The roles of transporters in the human metabolic network

当你吃一种食物或一粒药丸（即一种药物）时，重要的是相关分子会流向它们最有益的地方。因此，营养物质和药物如何被吸收和分布（并最终排出体外）是一个非常重要的话题。当药物出现问题时，它们可能无法正常工作，甚至可能有毒；后者被称为药物不良反应，它们占住院人数的5%以上。细胞被细胞膜包围着，细胞膜的作用是阻止任何旧垃圾进入细胞。相反，这些膜含有一种叫做转运蛋白的蛋白质，它可以将小分子运送进和运出细胞，作为维持我们生命的日常反应（新陈代谢）的一部分。这些转运体占这些生化网络中基因产物的三分之一。一般来说，由于它们所识别的只是一个分子，而不知道它的“目的”（营养物质、药物、维生素等），正是这些转运蛋白参与了将营养物质、维生素和药物运送到细胞中；问题是，我们并不倾向于知道哪种转运体运输哪种物质，而这种基于相关性的评估正是我们希望在这里找到的。为此，我们将研究转运蛋白水平和小分子摄取程度在不同细胞和组织之间是如何变化的；有了足够的这些成对测量，我们就可以找出哪些转运蛋白的变化最能解释小分子摄取的变化，包括在学习阶段没有使用的分子，然后可以直接测试它们确实运输了我们声称的分子。由于转运蛋白的水平在不同组织之间自然地变化，它们必须自然地被控制，而已知有几种物质（如维生素D）会显著地影响这些水平。这意味着我们可以通过添加第二个分子（所谓的“二元武器”）来调节转运蛋白和不同组织的表达，从而“迫使”特定物质只进入这些组织。我们将通过添加一个小的人工（人造）分子（所谓的“片段”）库来测试这一点，并看看其中哪些可以（至少在某种程度上）做到这一点。计算机方法帮助我们找到更大、更有效的分子，这些分子具有相同的片段特征，因此可以预测它们具有预期的靶向效果。重要的是，我们将在一个网络可访问的数据库中管理所有的数据。

When you eat a foodstuff or a pill (i.e. a pharmaceutical drug) it is important that the relevant molecules go to the places where they will be of most benefit. How nutrients and drugs are absorbed and distributed (and eventually excreted) is thus a topic of high importance. When it goes wrong in the case of drugs they may not work properly or may even be toxic; this latter is known as an Adverse Drug Reaction, and they account for more than 5% of hospital admissions. Cells are bounded by cell membranes, whose job it is to stop them letting in any old rubbish. Instead, these membranes contain proteins called transporters that serve to ferry small molecules into and out of cells as part of the day-to-day reactions (metabolism) that keep us alive. Such transporters account for fully one third of the gene products involved in these biochemical networks. It turns out in general terms that, since all they recognise is a molecule, without knowing its 'purpose' (nutrient, drug, vitamin, etc), just these same transporters are involved in transporting nutrients, vitamins and pharmaceutical drugs into cells; the problem is that we do not tend to know which transporters transport which substances, and this correlation-based assessment is what we wish to find out here.To do this we shall study how transporter levels and the extent of small molecule uptake vary together between different cells and tissues; given enough of these paired measurements we can work out which changes in which transporters best account for the changes in small molecule uptake, including molecules not used in the learning phase, and can then test directly that they do indeed transport the molecules we claim. Because the transporter levels naturally vary between tissues they must naturally be controlled, and several substances (such as vitamin D) are known to affect these levels dramatically. This means that we can expect to be able to modulate the expression of transporters and different tissues by adding a second molecule (a so-called 'binary weapon'), and thereby 'force' particular substances to go only to those tissues. We shall test this by adding a library of small artificial (man-made) molecules (so-called 'fragments') and seeing which of them can do this (at least to some degree). Computer methods help us to find larger and more potent molecules that possess these same fragment signatures and that would therefore be predicted to have the desired targeting effects. Importantly, we shall curate all of the data in a web-accessible database.