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Identification of sequences common to more than one therapeutic target to treat complex diseases: simulating the high variance in sequence interactivity evolved to modulate robust phenotypes

机译:识别多个用于治疗复杂疾病的治疗靶点共有的序列:模拟进化为调节稳健表型的序列相互作用的高度差异

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Genome-wide association studies show that most human traits and diseases are caused by a combination of environmental and genetic causes, with each one of these having a relatively small effect. In contrast, most therapies based on macromolecules like antibodies, antisense oligonucleotides or peptides focus on a single gene product. On the other hand, complex organisms seem to have a plethora of functional molecules able to bind specifically to multiple genes or genes products based on their sequences but the mechanisms that lead organisms to recruit these multispecific regulators remain unclear. The mutational biases inferred from the genomic sequences of six organisms show an increase in the variance of sequence interactivity in complex organisms. The high variance in the interactivity of sequences presents an ideal evolutionary substrate to recruit sequence-specific regulators able to target multiple gene products. For example, here it is shown how the 3’UTR can fluctuate between sequences likely to be complementary to other sites in the genome in the search for advantageous interactions. A library of nucleotide- and peptide-based tools was built using a script to search for candidates (e.g. peptides, antigens to raise antibodies or antisense oligonucleotides) to target sequences shared by key pathways in human disorders, such as cancer and immune diseases. This resource will be accessible to the community at www.wikisequences.org . This study describes and encourages the adoption of the same multitarget strategy (e.g., miRNAs, Hsp90) that has evolved in organisms to modify complex traits to treat diseases with robust pathological phenotypes. The increase in the variance of sequence interactivity detected in the human and mouse genomes when compared with less complex organisms could have expedited the evolution of regulators able to interact to multiple gene products and modulate robust phenotypes. The identification of sequences common to more than one therapeutic target carried out in this study could facilitate the design of new multispecific methods able to modify simultaneously key pathways to treat complex diseases.
机译:全基因组关联研究表明,大多数人类特征和疾病是由环境和遗传原因共同造成的,而每一种影响都相对较小。相反,大多数基于大分子的疗法,例如抗体,反义寡核苷酸或肽,都集中在单一基因产物上。另一方面,复杂的生物似乎具有过多的功能性分子,能够根据其序列与多种基因或基因产物特异性结合,但导致生物体募集这些多特异性调节剂的机制尚不清楚。从六个生物体的基因组序列推断出的突变偏倚表明复杂生物体中序列相互作用的方差增加。序列相互作用的高度差异为募集能够靶向多种基因产物的序列特异性调节子提供了理想的进化底物。例如,此处显示3’UTR如何在可能与基因组中其他位点互补的序列之间波动,以寻找有利的相互作用。使用脚本来构建基于核苷酸和肽的工具库,以搜索候选物(例如肽,产生抗体的抗原或反义寡核苷酸)以靶向人类疾病(如癌症和免疫性疾病)关键途径共有的靶序列。社区可以从www.wikisequences.org访问此资源。这项研究描述并鼓励采用相同的多靶点策略(例如miRNA,Hsp90),该策略已在生物体中进化为修饰复杂性状以治疗具有强大病理表型的疾病。与较不复杂的生物相比,在人类和小鼠基因组中检测到的序列相互作用的方差的增加可能加速了能够与多种基因产物相互作用并调节稳健表型的调节子的进化。在这项研究中,对一个以上的治疗靶标共有的序列进行鉴定可以促进新的多特异性方法的设计,这些方法能够同时修饰治疗复杂疾病的关键途径。

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