Functional Constraints on Insect Immune System Components Govern Their Evolutionary Trajectories.

Roles of constraints in shaping evolutionary outcomes are often considered in the contexts of developmental biology and population genetics, in terms of capacities to generate new variants and how selection limits or promotes consequent phenotypic changes. Comparative genomics also recognizes the role of constraints, in terms of shaping evolution of gene and genome architectures, sequence evolutionary rates, and gene gains or losses, as well as on molecular phenotypes. Characterizing patterns of genomic change where putative functions and interactions of system components are relatively well described offers opportunities to explore whether genes with similar roles exhibit similar evolutionary trajectories. Using insect immunity as our test case system, we hypothesize that characterizing gene evolutionary histories can define distinct dynamics associated with different functional roles. We develop metrics that quantify gene evolutionary histories, employ these to characterize evolutionary features of immune gene repertoires, and explore relationships between gene family evolutionary profiles and their roles in immunity to understand how different constraints may relate to distinct dynamics. We identified three main axes of evolutionary trajectories characterized by gene duplication and synteny, maintenance/stability and sequence conservation, and loss and sequence divergence, highlighting similar and contrasting patterns across these axes amongst subsets of immune genes. Our results suggest that where and how genes participate in immune responses limit the range of possible evolutionary scenarios they exhibit. The test case study system of insect immunity highlights the potential of applying comparative genomics approaches to characterize how functional constraints on different components of biological systems govern their evolutionary trajectories.

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation.

Characterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. To exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the community's best performing individual methods. CrowdGO therefore offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations.

Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies.

BACKGROUND: New sequencing technologies have lowered financial barriers to whole genome sequencing, but resulting assemblies are often fragmented and far from 'finished'. Updating multi-scaffold drafts to chromosome-level status can be achieved through experimental mapping or re-sequencing efforts. Avoiding the costs associated with such approaches, comparative genomic analysis of gene order conservation (synteny) to predict scaffold neighbours (adjacencies) offers a potentially useful complementary method for improving draft assemblies. RESULTS: We evaluated and employed 3 gene synteny-based methods applied to 21 Anopheles mosquito assemblies to produce consensus sets of scaffold adjacencies. For subsets of the assemblies, we integrated these with additional supporting data to confirm and complement the synteny-based adjacencies: 6 with physical mapping data that anchor scaffolds to chromosome locations, 13 with paired-end RNA sequencing (RNAseq) data, and 3 with new assemblies based on re-scaffolding or long-read data. Our combined analyses produced 20 new superscaffolded assemblies with improved contiguities: 7 for which assignments of non-anchored scaffolds to chromosome arms span more than 75% of the assemblies, and a further 7 with chromosome anchoring including an 88% anchored Anopheles arabiensis assembly and, respectively, 73% and 84% anchored assemblies with comprehensively updated cytogenetic photomaps for Anopheles funestus and Anopheles stephensi. CONCLUSIONS: Experimental data from probe mapping, RNAseq, or long-read technologies, where available, all contribute to successful upgrading of draft assemblies. Our evaluations show that gene synteny-based computational methods represent a valuable alternative or complementary approach. Our improved Anopheles reference assemblies highlight the utility of applying comparative genomics approaches to improve community genomic resources.

Identification of methylated GnTI-dependent N-glycans in Botryococcus brauni.

In contrast to mammals and vascular plants, microalgae show a high diversity in the N-glycan structures of complex N-glycoproteins. Although homologues for ß1,2-N-acetylglucosaminyltransferase I (GnTI), a key enzyme in the formation of complex N-glycans, have been identified in several algal species, GnTI-dependent N-glycans have not been detected so far. We have performed an N-glycoproteomic analysis of the hydrocarbon oils accumulating green microalgae Botryococcus braunii. Thereby, the analysis of intact N-glycopeptides allowed the determination of N-glycan compositions. Furthermore, insights into the role of N-glycosylation in B. braunii were gained from functional annotation of the identified N-glycoproteins. In total, 517 unique N-glycosylated peptides have been identified, including intact N-glycopeptides that harbored N-acetylhexosamine (HexNAc) at the nonreducing end. Surprisingly, these GnTI-dependent N-glycans were also found to be modified with (di)methylated hexose. The identification of GnTI-dependent N-glycans in combination with N-glycan methylation in B. braunii revealed an uncommon type of N-glycan processing in this microalgae.

Wei2GO: weighted sequence similarity-based protein function prediction.

BACKGROUND: Protein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases. METHODS: This paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations. RESULTS: Wei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes. AVAILABILITY: Wei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO.

Anopheles mosquitoes reveal new principles of 3D genome organization in insects.

Chromosomes are hierarchically folded within cell nuclei into territories, domains and subdomains, but the functional importance and evolutionary dynamics of these hierarchies are poorly defined. Here, we comprehensively profile genome organizations of five Anopheles mosquito species and show how different levels of chromatin architecture influence each other. Patterns observed on Hi-C maps are associated with known cytological structures, epigenetic profiles, and gene expression levels. Evolutionary analysis reveals conservation of chromatin architecture within synteny blocks for tens of millions of years and enrichment of synteny breakpoints in regions with increased genomic insulation. However, in-depth analysis shows a confounding effect of gene density on both insulation and distribution of synteny breakpoints, suggesting limited causal relationship between breakpoints and regions with increased genomic insulation. At the level of individual loci, we identify specific, extremely long-ranged looping interactions, conserved for ~100 million years. We demonstrate that the mechanisms underlying these looping contacts differ from previously described Polycomb-dependent interactions and clustering of active chromatin.

Summary Visualizations of Gene Ontology Terms With GO-Figure!

The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large-scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualizations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualization tools and resources there is a lack of standalone open-source software solutions that facilitate explorations of key features of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualizations of lists of GO terms, designed to support exploratory data analyses and dataset comparisons.

Of Genes and Genomes: Mosquito Evolution and Diversity.

Mosquitoes are widely despised for their exasperating buzzing and irritating bites, and more poignantly because, during blood-feeding, females may transmit pathogens that cause devastating diseases. However, the ability to transmit such viruses, filarial worms, or malaria parasites varies greatly amongst the ∼3500 recognised mosquito species. Applying omics technologies to sample this diversity and explore the biology underlying these variations is bringing increasingly greater resolution that enhances our understanding of mosquito evolution. Here we review the current status of mosquito omics, or 'mozomics', resources and recent advances in their applications to characterise mosquito biology and evolution, with a focus on the intersection of evolutionary and functional genomics to understand the putative links between gene and genome dynamism and mosquito diversity.

Genome Sequence of the Wheat Stem Sawfly, Cephus cinctus, Representing an Early-Branching Lineage of the Hymenoptera, Illuminates Evolution of Hymenopteran Chemoreceptors.

The wheat stem sawfly, Cephus cinctus, is a major pest of wheat and key ecological player in the grasslands of western North America. It also represents the distinctive Cephoidea superfamily of sawflies (Symphyta) that appeared early during the hymenopteran radiation, but after three early-branching eusymphytan superfamilies that form the base of the order Hymenoptera. We present a high-quality draft genome assembly of 162 Mb in 1,976 scaffolds with a scaffold N50 of 622 kb. Automated gene annotation identified 11,210 protein-coding gene models and 1,307 noncoding RNA models. Thirteen percent of the assembly consists of ∼58,000 transposable elements partitioned equally between Class-I and Class-II elements. Orthology analysis reveals that 86% of Cephus proteins have identifiable orthologs in other insects. Phylogenomic analysis of conserved subsets of these proteins supports the placement of the Cephoidea between the Eusymphyta and the parasitic woodwasp superfamily Orussoidea. Manual annotation and phylogenetic analysis of families of odorant, gustatory, and ionotropic receptors, plus odorant-binding proteins, shows that Cephus has representatives for most conserved and expanded gene lineages in the Apocrita (wasps, ants, and bees). Cephus has also maintained several insect gene lineages that have been lost from the Apocrita, most prominently the carbon dioxide receptor subfamily. Furthermore, Cephus encodes a few small lineage-specific chemoreceptor gene family expansions that might be involved in adaptations to new grasses including wheat. These comparative analyses identify gene family members likely to have been present in the hymenopteran ancestor and provide a new perspective on the evolution of the chemosensory gene repertoire.

Green genes: bioinformatics and systems-biology innovations drive algal biotechnology.

Many species of microalgae produce hydrocarbons, polysaccharides, and other valuable products in significant amounts. However, large-scale production of algal products is not yet competitive against non-renewable alternatives from fossil fuel. Metabolic engineering approaches will help to improve productivity, but the exact metabolic pathways and the identities of the majority of the genes involved remain unknown. Recent advances in bioinformatics and systems-biology modeling coupled with increasing numbers of algal genome-sequencing projects are providing the means to address this. A multidisciplinary integration of methods will provide synergy for a systems-level understanding of microalgae, and thereby accelerate the improvement of industrially valuable strains. In this review we highlight recent advances and challenges to microalgal research and discuss future potential.