Compensatory relationship between low-complexity regions and gene paralogy in the evolution of prokaryotes.

The evolution of genomes in all life forms involves two distinct, dynamic types of genomic changes: gene duplication (and loss) that shape families of paralogous genes and extension (and contraction) of low-complexity regions (LCR), which occurs through dynamics of short repeats in protein-coding genes. Although the roles of each of these types of events in genome evolution have been studied, their co-evolutionary dynamics is not thoroughly understood. Here, by analyzing a wide range of genomes from diverse bacteria and archaea, we show that LCR and paralogy represent two distinct routes of evolution that are inversely correlated. The emergence of LCR is a prominent evolutionary mechanism in fast evolving, young protein families, whereas paralogy dominates the comparatively slow evolution of old protein families. The analysis of multiple prokaryotic genomes shows that the formation of LCR is likely a widespread, transient evolutionary mechanism that temporally and locally affects also ancestral functions, but apparently, fades away with time, under mutational and selective pressures, yielding to gene paralogy. We propose that compensatory relationships between short-term and longer-term evolutionary mechanisms are universal in the evolution of life.

Analysis of lineage-specific protein family variability in prokaryotes combined with evolutionary reconstructions.

BACKGROUND: Evolutionary rate is a key characteristic of gene families that is linked to the functional importance of the respective genes as well as specific biological functions of the proteins they encode. Accurate estimation of evolutionary rates is a challenging task that requires precise phylogenetic analysis. Here we present an easy to estimate protein family level measure of sequence variability based on alignment column homogeneity in multiple alignments of protein sequences from Clade-Specific Clusters of Orthologous Genes (csCOGs). RESULTS: We report genome-wide estimates of variability for 8 diverse groups of bacteria and archaea and investigate the connection between variability and various genomic and biological features. The variability estimates are based on homogeneity distributions across amino acid sequence alignments and can be obtained for multiple groups of genomes at minimal computational expense. About half of the variance in variability values can be explained by the analyzed features, with the greatest contribution coming from the extent of gene paralogy in the given csCOG. The correlation between variability and paralogy appears to originate, primarily, not from gene duplication, but from acquisition of distant paralogs and xenologs, introducing sequence variants that are more divergent than those that could have evolved in situ during the lifetime of the given group of organisms. Both high-variability and low-variability csCOGs were identified in all functional categories, but as expected, proteins encoded by integrated mobile elements as well as proteins involved in defense functions and cell motility are, on average, more variable than proteins with housekeeping functions. Additionally, using linear discriminant analysis, we found that variability and fraction of genomes carrying a given gene are the two variables that provide the best prediction of gene essentiality as compared to the results of transposon mutagenesis in Sulfolobus islandicus. CONCLUSIONS: Variability, a measure of sequence diversity within an alignment relative to the overall diversity within a group of organisms, offers a convenient proxy for evolutionary rate estimates and is informative with respect to prediction of functional properties of proteins. In particular, variability is a strong predictor of gene essentiality for the respective organisms and indicative of sub- or neofunctionalization of paralogs.

The harsh microenvironment in early breast cancer selects for a Warburg phenotype.

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

Mutation-selection balance and compensatory mechanisms in tumour evolution.

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.

Proteomic and genomic signatures of repeat instability in cancer and adjacent normal tissues.

Repetitive sequences are hotspots of evolution at multiple levels. However, due to difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content, beyond microsatellites, in proteomes and genomes directly from proteomic and genomic raw data. This method was applied to a wide range of tumors and normal tissues. We identify high similarity between repeat instability patterns in tumors and their patient-matched adjacent normal tissues. Nonetheless, tumor-specific signatures both in protein expression and in the genome strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We observe an inverse relationship between repeat instability and point mutation load within and across patients independent of other somatic aberrations. Thus, repeat instability is a distinct, transient, and compensatory adaptive mechanism in tumor evolution and a potential signal for early detection.

Acid-Induced Downregulation of ASS1 Contributes to the Maintenance of Intracellular pH in Cancer.

Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) by either promoter methylation or by HIF1α is associated with increased metastasis and poor prognosis in multiple cancers. We have previously shown that in normoxic conditions, ASS1 downregulation facilitates cancer cell proliferation by increasing aspartate availability for pyrimidine synthesis by the enzyme complex CAD. Here we report that in hypoxia, ASS1 expression in cancerous cells is downregulated further by HIF1α-mediated induction of miR-224-5p, making the cells more invasive and dependent on upstream substrates of ASS1 for survival. ASS1 was downregulated under acidic conditions, and ASS1-depleted cancer cells maintained a higher intracellular pH (pHi), depended less on extracellular glutamine, and displayed higher glutathione levels. Depletion of substrates of urea cycle enzymes in ASS1-deficient cancers decreased cancer cell survival. Thus, ASS1 levels in cancer are differentially regulated in various environmental conditions to metabolically benefit cancer progression. Understanding these alterations may help uncover specific context-dependent cancer vulnerabilities that may be targeted for therapeutic purposes. SIGNIFICANCE: Cancer cells in an acidic or hypoxic environment downregulate the expression of the urea cycle enzyme ASS1, which provides them with a redox and pH advantage, resulting in better survival.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/3/518/F1.large.jpg.

Criticality in tumor evolution and clinical outcome.

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Melanoma, with the highest ML, evolves under purifying selection, whereas in low-ML cancers, signatures of positive selection are observed, demonstrating how selection affects tumor fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its nonlinear effects on survival.

Systems analysis of intracellular pH vulnerabilities for cancer therapy.

A reverse pH gradient is a hallmark of cancer metabolism, manifested by extracellular acidosis and intracellular alkalization. While consequences of extracellular acidosis are known, the roles of intracellular alkalization are incompletely understood. By reconstructing and integrating enzymatic pH-dependent activity profiles into cell-specific genome-scale metabolic models, we develop a computational methodology that explores how intracellular pH (pHi) can modulate metabolism. We show that in silico, alkaline pHi maximizes cancer cell proliferation coupled to increased glycolysis and adaptation to hypoxia (i.e., the Warburg effect), whereas acidic pHi disables these adaptations and compromises tumor cell growth. We then systematically identify metabolic targets (GAPDH and GPI) with predicted amplified anti-cancer effects at acidic pHi, forming a novel therapeutic strategy. Experimental testing of this strategy in breast cancer cells reveals that it is particularly effective against aggressive phenotypes. Hence, this study suggests essential roles of pHi in cancer metabolism and provides a conceptual and computational framework for exploring pHi roles in other biomedical domains.

Positive and strongly relaxed purifying selection drive the evolution of repeats in proteins.

Protein repeats are considered hotspots of protein evolution, associated with acquisition of new functions and novel phenotypic traits, including disease. Paradoxically, however, repeats are often strongly conserved through long spans of evolution. To resolve this conundrum, it is necessary to directly compare paralogous (horizontal) evolution of repeats within proteins with their orthologous (vertical) evolution through speciation. Here we develop a rigorous methodology to identify highly periodic repeats with significant sequence similarity, for which evolutionary rates and selection (dN/dS) can be estimated, and systematically characterize their evolution. We show that horizontal evolution of repeats is markedly accelerated compared with their divergence from orthologues in closely related species. This observation is universal across the diversity of life forms and implies a biphasic evolutionary regime whereby new copies experience rapid functional divergence under combined effects of strongly relaxed purifying selection and positive selection, followed by fixation and conservation of each individual repeat.

Proteomics-based metabolic modeling reveals that fatty acid oxidation (FAO) controls endothelial cell (EC) permeability.

Endothelial cells (ECs) play a key role to maintain the functionality of blood vessels. Altered EC permeability causes severe impairment in vessel stability and is a hallmark of pathologies such as cancer and thrombosis. Integrating label-free quantitative proteomics data into genome-wide metabolic modeling, we built up a model that predicts the metabolic fluxes in ECs when cultured on a tridimensional matrix and organize into a vascular-like network. We discovered how fatty acid oxidation increases when ECs are assembled into a fully formed network that can be disrupted by inhibiting CPT1A, the fatty acid oxidation rate-limiting enzyme. Acute CPT1A inhibition reduces cellular ATP levels and oxygen consumption, which are restored by replenishing the tricarboxylic acid cycle. Remarkably, global phosphoproteomic changes measured upon acute CPT1A inhibition pinpointed altered calcium signaling. Indeed, CPT1A inhibition increases intracellular calcium oscillations. Finally, inhibiting CPT1A induces hyperpermeability in vitro and leakage of blood vessel in vivo, which were restored blocking calcium influx or replenishing the tricarboxylic acid cycle. Fatty acid oxidation emerges as central regulator of endothelial functions and blood vessel stability and druggable pathway to control pathological vascular permeability.

Hierarchical partitioning of metazoan protein conservation profiles provides new functional insights.

The availability of many complete, annotated proteomes enables the systematic study of the relationships between protein conservation and functionality. We explore this question based solely on the presence or absence of protein homologues (a.k.a. conservation profiles). We study 18 metazoans, from two distinct points of view: the human's and the fly's. Using the GOrilla gene ontology (GO) analysis tool, we explore functional enrichment of the "universal proteins", those with homologues in all 17 other species, and of the "non-universal proteins". A large number of GO terms are strongly enriched in both human and fly universal proteins. Most of these functions are known to be essential. A smaller number of GO terms, exhibiting markedly different properties, are enriched in both human and fly non-universal proteins. We further explore the non-universal proteins, whose conservation profiles are consistent with the "tree of life" (TOL consistent), as well as the TOL inconsistent proteins. Finally, we applied Quantum Clustering to the conservation profiles of the TOL consistent proteins. Each cluster is strongly associated with one or a small number of specific monophyletic clades in the tree of life. The proteins in many of these clusters exhibit strong functional enrichment associated with the "life style" of the related clades. Most previous approaches for studying function and conservation are "bottom up", studying protein families one by one, and separately assessing the conservation of each. By way of contrast, our approach is "top down". We globally partition the set of all proteins hierarchically, as described above, and then identify protein families enriched within different subdivisions. While supporting previous findings, our approach also provides a tool for discovering novel relations between protein conservation profiles, functionality, and evolutionary history as represented by the tree of life.

Systematic analysis of compositional order of proteins reveals new characteristics of biological functions and a universal correlate of macroevolution.

We present a novel analysis of compositional order (CO) based on the occurrence of Frequent amino-acid Triplets (FTs) that appear much more than random in protein sequences. The method captures all types of proteomic compositional order including single amino-acid runs, tandem repeats, periodic structure of motifs and otherwise low complexity amino-acid regions. We introduce new order measures, distinguishing between 'regularity', 'periodicity' and 'vocabulary', to quantify these phenomena and to facilitate the identification of evolutionary effects. Detailed analysis of representative species across the tree-of-life demonstrates that CO proteins exhibit numerous functional enrichments, including a wide repertoire of particular patterns of dependencies on regularity and periodicity. Comparison between human and mouse proteomes further reveals the interplay of CO with evolutionary trends, such as faster substitution rate in mouse leading to decrease of periodicity, while innovation along the human lineage leads to larger regularity. Large-scale analysis of 94 proteomes leads to systematic ordering of all major taxonomic groups according to FT-vocabulary size. This is measured by the count of Different Frequent Triplets (DFT) in proteomes. The latter provides a clear hierarchical delineation of vertebrates, invertebrates, plants, fungi and prokaryotes, with thermophiles showing the lowest level of FT-vocabulary. Among eukaryotes, this ordering correlates with phylogenetic proximity. Interestingly, in all kingdoms CO accumulation in the proteome has universal characteristics. We suggest that CO is a genomic-information correlate of both macroevolution and various protein functions. The results indicate a mechanism of genomic 'innovation' at the peptide level, involved in protein elongation, shaped in a universal manner by mutational and selective forces.

Peptide markers of aminoacyl tRNA synthetases facilitate taxa counting in metagenomic data.

BACKGROUND: Taxa counting is a major problem faced by analysis of metagenomic data. The most popular method relies on analysis of 16S rRNA sequences, but some studies employ also protein based analyses. It would be advantageous to have a method that is applicable directly to short sequences, of the kind extracted from samples in modern metagenomic research. This is achieved by the technique proposed here. RESULTS: We employ specific peptides, deduced from aminoacyl tRNA synthetases, as markers for the occurrence of single genes in data. Sequences carrying these markers are aligned and compared with each other to provide a lower limit for taxa counts in metagenomic data. The method is compared with 16S rRNA searches on a set of known genomes. The taxa counting problem is analyzed mathematically and a heuristic algorithm is proposed. When applied to genomic contigs of a recent human gut microbiome study, the taxa counting method provides information on numbers of different species and strains. We then apply our method to short read data and demonstrate how it can be calibrated to cope with errors. Comparison to known databases leads to estimates of the percentage of novelties, and the type of phyla involved. CONCLUSIONS: A major advantage of our method is its simplicity: it relies on searching sequences for the occurrence of just 4000 specific peptides belonging to the S61 subgroup of aaRS enzymes. When compared to other methods, it provides additional insight into the taxonomic contents of metagenomic data. Furthermore, it can be directly applied to short read data, avoiding the need for genomic contig reconstruction, and taking into account short reads that are otherwise discarded as singletons. Hence it is very suitable for a fast analysis of next generation sequencing data.

Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex.

We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

Deriving enzymatic and taxonomic signatures of metagenomes from short read data.

BACKGROUND: We propose a method for deriving enzymatic signatures from short read metagenomic data of unknown species. The short read data are converted to six pseudo-peptide candidates. We search for occurrences of Specific Peptides (SPs) on the latter. SPs are peptides that are indicative of enzymatic function as defined by the Enzyme Commission (EC) nomenclature. The number of SP hits on an ensemble of short reads is counted and then converted to estimates of numbers of enzymatic genes associated with different EC categories in the studied metagenome. Relative amounts of different EC categories define the enzymatic spectrum, without the need to perform genomic assemblies of short reads. RESULTS: The method is developed and tested on 22 bacteria for which there exist many EC annotations in Uniprot. Enzymatic signatures are derived for 3 metagenomes, and their functional profiles are explored.We extend the SP methodology to taxon-specific SPs (TSPs), allowing us to estimate taxonomic features of metagenomic data from short reads. Using recent Swiss-Prot data we obtain TSPs for different phyla of bacteria, and different classes of proteobacteria. These allow us to analyze the major taxonomic content of 4 different metagenomic data-sets. CONCLUSIONS: The SP methodology can be successfully extended to applications on short read genomic and metagenomic data. This leads to direct derivation of enzymatic signatures from raw short reads. Furthermore, by employing TSPs, one obtains valuable taxonomic information.

Modeling of synchronized bursting events: the importance on inhomogeneity.

Cultured in vitro neuronal networks are known to exhibit synchronized bursting events (SBE), during which most of the neurons in the system spike within a time window of approximately 100 msec. Such phenomena can be obtained in model networks based on Markram-Tsodyks frequency-dependent synapses. In order to account correctly for the detailed behavior of SBEs, several modifications have to be implemented in such models. Random input currents have to be introduced to account for the rising profile of SBEs. Dynamic thresholds and inhomogeneity in the distribution of neuronal resistances enable us to describe the profile of activity within the SBE and the heavy-tailed distributions of interspike intervals and interevent intervals. Thus, we can account for the interesting appearance of Levy distributions in the data.