Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control.
Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control.
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DOI：
10.3390/v13060970
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发表时间：
2021-05-24
期刊：
影响因子：
--
通讯作者：
Leigh-Brown A
中科院分区：
文献类型：
--
作者：
Bbosa N;Ssemwanga D;Nsubuga RN;Kiwanuka N;Bagaya BS;Kitayimbwa JM;Ssekagiri A;Yebra G;Kaleebu P;Leigh-Brown A
系统发生推断有助于确定艾滋病毒传播网络的特征，并评估预防措施可能产生最大影响的地方。然而，影响网络结构的参数估计仍然是稀缺的，但重要的是在评估艾滋病毒传播的决定因素。我们分析了2017年的HIV pol序列（728个维多利亚湖社区（FFC），592名女性性工作者（FSW）和697名普通人群（GP）），以确定最大似然（ML）系统发育树上的传播网络，并使用时间分辨的遗传算法对其进行了改进。将网络生成模型拟合到观察到的度分布和网络参数，并估计校正的赤池信息准则和贝叶斯信息准则值。在ML树上链接了347个（17.2%）HIV序列（最大遗传距离≤ 4.5%，自举支持率≥ 95%），其中303个（86.7%）由纯A1（n = 168）和D（n = 135）亚型组成，在BEAST v1.8.4中进行了分析。大多数网络（至少40%）是在≤ 5年的时间深度发现的。Waring和Yule模型分别对FFC和FSW的网络拟合效果最好，而负二项模型对GP的网络拟合效果最好。与GP相反，HIV高流行性FFC中的网络结构可能是无标度的，并通过优先连接形成。研究结果支持及时针对FFC进行干预，以有效控制流行病。干预措施应根据目标人群中艾滋病毒流行的动态进行调整，了解网络结构对于确保艾滋病毒预防方案的成功至关重要。
Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.
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影响因子：
3.1
作者：
Kasamba, Ivan;Nash, Stephen;Weiss, Helen A.
通讯作者：
Weiss, Helen A.
影响因子：
3.7
作者：
Hladik W;Barker J;Ssenkusu JM;Opio A;Tappero JW;Hakim A;Serwadda D;Crane Survey Group
通讯作者：
Crane Survey Group
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
3.6
作者：
Kamali A;Nsubuga RN;Ruzagira E;Bahemuka U;Asiki G;Price MA;Newton R;Kaleebu P;Fast P
通讯作者：
Fast P
DOI：
10.1073/pnas.0611337104
发表时间：
2007-06-26
影响因子：
11.1
作者：
de Blasio, Birgitte Freiesleben;Svensson, Ake;Liljeros, Fredrik
通讯作者：
Liljeros, Fredrik