University of Electronic Science and Technology, 610051, Chengdu, People's Republic of China.
Institute of Cybernetics, Mathematics and Physics, 10400, Havana, Cuba.
Sci Rep. 2021 Apr 19;11(1):8470. doi: 10.1038/s41598-021-87764-0.
In many situations, the gene expression signature is a unique marker of the biological state. We study the modification of the gene expression distribution function when the biological state of a system experiences a change. This change may be the result of a selective pressure, as in the Long Term Evolution Experiment with E. Coli populations, or the progression to Alzheimer disease in aged brains, or the progression from a normal tissue to the cancer state. The first two cases seem to belong to a class of transitions, where the initial and final states are relatively close to each other, and the distribution function for the differential expressions is short ranged, with a tail of only a few dozens of strongly varying genes. In the latter case, cancer, the initial and final states are far apart and separated by a low-fitness barrier. The distribution function shows a very heavy tail, with thousands of silenced and over-expressed genes. We characterize the biological states by means of their principal component representations, and the expression distribution functions by their maximal and minimal differential expression values and the exponents of the Pareto laws describing the tails.
在许多情况下，基因表达特征是生物状态的独特标记。我们研究当系统的生物状态发生变化时，基因表达分布函数的变化。这种变化可能是选择压力的结果，如在大肠杆菌种群的长期进化实验中，或在老年大脑中阿尔茨海默病的进展中，或在正常组织向癌症状态的进展中。前两种情况似乎属于一类跃迁，其中初始状态和最终状态彼此相对接近，差异表达的分布函数具有短程性，只有少数几十个强烈变化的基因。在后一种情况下，即癌症中，初始状态和最终状态相距甚远，中间隔着一个低适应度的障碍。分布函数显示出非常重的尾部，有数千个沉默和过度表达的基因。我们通过主成分表示来描述生物状态，通过最大和最小差异表达值以及描述尾部的帕累托定律的指数来描述表达分布函数。
