July 8, 2014

Contributions to the bioRxiv, Summer 2014

I have been busy finishing up some work done in the Cellular Reprogramming Lab between 2010 and 2013. These two papers were submitted to and rejected as a single paper from PLoS One (after two rounds of revision). They were subsequently split them into their wet-lab molecular biology (written as an extended protocol) and computational components (written as a more conventional manuscript) for publication on bioRxiv.

The first paper (wet-lab molecular biology) is called "Using Polysome Isolation with Mechanism Alteration to Uncover Transcriptional and Translational Dynamics in Key Genes", in which we explore the world of mRNA regulation during adaptive cellular processes. The first part of the title (polysome isolation) involves harvesting mRNA from the polysome (translation-related mRNA). Harvesting this in tandem with mRNA associated with transcription provide us with a direct comparison between the transcriptome (TST) and translatome (TLT).

The second part of the title involves administering drug treatments to fibroblast populations which have systematic effects on transcription and protein production. These treatments are called "mechanism alteration" because they mimic changes that occur in a dying or transforming cell.

The third part of the title involves looking at transcriptional and translational dynamics for key genes. One criticism of the combined paper involved the use of candidate genes instead of high-throughput data. High-throughput data is great if one can afford it. On the other hand, large datasets can leave you with more questions than answers, which might be particularly true of this work. 

These figures demonstrate the analysis of experiments which validate the polysome recovery technique and the effects of drug treatments (mechanism disruption) on both transcriptome- and translatome- related mRNA (TST and TLT, respectively).

The second paper (computational) is called "Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA". There is also a Github repository that contains associated Matlab code and simulations. Work from the first and second papers have been presented previous to their bioRxiv release, notably at the Stem Cell and Regenerative Medicine Conference held on the Oakland University (MI) campus in 2012.

We will go through this title in backwards order this time. The last part (cellular mRNA) refers to its connection to the first paper. Gene expression measured at both the transcriptome (TST) and translatome (TLT) will be used to model the cell's general response to mechanism alteration. In this case, an assumption is made: fluctuations of both mRNA fractions and at multiple points in time represents a regulatory process. 

Thus, a first-order feedback model can be constructed, with a simplified set of feedforward, feedback, and decay components. While there are a multitude of mRNA decay pathways and processing functions, this model focuses on a much simpler abstraction: the path from DNA to protein with single sources of decay and feedback. Each fraction of mRNA can be represented as a point process controlled by inputs and outputs.

The middle part of the title (dynamical approximation) then refers to the simulation of mRNA dynamics using the model, its components, and biological data. The idea is to approximate meaningful trends at certain points in a biological process, which is expected to differ by gene and by model component. This is where the proof-of-concept nature of this paper is most evident. 

While a somewhat contrived means to approximate a complex biological process is used in both papers, the original application was to be for understanding the early stages of cellular reprogramming. However, it proved to be exceedingly difficult to go from iPS cultures to meaningful computational inference.

An example of the first-order feedback model. A: a graphical example of the model components. B: an example of activity among the components over time.

Finally, the first part of the title (modeling cellular information processing) is based on the interpretation of the model output. the notion of cellular information processing treats the regulation of mRNA as an information processing problem. That is, you have an input, a process, and an output. systematic noise can also be added to the model, depending on the application. The process itself (mRNA processing from DNA transcription to RNA translation) is a transformation of information. 

When a cell is challenged by an environmental stimulus or the need to change phenotype, information provided by mRNA can be operated upon in a number of ways (linear responses, accumulation, delayed responses). Three information processing principles are used to interpret phenomena such as linear decay, the sequestration of mRNA at either the TST or TLT, and the differential response among individual genes.

Finally, I must point out how cool the altimetric support is on the bioRxiv. Here is a screen shot showing the number of tweets, abstract views, and .pdf downloads for the wet-lab paper:

Almost as functional as the analytics data on Blogger (which is not saying much, but for a formal publication venue, it's pretty impressive). The people at Cold Spring Harbor Lab have done a good job on this, and are ahead of the people at arXiv on this. As it turns out, however, the arXiv has a principled policy on this. Whether viewership stats are a sign of vanity and worthy of scare quotes is another matter.

No comments:

Post a Comment