Our paper on modeling context-specific metabolism of C. difficile is finally accepted in mSystems! In it we generated and extensively curated a C. difficile GENRE for a hypervirulent isolate (str. R20291). In silico validation revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Then in collaboration with Dr. Rita Tamayo at UNC, whose research focuses on an evolutionary strategy in some bacteria for phenotypic heterogeneity and virulence known as phase variation. We utilized transcriptome sequencing from distinct phases of C. difficile, and in combination with our previously published integration algorithm, we created context-specific models of metabolism to identify possible metabolite signals that drive differences in virulence.

Using this GENRE-based analyses we found that glucose utilization through the pentose phosphate pathway is essential in the smooth phase variants of str. R20291. Here we show (A) Gene and reaction essentiality results for glycolysis and the pentose phosphate pathway across both the rough and smooth phase variant context-specific models. Components were deemed essential if models failed to generate <1% of optimal biomass flux. (B and C) Colony morphologies resulting from smooth and rough variants of C. difficile str. R20291 grown on either BHIS or BDM ± glucose after 48hr of growth. Defined medium colonies were then subcultured onto BHIS medium for an additional 24hr as indicated. Increased colony perimeter was found to be the defining characteristic of the rough colony morphology. This feature was quantified for multiple colonies under each permutation of colony variant and growth medium. (D) Colony perimeter for smooth and rough progenitor colony variants grown on BHIS. (E and F) Smooth (E) or rough (F) colony variant perimeter during subculture onto each of the BDM carbon source medium formulations.

Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.

You can read the whole open-access paper here!

Also, you can find all the analysis code here!

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!