Physiology and Bioinformatics

Physiologists strive to understand how and why things work in living tissues. I’ve been trying to think about physiology in the context of my research interests in genomics and bioinformatics in nonmodel species. We don’t discuss this at all in my physiology courses, but I can’t help thinking about sequencing data that I work with and how I’ve seen these gene names and pathways show up on functional annotations lists that I’ve delivered to people. Are they accurate annotations? We all struggle to understand the data. (Incidentally, this is one of the many reasons why I chose a physiology grad program rather than a bioinformatics- or genomics-focused grad program. I think it’s really important and want to understand the details of how and why living tissues work.)

These data are generated from preserved tissues, giving us a snapshot of processes in time. To understand genomic and transcriptomic (and proteomic and metabolomic) data, I think it is necessary to understand the biochemical processes that are going on, being encoded for by these data.

One of my favorite physiologists is August Krogh, founder of comparative physiology. He defined the concept of an animal model as one that enables us to study any question we want to ask:

“For such a large number of problems, there will be some animal of choice, or a few such animals, on which it can be most conveniently studied.”

In genomics, the concept of model organisms is slightly different. A model organism is one that is extensively studied. In contrast, nonmodel species are organisms with genomes that are not well characterized. In this new era of biology, utilizing Krogh’s principle, we can apply what we know about some organisms in the context of evolutionary relationship and conserved functions to others for which we know less to bridge the gap between genotype and phenotype.

Why is this important? We are accumulating more and more genomic-scale nucleic acid sequencing information from millions of species (nonmodel species) for a variety of reasons, e.g. organismal health, environmental health and management, species conservation. High-throughput automated methods of data collection are at our disposal. It’s all really exciting. Just looking at eukaryotes (prokaryotes an even larger group!):

pie_chart_NCBI_predicted_species_labeled

There are an estimated 8.74 million eukaryotic species on Earth (outer circle, data from Mora et al. 2011), of which 316,235 species are currently represented in the NCBI nucleotide database (inner circle, data accessed May 23, 2015). The majority, 88% (7.7 million) is predicted to be metazoan, of which we have nucleic acid sequences for approximately 2%. Data accessed from NCBI are numbers of species regardless of number of nucleotide records present. There are currently 150,279,335 eukaryotic nucleotide records in NCBI (May 23, 2015).

The majority of metazoans will likely be insects.

Wilson EO. 1987. The Little Things That Run the World (The Importance and Conservation of Invertebrates). Conservation Biology. 1(4): 344-346.

The problem is, sequences are just code. Once we obtain all this code, what does it mean? Annotations have to be assigned.

Most of what we know about molecular mechanisms of cellular processes is based on experimental evidence. How can we reliably connect experimental evidence with sequence annotations in an automated high-throughput way for data from nonmodel organisms? Mouse and human systems are relatively well-studied on an experimental level. But for new organisms whose physiology we know relatively little about, how reliable is it to annotate a new genome with protein function data from distantly-related species? There are often small changes in protein’s primary structure from one species to another. For example, connexins (below):

2015-10-31_05-58-32_22666604631_oHow distant is too distant? Even if there is complimentary proteomic data to the nucleic acid data from the same species, it will not always match up. When is it acceptable to use experimental evidence from distantly-related species to predict function at another species level?

I want to understand more about the evolutionary relationships among all of the protein and nucleic acid sequencing data.

I need to understand more about position scoring matrices to identify sensitive protein homologies between data sets. Hidden Markov model-based software HMMER (Eddy 2011) and existing hand-curated protein databases such as PROSITE (Sigrist et al. 2012), Pfam A and B (Finn et al. 2014) and ProDom (Servant et al. 2002). Also, phastCons:

Siepel A and Haussler D (2005). Phylogenetic hidden Markov models. In R. Nielsen, ed., Statistical Methods in Molecular Evolution, pp. 325-351, Springer, New York.

Siepel, A., Bejerano, G., Pedersen, J.S., Hinrichs, A., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L.W., Richards, S., Weinstock, G.M., Wilson, R. K., Gibbs, R.A., Kent, W.J., Miller, W., and Haussler, D. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomesGenome Res. 15, 1034-1050 (2005).

And then of course understand more about proteins themselves. For nonmodel species annotations, there seems to be a disconnect between: 1.) experimental evidence for molecular structures and functions and 2.) the sequences that encode for these.

With the extreme contrast between my research interests in bioinformatics and my physiology core courses at UC Davis, I’ve found it necessary to remind myself of why I’m motivated to study the fundamental basics of physiology.

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About Lisa Cohen

PhD student at UC Davis.
This entry was posted in Bioinformatics, Grad School, physiology. Bookmark the permalink.

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