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Background Different iron transport systems evolved in Gram-negative bacteria during evolution. Most of the transport systems depend on outer membrane localized TonB-dependent transporters (TBDTs), a periplasma-facing TonB protein and a plasma membrane localized machinery (ExbBD). So far, iron chelators (siderophores), oligosaccharides and polypeptides have been identified as substrates of TBDTs. For iron transport, three uptake systems are defined: the lactoferrin/transferrin binding proteins, the porphyrin-dependent transporters and the siderophore-dependent transporters. However, for cyanobacteria almost nothing is known about possible TonB-dependent uptake systems for iron or other substrates. Results We have screened all publicly available eubacterial genomes for sequences representing (putative) TBDTs. Based on sequence similarity, we identified 195 clusters, where elements of one cluster may possibly recognize similar substrates. For Anabaena sp. PCC 7120 we identified 22 genes as putative TBDTs covering almost all known TBDT subclasses. This is a high number of TBDTs compared to other cyanobacteria. The expression of the 22 putative TBDTs individually depends on the presence of iron, copper or nitrogen. Conclusions We exemplified on TBDTs the power of CLANS-based classification, which demonstrates its importance for future application in systems biology. In addition, the tentative substrate assignment based on characterized proteins will stimulate the research of TBDTs in different species. For cyanobacteria, the atypical dependence of TBDT gene expression on different nutrition points to a yet unknown regulatory mechanism. In addition, we were able to clarify a hypothesis of the absence of TonB in cyanobacteria by the identification of according sequences.
Phylogenetic relationships of the primarily wingless insects are still considered unresolved. Even the most comprehensive phylogenomic studies that addressed this question did not yield congruent results. In order to get a grip on these problems, we here analyzed the sources of incongruence in these phylogenomic studies using an extended transcriptome dataset.Our analyses showed that unevenly distributed missing data can be severely misleading by inflating node support despite the absence of phylogenetic signal. In consequence, only decisive datasets should be used which exclusively comprise data blocks containing all taxa whose relationships are addressed. Additionally, we employed Four-cluster Likelihood-Mapping (FcLM) to measure the degree of congruence among genes of a dataset, as a measure of support alternative to bootstrap. FcLM showed incongruent signal among genes, which in our case is correlated with neither functional class assignment of these genes, nor with model misspecification due to unpartitioned analyses. The herein analyzed dataset is the currently largest dataset covering primarily wingless insects, but failed to elucidate their interordinal phylogenetic relationships. While this is unsatisfying from a phylogenetic perspective, we try to show that the analyses of structure and signal within phylogenomic data can protect us from biased phylogenetic inferences due to analytical artefacts.
Ribosome biogenesis is fundamental for cellular life, but surprisingly little is known about the underlying pathway. In eukaryotes a comprehensive collection of experimentally verified ribosome biogenesis factors (RBFs) exists only for Saccharomyces cerevisiae. Far less is known for other fungi, animals or plants, and insights are even more limited for archaea. Starting from 255 yeast RBFs, we integrated ortholog searches, domain architecture comparisons and, in part, manual curation to investigate the inventories of RBF candidates in 261 eukaryotes, 26 archaea and 57 bacteria. The resulting phylogenetic profiles reveal the evolutionary ancestry of the yeast pathway. The oldest core comprising 20 RBF lineages dates back to the last universal common ancestor, while the youngest 20 factors are confined to the Saccharomycotina. On this basis, we outline similarities and differences of ribosome biogenesis across contemporary species. Archaea, so far a rather uncharted domain, possess 38 well-supported RBF candidates of which some are known to form functional sub-complexes in yeast. This provides initial evidence that ribosome biogenesis in eukaryotes and archaea follows similar principles. Within eukaryotes, RBF repertoires vary considerably. A comparison of yeast and human reveals that lineage-specific adaptation via RBF exclusion and addition characterizes the evolution of this ancient pathway.
Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. Orthologous genes that are detected across the full diversity of contemporary life allow reconstructing the gene set of LUCA, the last universal common ancestor. These genes presumably represent the functional repertoire common to – and necessary for – all living organisms. Design of artificial life has the potential to test this. Recently, a minimal gene (MG) set for a self-replicating cell was determined experimentally, and a surprisingly high number of genes have unknown functions and are not represented in LUCA. However, as similarity between orthologs decays with time, it becomes insufficient to infer common ancestry, leaving ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the evolutionary traceability, together with the software protTrace, that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. We show that the LUCA set comprises only high-traceable proteins most of which have catalytic functions. We further show that proteins in the MG set lacking orthologs outside bacteria mostly have low traceability, leaving open whether their eukaryotic orthologs have just been overlooked. On the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and non-detection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks.
Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. However, the similarity between orthologs decays with time, and ultimately it becomes insufficient to infer common ancestry. This leaves ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the "evolutionary traceability" as a measure that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. Using yeast, we show that genes that were thought to date back to the last universal common ancestor are of high traceability. Their functions mostly involve catalysis, ion transport, and ribonucleoprotein complex assembly. In turn, the fraction of yeast genes whose traceability is not sufficient to infer their presence in last universal common ancestor is enriched for regulatory functions. Computing the traceabilities of genes that have been experimentally characterized as being essential for a self-replicating cell reveals that many of the genes that lack orthologs outside bacteria have low traceability. This leaves open whether their orthologs in the eukaryotic and archaeal domains have been overlooked. Looking at the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and nondetection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks. "protTrace," a software tool for computing evolutionary traceability, is freely available at https://github.com/BIONF/protTrace.git; last accessed February 10, 2019.