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    • Small but statistically significant variance in PhyChem inde

    2022-05-13

    Small but statistically significant variance in 7 PhyChem indexes measured using intra-host HVR1 variants was shown to be strongly associated with CIP and MIP. All HVR1 sequences (N = 28,622) used here share only 6782 profiles of the selected 29 DAC’ of 7 PhyChem indexes. The significant majority (98–99%) of these profiles is specific to CIP or MIP, indicating that coevolution among HVR1 sites reflects HCV taselisib sale to HIV among CIP. It is important to note that, though the 2-D distributions of the PhyChem features are overlapping (Fig. 4), HCV HVR1 variants obtained from CIP and MIP are generally separable (Table 3). This observation suggests substantial differences in fitness between HVR1 variants circulating in infected hosts in the presence or absence on HIV. Reduction in the HVR1 genetic diversity was reported among CIP as compared to MIP (Lopez-Labrador et al., 2007). The data obtained in our study do not contradict this report since the PhyChem space occupied by CIP variants is less diverse than the MIP space (Fig. 5). The major finding, however, is that, rather than being different in size, the CIP and MIP 2-D PhyChem spaces have significantly different frequency distributions of HCV HVR1 variants (Fig. 4, inbox), emphasizing differences in HCV evolution among CIP vs MIP. Variation in intra-host HCV evolution between CIP and MIP is in concert with earlier observations of interaction between HCV and HIV resulting in a more rapid liver disease progression, greater HCV mutation rate, compartmentalization into peripheral-blood mononuclear cells (Alberti et al., 2005; Blackard et al., 2007; Mayor et al., 2006), and in increase of risk for AIDS-related death (Daar et al., 2001). Both, HCV and HIV are blood-borne pathogens. Co-infections with these two viruses, however, are not randomly distributed among different populations, with PWID and MSM being most affected by co-infections (Arain and Robaeys, 2014; Chen et al., 2009; Fraser et al., 2018; Hoornenborg et al., 2017; Lo Re 3rd et al., 2014; Taylor et al., 2013; Taylor et al., 2012). Taking into consideration a high HCV prevalence, a significant rate of re-infection with different HCV strains, HIV co-infection in these high-risk groups (Page et al., 2009; Peters et al., 2016), and variations in HCV adaptation detected here, HCV evolution among PWID and MSM should be very complex and may have a different path in comparison to the general population. The classification accuracy of both classifiers, especially the PNN model (Table 3), suggest that the PhyChem profiles of HVR1 (Table 2) can serve as new markers for identifying and differentiating HVR1 sequences associated with CIP and MIP. The classification performance on randomly labeled datasets (Table 3 and Fig. 2) and on the test dataset (Table 3 and Fig. 3) indicate that the found associations are likely due to CIP/MIP-specific traits rather than to the existence of random statistical correlations in NGS HVR1 data. Thus, the models generated here may serve as prototypes of cyber-molecular assays for the detection of CIP among HCV infected persons.
    Conclusions Intra-host HCV HVR1 evolution varies between CIP and MIP, which suggests that HIV-inflicted changes in the host environment exact a strong HCV genetic response. This finding further suggests possibility that HCV evolution might take a different path among HIV co-infected PWID and MSM, among whom the rates of HCV infection and HIV co-infection are high (Peters et al., 2016; Singh et al., 2017; Zibbell et al., 2015). HCV strains circulating in high-risk groups need to be carefully monitored for the identification of potentially new traits of clinical and public health relevance. The HCV “sensing” of HIV results in genetic changes of the intra-host HCV population, which can be detected by measuring coevolution among the HVR1 site using new PhyChem properties extracted from the NGS HVR1 data. The computational models built using these new markers provide novel opportunities for development of cyber-molecular diagnostics (Longmire et al., 2017) as has been reported for the detection of recent HCV infections (Astrakhantseva et al., 2011; Lara et al., 2017) and suggested here for the detection of HIV–HCV co-infections.