Biology subjects

Radecki, P.

Publications and source records attributed to Radecki, P..

4 recordsLinked to original sources

bioRxiv2026

Transmission of mutated SARS-CoV-2 variants is favored by relatively prolonged infections due to delayed immunity

SARS-CoV-2 evolution enhanced viral fitness and immune evasion, extending the COVID-19 pandemic and resulting in millions of excess deaths. Viral diversity is generated within infected individuals, yet the timing and interplay of viral and immunological forces that drive transmissible evolution are incompletely understood. We developed a multi-scale within host phylodynamic (WiPhy) model of SARS-CoV-2 infection which couples viral replication, innate and acquired immune responses, and viral mutation. We then validated the model against quantitative viral and phylodynamic metrics. Model output predicts that typical acute infections rapidly generate genetic diversity due to accumulation of minor variants which in most cases do not achieve sufficient concentrations for transmission. Delayed innate immune responses correlate with higher peak viral load and diversification, allowing higher transmission risk of the founder virus or with a novel variant that is equally or less fit. In contrast, the risk of transmitting a fitter variant is highest during the ~10% of infections in which viral loads remain sufficiently high for transmission after 10-14 days. In these cases, non-sustained innate and/or weak acquired immune responses allow sufficient time for selection of a variant with one or more fitness enhancing non-synonymous mutations. Across a simulated cohort of ~1500 individuals, 5% of transmission risk came from variants with enhanced fitness from nonsynonymous mutations, and 13% of simulated infections accounted for 90% of fitter variant transmission risk. Our results highlight how the timing and interplay of viral and immunological forces within a host create bottlenecks that severely limit between host evolution.

evolutionary biology↗

bioRxiv2024

Rapid Emergence and Evolution of SARS-CoV-2 Variants in Advanced HIV Infection

Previous studies have linked the evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic variants to persistent infections in people with immunocompromising conditions1-4, but the evolutionary processes underlying these observations are incompletely understood. Here we used high-throughput, single-genome amplification and sequencing (HT-SGS) to obtain up to [~]103 SARS-CoV-2 spike gene sequences in each of 184 respiratory samples from 22 people with HIV (PWH) and 25 people without HIV (PWOH). Twelve of 22 PWH had advanced HIV infection, defined by peripheral blood CD4 T cell counts (i.e., CD4 counts) <200 cells/L. In PWOH and PWH with CD4 counts [≥]200 cells/L, most single-genome spike sequences in each person matched one haplotype that predominated throughout the infection. By contrast, people with advanced HIV showed elevated intra-host spike diversity with a median of 46 haplotypes per person (IQR 14-114). Higher intra-host spike diversity immediately after COVID-19 symptom onset predicted longer SARS-CoV-2 RNA shedding among PWH, and intra-host spike diversity at this timepoint was significantly higher in people with advanced HIV than in PWOH. Composition of spike sequence populations in people with advanced HIV fluctuated rapidly over time, with founder sequences often replaced by groups of new haplotypes. These population-level changes were associated with a high total burden of intra-host mutations and positive selection at functionally important residues. In several cases, delayed emergence of detectable serum binding to spike was associated with positive selection for presumptive antibody-escape mutations. Taken together, our findings show remarkable intra-host genetic diversity of SARS-CoV-2 in advanced HIV infection and suggest that adaptive intra-host SARS-CoV-2 evolution in this setting may contribute to the emergence of new variants of concern (VOCs).

microbiology↗

bioRxiv2021

Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNAs pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNAs statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbor thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

genomics↗

bioRxiv2021

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g., transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens, and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

genomics↗