PlantFUNCO: Integrative Functional Genomics Database Reveals Clues into Duplicates Divergence Evolution.

Evolutionary epigenomics and, more generally, evolutionary functional genomics, are emerging fields that study how non-DNA-encoded alterations in gene expression regulation are an important form of plasticity and adaptation. Previous evidence analyzing plants' comparative functional genomics has mostly focused on comparing same assay-matched experiments, missing the power of heterogeneous datasets for conservation inference. To fill this gap, we developed PlantFUN(ctional)CO(nservation) database, which is constituted by several tools and two main resources: interspecies chromatin states and functional genomics conservation scores, presented and analyzed in this work for three well-established plant models (Arabidopsis thaliana, Oryza sativa, and Zea mays). Overall, PlantFUNCO elucidated evolutionary information in terms of cross-species functional agreement. Therefore, providing a new complementary comparative-genomics source for assessing evolutionary studies. To illustrate the potential applications of this database, we replicated two previously published models predicting genetic redundancy in A. thaliana and found that chromatin states are a determinant of paralogs degree of functional divergence. These predictions were validated based on the phenotypes of mitochondrial alternative oxidase knockout mutants under two different stressors. Taking all the above into account, PlantFUNCO aim to leverage data diversity and extrapolate molecular mechanisms findings from different model organisms to determine the extent of functional conservation, thus, deepening our understanding of how plants epigenome and functional noncoding genome have evolved. PlantFUNCO is available at https://rocesv.github.io/PlantFUNCO.

Integrative analysis in Pinus revealed long-term heat stress splicing memory.

Due to the current climate change, many studies have described main drivers in abiotic stress. Recent findings suggest that alternative splicing (AS) has a critical role in controlling plant responses to high temperature. AS is a mechanism that allows organisms to create an assortment of RNA transcripts and proteins using a single gene. However, the most important roles of AS in stress could not be rigorously addressed because research has been focused on model species, covering only a narrow phylogenetic and lifecycle spectrum. Thus, AS degree of diversification among more dissimilar taxa in heat response is still largely unknown. To fill this gap, the present study employs a systems biology approach to examine how the AS landscape responds to and 'remembers' heat stress in conifers, a group which has received little attention even though their position can solve key evolutionary questions. Contrary to angiosperms, we found that potential intron retention may not be the most prevalent type of AS. Furthermore, our integrative analysis with metabolome and proteome data places splicing as the main source of variation during the response. Finally, we evaluated possible acquired long-term splicing memory in a diverse subset of events, and although this mechanism seems to be conserved in seed plants, AS dynamics are divergent. These discoveries reveal the particular way of remembering past temperature changes in long-lived plants and open the door to include species with unique features to determine the extent of conservation in gene expression regulation.

Protein Interaction Networks: Functional and Statistical Approaches.

The evolution of next-generation sequencing and high-throughput technologies has created new opportunities and challenges in data science. Currently, a classic proteomics analysis can be complemented by going a step beyond the individual analysis of the proteome by using integrative approaches. These integrations can be focused either on inferring relationships among proteins themselves, with other molecular levels, phenotype, or even environmental data, giving the researcher new tools to extract and determine the most relevant information in biological terms. Furthermore, it is also important the employ of visualization methods that allow a correct and deep interpretation of data.To carry out these analyses, several bioinformatics and biostatistical tools are required. In this chapter, different workflows that enable the creation of interaction networks are proposed. Resulting networks reduce the complexity of original datasets, depicting complex statistical relationships (through PLS analysis and variants), functional networks (STRING, shinyGO), and a combination of both approaches. Recently developed methods for integrating different omics levels, such as coinertial analyses or DIABLO, are also described. Finally, the use of Cytoscape or Gephi was described for the representation and mining of the different networks.This approach constitutes a new way of acquiring a deeper knowledge of the function of proteins, such as the search for specific connections of each group to identify differentially connected modules, which may reflect involved protein complexes and key pathways.