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Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences.

Bacterial and archaeal evolution involve extensive gene gain and loss. Thus, phylogenetic trees of prokaryotes can be constructed both by traditional sequence-based methods (gene trees) and by comparison of gene compositions (genome trees). Comparing the branch lengths in gene and genome trees with identical topologies for 34 clusters of closely related bacterial and archaeal genomes, we show here that terminal branches of gene trees are systematically compressed compared to those of genome trees. Thus, sequence evolution is delayed compared to genome evolution by gene gain and loss. The extent of this delay differs widely among bacteria and archaea. Mathematical modeling shows that the divergence delay can result from sequence homogenization by homologous recombination. The model explains how homologous recombination maintains the cohesiveness of the core genome of a species while allowing extensive gene gain and loss within the accessory genome. Once evolving genomes become isolated by barriers impeding homologous recombination, gene and genome evolution processes settle into parallel trajectories, and genomes diverge, resulting in speciation. A significant proportion of the molecular evolution of bacteria and archaea occurs through gene gain and loss. Here Iranzo et al. develop a mathematical model that explains observed differential patterns of sequence evolution vs. gene content evolution as a consequence of homologous recombination.

Background The genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical U-shaped distribution of gene commonality. However, the evolutionary factors that define the specific shape of this distribution are not thoroughly understood. Results We investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genome intersections for 33 groups of closely related bacterial genomes. A model with an infinite external gene pool available for gene acquisition and constant genome size (IGP-CGS model), and two gene turnover rates, one for slow- and the other one for fast-evolving genes, allows two approaches to estimate the parameters for gene content dynamics. One is by fitting the model prediction to the distribution of the number of genes shared by precisely k genomes (gene commonality distribution) and another by analyzing the distribution of the number of genes common for k genome sets ( k -cores). Both approaches produce a comparable overall quality of fit, although the former significantly overestimates the number of the universally conserved genes, while the latter overestimates the number of singletons. We further explore the effect of dropping each of the assumptions of the IGP-CGS model on the fit to the gene commonality distributions and show that models with either a finite gene pool or unequal rates of gene loss and gain (greater gene loss rate) eliminate the overestimate of the number of singletons or the core genome size. Conclusions We examine the assumptions that are usually adopted for modeling the evolution of the U-shaped gene commonality distributions in prokaryote genomes, namely, those of infinitely many genes and constant genome size. The combined analysis of genome intersections and gene commonality suggests that at least one of these assumptions is invalid. The violation of both these assumptions reflects the limited ability of prokaryotes to gain new genes. This limitation seems to stem, at least partly, from the horizontal gene transfer barrier, i.e., the cost of accommodation of foreign genes by prokaryotes. Further development of models taking into account the complexity of microbial evolution is necessary for an improved understanding of the evolution of prokaryotes.

Background Single nucleotide substitutions in protein-coding genes can be divided into synonymous (S), with little fitness effect, and non-synonymous (N) ones that alter amino acids and thus generally have a greater effect. Most of the N substitutions are affected by purifying selection that eliminates them from evolving populations. However, additional mutations of nearby bases potentially could alleviate the deleterious effect of single substitutions, making them subject to positive selection. To elucidate the effects of selection on double substitutions in all codons, it is critical to differentiate selection from mutational biases. Results We addressed the evolutionary regimes of within-codon double substitutions in 37 groups of closely related prokaryotic genomes from diverse phyla by comparing the fractions of double substitutions within codons to those of the equivalent double S substitutions in adjacent codons. Under the assumption that substitutions occur one at a time, all within-codon double substitutions can be represented as “ancestral-intermediate-final” sequences (where “intermediate” refers to the first single substitution and “final” refers to the second substitution) and can be partitioned into four classes: (1) SS, S intermediate–S final; (2) SN, S intermediate–N final; (3) NS, N intermediate–S final; and (4) NN, N intermediate–N final. We found that the selective pressure on the second substitution markedly differs among these classes of double substitutions. Analogous to single S (synonymous) substitutions, SS double substitutions evolve neutrally, whereas analogous to single N (non-synonymous) substitutions, SN double substitutions are subject to purifying selection. In contrast, NS show positive selection on the second step because the original amino acid is recovered. The NN double substitutions are heterogeneous and can be subject to either purifying or positive selection, or evolve neutrally, depending on the amino acid similarity between the final or intermediate and the ancestral states. Conclusions The results of the present, comprehensive analysis of the evolutionary landscape of within-codon double substitutions reaffirm the largely conservative regime of protein evolution. However, the second step of a double substitution can be subject to positive selection when the first step is deleterious. Such positive selection can result in frequent crossing of valleys on the fitness landscape.

In prokaryotes, the number of genes in different functional classes shows apparent universal scaling with the total number of genes that can be approximated by a power law, with a sublinear, near-linear, or superlinear scaling exponent. These dependences are gene class specific but hold across the entire diversity of bacteria and archaea. Several models have been proposed to explain these universal scaling laws, primarily based on the specifics of the respective biological functions. However, a population-genetic theory of universal scaling is lacking. We employ a simple mathematical model for prokaryotic genome evolution, which, together with the analysis of 34 clusters of closely related bacterial genomes, allows us to identify the underlying factors that govern the evolution of the genome content. Evolution of the gene content is dominated by two functional class-specific parameters: selection coefficient and genome plasticity. The selection coefficient quantifies the fitness cost associated with deletion of a gene in a given functional class or the advantage of successful incorporation of an additional gene. Genome plasticity reflects both the availability of the genes of a given class in the external gene pool that is accessible to the evolving population and the ability of microbes to accommodate these genes in the short term, that is, the class-specific horizontal gene transfer barrier. The selection coefficient determines the gene loss rate, whereas genome plasticity is the principal determinant of the gene gain rate.

Immune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance. Pre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes using clinical benefit and overall survival as endpoints. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes. The levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status, and, in the ICI monotherapy dataset, an association with improved overall survival. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network, including CD8A connected to ten other proteins, suggestive of T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage. Our study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and biomarkers for outcome.

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized the cancer therapy landscape due to long-term benefits in patients with advanced metastatic disease. However, robust predictive biomarkers for response are still lacking and treatment resistance is not fully understood.MethodsWe profiled approximately 800 pre-treatment and on-treatment plasma proteins from 143 ICI-treated patients with non-small cell lung cancer (NSCLC) using ELISA-based arrays. Different clinical parameters were collected from the patients including specific mutations, smoking habits, and body mass index, among others. Machine learning algorithms were used to identify a predictive signature for response. Bioinformatics tools were used for the identification of patient subtypes and analysis of differentially expressed proteins and pathways in each response group.ResultsWe identified a predictive signature for response to treatment comprizing two proteins (CXCL8 and CXCL10) and two clinical parameters (age and sex). Bioinformatic analysis of the proteomic profiles identified three distinct patient clusters that correlated with multiple parameters such as response, sex and TNM (tumors, nodes, and metastasis) staging. Patients who did not benefit from ICI therapy exhibited significantly higher plasma levels of several proteins on-treatment, and enrichment in neutrophil-related proteins.ConclusionsOur study reveals potential biomarkers in blood plasma for predicting response to ICI therapy in patients with NSCLC and sheds light on mechanisms underlying therapy resistance.

The results of our recent study on mathematical modeling of microbial genome evolution indicate that, on average, genomes of bacteria and archaea evolve in the regime of mutation-selection balance defined by positive selection coefficients associated with gene acquisition that is counter-acted by the intrinsic deletion bias. This analysis was based on the strong assumption that parameters of genome evolution are universal across the diversity of bacteria and archaea, and yielded extremely low values of the selection coefficient. Here we further refine the modeling approach by taking into account evolutionary factors specific for individual groups of microbes using two independent fitting strategies, an ad hoc hard fitting scheme and a mixture model. The resulting estimate of the mean selection coefficient of s∼10-10 associated with the gain of one gene implies that, on average, acquisition of a gene is beneficial, and that microbial genomes typically evolve under a weak selection regime that might transition to strong selection in highly abundant organisms with large effective population sizes. The apparent selective pressure towards larger genomes is balanced by the deletion bias, which is estimated to be consistently greater than unity for all analyzed groups of microbes. The estimated values of s are more realistic than the lower values obtained previously, indicating that global and group-specific evolutionary factors synergistically affect microbial genome evolution that seems to be driven primarily by adaptation to existence in diverse niches.

The 'Cerebellar Deficit Theory' of developmental dyslexia proposes that a subtle developmental cerebellar dysfunction leads to deficits in attaining 'automatic' procedures and therefore manifests as subtle motor impairments (e.g., balance control, motor skill learning) in addition to the reading and phonological difficulties. A more recent version of the theory suggests a core deficit in motor skill acquisition. This study was undertaken to compare the time-course and the nature of practice-related changes in volitional (manual) and non-volitional (posture) motor performance in dyslexic and typical readers while learning a new movement sequence. Seventeen dyslexic and 26 skilled young adult readers underwent a three-session training program in which they practiced a novel sequence of manual movements while standing in a quiet stance position. Both groups exhibited robust and well-retained gains in speed, with no loss of accuracy, on the volitional, manual, aspects of the task, with a time-course characteristic of procedural learning. However, the dyslexic readers exhibited a pervasive slowness in the initiation of volitional performance. In addition, while typical readers showed clear and well-retained task-related adaptation of the balance and posture control system, the dyslexic readers had significantly larger sway and variance of sway throughout the three sessions and were less efficient in adapting the posture control system to support the acquisition of the novel movement sequence. These results support the notion of a non-language-related deficit in developmental dyslexia, one related to the recruitment of motor systems for effective task performance rather than to a general motor learning disability.

Data indicated that dyslexic individuals exhibited difficulties on tasks involving Working Memory (WM). Previous studies have suggested that these deficits stem from impaired processing in the Phonological Loop (PL). The PL impairment was connected to poor phonological processing. However, recent data has pointed to the Central Executive (CE) system as another source of WM deficit in dyslexic readers. This opened a debate whether the WM deficit stems solely from PL or can also be seen as an outcome of poor CE processing. In an attempt to verify this question, the current study compared adult skilled and compensated dyslexic readers with no impairment of phonological skills. The participants' PL and CE processing were tested by using the fNIR device attached to the frontal lobe and measured the changes in brain oxygen values when performing N-back task. As it was previously suggested, the N = 0 represented PL and N = 1 to 3 represent CE processing. It was hypothesized that dyslexic readers who show non-impaired phonological skills will exhibit deficits mainly in the CE subsystem and to a lesser extent in the PL. Results indicated that the two reading level groups did not differ in their accuracy and reaction times in any of the N-Back conditions. However, the dyslexic readers demonstrated significant lower maximum oxyHb values in the upper left frontal lobe, mainly caused due to a significant lower activity under the N = 1 condition. Significant task effects were found in the medial left hemisphere, and the high medial right hemisphere. In addition, significant correlations between fNIR-features, reading performance and speed of processing were found. The higher oxyHb values, the better reading and speed of processing performance obtained. The results of the current study support the hypothesis that at least for the group of dyslexics with non-impaired PL, WM deficit stems from poor CE activity.