A novel method for assigning functional linkages to proteins using enhanced phylogenetic trees

Genome-wide investigation of the OR gene family in Helicoverpa armigera and functional analysis of OR48 and OR75 in metamorphosis development

The cotton bollworm, Helicoverpa armigera, is a significant global agricultural pest, particularly detrimental during its larval feeding period. Insects' odorant receptors (ORs) are crucial for their crop-feeding activities, yet a comprehensive analysis of H. armigera ORs has been lacking, and the influence of hormones on ORs remain understudied. Herein, we conducted a genome-wide study and identified 81 ORs, categorized into 15 distinct groups. Analyses of protein motifs and gene structures revealed both conservation within groups and divergence among them. Comparative gene duplication analysis between H. armigera and Bombyx mori highlighted different duplication patterns. We further investigated subcellular localization and protein interactions within the odorant receptor family, providing valuable insights for future functional and interaction studies of ORs. Specifically, we identified that OR48 and OR75 were abundantly expressed during molting/metamorphosis and feeding stages, respectively. We demonstrated that 20E induced the upregulation of OR48 via EcR, while insulin upregulated OR75 expression through InR. Moreover, 20E induced the translocation of OR48 to the cell membrane, mediating its effects. Functional studies involving the knockdown of OR48 and OR75 revealed their roles in metamorphosis development, with OR48 knockdown resulting in delayed pupation and OR75 knockdown leading to premature pupation. OR48 can promote autophagy and apoptosis in fat body, while OR75 can significantly inhibit apoptosis and autophagy. These findings significantly contribute to our understanding of OR function in H. armigera and shed light on potential avenues for pest control strategies.

Inverse Potts model improves accuracy of phylogenetic profiling

MOTIVATION

Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias.

RESULTS

To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/fukunagatsu/Ipm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mirage: estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families

Motivation

Reconstruction of gene copy number evolution is an essential approach for understanding how complex biological systems have been organized. Although various models have been proposed for gene copy number evolution, existing evolutionary models have not appropriately addressed the fact that different gene families can have very different gene gain/loss rates.

Results

In this study, we developed Mirage (MIxtuRe model for Ancestral Genome Estimation), which allows different gene families to have flexible gene gain/loss rates. Mirage can use three models for formulating heterogeneous evolution among gene families: the discretized Γ model, probability distribution-free model and pattern mixture (PM) model. Simulation analysis showed that Mirage can accurately estimate heterogeneous gene gain/loss rates and reconstruct gene-content evolutionary history. Application to empirical datasets demonstrated that the PM model fits genome data from various taxonomic groups better than the other heterogeneous models. Using Mirage, we revealed that metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated.

Availability and implementation

The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.

Genes that are involved in the same biological process tend to co-evolve. This property is exploited by the technique of phylogenetic profiling, which identifies co-evolving (and therefore likely functionally related) genes through patterns of correlated gene retention and loss in evolution and across species. However, conventional methods to computing and clustering these correlated genes do not scale with increasing numbers of genomes. HogProf is a novel phylogenetic profiling tool built on probabilistic data structures. It allows the user to construct searchable databases containing the evolutionary history of hundreds of thousands of protein families. Such fast detection of coevolution takes advantage of the rapidly increasing amount of genomic data publicly available, and can uncover unknown biological networks and guide in-vivo research and experimentation. We have applied our tool to describe the biological networks underpinning sexual reproduction in eukaryotes.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.

Interactomics: toward protein function and regulation

Protein-protein interactions are central to all cellular processes. Understanding of protein-protein interactions is therefore fundamental for many areas of biochemical and biomedical research and will facilitate an understanding of the cell process-regulating machinery, disease causative mechanisms, biomarkers, drug target discovery and drug development. In this review, we summarize methods for populating and analyzing the interactome, highlighting their advantages and disadvantages. Applications of interactomics in both the biochemical and clinical arenas are presented, illustrating important recent advances in the field.

The Glanville fritillary genome retains an ancient karyotype and reveals selective chromosomal fusions in Lepidoptera

Previous studies have reported that chromosome synteny in Lepidoptera has been well conserved, yet the number of haploid chromosomes varies widely from 5 to 223. Here we report the genome (393 Mb) of the Glanville fritillary butterfly (Melitaea cinxia; Nymphalidae), a widely recognized model species in metapopulation biology and eco-evolutionary research, which has the putative ancestral karyotype of n=31. Using a phylogenetic analyses of Nymphalidae and of other Lepidoptera, combined with orthologue-level comparisons of chromosomes, we conclude that the ancestral lepidopteran karyotype has been n=31 for at least 140 My. We show that fusion chromosomes have retained the ancestral chromosome segments and very few rearrangements have occurred across the fusion sites. The same, shortest ancestral chromosomes have independently participated in fusion events in species with smaller karyotypes. The short chromosomes have higher rearrangement rate than long ones. These characteristics highlight distinctive features of the evolutionary dynamics of butterflies and moths.

Butterflies and moths (Lepidoptera) vary in chromosome number. Here, the authors sequence the genome of the Glanville fritillary butterfly, Melitaea cinxia, show it has the ancestral lepidopteran karyotype and provide insight into how chromosomal fusions have shaped karyotype evolution in butterflies and moths.

Tracing evolutionary footprints to identify novel gene functional linkages

Systematic determination of gene function is an essential step in fully understanding the precise contribution of each gene for the proper execution of molecular functions in the cell. Gene functional linkage is defined as to describe the relationship of a group of genes with similar functions. With thousands of genomes sequenced, there arises a great opportunity to utilize gene evolutionary information to identify gene functional linkages. To this end, we established a computational method (called TRACE) to trace gene footprints through a gene functional network constructed from 341 prokaryotic genomes. TRACE performance was validated and successfully tested to predict enzyme functions as well as components of pathway. A so far undescribed chromosome partitioning-like protein ro03654 of an oleaginous bacteria Rhodococcus sp. RHA1 (RHA1) was predicted and verified experimentally with its deletion mutant showing growth inhibition compared to RHA1 wild type. In addition, four proteins were predicted to act as prokaryotic SNARE-like proteins, and two of them were shown to be localized at the plasma membrane. Thus, we believe that TRACE is an effective new method to infer prokaryotic gene functional linkages by tracing evolutionary events.

Emerging methods in protein co-evolution

Co-evolution is a fundamental component of the theory of evolution and is essential for understanding the relationships between species in complex ecological networks. A wide range of co-evolution-inspired computational methods has been designed to predict molecular interactions, but it is only recently that important advances have been made. Breakthroughs in the handling of phylogenetic information and in disentangling indirect relationships have resulted in an improved capacity to predict interactions between proteins and contacts between different protein residues. Here, we review the main co-evolution-based computational approaches, their theoretical basis, potential applications and foreseeable developments.