Weimer Micro Lab

Contributed by Darwin Bandoy, DVM

I participated in the 2018 ASM Conference on Rapid Applied Microbial Next-Generation Sequencing and Bioinformatic Pipelines, September 23–26, 2018, in Tysons, Virginia and presented my work in the lab using next-generation sequencing of Campylobacter hyointestinalis. The most interesting talk for me, surprisingly, is the use of data analytics by the city of Chicago as they even have a chief data analytics officer. This is surprising because the conference is about next-generation sequencing and bioinformatics pipeline, however, I find the use of models to guide government decisions quite fascinating. Some of the applications of data modeling include predicting violations of food inspections and coliform count in beaches. Combining genomics data with modeling is powerful and can improve city services, which makes me think how can I integrate my data with models for practical applications.

Contributed by Shawn Higdon

Microbial Genomics involves the isolation of microbes from environmental samples, often times with the intent of generating a pure culture comprised by an organism of a single type of strain. While many isolation events lead to the generation of such cultures, large-scale isolation endeavors often lead to collections of banked isolates that are rife in cultures that possess high levels of genomic identity with subtle variations (i.e. different genotypes). These genotypic differences among isolates are often subtle, but in many cases contribute either directly or indirectly to an observed phenotype. In any case, identifying the presence of genetic differences that are present in select isolates is of great interest.

Once the isolates in question have been subjected to whole genome sequencing, DNA sequence reads are then assembled into contiguous sequences that contain the information associated with individual genes (Some popular assembly programs include MEGAhit and SPAdes). These contiguous sequences are then scanned for many genomic features, the likes of which include RNA and Protein coding genes. A great way to identify the features of a microbial genome assembly is using the program Prokka. This program provides genome annotation information in multiple output formats, providing solid versatility for input to downstream analyses.

Getting back to the identification of genetic features that lead to differences among type strains for prokaryotic isolates that display high levels of genomic similarity, one program that provides a solid strategy for carrying out Pan-genome analysis with bacterial genomes to identify accessory genes among a collection of isolates is Roary. In my opinion, something that makes Roary great is that the developers accommodate researchers who already use Prokka for genome annotation by allowing them to input GFF3 output files from Prokka directly into the Roary pipeline. By providing Roary with multiple GFF3 files from the isolates in question, a Pan-genome and Core-genome will be constructed for the population under investigation and a list of genes present in some but not all isolate genomes will be made available. This strategy is useful for identifying genes of interest, such as virulence genes that may lead to outbreaks of infectious diseases. In fact, the Applied Bioinformatics course at UC Davis (BIT150) taught a lab session on this subject in the Fall of 2017, and will likely continue in doing so this year. Check out the course website for details! (https://deruncie.github.io/BIT150_website/)

Contributed by Carol Huang

Metabolic disease diabetes and obesity have impacted quite some percentage of the population, over 600 million people in the world are obese, and over 400 million have diabetes. Type I diabetes often involves the loss of beta islet cells which produce insulin; Type II diabetes is a disorder of elevated blood glucose levels (hyperglycemia) primarily due to insulin resistance and inadequate insulin secretion. Obesity contributes to T2D by decreasing insulin sensitivity in adipose tissue, liver, and skeletal muscle, and subsequently impaired beta-cell function, but not all obese individuals will develop type II diabetes.

Scientists have devoted plenty of efforts to understand the complex interaction of genes and environment involved. The gut microbiome is one of the factors that can play a role in diabetes risk. Therefore more and more attention has been drawn to study of the gut microbiota.  There’s an old saying “All disease begins in the gut”. The gut microbiota is a collection of the microbial community in the gut, and the gut microbiome is the full collection of genes in the gut microbiota.16S rRNA gene sequencing created phylogenetic information to distinguish microbial groups in phylotypes but it lacks specificity to describe bacterial species for identifying the diverse composition of microbes (personal microbiota), while more advanced techniques like metagenomic sequencing would provide more sufficient and personalized information for the needs.

Gut microbiota has the function to communicate with each other, balance host immune system, depredate indigestible components in host diet, harvest energy and nutrition. Microbial population diversity and density vary along the gastrointestinal (GI) tract. Low bacterial diversity impairs the gut integrity causing low-grade inflammation through endotoxemia, which plays a profound role in the innate immune system in insulin resistance. Increased Gram-negative bacterial strains have also been seen in T2D patients “Diabetes and obesity are both associated with less diversity and less redundancy in the gut microbiome,” says Dr. Mathur. Diet is a major source of substrates for the production of small molecules by the gut microbiota. Diet change could significantly alter gut microbiota composition in 48 hours. Fecal samples are more representative for gut microbiome studies, which considered to be the full collection, therefore samples collection time, patient treatment and samples processing are critical, which will have a great impact on final conclusion.

Reference

The Gut Microbiome as a Target for the Treatment of Type 2 Diabetes.  Ömrüm Aydin^1,2& Max Nieuwdorp^2,3,4 & Victor Gerdes^1,2  Published online: 21 June 2018, Current Diabetes Reports (2018) 18: 55 https://doi.org/10.1007/s11892-018-1020-6

Contributed by Shawn Higdon

As I begin the newest chapter of my PhD journey – a cross-college internship at UC Davis with the Weimer lab in School of Veterinary Medicine – I am faced with new computational tasks that demand the implementation of numerous bioinformatic programs. Specifically, the focus of my internship involves the investigation of potential shifts in microbial metabolism and population structure within various components of the human microbiome. The overall approach of the lab is to adopt more of a systems biology approach in which the power of metabolomics and meta-transcriptomics will be employed to uncover potential shifts in metabolic output as a result of dietary changes or adverse human health conditions.

My particular role on these projects during the internship will be to carry out bioinformatic analysis on meta-RNAseq data sets that were generated from patients of clinical experiments. While this type of analysis may be new to me, these types of studies will undoubtedly continue to be carried out to answer the myriad biological questions surrounding the impact of the human microbiome on human population health and ecology. Because of this, my primary goal for this internship in terms of deliverables is to generate reproducible bioinformatic analysis pipelines that are capable of being called upon for future analysis of similar types of studies that incorporate meta-RNAseq experiments.

After having spent time with computationally savvy members of The Lab for Data Intensive Biology at U.C. Davis, I came to learn of an incredibly powerful Python package called Snakemakethat provides the programming community with a workflow management tool. While my experience in using Snakemakeis just beginning, I aim to emerge from the internship having acquired the skill of calling on Snakemake to develop at least two different flavors of bioinformatic workflows: one workflow that can be used to analyze meta-RNAseq datasets and another that can be used to annotate microbial isolates for plant growth promoting functionalities.

Contributed by Nguyet Kong

Chitin is a fibrous substance containing polysaccharides and form the exoskeleton of arthropods such as insects and crustaceans and are commonly found on the cell walls of fungi. Non- mammals protect their gut walls with a layer of chitin and that layer will prevent bacterial infections. Whereas a mammal’s gut is lined with a mucous layer that will allow the bacteria to colonize but not invade the cell wall.

Dr. Keisuke Nakashima led his team to map the evolution of the animal gut and have it evolved to defend itself from bacterial infection.  They have noticed that Tunicates (sea squirts) have both chitin and mucous in their guts and are closely related to mammals and is an ideal model to study the evolution of the gut lining. When the team chemically prevent the tunicates to produce chitin they would die unless treated with antibiotics. It shows that the chitin layer has some antimicrobial properties preventing bacterial infection. After years, Dr. Nakashima’s team was able to map out an evolutionary path from one gut model to the next, showing how one body structure is able to keep everyone safe from bacterial infection.

References:

https://www.nature.com/articles/s41467-018-05884-0

Nakashima, K. et al. Chitin-based barrier immunity and its loss predated mucus-colonization by indigenous gut microbiota. Nature Communications, 2018; 9: 3402.

Contributed by Shawn Higdon

Biological Nitrogen fixation energy-intensive process carried out by Bacteria and Archaebacteria that have been classified across many phyla. While there is an overwhelming amount of research that describes and characterizes the process of biological nitrogen fixation by microbes that have been proven to convert atmospheric dinitrogen into the organic form of ammonia, one question that comes to mind is whether or not the described mechanism that involves the nitrogenase enzyme complex is the only strategy that has evolved in nature.

The focus of my PhD research is heavily centered around understanding how an isolated variety of maize is capable of completing its life cycle without the addition of synthetic nitrogen fertilizers on fields that are essentially N-depleted. I have sequenced the genomes of over 600 microbial samples that derive from the corn plant’s root exudate. These cultures were isolated on growth media that lacked the addition of nitrogen sources and streaked in a serial fashion as an attempt to isolate pure cultures. Furthermore, all of the isolates that were sequenced were also subjected to nitrogen fixing assays and scored based on their ability to assimilate atmospheric nitrogen in metabolites.

My approach to bioinformatic analysis so far involves the use of Hidden Markov Models for proteins involved in nitrogen fixation. While these models serve the purpose of screening the identified proteins within each isolate’s whole genome sequence for gene products that match known nitrogen fixation proteins, the possibility that unknown nitrogen fixation proteins within our population of putative diazotrophs certainly exists. Today, it was brought to my attention that I should also consider the administration of a sequence alignment-based approach to identify any nitrogen fixation proteins present within the genomes of isolates I have sequenced. My next step will be to carry out this analysis in order to corroborate any findings that suggest isolates may be fixing nitrogen with mechanisms that are yet to have been discovered or described by the scientific community. So far, we have a hypothesis and the data is propelling us forward in our pursuit of an answer; but, the road ahead appears to be at a steep incline and we must persevere to uncover the truth…

Contributed by Nguyet Kong

A recent Listeria outbreak in Europe has been reported due to the insufficient cooking of frozen vegetables, which led to the question if frozen vegetables even safe to eat at all. The European Food Agency is reminding people frozen food is not labeled ready to eat and that it needs to be cooked properly before consumption.

Listeria can cause a serious infection after consuming contaminated food. The infection will sicken pregnant women, newborns, elderly and people with weakened immune systems. This outbreak is caused by Listeria monocytogenes, which is a species of bacteria that can grow at low temperatures. Currently in Europe – in 5 different countries, there have been 47 cases of illness which resulted in 9 deaths for 2018. The root of the outbreak is still under investigation. The frozen vegetables were produced at a Hungarian Company and there was recalls from the company in the years 2016 to 2018. Currently, the company is not producing frozen vegetables, it is said that it left a financial impact of 24 million Euro.

References:

https://www.cdc.gov/listeria/index.html

http://www.efsa.europa.eu/en/press/news/180703

https://www.hulldailymail.co.uk/news/hull-east-yorkshire-news/frozen-vegetable-recall-2018-listeria-1784978

http://www.foodsafetynews.com/2018/07/listeria-deaths-in-five-eu-counties-linked-to-frozen-vegetables/#.W0zN_7gnY2w

Contributed by Shawn Higdon

As a graduate student at the interface of plant biology and microbiology working with pure isolates, analyzing microbial growth curve data is unavoidable. I was recently in a situation where I needed to calculate the slope of the logarithmic phase of growth for about 1,500 well-based growth experiments (about fifteen full 96-well plates worth of data). The task was to get an estimate for mu, the intrinsic growth rate, which is something I had neglected to do previously when I only seemed to care about the maximum cell density achieved over the course of the entire experiment.

My initial approach was to visualize the data in R using ggplot2 and display the actual OD600 absorbance values on the plots, followed by manually reading each plot to select two coordinate points and enter the values into a spreadsheet using excel. This meant that I was going to manually plot the data of all the wells! While this seemed like the most straightforward approach, I found it to be extremely time-consuming, tedious and boring (as was expected). After completing maybe 5 percent of the work, I decided there must be a more efficient solution and began carrying out string searches with Google. I searched for, “r for loop to calculate the slope of microbial growth curves.” To my surprise, the top hit was to an R package that already existed called Growthcurverwritten by Kathleen Sprouffske, which is available for download at https://cran.r-project.org/web/packages/growthcurver/index.htmlor by placing the phrase “growthcurver” into the quotes of the install packages command in R.

My experience with Growthcurver was extremely positive, as it was able to calculate the intrinsic growth rate of each well on each plate in a matter of seconds. In addition, Growthcurver makes analyzing an entire 96-well plate worth of data as easy as typing the single command: SummarizeGrowthByPlate(). Growthcurver will provide tabulated data output that provides estimation for many variables of the logistic equation it uses to model the growth of your data. Additionally, Growthcurver will provide plots for each well of data on the plate, either individually or output as a pdf file that displays all wells included in the imported dataset. The only requirement is that the data be formatted with a single column containing the Time values (in hours), and the absorbance values of each well descending in individual columns. The best part about this package is that Kathleen has provided excellent user documentation in the form of an introductory tutorial that explains how to use the package (https://cran.r-project.org/web/packages/growthcurver/vignettes/Growthcurver-vignette.html).