Mitochondria are crucial for energy production in plant tissues, but their quantity and activity vary in different tissues and developmental processes. Determining the factors underlying differential molecular evolutionary rates has long been a central question in evolutionary biology, with expression level emerging as the prime predictor. Although we have previously observed an anti-correlation between expression level (E) and evolutionary rate (R) in chloroplast genes, it remains unclear whether such an anti-correlation exists in plant mitochondrial genes. Ophioglossum vulgatum is a typical plant belonging to the Ophioglossaceae, characterized by its unique morphology with only a single leaf above ground. It holds significant scientific and medicinal value. Using the mitochondrial genome and transcriptome data of O. vulgatum, we first analyzed the correlation between mitochondrial gene expression, codon usage bias, and evolutionary rates in different tissues. Our findings indicated that mitochondrial gene expression level was the strongest between stem and leaf, while the weakest was between sporangium and root. Kruskal-Wallis tests revealed significant differences across various tissue types. Codon usage bias was influenced by both mutation and selection, with selection exerting a greater impact. The Spearman’s rank correlation coefficients between codon adaptation index and expression levels of sporangium, stem, leaf, and root were 0.1178, 0.3926, 0.4463, and 0.2945, respectively, with significance in stem and leaf (P < 0.05). The correlation coefficients between the nonsynonymous substitution rate (dN) and expression levels in sporangium, stem, leaf, and root were -0.0840, -0.1786, -0.1714, and -0.0857, respectively, yet none are statistically significant. The correlation coefficient between the synonymous substitution rate (dS) and expression levels in sporangium was negative, whereas those between dS and the stem, leaf, and root were positive, although they were not significant. The dN/dS ratio exhibited a significant negative correlation with expression levels in both leaf and root (P < 0.05). For the first time, our study revealed differences in the correlation between mitochondrial gene expression and codon usage bias, as well as evolutionary rates, across various tissues of O. vulgatum. Moreover, we also provide novel insights into understanding the effects of plant mitochondrial gene expression on evolutionary patterns.

Many plant mitochondrial (mt) genomes have been sequenced but few in ferns. Ophioglossum vulgatum represents a typical species of fern genus Ophioglossum with medicinal and scientific value. However, its mt genome structure remains to be characterized. This study assembled and annotated the complete O. vulgatum mt genome and presented its structural characters and repeat sequences firstly. Its mt and chloroplast (cp) transfer sequences were explored, and the phylogenetic significance of both mt and cp genomes was also evaluated at the family level. Our results showed that the complete mt genome of O. vulgatum is a single circular genome of 369,673 bp in length, containing 5000 dispersed repetitive sequences. Phylogenetic trees reconstructed from cp and mt genomes displayed similar topologies, but also showed subtle differences at certain nodes. There exist 4818 bp common gene fragments between cp and mt genomes, of which more than 70% are located in tRNA intergenic regions (in mt). In conclusion, we assembled the complete mt genome of O. vulgatum, identified its remarkable structural characters, and provided new insights on ferns. The complementary results derived from mt and cp phylogeny highlighted that some higher taxonomic-level phylogenetic relationships among ferns remain to be resolved.

Simple sequence repeats (SSRs) are found in nonrandom distributions in genomes and are thought to impact gene expression. The distribution patterns of 48 295 SSRs of Paphiopedilum malipoense are mined and characterized based on the first full‐length transcriptome and comprehensive transcriptome dataset from 12 organs. Statistical genomics analyses are used to investigate how SSRs in transcripts affect gene expression. The results demonstrate the correlations between SSR distributions, characteristics, and expression level. Nine expression‐modulating motifs (expMotifs) are identified and a model is proposed to explain the effect of their key features, potency, and gene function on an intra‐transcribed region scale. The expMotif‐transcribed region combination is the most predominant contributor to the expression‐modulating effect of SSRs, and some intra‐transcribed regions are critical for this effect. Genes containing the same type of expMotif‐SSR elements in the same transcribed region are likely linked in function, regulation, or evolution aspects. This study offers novel evidence to understand how SSRs regulate gene expression and provides potential regulatory elements for plant genetic engineering.

Based on the fundamental principles of spatial heterogeneity and landscape ecology, landscape mosaic (LM) offers a more effective method for capturing variations in landscape spatial components, patterns, and ecological functions compared to land use and land cover (LULC). This advantage is particularly pronounced when employing the InVEST model to evaluate habitat quality (HQ), as field surveys often yield highly variable results that challenge the accuracy and applicability of LULC-based assessments. This paper focuses on the Taihang Mountain area in Hebei Province as the study region, utilizing the Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Euclidean Distance (ED) model to achieve LM classification of the area. Based on this, the InVEST-HQ assessment is conducted, employing both OLS and GWR models to analyze the correlation between HQ and LM landscape patterns. The results indicate that (1) seven major LULC types were reclassified into nine pillar LM types and eleven transitional LM types, with a significant number of ecotone types emerging between different LULC types, among which cultivated land plays the most prominent role; (2) from 2000 to 2020, the overall HQ in the study area exhibited a continuous deterioration trend, particularly marked by a notable increase in functional areas of HQ areas classified as Level I; (3) factors such as the complexity of patch edges, the continuity between patches, and the diversity of patch types all significantly impact HQ. This study introduces an innovative methodological framework for HQ assessment using LM classifications within InVEST model, offering a robust foundation for comprehensive biodiversity monitoring and informed ecological management in the study area.