My first paper from post-doc is out! Transitioning into from sequence-based analyses into linear-algebra-driven metabolic modeling and flux balance analysis was a challenge, so I’m very proud of this new publication. Our new platform integrates transcriptomic data in genome scale models with repect to maximizing cellular economy in light of the transcriptomic investment made into the enzymes indicated by the data.

Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches have been shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. This method, known as RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

This figure is a representation of the impact of RIPTiDe on the original reconstruction, converting it to a context-specific model of metabolism with a parsimonious metabolic solution space with respect to the given transcriptomic data.

You can read the whole open-access paper here!

Also, you can find all the analysis code here!

And finally, you can download and use RIPTiDe from here!

After a long review process, the final publication from my dissertation work is finally published! In it were able to show that not only did Clostridium difficile differentially alter the transcriptional programs of communities it was invading, but that the degree of change positively correlates lasting infection. What this means is that those individuals where C. difficile infection is more persistant may possess a microbiota that is more susceptible to changes induced by the pathogen. We accomplished this by comparing the metatranscriptomes of mock and infection mouse intestinal communities across multiple antibiotic pretreatments that each result in equal initial susceptibility to infection but differ in resultant community structure and rate of pathogen clearance. You can see one example of changes caused by C. difficile to streptomycin-pretreated gut communities below.

This figure illustrates metatranscriptomic expression of many genes in the community are more highly upregulated in the absence of C. difficile and that genes primarily belong to bacteria within the genus Lactobacillus. What we found interesting about this finding was that streptomycin results in a gut community where Lactobacillus represents less than 1% of the detectable bacteria. This proved to be the case across each antibiotic pretreatment tested, where the minority members of the community appear to be more greatly impacted by infection than those taxa that dominate those communities. This was quantified below by combined the metatranscriptomic results with 16S rRNA gene amplicon sequencing relative abundance data.

What this shows is that those taxonomic groups of bacteria that are more greater affected by C. difficile infection seem to be always in underrepresented in each community. You could say that C. difficile is attacking the loser is each of these instances to try and promote it’s own existence, a pretty low blow even for a pathogen. There are instances similar to this in bacterial communities, but this is the first time it has been demonstrated in the context of a bacterial infection in a host-associated community of microbes.

You can read in much more detail here!