Identifying horizontally acquired elements in bacterial genomes

Posted on June 30, 2020 by WeimerMicroLab
Contributed by Cory Schlesener
Bacterial genomes are composed of diverse genetic elements. Many of these elements are not exclusively passed down a parental lineage (vertically acquired), but can also be horizontally acquired from other bacteria of varying degrees of relatedness. As bacteria are asexual, mechanisms of horizontal transfer of genetic information provide ways to add diversity to a population. Novel genes and accompanying traits can allow a strain to take advantage of new niches (e.g. enzymes to break down new metabolites), endure formally inhospitable conditions (e.g. antimicrobial resistance genes), or improve fitness in other ways. These elements can often be the defining feature that make a strain stand out from typical members of its species (e.g. becoming a pathogen or one with enhanced virulence). As there are many mechanisms for bacteria to acquire genes horizontally (e.g. plasmids, bacteriophage, DNA uptake/integration) and differences in technical genetic trends (e.g. GC% content and codon usage) can homogenize overtime, they do not always stand out when integrated into the genome. Luckily, there are many programs that can bioinformatically predict regions that are horizontally acquired genomic islands. These programs utilize a variety of techniques that basically work off contrasts. They can contrast a genome to comparable genomes to assess differences, or they can contrast within a genome, comparing different regions to one another (some programs are enhanced when working with annotated gene data). There are still ambiguities when predicting horizontally acquired regions, with varying levels of prediction accuracy. The recent review article (given below) details how many of these programs work, compares their performance, and gives a schematic for how best to choose the program(s) for your needs (based on methodology, user interface, data inputs, precision and accuracy).
Microbial genomic island discovery, visualization and analysis
Claire Bertelli, Keith E Tilley, Fiona S L Brinkman
Briefings in Bioinformatics (September 2019)
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A genetic clue on RNA virus tissue tropism (selection by ZAP)

Posted on May 22, 2020 by WeimerMicroLab
Contributed by Cory Schlesener
It is useful to apply known biological concepts to empower bioinformatics to make the most out of analyzing genetic sequences (in proper context). Here is an example of a simple genetic analysis that can carry a lot of meaning when applied in a specific context.
Zinc finger antiviral protein (ZAP) is part of the innate immune response mediated by interferon, and is expressed at different levels by tissue type in mammals. The protein’s RNA binding domain specifically targets CpG sites in the viral RNA genome. This leads to selection against having CpG dinucleotide sequences in viral genomes. Indexing the relative deficiency of CpG sites, from what would be expected by GC content, indicates a selective pressure (likely by ZAP).  The difference in CpG deficiencies between strains has been observed to correlate with host tissue tropism (e.g. between respiratory and gastrointestinal tracts) where ZAP levels differ. In a recent analysis, SARS-CoV-2 was observed to have a much greater deficiency in CpG sites than other beta-coronaviruses. This indicates much of the genome’s sequence is showing a history of
replication in higher ZAP level cellular environments, likely in the gastrointestinal tract of bats.

Reference:
Xuhua Xia (April 2020) Molecular Biology and Evolution
https://doi.org/10.1093/molbev/msaa094
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The Equine Microbiome: A New Frontier in Veterinary Research

Posted on April 29, 2020 by WeimerMicroLab

Contributed by Ashleigh Flores

There has been growing interest in the link between the microbiota of the gut and disease mediated changes in both human and veterinary medicine. The horse gastrointestinal tract is one of the most unique displays of physiological engineering in any mammal. Horses are non-ruminant herbivores with the ability for microbial fermentation in the hindgut. The complexities of the microbial community within the equine gut and how those intricacies pertain to disease processes are just beginning to be understood. This is a new frontier in equine veterinary research with much to be discovered and a vast wealth of knowledge to be gained.
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Read our latest manuscript published in Microorganisms

Posted on April 13, 2020 by WeimerMicroLab

Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease

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New pre-prints from Weimer Micro Lab

Posted on March 30, 2020 by WeimerMicroLab

Pandemic dynamics of COVID-19 using epidemic stage, instantaneous reproductive number and pathogen genome identity (GENI) score: modeling molecular epidemiology

Biological machine learning combined with bacterial population genomics reveals common and rare allelic variants of genes to cause disease

Salmonella Enhances Osteogenic Differentiation in Adipose-Derived Mesenchymal Stem Cells
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Biorkiv first

Posted on February 11, 2020 by WeimerMicroLab

Contributed by DJ Bandoy, DVM

Covid-19 is the name given by the World Health Organization to the severe respiratory disease due to 2019-nCOV. What is astonishing is the speed of release of preprints in Biorkiv of several studies from phylogenetics to virus pseudotyping. While public health concern triumphs over the need for peer review, uncurated papers lead to wild conspiracy theories. This is where social media scientists play a role, hence the heavily criticized paper linking coronavirus with HIV virus is retracted. All of my papers so far are biorkived because coming from a Third World country, my inability to access journal articles hindered my research before coming to UC Davis.

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Why a biologist should code

Posted on December 11, 2019 by WeimerMicroLab

Contributed by DJ Darwin Bandoy, DVM

I personally believe that biology is a big data puzzle. That is also the main reason why I took my PhD in the Weimer lab with the 100K Pathogen Genome Project. But when I started in the lab, I also realized that before you can do any biology with large scale sequencing data, you need to tame the metadata. This requires at the minimum scripting techniques in Excel, which is practically a form of coding. But transposition of multimillion rows and columns is limited in Excel, hence the need for more robust tools like OpenRefine and eventually command-line tools. So yes, a biologist should and must code.

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Food authentication from shotgun sequencing reads with an application on high protein powders

Posted on December 2, 2019 by WeimerMicroLab

Click on the link to read the latest manuscript that Dr. Weimer was involved with which has been published in Nature.  Food authentication from shotgun sequencing reads with an application on high protein powders.

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Next Generation Antimicrobial Resistance Profiling Using Whole Genome Sequences

Posted on November 4, 2019 by WeimerMicroLab

Contributed by Darwin Bandoy, DVM

Antimicrobial resistance is a key issue as pathogens develop resistance due to the misuse of antimicrobials in food animals. While some countries have already banned the use of antibiotics as growth promotants, emerging economies find it difficult to implement due to numerous factors. From a monitoring point of view, large harmonization of standards make it difficult to compare the AMR profile across different countries. Whole-genome sequencing has recently been used to profile antimicrobial resistance and is shown to be robust in detecting the antibiotic resistance genes for the most commonly used antibiotics. One of my favorite tools is Abricate by Torsten Seeman (who also developed the hugely popular Prokka tool). This tool allows me to perform in silico AMR profiling of whole genomes of pathogenic organisms.

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How I catch up with academic literature

Posted on September 30, 2019 by WeimerMicroLab

Contributed by DJ Darwin Bandoy, DVM

Catching up with scientific literature is definitely a big challenge. While Google makes it easy to find materials you are searching for, it is not easy for articles that are tangential to your interest. These are the type of readings outside your field of interest but can complement your trajectory. One form of innovation is applying a technique from another field.

This is where Twitter is useful. I view Twitter as a source of curated scientific material for discovery from peripheral fields. It allows me to sample the minds of the brightest people, and see the hottest publications. I have encountered a lot of bioinformatic tools by eavesdropping on the tweets of the leading bioinformaticians.

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