GC-linked lncRNA organization in Arabidopsis resolves into composition-constrained entropy and species-dependent transcript architecture.

Long noncoding RNAs (lncRNAs) show extensive sequence and structural heterogeneity, but whether GC-linked variation reflects a single organizational axis or distinct relationships with sequence composition and transcript architecture remains unclear. Here, we analyzed 13,948 CANTATAdb-derived lncRNAs from Arabidopsis halleri, Arabidopsis lyrata and Arabidopsis thaliana to disentangle these relationships. Exact entropy decomposition showed that the strong raw association between GC content and four-state nucleotide entropy largely reflects a mathematical constraint imposed by base composition; after conditioning on the GC-dependent entropy ceiling, the residual association was markedly reduced. In contrast, GC-related composition retained substantial associations with transcript architecture under stricter analytical control. Thirty-four of 42 adjusted predictor-outcome associations remained significant after FDR correction, and all nine species-by-outcome random-forest models showed positive median out-of-sample predictive gain over their corresponding baselines. These architecture-associated signals varied markedly among species and structural features, with the clearest positive pattern in A. thaliana, indicating a heterogeneous rather than universally conserved relationship. Repeat-aware analyses and species-specific matched-mRNA comparators further showed that the entropy pattern was neither uniformly explained by annotated repeat sequence nor uniquely characteristic of lncRNAs. In A. thaliana, no robust difference in maximum expression was detected, whereas GC-rich lncRNAs occurred in distinct local protein-coding-gene contexts. Together, these findings resolve GC-linked lncRNA organization into two analytically distinct layers: a largely composition-constrained entropy relationship and a species-dependent composition-architecture signal that persists after covariate adjustment and out-of-sample prediction. This distinction provides a more rigorous framework for understanding compositional organization in plant lncRNAs.