Developing methods and bioinformatics tools for the global analysis of accessible regions in chromatin

现代生物学的一大挑战是理解为什么遗传信息在不同类型的细胞中有不同的表达。一旦我们牢牢掌握了这一点，我们应该能够对身体的任何细胞进行重新编程，并将分化的细胞转变为能够成为任何细胞类型的干细胞。这种重新编程确实是可能的，这一发现证明了某些染色体易位会产生异常的转录因子，这些转录因子会将正常的造血前体细胞转变为恶性的白血病细胞，只是与正常细胞类型有少许相似之处。此外，最近的研究表明，只有四种转录因子足以将人成纤维细胞重新编程为干细胞。然而，白血病细胞的发展需要很长的时间，而且众所周知，通常需要继发性事件，即其他基因的激活，才能完全将正常细胞转变为癌细胞。此外，成纤维细胞重新编程的效率很低，这表明需要激活的靶细胞超过了这四个因子的靶点。如果我们有方法可以准确地识别可接近的染色质区域，我们就可以使用序列特异的转录因子或小的DNA结合分子来靶向这些区域。此外，诊断细胞分化过程中即将打开的染色质区域将极大地增强我们对哪些基因在发育过程中被激活以及如何激活的理解。在这项提案中，我们将使用高度敏感的技术来识别这些地区。这项技术的发展需要特别的努力，因为它将需要实验科学家和专家在处理和分析大型分子数据集方面的密切合作。这样做的原因是，实验科学家将产生海量的DNA序列数据，然后生物信息学家将这些数据拼凑在一起，集中研究老鼠或人类基因组中可访问的序列。这项任务绝不是微不足道的。

One of the great challenges in modern biology is to understand why genetic information is differentially expressed in different cell types. Once we have a firm grip on this, we should be able to reprogram any cell of the body, and turn differentiated cells into stem cells able to become any cell type. That such reprogramming is indeed possible is exemplified by the finding that certain chromosomal translocations produce aberrant transcription factors which turn normal haematopoietic precursor cells into malignant leukaemic cells that only remotely resemble normal cell types. In addition, it was recently shown that only four transcription factors are sufficient to reprogram human fibroblast cells into stem cells. However, leukaemic cells take a long time to develop and it is known that often secondary events, i.e. the activation of other genes are required to fully turn normal cells into cancer cells. Also fibroblast reprogramming occurs with low efficiency, indicating that more than the targets of these four factors need to be activated. If we had methods where we could precisely identify regions of accessible chromatin, we could target these regions using either sequence specific transcription factors or small DNA-binding molecules. In addition, diagnosing regions of chromatin about to be opened up during cell differentiation will greatly enhance our understanding of which genes are activated in development and how. In this proposal we will use highly sensitive techniques enabling identification of such regions. The development of this technology needs a special effort, because it will require the close collaboration between experimental scientists and experts in handling and analysing large molecular data sets. The reason for this is that the experimental scientists will generate vast amounts of DNA sequence data which will then be puzzled together by the bioinformaticians to home in on those sequences in the mouse or human genome that are accessible. This task is anything but trivial.