Ome sweet ome?

Here’s my latest piece for the Prospect blog.

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Chances are that every biologist now has an ome to go to. This suffix, first introduced in the genome (the sum total of all an organism’s genes), can now be found attached to just about every aspect of life’s molecular basis. There is the proteome (the full complement of protein molecules in an organism), the glycome (all the sugars), the epigenome (all the non-genetically encoded regulation of gene activity), the lipidome (all the fatty-acid lipids of cell membranes). Omes embrace wider concepts too. The metabolome comprises all the molecules involved in metabolism; the interactome is the network of interactions between genes and other molecules; the phenome is the total of all distinct observable traits (phenotypes), and so on. The integrome is the ome of all the omes: an ome from ome, you might say.

The proliferation of these neologisms has understandably attracted criticisms and ridicule, and even the founding editor of a new journal called Omics told Nature that “most of them will not make sense.” Some researchers suggest that they are just a way of investing an established field – such as the study of metabolic biochemical processes – with the kudos that has become attached to genomics. They are also a marketing ploy: if you have an ome, you surely need your own distinct funding stream.

Geneticist Jonathan Eisen of the University of California at Davis talks about “badomics”, and sees the spread of omes as a pernicious meme that adds clutter and confusion, as well as implying a sometimes misleading analogy to the aims and concepts of genomics. He compares it with the indiscriminate appending of -gate to every political blunder post-Watergate. “Some of the omes I have the most trouble with are not even remotely comprehensive, but are simply collections of a small set of some facts about one minor entity”, says Eisen, citing for example the nascentosome (incompletely assembled protein molecules) and the predatosome (genes involved in bacterial predation).

This scepticism is valid, but it doesn’t necessarily get to the core of what is both bad and potentially constructive in the omics fad. An ome is basically a list of parts, whether those are physical entities such as molecules or more abstract such as connections or properties. There is great potential value in such a list, provided that it is comprehensive. If one can consult the proteome to look up the chemical structure of a protein associated with a disease-linked gene, say, then one might be able to design a drug molecule that intervenes in the protein’s behaviour. But a list of parts is not an explanation for their collective function, as any electrical engineer or car mechanic will tell you. Omes are in fact the modern equivalent of what Francis Bacon in the seventeenth century called ‘histories’ – exhaustive collections of all possible facts about a given phenomenon, such as cold or comets. Bacon was convinced that preparing histories was the essential first step in natural philosophy, and he set about devising a scheme for distilling these heaps of facts into real knowledge and insight. But that scheme was absurdly elaborate and never even completed, let alone put into practice. The early scientists found, in spite of their Baconian convictions, that this could never be the way to do science – they were compelled to draw up hypotheses and theories, even before all the ‘facts’ were in, for otherwise there is no way to prioritize or organise what you are looking for.

This is another way of saying that omics will not be science until it works within a framework that allows for hypothesis-testing. Merely searching vast databases for correlations is worse than futile, because it will inevitably produce false positives – spurious relationships between events or entities – while remaining silent about the root mechanisms. There’s a difference between knowing which parts work together and knowing how they do so.

It seems that the converse is also true: causative principles might not announce themselves at the level of the basic components. This has become embarrassingly clear in genomics: for many traits or diseases that are evidently inheritable, it has proved possible to identify only a small fraction of the genes responsible, even with the whole human genome at our fingertips. Causation might stem instead from higher levels of organization.

But that leads to one of the positive aspects of the omics craze. It was largely stimulated in the first place by the anticlimactic realization of how much was left unsaid by the human genome projects. We need to know not just what genes we have, but what protein molecules they encode (for these are ultimately the cell’s primary machinery), and how much the gene is actually used, or ‘transcribed’. Enter the proteome and transcriptome. Then we need to know how genes and proteins act together – the interactome, metabolome and so forth – and what other molecules are crucially involved – the glycome, lipidome and so on. What’s more, because some of these sets of molecules are closer to the physiological end of an organism’s functioning, it seems likely that we might find clearer, less ambiguous and more immediate markers of disease and pathology in these other omes than in the genome. Profiling of lipids, for example, might point to incipient diet-related disease.

In other words, the proliferation of omes marks a recognition – never doubted, but long sidelined by the glamour of genomics – that there is much more to life than genes, many of which are better regarded not as ruthless dictators of the cell but as referees that keep the game on track. Omics could thus represent the start – even if clumsy and too overtly list-obsessed – of a return to a more integrated view of what life is.