Creating Prokaryotic Taxonomies with Whole Genomes

Contributed by Cory Schlesener, B.S.
Historically it has been difficult to categorize prokaryotes into taxonomic units. Advances made have utilized DNA sequence identity to profile organisms at a higher resolution.  Older technologies based on genomic DNA hybridization, or sequence comparison of the 16s rDNA gene (PCR amplified), have greatly advanced our understanding of phylogeny, but have limitations in resolution or scope. While 16s comparison has at times been the go-to method, there are challenges to perform unbiased PCR targeting, and while 16s is a core gene it is only one part of a genomic skeleton. Along with ambiguities brought in with horizontal gene transfer, there is a wide array of genetic variation that exists, detailing the genetic relations between organisms. With modern whole genome sequencing, an expanded view of genetic identity can be applied at a broad scale. Scaling up to categorize many organisms requires more computational resources and efficient informatic strategies. Some recent large-scale analyses demonstrating new approaches to prokaryotic taxonomy, have been based on a combined scheme of average nucleotide identity (ANI) and alignment fraction (AF). This can utilize genes shared among the organisms compared, or k-mer sippets of genetic sequence, for identity comparison and alignment of orthologous regions of the genome. The studies referenced here utilize thresholds of 95-97% identity (ANI), with 60-65% alignment (AF) as criteria for species level grouping. While these thresholds cannot produce perfect cut offs every time, they provide robust standardized metrics to group organisms. These broader analyses have been able to group previously unidentified samples, separate out divergent groups (some previously hypothesized), and parse out edge cases of misfits that may belong to their own group. These advancing strategies help us make sense of a nebulous picture (ever incomplete) of genomic spaces that are complex and interwoven.

References:
Barco, R. A., G. M. Garrity, J. J. Scott, J. P. Amend, K. H. Nealson, and D. Emerson. “A genus definition for Bacteria and Archaea based on a standard genome relatedness index.” Mbio 11, no. 1 (2020).
https://dx.doi.org/10.1128%2FmBio.02475-19

Parks, Donovan H., Maria Chuvochina, Pierre-Alain Chaumeil, Christian Rinke, Aaron J. Mussig, and Philip Hugenholtz. “A complete domain-to-species taxonomy for Bacteria and Archaea.” Nature Biotechnology (2020): 1-8.
https://doi.org/10.1038/s41587-020-0501-8
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Year Four

Contributed by DJ Darwin Bandoy, PhD Candidate

I am now in my year four of my PhD studies in Integrative Pathobiology in UC Davis, still in the middle of the COVID-19 Pandemic. I presented in the lab meeting yesterday the theoretical framework of my epidemic modelling work from the UP Pandemic Response Team.  Understanding compartmental models using differential equations is challenging given my limited background in calculus and hence I learned it from the ground up and culminated with a training in infectious disease modelling at the London School of Tropical Hygiene and Medicine.

But the key principles are the same, we effectively reduce infections by reducing effective contact rate and by physically blocking the viruses by wearing masks and killing the virus using handwashing with soap.

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New Quality Control Program Optimized for Long Read Nucleotide Sequencing

Contributed by Cory Schlesener, B.S.

DNA sequencing, in a high throughput process, generates errors in the reads, such as low confidence nucleotide based calling. Quality control is needed to evaluate the quality of the sequencing output and identify undesired features. The program FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc) has become one of the most popular tools for assessing quality of short read sequencing output (e.g. illumina sequencing). However, the tool is not optimized for newer long read sequencing technologies (e.g. PacBio and Nanopore sequencing), which can have higher error rates and have non-standard formats for recording raw sequence data. These newer long read outputs need alternatively optimized metrics for assessment, as the structure of individual reads and population of reads is quite different compared to the massive population of short reads from older technologies. A useful new program, LongQC (https://github.com/yfukasawa/LongQC), provides an alternative QC analysis optimized for long reads. LongQC assessment includes general statistics, read length, read coverage, GC content, sequence complexity, and sequence error estimation.

Fukasawa, Y., Ermini, L., Wang, H., Carty, K. and Cheung, M.S., 2020. LongQC: A Quality Control Tool for Third Generation Sequencing Long Read Data. G3: Genes, Genomes, Genetics, 10(4), pp.1193-1196. https://doi.org/10.1534/g3.119.400864

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Gut Microbiota and Unhealthy Aging

Contributed by Carol Huang, Sr. Research Specialist                      @BartWeimersLab

The equilibrium of gut microbiota is fundamental to our health. The gut microbiota composition established at birth which changes with aging, diet, health condition and other factors.  As growing up the dynamic nature of gut microbiota getting built up, diversified and stabilized till middle age.  At older age, the profound changes occur again. During this long process, the composition may be regulated by diet. The dominant microbiota is a signature for each individual, but it always retains the imprint of the early childhood profile.  A recent published review paper discussed factors that  have impact on gut microbiota and unhealthy aging (Cell Host & Microbe 28, August 12, 2020, p.180-189)

The gut microbe organism composition may be modified by aging related gut physiological changes, food structure, lifestyle and geological living conditions. All these factors would contribute to the changes in some microbiome members. Around 60 bacterial species are carried by 50% of individuals in the same geographic area. Studies shown that upon their roles and functions in the community, the changes on level of some species are related diseases and unhealthy aging.  Low levels of beneficial microbes like Akkarmansiaceae, shown in obesity, diabetes and HIV cases and high level of which in Healthy colons, while low level of Bifidobacterium shown in HIV cases and high in centenarians. Microbes like Clostridiaceae, Bidobacteriaceae, Lachnospiraceae Coriobacteriaceae are related to immune senescence. In model study shown that preventing of age-related changes in intestinal physiology would reduce microbial imbalance and extends lifespan.

The review discussed the role of inflammation as drive of gut permeability and microbial dysbiosis. The slowly increased chronic inflammation is a character of aging, chronic health and age-related metabolic conditions. Inflammation caused aging impacts gut integrity and is linked to specific microbiota changes. Observation shows all chronic inflammatory conditions are associated with microbial dysbiosis. In response to inflammation some specific microbial niches are lost or disordered. Increased expression of inflammatory cytokines can decrease expression of tight junction proteins and lead to increasing permeability and further to retain inflammation.

The age-related intestinal function changes are not clear, but it has been reported that increased paracellular permeability colonic transit is likely due to physiologic changes in the aging gut. Studies have found that the extreme longevity individuals tend to have a more diversified gut microbiota and greater number of microbial taxa than less healthy individuals.

Physiology difference between genders plays a role in age-related immune disfunction and further to microbiome imbalance. Hormonal difference makes different microbiome abundance in gut physiology, such as adipose distribution, glucose metabolism, and specific inflammatory mediators likely contribute to age-related immune dysfunction and ultimately contribute to microbial dysbiosis.

To promote healthy aging, we need to understand the interactions between host and gut microbial community, intervene within the microbiome community.  It might not be easy to change the aging microbiome, which changed due to gut physiological change. Interventions can be fulfilled by change diet for more favorable environment to beneficials microbiomes abundance and reduce inflammatory conditions; promote more diversified microbiota community and reduce microbial dysbiosis; provide proper balanced nutrient, prebiotics, control antibiotic intake, to some degree can supplement probiotics. Keep healthy lifestyle physically and mentally, being active are also very important for healthy aging process.

Ref:  The Gut Microbiota and Unhealthy Aging: Disentangling Cause from Consequence.  Cell Host & Microbe 28, August 12, 2020, p.180-189 ( https://doi.org/10.1016/j.cell.2020.08.017)

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Where is the metadata?

Contributed by DJ Darwin Bandoy, PhD Candidate

One distinguishing feature of this pandemic is the rapid release of whole genome sequencing data. These sequences are usually uploaded in public databases with minimal accompanying metadata. While dates and geographic origin are useful for creation of phylogenetic analysis, further sophisticated analysis requires more metadata, particularly associated with pathogen virulence, risk factors of patients. Without the metadata, we miss finding valuable insight from whole genome sequences.

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Quantify Structural Similarity Comparisons of Genome Assemblies

Contributed by Cory Schlesener, B.S.
One important component of a genome’s overall composition is the larger structure of how conserved blocks of genetic sequence are arranged. As segments of DNA recombine, sequences are introduced into new locations and/or orientations in a genome. However, this composition of large genetic blocks can artificially be rearranged in a constructed genome sequence, depending on the strategies and methods used for sequencing and assembly. Constructing genome assemblies from short sequencing reads can yield regions of low sequence coverage and assembly accuracy. This can lead to inaccurate constructions (order and orientation) of genetic blocks. composition can also be artificially fitted to the reference genome when used. Comparing genome assembly structural identity can give another metric on how divergent two genomes are, and comparing to differences in sequence identity can give hits on assembly accuracy. Additionally, assemblies of a genome by different methods can be compared. This can give insight into what the most accurate and consistent method of assembly is for a particular organism ‘s genomes and sequencing platform used. Here is an interesting newer approach for quantifying genome structural similarity for such comparisons.
“GMASS: a novel measure for genome assembly structural similarity”
Daehong Kwon, Jongin Lee & Jaebum Kim
BMC Bioinformatics volume 20, Article number: 147 (2019)
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More on the Equine Microbiome

Contributed by Ashleigh Flores, M.S.

Within the GI Tract, horses can host up to 1015 bacterial cells, with the highest population residing within the caecum. Recent studies have revealed that distinct individual ecosystems for each compartment of the equine gut exist, with adjacent compartments having the most similarities in microbiome composition. The upper GI tract shows a more variable microbiota compared to the lower GI tract, likely due to a high throughput of environmental bacteria present in forage. Members of the α-Proteobacteria such as Methylobacterium sp., Rhizobium sp. and Sphingomonas sp. are commonly abundant in the upper GI tract while Firmicutes, Bacteroidetes and Verrucomicrobia have been found to be amongst the predominating phyla in the equine hindgut. It is thought that changes in the microbiome are strongly associated with the development of colic in horses. At present, there is a lack of data regarding the equine microbiome, further research in this area is needed for improved treatment and prevention of equine GI tract diseases.

References:

Kauter, A., Epping, L., Semmler, T. et al. The gut microbiome of horses: current research on equine enteral microbiota and future perspectives. anim microbiome 1, 14 (2019). https://doi.org/10.1186/s42523-019-0013-3

Costa MC, Silva G, Ramos RV, et al. Characterization and comparison of the bacterial microbiota in different gastrointestinal tract compartments in horses. Vet J. 2015;205(1):74-80. doi:10.1016/j.tvjl.2015.03.018

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Pandemic Status as of July 12, 2020

Contributed by DJ Darwin Bandoy, Ph.D. Candidate

California is in full lockdown again after a surge in cases. This is a consequence of opening too early when the cases are not fully suppressed. This requires people to wear masks, practice physical distancing, and a reduction of unnecessary travel. Heterogeneous properties of infection indicate super-spreading events as a key driver of the pandemic. We are in the middle of deriving interesting insights from this angle, using pathogen genomic data. I will be presenting interesting data soon.

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Professor Bart C. Weimer, Ph.D.

Professor, School of Veterinary Medicine,       UC Davis       

Director, 100K Pathogen Genome Project 

Publication Summary:

Leadership Experience:

  • Chair; Department of Population Health and Reproduction. 2019 – present
  • Director, founder; Genomes4Health. 2019 – present
  • Director, 100K Pathogen Genome Sequencing Project.  2012 – present
  • Co-founder, Molecular Food Safety Consortium.  2014 – present
  • Director, Genomics Integration Core, West Coast Metabolomics Center (Davis).  2012 – 2013
  • Faculty Coordinator Corporate Relations (UC Davis; Office of Research).  2010 – 2015
  • Director, BGI@UC Davis Genome Sequencing Center.  2011 – 2015
  • Executive Director, Center for Integrated BioSystems (Utah State University).  2002 – 2009
  • Co-founder, Lactic Acid Bacteria Genome Consortium.  2000
  • Director, Center for Microbe Detection & Physiology (Utah State University).  1998 – 2008

Academic Experience:

  • Professor, Population Health & Reproduction (UC Davis).  2008 – present
  • Professor, Nutrition & Food Sciences (Utah State University).  1991 – 2008
  • Adjunct Professor, School of Life Sciences (Beijing Normal University).  2014 – 2019
  • Adjunct Professor, Biology (College of Life Sciences; Xiamen University).  2007 – 2010
  • Adjunct Professor, Horticulture Department (Kasetsart University, Thailand).  2008 – 2012
  • Adjunct Professor, Biology Department (College of Science, USU).  2003 – 2008
  • Adjunct Professor, Computer Science Department (College of Science, USU).  2006 – 2010
  • Adjunct Professor, Biological Engineering (College of Engineering, USU).  2003 – 2007

Education:

  • Post-Doctoral Fellow, University of Melbourne, Biochemistry & Genetics, Melbourne, Australia. 1991-1992
  • D., Nutrition & Food Sciences – Microbiology, Utah State University, Logan, UT.  1990
  • BS, Microbiology & Immunology (Honors), University of Arizona, Tucson, AZ. 1986

Awards and Honors:

  • IBM Shared University Research Award. 2015, 2016
  • FDA Food Safety Grand Challenge Finalist (1/5 from over 100 Applicants). 2015
  • Aglient Thought Leader (DNA modification, genome restricted metabolomics). 2010, 2015
  • HHSInnovate – 100K Pathogen Genome Sequencing Project. 2012
  • Best agronomic paper – Canadian Agronomic Society.  2010
  • Researcher of the Year – USU Dept. of Nutrition & Food Sciences . 2008
  • Inducted into Academic Keys Who’s Who in Agriculture in Higher Education. 2006
  • Inducted into Empire Who’s Who.  2003
  • Inducted into International Who’s Who Historical Society.  2002
  • Winder Chair in Food Science – USU, Nutrition and Food Services.  1996, 2001 – 2004
  • Researcher of the Year – USU, College of Family Life.  1995
  • S. – Graduated with Honors.  1986

 

 

 

 

 

 

 

 

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Horses and “Pigeon Fever”

Contributed by Ashleigh Flores, M.S.

Corynebacterium pseudotuberculosis biovar equi is responsible for the highly contagious disease in horses commonly known as “Pigeon Fever”. Although flies act as the primary vector, soil serves as a reservoir for this robust microorganism. Research has shown that C. pseudotuberculosis can persist in soil for months at a time, particularly in dry, arid environments. When manure contacts soil laden with the bacteria, survival and growth rates increase due to the additional micronutrients available in feces. The ability of C. pseudotuberculosis to survive under an array of environmental conditions along with its slow growth rate, make it a challenge to treat and prevent. Further research is needed to enhance treatment and prevention methods available in the clinical setting.

Spier SJ, Toth B, Edman J, et al. Survival of Corynebacterium pseudotuberculosis biovar equi in soil. Vet Rec. 2012;170(7):180. doi:10.1136/vr.100543

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