Nitrogen (N) and/or phosphorus (P) availability can limit growth of primary producers across most of the world's aquatic and terrestrial ecosystems. These constraints are commonly overcome in agriculture by applying fertilizers to improve yields. However, excessive anthropogenic N and P inputs impact natural environments and have far-reaching ecological and evolutionary consequences, from individual species up to entire ecosystems. The extent to which global N and P cycles have been perturbed over the past century can be seen as a global fertilization experiment with significant redistribution of nutrients across different ecosystems. Here we explore the effects of N and P availability on stoichiometry and genomic traits of organisms, which, in turn, can influence: (i) plant and animal abundances; (ii) trophic interactions and population dynamics; and (iii) ecosystem dynamics and productivity of agricultural crops. We articulate research priorities for a deeper understanding of how bioavailable N and P move through the environment and exert their ultimate impacts on biodiversity and ecosystem services.

We investigate how the dynamics and outcomes of adaptation by natural selection are affected by environmental stability by simulating adaptive walks in response to an environmental change of fixed magnitude but variable speed. Here we consider monomorphic lineages that adapt by the sequential fixation of beneficial mutations. This is modeled by selecting short RNA sequences for folding stability and secondary structure conservation at increasing temperatures. Using short RNA sequences allows us to describe adaptive outcomes in terms of genotype (sequence) and phenotype (secondary structure) and to follow the dynamics of fitness increase. We find that slower rates of environmental change affect the dynamics of adaptive walks by reducing the fitness effect of fixed beneficial mutations, as well as by increasing the range of time in which the substitutions of largest effect are likely to occur. In addition, adaptation to slower rates of environmental change results in fitter endpoints with fewer possible end phenotypes relative to lineages that adapt to a sudden change. This suggests that care should be taken when experiments using sudden environmental changes are used to make predictions about adaptive responses to gradual change. F OLLOWING an environmental change, lineages adapt either by using standing genetic variation or by fixing novel beneficial mutations, depending in part on the timescale considered. Traditionally, adaptation is studied by considering the changes that take place in a population or lineage after it is suddenly placed in an environment to which it is poorly adapted. When this adaptation occurs by fixing sequential novel beneficial mutations, it is often described as an adaptive walk. The majority of experimental and theoretical studies of adaptation follow a change in phenotype in a novel constant environment (reviewed by Orr 2002;Elena and Lenski 2003). Some experimental studies also document adaptation to sequential environments (Travisano et al. 1995;Collins et al. 2006). However, few environmental changes outside of laboratories and natural disasters occur instantaneously, and few natural environments remain constant over the time needed to fix beneficial mutations. Because of this discrepancy between the stability of environments used to study adaptation and that of natural environments, there is a growing concern that changing environments should be taken into account in experiments and models of adaptation (Wilson et al. 2006).Adaptive walks toward stationary optima have been described by both theory and experiments. Adaptation in a stable novel environment happens by first fixing beneficial mutations of large effect and then those of smaller effect, with adaptation following a ''decreasing returns '' scenario (Orr 1998). This has been shown to occur in large microbial populations (Gerrish 2001;Imhof and Schlö tterer 2001). In addition, it has been suggested that the number of possible beneficial mutations decreases with the magnitude of effect of these mutations (Wichman et ...

We observe that the time of appearance of cellular compartmentalization correlates with atmospheric oxygen concentration. To explore this correlation, we predict and characterize the topology of all transmembrane proteins in 19 taxa and correlate differences in topology with historical atmospheric oxygen concentrations. Here we show that transmembrane proteins, individually and as a group, were probably selectively excluding oxygen in ancient ancestral taxa, and that this constraint decreased over time when atmospheric oxygen levels rose. As this constraint decreased, the size and number of communication-related transmembrane proteins increased. We suggest the hypothesis that atmospheric oxygen concentrations affected the timing of the evolution of cellular compartmentalization by constraining the size of domains necessary for communication across membranes.One of the major transitions in macroevolution was the appearance of eukaryotic cells between 2.1 and 1.8 billion years ago [1][2][3] . Cellular compartmentalization by membranes that are impermeable to large or charged molecules requires transport and communication across intracellular membranes. Eukaryotes devote more proteins to roles in communication than prokaryotes; this innovation involved a shift in the dominant secondary structures of transmembrane proteins 4 . Protein secondary structure is largely determined by hydrophobicity 5 , where oxygen and nitrogen are vital to forming hydrophilic residues. Transmembrane protein topology is further influenced by charge, where positively charged amino acids are more prevalent in cytoplasmic domains and negatively charged amino acids are more prevalent in extracellular domains [6][7][8] . This implies that changes in protein atomic composition may occur in parallel with changes in protein function. Traditionally, functional changes were thought to be associated with changes in amino acid sequence 9 , but an alternative approach is to consider proteins at the atomic level. This may be appropriate when large fluctuations in the elemental components of proteins occur through changes in absolute abundance, relative abundance, or form. In this case, nutritional constraints, metabolic optimization and chemical properties such as redox state may have important roles in protein evolution.The atomic content of biomolecules has a role in evolution Several examples of stoichiometric constraints on evolutionary and ecological outcomes have been reported recently. For example, variation in the atomic content of proteins in cyanobacterial lightharvesting proteins and microbial sulphur assimilatory enzymes correlates with nutrient availability 10,11 . Similarly, the carbon content of proteomes differs between species and correlates with genomic G1C content, which may reflect carbon availability in natural habitats 12 . The nitrogen content of proteins is lower in plants than in animals and is related to gene expression levels in plants 13 . These studies indicate that physiology, proteomes and genomes may bear detectable eco...

The new field of “stoichiogenomics” integrates evolution, ecology, and bioinformatics to reveal surprising patterns of differential usage of key elements (e.g., nitrogen, N) in proteins and nucleic acids. Because the canonical amino acids as well as nucleotides differ in element counts, natural selection due to limited element supplies might bias monomer usage to reduce element costs. For example, proteins that respond to N limitation in microbes use a lower proportion of N-rich amino acids, whereas proteome- and transcriptome-wide element contents differ significantly for plants as compared to animals, likely because of differential severity of element limitations. In this review, we show that with these findings, new directions for future investigations are emerging, particularly via the increasing availability of diverse meta-genomic and meta-transcriptomic data sets.

Phenotypes and behaviors respond to resource constraints via adaptation, but the influence of ecological limitations on the composition of eukaryotic genomes is still unclear. We trace connections between plant ecology and genomes through their elemental composition. Inorganic sources of nitrogen (N) are severely limiting to plants in natural ecosystems. This constraint would favor the use of N-poor nucleotides in plant genomes. We show that the transcribed segments of undomesticated plant genomes are the most N poor, with genomes and proteomes bearing signatures of N limitation. Consistent with the predictions of natural selection for N conservation, the precursors of transcriptome show the greatest deviations from Chargaff's second parity rule. Furthermore, crops show higher N contents than undomesticated plants, likely due to the relaxation of natural selection owing to the use of N-rich fertilizers. These findings indicate a fundamental role of N limitation in the evolution of plant genomes, and they link the genomes with the ecosystem context within which biota evolve.

BackgroundNullomers are short DNA sequences that are absent from the genomes of humans and other species. Assuming that nullomers are the signatures of natural selection against deleterious sequences in humans, the use of nullomers in drug target identification, pesticide development, environmental monitoring, and forensic applications has been envisioned.ResultsHere, we show that the hypermutability of CpG dinucleotides, rather than the natural selection against the nullomer sequences, is likely the reason for the phenomenal event of short sequence motifs becoming nullomers. Furthermore, many reported human nullomers differ by only one nucleotide, which reinforces the role of mutation in the evolution of the constellation of nullomers in populations and species. The known nullomers in chimpanzee, cow, dog, and mouse genomes show patterns that are consistent with those seen in humans.ConclusionsThe role of mutations, instead of selection, in generating nullomers cast doubt on the utility of nullomers in many envisioned applications, because of their dependence on the role of lethal selection on the origin of nullomers.

Nitrogen (N) is a fundamental component of nucleotides and amino acids and is often a limiting nutrient in natural ecosystems. Thus, study of the N content of biomolecules may establish important connections between ecology and genomics. However, while significant differences in the elemental composition of whole organisms are well documented, how the flux of nutrients in the cell has shaped the evolution of different cellular processes remains poorly understood. By examining the elemental composition of major functional classes of proteins in four multicellular eukaryotic model organisms, we find that the catabolic machinery shows substantially lower N content than the anabolic machinery and the rest of the proteome. This pattern suggests that ecological selection for N conservation specifically targets cellular components that are highly expressed in response to nutrient limitation. We propose that the RNA component of the anabolic machineries is the mechanistic force driving the elemental imbalance we found, and that RNA functions as an intracellular nutrient reservoir that is degraded and recycled during starvation periods. A comparison of the elemental composition of the anabolic and catabolic machineries in species that have experienced different levels of N limitation in their evolutionary history (animals versus plants) suggests that selection for N conservation has preferentially targeted the catabolic machineries of plants, resulting in a lower N content of the proteins involved in their catabolic processes. These findings link the composition of major cellular components to the environmental factors that trigger the activation of those components, suggesting that resource availability has constrained the atomic composition and the molecular architecture of the biotic processes that enable cells to respond to reduced nutrient availability.

We investigate on a possible way to connect the presence of low-complexity sequences (LCS) in DNA genomes and the non-stationary properties of base correlations. Under the hypothesis that these variations signal a change in the DNA function, we use a new technique, called non-stationarity entropic index (NSEI) method, and we prove that this technique is an efficient way to detect functional changes with respect to a random baseline. The remarkable aspect is that NSEI does not imply any training data or fitting parameter, the only arbitrarity being the choice of a marker in the sequence. We make this choice on the basis of biological information about LCS distributions in genomes. We show that there exists a correlation between changing the amount in LCS and the ratio of long- to short-range correlation. (C) 2003 Elsevier Ltd. All rights reserved