of my time variables, so don’t want to get rid of all of the variation on that axis. But, since my negative kit controls showed a lot of variation in number and types of taxa, I don’t want to remove everything found there from all samples indiscriminately.
Additionally, I don’t favor throwing sequences out just because they were a contaminant somewhere, particularly for dust samples. Contamination can be situational, particularly if a microbe is found in the local air or water supply and would be legitimately found in house dust but would have also accidentally gotten into the extraction process.
Approach 3: “To each its own”
This approach removes all the sequences from PCR and swab contaminant SVs fully from each cohort, respectively, and removes extraction kit contaminants fully from each DNA extraction batch, respectively. I took all the sequences of the SVs found in my dust samples and made them into a vector (list), and then I took all the sequences of the SVs found in my controls and made them into a different vector. I effectively subtracted out the contaminant SVs by name, but asking to find the sequences which were different between my two lists (thus returning the sequences which were in my dust samples but not in my control samples). I did this respective to each sequencing cohort and batch, so that I only remove the pertinent sequences (ex. using kit control 1 to subtract from DNA extraction batch 1).
In Figure 4, potential batch effect is solidly my secondary axis and not the primary driving force behind clustering. The primary axis (axis 1) shows a clear separation by climate zone, or location of homes, once the batch contamination has been removed. When I recolor by different variables, there is much more clustering by Treatment and almost none by batch effects. I say almost none, because some of my DNA extraction batches also happen to be Treatment batches, as they represent a subset of samples from a different location. Thus, I can’t tell if those samples cluster separately solely because of location or also because of batch effect. However, I am satisfied with the results and ready to move on.
Unlike its namesake, this tale has a happier ending.
Last night I participated in the Oregon Museum of Science and Industry (OMSI) After Dark event: “It’s Alive! (Mind and Body)”. OMSI regularly puts on After Dark events, where adults can check out the museum, listen to lectures in the planetarium, and engage in interactive science experiments and activities, all while enjoying an open bar. Last night, I had a great time giving a short presentation on “Ishaq OMSI After Dark 20180425“!
The Hungate 1000 Project was a massive undertaking: namely, sequencing the genome of 1000 microorganisms cultured from ruminant animals all over the world, and was both coordinated and led by the Rumen Microbial Genomics Network. After years of hard work by some incredible researchers, the Hungate 1000 has just been published in the Nature Biotechnology Journal! The […]
I’m pleased to announce that one of my collaborators, Dr. Huawei Zeng of the USDA Agricultural Research Service, recently published another study of his, to which I contributed some analysis of bacterial communities from mice. Several years ago, during my Ph.D. at the University of Vermont, I provided wet-lab and DNA sequence analysis work for a previous project of Dr. Zeng, investigating the health effects of a low or high fat diet on mice, which can be found here.
Zeng, H., Ishaq, S.L., Liu, Z., Bukowski, M.R. 2017. Journal of Nutritional Biochemistry. In press, doi.org/10.1016/j.jnutbio.2017.11.001.
The increasing worldwide incidence of colon cancer has been linked to obesity and consumption of a high-fat western diet. To test the hypothesis that a high fat diet (HFD) promotes colonic aberrant crypt (AC) formation in a manner associated with gut bacterial dysbiosis, we examined the susceptibility to azoxymethane (AOM)-induced colonic AC and microbiome composition in C57/BL6 mice fed a modified AIN93G diet (AIN, 16% fat, energy) or a HFD (45% fat, energy) for 14 weeks. Mice receiving the HFD exhibited increased plasma leptin, body weight, body fat composition and inflammatory cell infiltration in the ileum compared with those in the AIN group. Consistent with the gut inflammatory phenotype, we observed an increase in colonic AC, plasma interleukin 6 (IL6), tumor necrosis factor α (TNF α), monocyte chemoattractant protein 1 (MCP1), and inducible nitric oxide synthase (iNOS) in the ileum of the HFD-AOM group compared with the AIN-AOM group. Although the HFD and AIN groups did not differ in bacterial species number, the HFD and AIN diets resulted in different bacterial community structures in the colon. The abundance of certain short chain fatty acid (SCFA) producing bacteria (e.g., Barnesiella) and fecal SCFA (e.g., acetic acid) content were lower in the HFD-AOM group compared with the AIN and AIN-AOM groups. Furthermore, we identified a high abundance of Anaeroplasma bacteria, an opportunistic pathogen in the HFD-AOM group. Collectively, we demonstrate that a HFD promotes AC formation concurrent with an increase of opportunistic pathogenic bacteria in the colon of C57BL/6 mice.
In 2015, while working in the Yeoman Lab, I was invited to perform the sequence analysis on some samples from a previously-run diet study. The study was part of ongoing research by Dr. Travis Whitney at Texas A & M on the use of juniper as a feed additive for sheep. The three main juniper species in Texas can pose a problem- while they are native, they have significantly increased the number of acres they occupy due to changes in climate, water availability, and human-related land use. And, juniper can out-compete other rangeland species, which can make forage less palatable, less nutritious, or unhealthy for livestock. Juniper contains essential oils and compounds which can affect some microorganisms living in their gut. We wanted to know how the bacterial community in the rumen might restructure while on different concentrations of juniper and urea.
Coupled with the animal health and physiology aspect led by Travis, we published two companion papers in the Journal of Animal Science. We had also previously presented these results at the Joint Annual Meeting of the American Society for Animal Science, the American Dairy Science Association, and the Canadian Society for Animal Science in Salt Lake City, UT in 2016. Travis’ presentation can be found here, and mine can be found here. The article can be found here.
Ground redberry juniper and urea in supplements fed to Rambouillet ewe lambs.
Part 1: Growth, blood serum and fecal characteristics, T.R. Whitney
This study evaluated effects of ground redberry juniper (Juniperus pinchotii) and urea in dried distillers grains with solubles-based supplements fed to Rambouillet ewe lambs (n = 48) on rumen physiological parameters and bacterial diversity. In a randomized study (40 d), individually-penned lambs were fed ad libitum ground sorghum-sudangrass hay and of 1 of 8 supplements (6 lambs/treatment; 533 g/d; as-fed basis) in a 4 × 2 factorial design with 4 concentrations of ground juniper (15%, 30%, 45%, or 60% of DM) and 2 levels of urea (1% or 3% of DM). Increasing juniper resulted in minor changes in microbial β-diversity (PERMANOVA, pseudo F = 1.33, P = 0.04); however, concentrations of urea did not show detectable broad-scale differences at phylum, family, or genus levels according to ANOSIM (P> 0.05), AMOVA (P > 0.10), and PERMANOVA (P > 0.05). Linear discriminant analysis indicated some genera were specific to certain dietary treatments (P < 0.05), though none of these genera were present in high abundance; high concentrations of juniper were associated with Moraxella and Streptococcus, low concentrations of urea were associated with Fretibacterium, and high concentrations of urea were associated with Oribacterium and Pyramidobacter. Prevotella were decreased by juniper and urea. Ruminococcus, Butyrivibrio, and Succiniclasticum increased with juniper and were positively correlated (Spearman’s, P < 0.05) with each other but not to rumen factors, suggesting a symbiotic interaction. Overall, there was not a juniper × urea interaction for total VFA, VFA by concentration or percent total, pH, or ammonia (P > 0.29). When considering only percent inclusion of juniper, ruminal pH and proportion of acetic acid linearly increased (P < 0.001) and percentage of butyric acid linearly decreased (P = 0.009). Lamb ADG and G:F were positively correlated with Prevotella(Spearman’s, P < 0.05) and negatively correlated with Synergistaceae, the BS5 group, and Lentisphaerae. Firmicutes were negatively correlated with serum urea nitrogen, ammonia, total VFA, total acetate, and total propionate. Overall, modest differences in bacterial diversity among treatments occurred in the abundance or evenness of several OTUs, but there was not a significant difference in OTU richness. As diversity was largely unchanged, the reduction in ADG and lower-end BW was likely due to reduced DMI rather than a reduction in microbial fermentative ability.