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Kornilov, S.

Publications and source records attributed to Kornilov, S..

2 recordsLinked to original sources

Robust error-minimization in the genetic code across physicochemical metrics and variant codes: a graph-theoretic analysis in GF(2)6

The standard genetic code reduces the impact of point mutations, but the robustness of this property across physicochemical metrics, naturally occurring variant codes, and codon-reassignment mechanisms remains incompletely quantified. Embedding the 64 codons in GF(2)6 represents the hypercube Q6 as a coordinate-dependent subgraph of the encoding-independent single-nucleotide mutation graph H(3, 4), and enables continuous{rho} -interpolation between the two. Under a quartet-pattern shuffie null (n =10,000), the standard code is significantly low-cost across four established, code-independent physicochemical distance metrics with partially overlapping content (Grantham p = 0.0062; Miyata p < 0.001; Woese polar requirement p = 0.003; Kyte-Doolittle hydropathy p = 0.001), and the signal strengthens monotonically as{rho} moves Q6 [->] H(3, 4). A structure-aware sensitivity analysis under the alignment-derived ProtSub matrix (Jia & Jernigan 2021) yields the most extreme percentile of any measure tested (p = 0.0004; all five p-values pass Bonferroni at = 0.05). Across the 27 NCBI translation tables, near-optimality is preserved: 11 of 12 informative-distance variants retain top-5% placement after BH-FDR correction. Natural codon reassignments avoid disrupting codon-family connectivity: under the encoding-independent H(3, 4) adjacency, observed events are topology-breaking at relative risk 0.32 versus the candidate landscape (permutation p [&le;] 10-4). The H(3, 4) result is stable by construction; the Q6 decomposition is representation-specific and fails to show depletion under 8 of 24 base-to-bit encodings, so we report H(3, 4) as the primary test and Q6 as a sensitivity. Event-level conditional-logit modelling shows that topology avoidance and local physicochemical cost provide complementary, only weakly correlated signal (rs = 0.15), and that topology adds explanatory value beyond physicochemistry under both Q6 and encoding-independent H(3, 4) adjacency. Retrospective reanalysis of nine genome-recoding datasets is consistent with codon-family topology operating as an evolutionary-trajectory constraint distinct from acute engineering fitness. The contribution is the second axis: code evolution is jointly constrained by physicochemical smoothness and codon-family topological integrity, and these two constraints are partly independent. HighlightsO_LICodon-space geometry links genetic-code robustness and reassignment paths C_LIO_LIStandard and variant codes preserve broad physicochemical error minimization C_LIO_LIReassignments are depleted for codon-family topology-breaking moves C_LIO_LIConditional-logit models separate topology from physicochemical similarity C_LIO_LISynthetic recoding shows boundary conditions for natural-code constraints C_LI

genetics↗

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗