I'm an assistant professor of animal and veterinary studies at the University of Maine, Orono, studying how animals get their microbes. I am also the Founder and Lead of the Microbes and Social Equity working group.
Several years ago, during my Ph.D. at the University of Vermont, I provided wet-lab and DNA sequence analysis work for a project investigating the health effects of a low or high fat diet on mice with Dr. Huawei Zeng of the USDA Agricultural Research Service. It was just recently published in the Journal of Nutritional Biochemistry!
Abstract
Consumption of an obesigenic/high-fat diet (HFD) is associated with a high colon cancer risk and may alter the gut microbiota. To test the hypothesis that long-term high-fat (HF) feeding accelerates inflammatory process and changes gut microbiome composition, C57BL/6 mice were fed HFD (45% energy) or a low-fat (LF) diet (10% energy) for 36 weeks. At the end of the study, body weights in the HF group were 35% greater than those in the LF group. These changes were associated with dramatic increases in body fat composition, inflammatory cell infiltration, inducible nitric oxide synthase protein concentration and cell proliferation marker (Ki67) in ileum and colon. Similarly, β-catenin expression was increased in colon (but not ileum). Consistent with gut inflammation phenotype, we also found that plasma leptin, interleukin 6 and tumor necrosis factor α concentrations were also elevated in mice fed the HFD, indicative of chronic inflammation. Fecal DNA was extracted and the V1–V3 hypervariable region of the microbial 16S rRNA gene was amplified using primers suitable for 454 pyrosequencing. Compared to the LF group, the HF group had high proportions of bacteria from the family Lachnospiraceae/Streptococcaceae, which is known to be involved in the development of metabolic disorders, diabetes and colon cancer. Taken together, our data demonstrate, for the first time, that long-term HF consumption not only increases inflammatory status but also accompanies an increase of colonic β-catenin signaling and Lachnospiraceae/Streptococcaceae bacteria in the hind gut of C57BL/6 mice.
For the last four days I was in Boston for the American Society for Microbiology (ASM) Microbe 2016 meeting.The meeting is held in Boston on even years, and New Orleans on odd.
The conference brings together all sorts of microbiologists: from earth sciences, to host-associated, to clinical pathologists and epidemiologists, to educators. This year, there were reportedly over 11,000 participants! Because of the wide variety of topics, there is always an interesting lecture going on related to your topic, and it was a wonderful experience to be able to talk directly to other researchers to learn about the clever techniques they are using. I posted about a tiny fraction of those interesting projects on Give Me The Short Version.
On Sunday, I presented a poster on “Farming Systems Modify The Impact Of Inoculum On Soil Microbial Diversity.” I analyzed the data from this project for the Menalled Lab last year, and it has developed into a manuscript in review, as well as several additional projects in development.
One of the best parts of ASM meetings is that you never know who you are going to run into, and I was able to meet up with several friends and colleagues, including Dr. Benoit St-Pierre, who was a post-doc in the Wright lab at the University of Vermont while I was a student, and Laura Cersosimo, the other Ph.D. candidate from the UVM Wright lab who will be defending in just a few months! I also ran into Ph.D. candidate Robert Mugabi, who is hoping to defend by March and in the Barlow lab at UVM while I was there. Most unexpectedly, I ran into a A Lost Microbiologist who had wandered in from Norway: Dr. Nicole Podnecky, who I met at UVM back when we were undergraduates!
This slideshow requires JavaScript.
Of course, no conference would be complete without vendor swag.
Vendor swag! And not even all of it…
Ice cream made using liquid nitrogen from the Witches of Boston.
Today I’ll be presenting a poster on “Farming Systems Modify The Impact Of Inoculum On Soil Microbial Diversity.” at the American Society for Microbiology (ASM) Microbe 2016 meeting.
Come find me in the poster hall from 12:30 pm to 2:30 pm, poster number SUNDAY-053.
Stay tuned for the electronic poster, which will be uploaded after my presentation (as per ASM regulations).
Scientific conferences are a great place to get your name out there, discuss research with colleagues, and meet other researchers with whom you might one day collaborate. It can be difficult to get noticed as a graduate student or post-doctoral researcher, especially if it’s your first time at a certain conference, if your poster time conflicts with more interesting events, or if you find yourself way at the back of a 1,000 poster hall. You need to be ready to introduce yourself and get your point across, and to do it in a memorable and concise way. There may be hundreds or even thousands of people in attendance, so you need to make a fast impression.
Too much info on your card? A black background is slimming.
Though a bit outdated these days, I find business cards really handy. Not only can you quickly hand out all your information, but you can write notes on the back about what you discussed with someone so you can follow up with them later. It’s easy to leave a bag of them at your poster for people to take, too.
Not only is your poster or presentation’s content important, its visual appeal will help draw in people who are “browsing”. Make sure your font is large enough to read from 5-8 ft away, and that you have some color, but not enough to make text illegible. Bolding or bulleting take-home messages can also be really helpful. Make sure you can describe your poster in a variety of ways: in under 60 seconds to the person with a mild passing interest, and in-depth with the person that is curious about your methods or your other projects.
The most important thing to prepare, though, is yourself. You are representing yourself, your institution, and your science. Cleanliness, organization, and confidence make a huge difference when meeting new people, and will make you more approachable. Make eye contact, try to avoid filler words, and smile! I have watched posters get overwhelmingly passed by because the presenter was on their phone, or looked bored or annoyed. Making eye contact and saying hello to someone as they walk by is often enough to get them to slow down and ask you about your work.
If nothing else, a brightly colored shirt will attract attention to you and your poster.
When asking questions at other presentations, be sure to be polite; being demanding or rude is guaranteed to be met with disapproval from the rest of the audience. And go ahead and introduce yourself to other researchers, just be sure to keep it brief and don’t interrupt another meeting.
One more thing to consider at a conference is your behavior outside of your presentation. You are at a gathering of intellectuals who may one day be your boss, your colleague, your grant reviewer, or otherwise influential in your career. They may remember that they saw you talking loudly to a friend during a presentation, or that you got too drunk at the opening session. Conferences are often used as an excuse to take a concurrent vacation, especially for those in academia who generally can’t take a week off during the semester. But you should remember why you are there and act professionally, especially as a graduate student or post-doc, because you never know who’ll remember you in the future.
Bioinformatics brings statistics, mathematics, and computer programming to biology and other sciences. In my area, it allows for the analysis of massive amounts of genomic (DNA), transcriptomic (RNA), proteomic (proteins), or metabolomic (metabolites) data.
In recent years, the advances in sequencing have allowed for the large-scale investigation of a variety of microbiomes. Microbiome refers to the collective genetic material or genomes of all the microorganisms in a specific environment, such as the digestive tract or the elbow. The term microbiome is often casually thrown around: some people mistakenly use it interchangeably with “microbiota”, or use it to describe only the genetic material of a specific type of microorganism (i.e. “microbiome” instead of “bacterial microbiome”). Not only have targeted, or amplicon sequencing techniques improved, but methods that use single or multiple whole genomes have become much more efficient. In both cases, this has resulted in more sequences being amplified more times. This creates “sequencing depth”, a.k.a. better “coverage”: if you can sequence one piece of DNA 10 times instead of just once of twice, then you can determine if changes in the sequence are random errors or really there. Unfortunately, faster sequencing techniques usually have more spontaneous errors, so your data are “messy” and harder to deal with. More and messier data creates the problem of handling data.
The grey lines on the right represent sequence pieces reassembled into a genome, with white showing gaps. The colored lines represent a nucleotide that is different from the reference genome, usually just a random error in one sequence. The red bar shows where each sequence has a nucleotide different from that of the reference genome, indicating that this bacterial strain really is different there. This is a single nucleotide polymorphism (SNP).
DNA analysis requires very complex mathematical equations in order to have a standardized way to quantitatively and statistically compare two or two million DNA sequences. For example, you can use equations for estimating entropy (chaos) and estimate how many sequences you might be missing due to sequencing shortcomings based on how homogeneous (similar) or varied your dataset is. If you look at your data in chunks of 100 sequences, and 90 of them are different from each other, then sequencing your dataset again will probably turn up something new. But if 90 are the same, you have likely found nearly all the species in that sample.
Bioinformatics takes these complex equations and uses computer programs to break them down into many simple pieces and automate them. However, the more data you have, the more equations the computer will need to do, and the larger your files will be. Thus, many researchers are limited by how much data they can process.
Mr. DNA, Jurassic Park (1993)
There are several challenges to analyzing any dataset. The first is assembly.
Sequencing technology can only add so many nucleotide bases to a synthesized sequence before it starts introducing more and more errors, or just stops adding altogether. To combat this increase in errors, DNA or RNA is cut into small fragments, or primers are used to amplify only certain small regions. These pieces can be sequenced from one end to another, or can be sequenced starting at both ends and working towards the middle to create a region of overlap. In that case, to assemble, the computer needs to match up both ends and create one contiguous segment (“contig”). With some platforms, like Illumina, the computer tags each sequence by where on the plate it was, so it knows which forward piece matches which reverse.
When sequencing an entire genome (or many), the pieces are enzymatically cut, or sheared by vibrating them at a certain frequency, and all the pieces are sequenced multiple times. The computer then needs to match the ends up using short pieces of overlap. This can be very resource-intensive for the computer, depending on how many pieces you need to put back together, and whether you have a reference genome for it to use (like the picture on a puzzle box), or whether you are doing it de novo from scratch (putting together a puzzle without a picture, by trial and error, two pieces at a time).
Once assembled into their respective consensus sequences, you need to quality-check the data.
This can take a significant amount of time, depending on how you go about it. It also requires good judgement, and a willingness to re-run the steps with different parameters to see what will happen. An easy and quick way is to have the computer throw out any data below a certain threshold: longer or shorter than what your target sequence length was, ambiguous bases (N) which the computer couldn’t call as a primary nucleotide (A, T, C, or G), or the confidence level (quality score) of the base call was low. These scores are generated by the sequencing machine as a relative measure of how “confident” the base call is, and this roughly translates to potential number of base call errors (ex. marking it an A instead of a T) per 1,000 bases. You can also cut off low-quality pieces, like the very beginning or ends of sequences which tend to sequence poorly and have low quality. This is a great example of where judgement is needed: if you quality-check and trim off low quality bases first, and then assemble, you are likely to have cut off the overlapping ends which end up in the middle of a contig and won’t be able to put the two halves together. If you assemble first, you might end up with a sequence that is low-quality in the middle, or very short if you trim it on the low quality portions. If your run did not sequence well and you have lot of spontaneous errors, you will have to decide whether to work with a lot of poor-quality data, or a small amount of good-quality data leftover after you trim out the rest, or spend the money to try and re-sequence.
There are several steps that I like to add, some of which are necessary and some which are technically optional. One of them is to look for chimeras, which are two sequence pieces that mistakenly got joined together. This happens during the PCR amplification step, often if there is an inconsistent electrical current or other technical problem with the machine. While time- and processor-consuming, chimera checking can remove these fake sequences before you accidentally think you’ve discovered a new species. Your screen might end up looking something like this…
Actual and common screen-shot… but I am familiar enough with it to be able to interpret!
Eventually, you can taxonomically and statistically assess your data.
Ishaq and Wright, 2014, Microbial Ecology
In order to assign taxonomic identification (ex. genus or species) to a sequence, you need to have a reference database. This is a list of sequences labelled with their taxonomy (ex. Bacillus licheniformis), so that you can match your sequences to the reference and identify what you have. There are several pre-made ones publicly available, but in many cases you need to add to or edit these, and several times I have made my own using available data in online databases.
Ishaq and Wright, 2014, Microbial Ecology
You can also statistically compare your samples. This can get complicated, but in essence tries to mathematically compare datasets to determine if they are actually different, and if that difference could have happened by chance or not. You can determine if organically-farmed soil contains more diversity than conventionally-farmed soils. Or whether you have enough sequencing coverage, or need to go back and do another run. You can also see trends across the data, for example, whether moose from different geographic locations have similar bacterial diversity to each other (left). Or whether certain species or environmental factors have a positive/negative/ or no correlation (below).
Bioinformatics can be complicated and frustrating, especially because computers are very literal machines and need to have things written in very specific ways to get them to accomplish tasks. They also aren’t very good at telling you what you are doing wrong; sometimes it’s as simple as having a space where it’s not supposed to be. It takes dedication and patience to go back through code to look for minute errors, or to backtrack in an analysis and figure out at which step several thousand sequences disappeared and why. Like any skill, computer science and bioinformatics take time and practice to master. In the end, the interpretation of the data and identifying trends can be really interesting, and it’s really rewarding when you finally manage to get your statistical program to create a particularly complicated graph!
Stay tuned for an in-depth look at my current post-doctoral work with weed management in agriculture and soil microbial diversity!
A manuscript that I helped co-author, “Rumen and cecum microbiomes in reindeer (Rangifer tarandus tarandus) are changed in response to a lichen diet and may effect enteric methane emissions” was just accepted for publication in PLOS ONE. In 2012 I went to Norway to apprentice for two weeks in the lab of Dr. Monica Sundset, and in 2013, Monica’s graduate student Alex came to the University of Vermont to apprentice in Dr. Andre Wright’s lab, where I taught him quantitative real-time PCR and some bioinformatics. Alex performed a feeding trial back in Norway, in which reindeer were fed a lichen-based diet, in order to assess changes in microbial diversity. Lichens contain usnic acid, which is toxic to ruminants. Reindeer; however, host some unique bacteria which degrade usnic acid in the rumen and allow the reindeer to eat them without dietary problems.
“Abstract
Reindeer (Rangifer tarandus tarandus) are large Holarctic herbivores whose heterogeneous diet has led to the development of a unique gastrointestinal microbiota, essential for the digestion of arctic flora, which may include a large proportion of lichens during winter. Lichens are rich in plant secondary metabolites, which may affect members of the gut microbial consortium, such as the methane-producing methanogenic archaea. Little is known about the effect of lichen consumption on the rumen and cecum microbiotas and how this may affect methanogenesis in reindeer. Here, we examined the effects of dietary lichens on the reindeer gut microbiota, especially methanogens. Samples from the rumen and cecum were collected from two groups of reindeer, fed either lichens (Ld: n = 4), or a standard pelleted feed (Pd: n = 3). Microbial densities (methanogens, bacteria and protozoa) were quantified using quantitative real-time PCR and methanogen and bacterial diversities were determined by 454 pyrosequencing of the 16S rRNA genes.
In general, the density of methanogens were not significantly affected (p>0.05) by the intake of lichens. Methanobrevibacter constituted the main archaeal genus (>95% of reads), with Mbr. thaueri CW as the dominant species in both groups of reindeer. Bacteria belonging to the uncharacterized Ruminococcaceae and the genus Prevotella were the dominant phylotypes in the rumen and cecum, in both diets (ranging between 16–38% total sequences). Bacteria belonging to the genus Ruminococcus (3.5% to 0.6%; p = 0.001) and uncharacterized phylotypes within the order Bacteroidales (8.4% to 1.3%; p = 0.027), were significantly decreased in the rumen of lichen-fed reindeer, but not in the cecum (p = 0.2 and p = 0.087, respectively). UniFrac-based analyses showed archaeal and bacterial libraries were significantly different between diets, in both the cecum and the rumen (vegan::Adonis: pseudo-F<0.05). Based upon previous literature, we suggest that the altered methanogen and bacterial profiles may account for expected lower methane emissions from lichen-fed reindeer.”
Microbiome studies do not usually employ culturing techniques, and many microorganisms are too recalcitrant to grow in the laboratory. Instead, presumptive identification is made using gene sequence comparisons to known species. The ribosome is an organelle found in all living cells (they are ubiquitous), and it is responsible for translating RNA into amino acid chains. The genes in DNA which encode the parts of the ribosome are great targets for identification-based sequencing. In particular, the small subunit of the ribosome (SSU rRNA) provides a good platform for current molecular methods, although the gene itself does not provide any information about the phenotypic functionality of the organism.
Prokaryotes, such as bacteria and archaea, have a 16S rRNA gene which is approximately 1,600 nucleotide base pairs in length. Eukaryotes, such as protozoa, fungi, plants, animals, etc., have an 18S rRNA gene which is up to 2,300 base pairs in length, depending on the kingdom. In both cases, the 16 or 18 refers to sedimentation rates, and the S stands for Svedberg Units, all-together it is a relative measure of weight and size. Thus, the 18S is larger than the 16S, and would sink faster in water. In both genes, there exist regions which are conserved (identical or near-identical) across taxa, and nine variable regions (V1-V9) [1]. The variable regions are generally found on the exterior of the ribosome, where they are more exposed and prone to higher evolutionary rates. Since the outside of the ribosome is not integral to maintaining its structure, the variable regions are not under functional constraint and may evolve without destroying the ribosome. They provide a means for identification and classification through analysis [2-6]. The conserved areas are targets for primers, as a single primer can bind universally (to all or nearly-all) to its target taxa. The conserved regions are all on the internal structure of the ribosome, and too much change in the sequence will cause its 3D (tertiary) structure to change, thus it won’t be able to interact with the many components in the cell. Mutations or changes in the conserved regions often causes a non-functional ribosome and will kill the cell.
Image: alimetrics.net
In addition to a small subunit, ribosomes also possess a large subunit (LSU rRNA), the 23S rRNA in prokaryotes, and the 28S rRNA in eukaryotes. Eukaryotes have an additional 5.8S subunit which is non-coding, and all small and large units of RNA have associated proteins which aid in structure and function. Taken together, this gives a combined 70S ribosome in prokaryotes, and a combined 80S ribosome rRNA in eukaryotes.
The ribosome assembles amino acids into protein chains based on the instructions of messenger RNA (mRNA) sequences. (Image: shmoop.com)
The way to study the rRNA gene is to sequence it. First, you need to extract the DNA from cells, and then you need to make millions of copies of the gene you want using Polymerase Chain Reaction (PCR). PCR and sequencing technology more or less work the same way as a cell would make copies of DNA for cell processes or division (mitosis). You take template DNA, building block nucleotides, and a polymerase enzyme which is responsible for reading the DNA sequence and making an identical copy, and with hours of troubleshooting get a billion copies! Many sequencing machines use nucleotides that have colored dyes attached, and when a nucleotide is added, that dye gets cut (cleaved) off, and the camera can catch and interpret that action. It then records each nucleotide being added to each separate DNA strand, and outputs the sequences for the microorganisms that were in your original sample!
Image: nature.com
The two main challenges facing high-throughput sequencing are in choosing a target for amplification, and being able to integrate the generated data into an increased understanding of the microbiome of the environment being studied. High-throughput sequencing can currently sequence thousands to millions of reads which are up to 600-1000 bases in length, depending on the platform. This has forced studies to choose which variable regions of the rRNA gene to amplify and sequence, and has opened up an arena for debate on which variable region to choose [2]. And of course, the DNA analysis of all this data you’ve now created is quickly being recognized as the most difficult part- which is what I focused on during my post-doc in the Yeoman Lab. Stay tuned for a blog post on the wonderful world of bioinformatics!
Neefs J-M, Van de Peer Y, Hendriks L, De Wachter R: Compilation of small ribosomal subunit RNA sequences. Nucleic Acids Res 1990, 18:2237–2318.
Kim M, Morrison M, Yu Z: Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes.J Microbiol Methods 2010, 84:81–87.
Doud MS, Light M, Gonzalez G, Narasimhan G, Mathee K: Combination of 16S rRNA variable regions provides a detailed analysis of bacterial community dynamics in the lungs of cystic fibrosis patients.Hum. Genomics 2010, 4:147–169.
Yu Z, Morrison M: Comparisons of different hypervariable regions of rrs genes for use in fingerprinting of microbial communities by PCR-denaturing gradient gel electrophoresis.Appl Env Microbiol 2004, 70:4800–4806.
Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR: Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses.Proc Natl Acad Sci USA 1985, 82:6955–6959.
Yu Z, García-González R, Schanbacher FL, Morrison M: Evaluations of different hypervariable regions of archaeal 16S rRNA genes in profiling of methanogens by archaea-specific PCR and denaturing gradient gel electrophoresis.Appl Env Microbiol 2007, 74:889–893.
The “Women in Science” debate has been raging on in a variety of ways, from wondering why there aren’t more of us to whether or not a mixed-gender lab is too distractingly sexy. The amount of women in science, the pay gap, and career advancement potential varies wildly by country and research field. So does public opinion about whether or not there is an actual problem, what might be causing it, and what we might do about it. In 2011, women only earned about 18% of undergraduate computer science degrees, down from its peak of 37% in 1985. The percentage of women earning graduate-level degrees has been slowly increasing since 1970, with 28% of the masters degrees and 20% of the doctoral degrees (Ph.D ,s) being earned by women in 2011. Women make up roughly 41% of total STEM doctoral degrees earned; however, women only fill 24% of STEM jobs in the US, and only 25% of STEM managers are female. Universities are only slightly better, with 28% of tenure-track faculty positions being held by women in the US, but only 12% worldwide.
This debate isn’t just specific to science in academia, but a lack of diversity in the educational system can have interesting effects. First, a lack of female (or other demographic) role models means that female children are less likely to go into that field: if they don’t see anyone paving the way, then the idea that they might also become a physicist doesn’t occur to them or doesn’t sound like an attractive career. While boys and girls are taking math and science in equal numbers in grade school, this doesn’t translate into the same number of men and women in math or science undergraduate fields, where women only earn 18% of undergraduate computer science degrees, down from 37% in 1985, and only 11.5% of software developers are female. Part of this is the perception that men are better than women at math and science, even though women have been shown to be better at writing computer code than men, but only when reviewers did not know the coder was female. Science faculty, regardless of their own gender, were more likely to hire a male applicant over an identical female applicant, and offered them several thousand dollars more starting salary for the same position. The male applicant was perceived as more competent, more hirable, and more in need of mentoring than the identical female applicant.
Another problem is that women are less likely to have people sponsoring or advocating for them in the work place (available here and discussed here). People with sponsors were 30% more likely to be promoted or given raises. As of 2014, only 23% of Americans polled preferred a female boss, which is and has always been lower than the number of respondents preferring a male boss, which may account for the lack of support women find in climbing the ladder. Surprisingly, women were 13% more likely to want a male boss, which may be a reaction to fierce competition to become the “token woman” at a company or working group, as women or other minorities who advocate hiring another woman or minority are rated poorly. There is also the perception among women that a female boss is less likely to promote you over herself, as she doesn’t want competition, known as Queen Bee Syndrome. This too, has been refuted, as women are shown to be more likely to mentor and develop female employees lower down on the ladder (discussed here).
Finally, one of the reasons that women are not found in some fields or levels of management, which no one really wants to discuss, is the disparaging levels of sexism and harassment we may face. For female graduate students, post-docs, or new professionals, sexual harassment at work can increase attrition rates. Due to the close nature of the working relationship of graduates/post-docs with their advisors, many students feel they can’t report inappropriate behavior (of any nature) for fear of losing their position in the program. As a student, you need your advisor to approve everything, from the courses you have taken to manuscripts before publishing, and a poor relationship with your advisor can make it nearly impossible for you to complete your work. Tenured faculty who have been accused of harassment also seem to be acting with impunity, as it can be difficult, time-consuming, and costly to fire a tenure professor (in the absence of proven criminal activity). In field situations, sexual harassment can take on a more sinister tone, as you may be the only female in a group and depending on your abuser to keep you alive.
So what can we do about this? Because this isn’t just a woman’s issue. That’s what I discussed here because I have some expertise with being a woman, but in general, diversity in society is a hotly contested issue. It really shouldn’t be, increasing the diversity in a group can increase performance and improve decision making (discussed here). Having a diverse group of people (in terms of gender, race, sexuality, education, economic status, birth order, pets owned, places lived, live experiences learned from..) gives the group a wider range of previous experience to lean upon when solving problems. It’s why we evolved into a social society in the first place- it was better for survival.
The first step to solving our diversity issues is to let go of preconceived notions about yourself or others. Stop thinking about life-related obstacles to your career trajectory, such as whether you want kids or having to relocate your family, and stop assuming that others might be better or worse at their job because they have chosen a certain family dynamic. Stop thinking you might not get a job because of what the employer might thinking about women as bioinformaticians, and in turn stop stereotyping applicants based on your ideas of who they are and of what they are capable.
The second step is to be a role model, and to actively engage the next generations of computer scientists, astronauts, microbial ecologists, astrophysicists, and educators. As a woman in science, it’s important to me to encourage other women and girls in science, because I would not be here today without the positive female role models I have had. It’s important to support programs that encourage different minorities to achieve in fields where they are underrepresented, because it benefits all of us.
And the third step, perhaps the most difficult. is to have an open conversation about the difficulties and prejudices facing women, or anyone, in different science fields. Often people can fall back on stereotypes or be sexist or racist without realizing it, and it’s important to speak up and have a conversation with them to come to a better understanding of how to get along. When someone’s words or actions are creating a hostile work environment, tell them directly, as well as their supervisor or relevant reporting agency as needed. If we don’t address the problem on an individual basis, then individuals will never amend their actions. In addition, it’s important to validate the feelings of and listen to someone who has been the victim of harassment or a crime (of any nature), because it’s important to make them feel safe and believed. Often, victims of sexual harassment state that not having their reports believed or treating seriously by supervisors was worse than the harassment itself. And personally, I have plenty to do on a daily basis without having to deal with casual or institutional sexism. Working women are simply too busy quietly doing well at ours jobs to deal with men’s feelings about us.
Medora Lachman, one of the graduate students in the Yeoman lab, won an award for a poster last week. I have been helping Medora to learn bioinformatics, and to wrangle her 1500 sequencing samples!
Repost from Yeoman Lab: “Congratulations to graduate student Medora Lachman who was awarded the Laurie Henneman Outstanding Student Presentation award for best graduate poster at the 2016 Montana Academy of Sciences annual meeting!!! Medora is studying the interrelationships between maternal nurturing, gut microbial succession, and immune maturation in ruminants. Medora’s research is supported by the Montana Agricultural Experiment Station, USDA’s W3177 multi-state project (Enhancing the competitiveness of US beef) funding, and by Land ‘o’ Lakes.”
In addition to the poster I’ll be presenting at ASM Microbe in Boston, I’ll be presenting two oral presentations at the Joint Annual Meeting in Salt Lake City, Utah in July. JAM brings together the American Society of Animal Science (ASAS), the American Dairy Science Association (ADSA), the Western Section of the American Society of Animal Science (WSASAS), and the Canadian Society of Animal Science (CSAS).
I’ll be presenting “Influence of colostrum on the microbiological diversity of the developing bovine intestinal tract” in the Ruminant Nutrition section, and “Ground redberry juniper and urea in DDGS-based supplements do not adversely affect ewe lamb rumen microbial communities” in the Small Ruminant section. Both projects are collaborations with the Yeoman Lab. Check back to my calendar in a few weeks to get more details on my presentations!
The Ishaq Lab 