Genotype networks, innovation, and robustness in sulfur metabolism

Background: A metabolism is a complex network of chemical reactions. This network synthesizes multiple small precursor molecules of biomass from chemicals that occur in the environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined as the set of enzyme-coding genes whose products catalyze the network's reactions. Each metabolic genotype has a metabolic phenotype. We define this metabolic phenotype as the spectrum of different sources of a chemical element that a metabolism can use to synthesize biomass. We here focus on the element sulfur. We study properties of the space of all possible metabolic genotypes in sulfur metabolism by analyzing random metabolic genotypes that are viable on different numbers of sulfur sources. Results: We show that metabolic genotypes with the same phenotype form large connected genotype networks - networks of metabolic networks - that extend far through metabolic genotype space. How far they reach through this space depends linearly on the number of super-essential reactions. A super-essential reaction is an essential reaction that occurs in all networks viable in a given environment. Metabolic networks can differ in how robust their phenotype is to the removal of individual reactions. We find that this robustness depends on metabolic network size, and on other variables, such as the size of minimal metabolic networks whose reactions are all essential in a specific environment. We show that different neighborhoods of any genotype network harbor very different novel phenotypes, metabolic innovations that can sustain life on novel sulfur sources. We also analyze the ability of evolving populations of metabolic networks to explore novel metabolic phenotypes. This ability is facilitated by the existence of genotype networks, because different neighborhoods of these networks contain very different novel phenotypes. Conclusions: We show that the space of metabolic genotypes involved in sulfur metabolism is organized similarly to that of carbon metabolism. We demonstrate that the maximum genotype distance and robustness of metabolic networks can be explained by the number of superessential reactions and by the sizes of minimal metabolic networks viable in an environment. In contrast to the genotype space of macromolecules, where phenotypic robustness may facilitate phenotypic innovation, we show that here the ability to access novel phenotypes does not monotonically increase with robustness

HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences

Motivation: Nucleotide sequence data are being produced at an ever increasing rate. Clustering such sequences by similarity is often an essential first step in their analysis—intended to reduce redundancy, define gene families or suggest taxonomic units. Exact clustering algorithms, such as hierarchical clustering, scale relatively poorly in terms of run time and memory usage, yet they are desirable because heuristic shortcuts taken during clustering might have unintended consequences in later analysis steps. Results: Here we present HPC-CLUST, a highly optimized software pipeline that can cluster large numbers of pre-aligned DNA sequences by running on distributed computing hardware. It allocates both memory and computing resources efficiently, and can process more than a million sequences in a few hours on a small cluster. Availability and implementation: Source code and binaries are freely available at http://meringlab.org/software/hpc-clust/; the pipeline is implemented in C++ and uses the Message Passing Interface (MPI) standard for distributed computing. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics onlin

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels

Toxicological effects of the rare earth element neodymium in Mytilus galloprovincialis

The wide range of applications of rare earth elements (REE) is leading to their occurrence in worldwide aquatic environments. Among the most popular REE is Neodymium (Nd), being widely used in permanent magnets, lasers, and glass additives. Neodymium–iron–boron (NdFeB) magnets is the main application of Nd since they are used in electric motors, hard disk drives, speakers and generators for wind turbines. Recent studies have already evaluated the toxic potential of different REE, but no information is available on the effects of Nd towards marine bivalves. Thus, the present study evaluated the biochemical alterations caused by Nd in the mussel Mytilus galloprovincialis exposed to this element for 28 days. The results obtained clearly demonstrated that Nd was accumulated by mussels, leading to mussel’s metabolic capacity increase and GLY expenditure, in an attempt to fuel up defense mechanisms. Antioxidant and biotransformation defenses were insufficient in the elimination of ROS excess, resulting from the presence of Nd and increased electron transport system activity, which caused cellular damages (measured by lipid peroxidation) and loss of redox balance (assessed by the ratio between reduced and oxidized glutathione). The results obtained clearly highlight the potential toxicity of REEs and, in particular of Nd, with impacts at cellular level, which may have consequences in mussel’s survival, growth and reproduction, affecting mussel’s population.publishe

The Evolution of Ecological Diversity in Acidobacteria

Acidobacteria occur in a large variety of ecosystems worldwide and are particularly abundant and highly diverse in soils. In spite of their diversity, only few species have been characterized to date which makes Acidobacteria one of the most poorly understood phyla among the domain Bacteria. We used a culture-independent niche modeling approach to elucidate ecological adaptations and their evolution for 4,154 operational taxonomic units (OTUs) of Acidobacteria across 150 different, comprehensively characterized grassland soils in Germany. Using the relative abundances of their 16S rRNA gene transcripts, the responses of active OTUs along gradients of 41 environmental variables were modeled using hierarchical logistic regression (HOF), which allowed to determine values for optimum activity for each variable (niche optima). By linking 16S rRNA transcripts to the phylogeny of full 16S rRNA gene sequences, we could trace the evolution of the different ecological adaptations during the diversification of Acidobacteria. This approach revealed a pronounced ecological diversification even among acidobacterial sister clades. Although the evolution of habitat adaptation was mainly cladogenic, it was disrupted by recurrent events of convergent evolution that resulted in frequent habitat switching within individual clades. Our findings indicate that the high diversity of soil acidobacterial communities is largely sustained by differential habitat adaptation even at the level of closely related species. A comparison of niche optima of individual OTUs with the phenotypic properties of their cultivated representatives showed that our niche modeling approach (1) correctly predicts those physiological properties that have been determined for cultivated species of Acidobacteria but (2) also provides ample information on ecological adaptations that cannot be inferred from standard taxonomic descriptions of bacterial isolates. These novel information on specific adaptations of not-yet-cultivated Acidobacteria can therefore guide future cultivation trials and likely will increase their cultivation success

Differences in Cell Division Rates Drive the Evolution of Terminal Differentiation in Microbes

Multicellular differentiated organisms are composed of cells that begin by developing from a single pluripotent germ cell. In many organisms, a proportion of cells differentiate into specialized somatic cells. Whether these cells lose their pluripotency or are able to reverse their differentiated state has important consequences. Reversibly differentiated cells can potentially regenerate parts of an organism and allow reproduction through fragmentation. In many organisms, however, somatic differentiation is terminal, thereby restricting the developmental paths to reproduction. The reason why terminal differentiation is a common developmental strategy remains unexplored. To understand the conditions that affect the evolution of terminal versus reversible differentiation, we developed a computational model inspired by differentiating cyanobacteria. We simulated the evolution of a population of two cell types –nitrogen fixing or photosynthetic– that exchange resources. The traits that control differentiation rates between cell types are allowed to evolve in the model. Although the topology of cell interactions and differentiation costs play a role in the evolution of terminal and reversible differentiation, the most important factor is the difference in division rates between cell types. Faster dividing cells always evolve to become the germ line. Our results explain why most multicellular differentiated cyanobacteria have terminally differentiated cells, while some have reversibly differentiated cells. We further observed that symbioses involving two cooperating lineages can evolve under conditions where aggregate size, connectivity, and differentiation costs are high. This may explain why plants engage in symbiotic interactions with diazotrophic bacteria

Pathogens and host immunity in the ancient human oral cavity.

Calcified dental plaque (dental calculus) preserves for millennia and entraps biomolecules from all domains of life and viruses. We report the first, to our knowledge, high-resolution taxonomic and protein functional characterization of the ancient oral microbiome and demonstrate that the oral cavity has long served as a reservoir for bacteria implicated in both local and systemic disease. We characterize (i) the ancient oral microbiome in a diseased state, (ii) 40 opportunistic pathogens, (iii) ancient human-associated putative antibiotic resistance genes, (iv) a genome reconstruction of the periodontal pathogen Tannerella forsythia, (v) 239 bacterial and 43 human proteins, allowing confirmation of a long-term association between host immune factors, 'red complex' pathogens and periodontal disease, and (vi) DNA sequences matching dietary sources. Directly datable and nearly ubiquitous, dental calculus permits the simultaneous investigation of pathogen activity, host immunity and diet, thereby extending direct investigation of common diseases into the human evolutionary past

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio