Special Care Is Needed in Applying Phylogenetic Comparative Methods to Gene Trees with Speciation and Duplication Nodes

Genome-wide identification and expression pattern analysis of the Aux/IAA (auxin/indole-3-acetic acid) gene family in alfalfa (Medicago sativa) and the potential functions under drought stress

BACKGROUND

Auxin/induced-3-acetic acid (Aux/IAA) is an important plant hormone that affects plant growth and resistance to abiotic stresses. Drought stress is a vital factor in reducing plant biomass yield and production quality. Alfalfa (Medicago sativa L.) is the most widely planted leguminous forage and one of the most economically valuable crops in the world. Aux/IAA is one of the early responsive gene families of auxin, playing a crucial role in response to drought stress. However, the characteristics of the Aux/IAA gene family in alfalfa and its potential function in response to drought stress are still unknown.

RESULT

A total of 41 Aux/IAA gene members were identified in alfalfa genome. The physicochemical, peptide structure, secondary and tertiary structure analysis of proteins encoded by these genes revealed functional diversity of the MsIAA gene. A phylogenetic analysis classified the MsIAA genes into I-X classes in two subgroups. And according to the gene domain structure, these genes were classified into typical MsIAA and atypical MsIAA. Gene structure analysis showed that the MsIAA genes contained 1-4 related motifs, and except for the third chromosome without MsIAAs, they were all located on 7 chromosomes. The gene duplication analysis revealed that segmental duplication and tandem duplication greatly affected the amplification of the MsIAA genes. Analysis of the Ka/Ks ratio of duplicated MsAux/IAA genes suggested purification selection pressure was high and functional differences were limited. In addition, identification and classification of promoter cis-elements elucidated that MsIAA genes contained numerous elements associated to phytohormone response and abiotic stress response. The prediction protein-protein interaction network showed that there was a complex interaction between the MsAux/IAA genes. Gene expression profiles were tissue-specific, and MsAux/IAA had a broad response to both common abiotic stress (ABA, salt, drought and cold) and heavy metal stress (Al and Pb). Furthermore, the expression patterns analysis of 41 Aux/IAA genes by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) showed that Aux/IAA genes can act as positive or negative factors to regulate the drought resistance in alfalfa.

CONCLUSION

This study provides useful information for the alfalfa auxin signaling gene families and candidate evidence for further investigation on the role of Aux/IAA under drought stress. Future studies could further elucidate the functional mechanism of the MsIAA genes response to drought stress.

Genome-wide identification and expression analysis of ARF gene family in embryonic development of Korean pine (Pinus koraiensis)

BACKGROUND

The Auxin Responsive Factor (ARF) family plays a crucial role in mediating auxin signal transduction and is vital for plant growth and development. However, the function of ARF genes in Korean pine (Pinus koraiensis), a conifer species of significant economic value, remains unclear.

RESULTS

This study utilized the whole genome of Korean pine to conduct bioinformatics analysis, resulting in the identification of 13 ARF genes. A phylogenetic analysis revealed that these 13 PkorARF genes can be classified into 4 subfamilies, indicating the presence of conserved structural characteristics within each subfamily. Protein interaction prediction indicated that Pkor01G00962.1 and Pkor07G00704.1 may have a significant role in regulating plant growth and development as core components of the PkorARFs family. Additionally, the analysis of RNA-seq and RT-qPCR expression patterns suggested that PkorARF genes play a crucial role in the development process of Korean pine.

CONCLUSION

Pkor01G00962.1 and Pkor07G00704.1, which are core genes of the PkorARFs family, play a potentially crucial role in regulating the fertilization and developmental process of Korean pine. This study provides a valuable reference for investigating the molecular mechanism of embryonic development in Korean pine and establishes a foundation for cultivating high-quality Korean pine.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Estimating transcriptome complexities across eukaryotes

BACKGROUND

Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the "remarkable lack of correspondence" between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study, we use a set of complexity metrics that allow for evaluating changes in complexity using TranD.

RESULTS

We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics - TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts.

CONCLUSIONS

With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step toward whole-transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity.

Genome-Wide Identification and Expression Analysis of the Aux/IAA Gene Family of the Drumstick Tree (Moringa oleifera Lam.) Reveals Regulatory Effects on Shoot Regeneration

Auxin plays a critical role in organogenesis in plants. The classical auxin signaling pathway holds that auxin initiates downstream signal transduction by degrading Aux/IAA transcription repressors that interact with ARF transcription factors. In this study, 23 MoIAA genes were identified in the drumstick tree genome. All MoIAA genes were located within five subfamilies based on phylogenetic evolution analysis; the gene characteristics and promoter cis-elements were also analyzed. The protein interaction network between the MoIAAs with MoARFs was complex. The MoIAA gene family responded positively to NAA treatment, exhibiting different patterns and degrees, notably for MoIAA1, MoIAA7 and MoIAA13. The three genes expressed and functioned in the nucleus; only the intact encoding protein of MoIAA13 exhibited transcriptional activation activity. The shoot regeneration capacity in the 35S::MoIAA13-OE transgenic line was considerably lower than in the wild type. These results establish a foundation for further research on MoIAA gene function and provide useful information for improved tissue culture efficiency and molecular breeding of M. oleifera.