Divergent trajectory of replication and intrinsic pathogenicity of SARS-CoV-2 Omicron post-BA.2/5 subvariants in the upper and lower respiratory tract.

BACKGROUND: Earlier Omicron subvariants including BA.1, BA.2, and BA.5 emerged in waves, with a subvariant replacing the previous one every few months. More recently, the post-BA.2/5 subvariants have acquired convergent substitutions in spike that facilitated their escape from humoral immunity and gained ACE2 binding capacity. However, the intrinsic pathogenicity and replication fitness of the evaluated post-BA.2/5 subvariants are not fully understood. METHODS: We systemically investigated the replication fitness and intrinsic pathogenicity of representative post-BA.2/5 subvariants (BL.1, BQ.1, BQ.1.1, XBB.1, CH.1.1, and XBB.1.5) in weanling (3-4 weeks), adult (8-10 weeks), and aged (10-12 months) mice. In addition, to better model Omicron replication in the human nasal epithelium, we further investigated the replication capacity of the post-BA.2/5 subvariants in human primary nasal epithelial cells. FINDINGS: We found that the evaluated post-BA.2/5 subvariants are consistently attenuated in mouse lungs but not in nasal turbinates when compared with their ancestral subvariants BA.2/5. Further investigations in primary human nasal epithelial cells revealed a gained replication fitness of XBB.1 and XBB.1.5 when compared to BA.2 and BA.5.2. INTERPRETATION: Our study revealed that the post-BA.2/5 subvariants are attenuated in lungs while increased in replication fitness in the nasal epithelium, indicating rapid adaptation of the circulating Omicron subvariants in the human populations. FUNDING: The full list of funding can be found at the Acknowledgements section.

A Bayesian approach to estimate MHC-peptide binding threshold.

Major histocompatibility complex (MHC)-peptide binding is a critical step in enabling a peptide to serve as an antigen for T-cell recognition. Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.