Teppa Elin, Nadalin Francesca, Combet Christophe, Zea Diego Javier, David Laurent, Carbone Alessandra
Sorbonne Université, Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB) - UMR 7238, 4 Place Jussieu, 75005 Paris, France.
Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), 4 Place Jussieu, 75005 Paris, France.
Virus Evol. 2020 Feb 6;6(1):veaa006. doi: 10.1093/ve/veaa006. eCollection 2020 Jan.
The study of mutational landscapes of viral proteins is fundamental for the understanding of the mechanisms of cross-resistance to drugs and the design of effective therapeutic strategies based on several drugs. Antiviral therapy with nucleos(t)ide analogues targeting the hepatitis B virus (HBV) polymerase protein (Pol) can inhibit disease progression by suppression of HBV replication and makes it an important case study. In HBV, treatment may fail due to the emergence of drug-resistant mutants. Primary and compensatory mutations have been associated with lamivudine resistance, whereas more complex mutational patterns are responsible for resistance to other HBV antiviral drugs. So far, all known drug-resistance mutations are located in one of the four Pol domains, called reverse transcriptase. We demonstrate that sequence covariation identifies drug-resistance mutations in viral sequences. A new algorithmic strategy, BIS2TreeAnalyzer, is designed to apply the coevolution analysis method BIS2, successfully used in the past on small sets of conserved sequences, to large sets of evolutionary related sequences. When applied to HBV, BIS2TreeAnalyzer highlights diversified viral solutions by discovering thirty-seven positions coevolving with residues known to be associated with drug resistance and located on the four Pol domains. These results suggest a sequential mechanism of emergence for some mutational patterns. They reveal complex combinations of positions involved in HBV drug resistance and contribute with new information to the landscape of HBV evolutionary solutions. The computational approach is general and can be applied to other viral sequences when compensatory mutations are presumed.
对病毒蛋白突变图谱的研究对于理解药物交叉耐药机制以及基于多种药物设计有效治疗策略至关重要。针对乙型肝炎病毒（HBV）聚合酶蛋白（Pol）的核苷（酸）类似物抗病毒治疗可通过抑制HBV复制来抑制疾病进展，这使其成为一个重要的案例研究。在HBV中，治疗可能因耐药突变体的出现而失败。原发性和补偿性突变与拉米夫定耐药有关，而更复杂的突变模式则导致对其他HBV抗病毒药物的耐药。到目前为止，所有已知的耐药突变都位于称为逆转录酶的四个Pol结构域之一。我们证明序列共变可识别病毒序列中的耐药突变。一种新的算法策略BIS2TreeAnalyzer被设计用于将过去成功应用于少量保守序列集的共进化分析方法BIS2应用于大量进化相关序列集。当应用于HBV时，BIS2TreeAnalyzer通过发现与已知与耐药相关的残基共进化且位于四个Pol结构域上的37个位置，突出了多样化的病毒解决方案。这些结果提示了某些突变模式出现的顺序机制。它们揭示了参与HBV耐药的位置的复杂组合，并为HBV进化解决方案的图景提供了新信息。该计算方法具有通用性，当假定存在补偿性突变时，可应用于其他病毒序列。
