Bernal, B. | Search bioRxiv

bioRxiv2026

Ultra-deep sequencing reveals intra-host diversity and co-infection-driven evolution of SARS-CoV-2

As COVID-19 enters an endemic phase, SARS-CoV-2 continues to diversify under ongoing immune pressure, with Omicron sublineages and episodic emergent variants sustaining reinfections worldwide. Intra-host evolution represents the earliest stage of this diversification, yet remains undercharacterized, particularly in regions with limited genomic surveillance. Here, we conducted high-throughput sequencing on 96 nasopharyngeal swab samples from Chilean individuals (2020-2022), achieving an average per-base genome coverage of [~]60,000x across the viral genome. This ultra-deep sequencing coverage enabled the identification of intra-host single-nucleotide variants (iSNVs) and co-infection events with high sensitivity and accuracy. Co-infections, especially with Omicron, significantly increased iSNV frequency and recombination, driving viral diversity. Evolutionary analysis based on the non-synonymous to synonymous ratio (dN/dS) shows that Omicron is under extensive purifying selection (global dN/dS [~] 0.55). However, Omicron co-infection cases exhibited higher dN/dS ratios ([~]0.58), suggesting a lower level of purifying selection and increased genetic diversity. Notably, the Spike gene showed dN/dS ratios indicative of positive selection (dN/dS > 1), which are more pronounced in co-infection cases than in Omicron alone. This suggests that co-infections are providing the substrate for the emergence of new variants with enhanced transmissibility and immune evasion capabilities. Together, these findings demonstrate that ultra-deep sequencing is crucial for mapping the evolutionary forces driving SARS-CoV-2 intra-host adaptation and the emergence of new variants.

genetics↗

bioRxiv2018

Identification negative BOLD responses using windkessel models

Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

neuroscience↗