orsum: a Python package for filtering and comparing enrichment analyses using a simple principle.

BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the comparison of results obtained from different enrichment analyses. An approach to overcome these issues is using down-sized annotation collections composed of non-redundant terms. However, down-sized collections are generic and the level of detail may not fit the user's study. Other available approaches include clustering and filtering tools, which are based on similarity measures and thresholds that can be complicated to comprehend and set. RESULT: We propose orsum, a Python package to filter enrichment results. orsum can filter multiple enrichment results collectively and highlight common and specific annotation terms. Filtering in orsum is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes; the remaining more significant term becomes the representative term for the discarded term. This principle ensures that the main biological information is preserved in the filtered results while reducing redundancy. In addition, as the representative terms are selected from the original enrichment results, orsum outputs filtered terms tailored to the study. As a use case, we applied orsum to the enrichment analyses of four lists of genes, each associated with a neurodegenerative disease. CONCLUSION: orsum provides a comprehensible and effective way of filtering and comparing enrichment results. It is available at https://anaconda.org/bioconda/orsum .

A multi-objective genetic algorithm to find active modules in multiplex biological networks.

The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.

A reference genome for pea provides insight into legume genome evolution.

We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.

Transcriptional Reprogramming of Pea Leaves at Early Reproductive Stages.

Pea (Pisum sativum L.) is an important source of dietary proteins. Nutrient recycling from leaves contributes to the accumulation of seed proteins and is a pivotal determinant of protein yields in this grain legume. The aim of this study was to unveil the transcriptional regulations occurring in pea leaves before the sharp decrease in chlorophyll breakdown. As a prelude to this study, a time-series analysis of 15N translocation at the whole plant level was performed, which indicated that nitrogen recycling among organs was highly dynamic during this period and varied depending on nitrate availability. Leaves collected on vegetative and reproductive nodes were further analyzed by transcriptomics. The data revealed extensive transcriptome changes in leaves of reproductive nodes during early seed development (from flowering to 14 days after flowering), including an up-regulation of genes encoding transporters, and particularly of sulfate that might sustain sulfur metabolism in leaves of the reproductive part. This developmental period was also characterized by a down-regulation of cell wall-associated genes in leaves of both reproductive and vegetative nodes, reflecting a shift in cell wall structure. Later on, 27 days after flowering, genes potentially switching the metabolism of leaves toward senescence were pinpointed, some of which are related to ribosomal RNA processing, autophagy, or transport systems. Transcription factors differentially regulated in leaves between stages were identified and a gene co-expression network pointed out some of them as potential regulators of the above-mentioned biological processes. The same approach was conducted in Medicago truncatula to identify shared regulations with this wild legume species. Altogether the results give a global view of transcriptional events in leaves of legumes at early reproductive stages and provide a valuable resource of candidate genes that could be targeted by reverse genetics to improve nutrient remobilization and/or delay catabolic processes leading to senescence.

Water stress combined with sulfur deficiency in pea affects yield components but mitigates the effect of deficiency on seed globulin composition.

Water stress and sulfur (S) deficiency are two constraints increasingly faced by crops due to climate change and low-input agricultural practices. To investigate their interaction in the grain legume pea (Pisum sativum), sulfate was depleted at the mid-vegetative stage and a moderate 9-d water stress period was imposed during the early reproductive phase. The combination of the stresses impeded reproductive processes in a synergistic manner, reducing seed weight and seed number, and inducing seed abortion, which highlighted the paramount importance of sulfur for maintaining seed yield components under water stress. On the other hand, the moderate water stress mitigated the negative effect of sulfur deficiency on the accumulation of S-rich globulins (11S) in seeds, probably due to a lower seed sink strength for nitrogen, enabling a readjustment of the ratio of S-poor (7S) to 11S globulins. Transcriptome analysis of developing seeds at the end of the combined stress period indicated that similar biological processes were regulated in response to sulfur deficiency and to the combined stress, but that the extent of the transcriptional regulation was greater under sulfur deficiency. Seeds from plants subjected to the combined stresses showed a specific up-regulation of a set of transcription factor and SUMO ligase genes, indicating the establishment of unique regulatory processes when sulfur deficiency is combined with water stress.

The loss of adipokine genes in the chicken genome and implications for insulin metabolism.

Gene loss is one of the main drivers in the evolution of genomes and species. The demonstration that a gene has been lost by pseudogenization is truly complete when one finds the pseudogene in the orthologous genomic region with respect to active genes in other species. In some cases, the identification of such orthologous loci is not possible because of chromosomal rearrangements or if the gene of interest has not yet been sequenced. This question is particularly important in the case of birds because the genomes of avian species possess only about 15,000 predicted genes, in comparison with 20,000 in mammals. Yet, gene loss raises the question of which functions are affected by the changes in gene counts. We describe a systematic approach that makes it possible to demonstrate gene loss in the chicken genome even if a pseudogene has not been found. By using phylogenetic and synteny analysis in vertebrates, genome-wide comparisons between the chicken genome and expressed sequence tags, RNAseq data analysis, statistical analysis of the chicken genome, and radiation hybrid mapping, we show that resistin, TNFα, and PAI-1 (SERPINE1), three genes encoding adipokines inhibiting insulin sensitivity, have been lost in chicken and zebra finch genomes. Moreover, omentin, a gene encoding an adipokine that enhances insulin sensitivity, has also been lost in the chicken genome. Overall, only one adipokine inhibiting insulin sensitivity and five adipokines enhancing insulin sensitivity are still present in the chicken genome. These genetic differences between mammals and chicken, given the functions of the genes in mammals, would have dramatic consequences on chicken endocrinology, leading to novel equilibriums especially in the regulation of energy metabolism, insulin sensitivity, as well as appetite and reproduction.