Fighting Assessment Triggers Rapid Changes in Activity of the Brain Social Decision-Making Network of Cichlid Fish.

Social living animals have to adjust their behavior to rapid changes in the social environment. It has been hypothesized that the expression of social behavior is better explained by the activity pattern of a diffuse social decision-making network (SDMN) in the brain than by the activity of a single brain region. In this study, we tested the hypothesis that it is the assessment that individuals make of the outcome of the fights, rather than the expression of aggressive behavior per se, that triggers changes in the pattern of activation of the SDMN which are reflected in socially driven behavioral profiles (e.g., dominant vs. subordinate specific behaviors). For this purpose, we manipulated the perception of the outcome of an agonistic interaction in an African cichlid fish (Oreochromis mossambicus) and assessed if either the perception of outcome or fighting by itself was sufficient to trigger rapid changes in the activity of the SDMN. We have used the expression of immediate early genes (c-fos and egr-1) as a proxy to measure the neuronal activity in the brain. Fish fought their own image on a mirror for 15 min after which they were allocated to one of three conditions for the two last minutes of the trial: (1) they remained fighting the mirror image (no outcome treatment); (2) the mirror was lifted and a dominant male that had just won a fight was presented behind a transparent partition (perception of defeat treatment); and (3) the mirror was lifted and a subordinate male that had just lost a fight was presented behind a transparent partition (perception of victory treatment). Results show that these short-term social interactions elicit distinct patterns in the SDMN and that the perception of the outcome was not a necessary condition to trigger a SDMN response as evidenced in the second treatment (perception of defeat treatment). We suggest that the mutual assessment of relative fighting behavior drives these acute changes in the state of the SDMN.

Audience Effects in Territorial Defense of Male Cichlid Fish Are Associated with Differential Patterns of Activation of the Brain Social Decision-Making Network.

Animals communicate by exchanging signals frequently in the proximity of other conspecifics that may detect and intercept signals not directed to them. There is evidence that the presence of these bystanders modulates the signaling behavior of interacting individuals, a phenomenon that has been named audience effect. Research on the audience effect has predominantly focused on its function rather than on its proximate mechanisms. Here, we have investigated the physiological and neuromolecular correlates of the audience effect in a cichlid fish (Mozambique tilapia, Oreochromis mossambicus). A male was exposed to a territorial intrusion in the presence or absence of a female audience. Results showed that the presence of the female audience increased territorial defense, but elicited a lower androgen and cortisol response to the territorial intrusion. Furthermore, analysis of the expression of immediate early genes, used as markers of neuronal activity, in brain areas belonging to the social decision-making network (SDMN) revealed different patterns of network activity and connectivity across the different social contexts (i.e., audience × intrusion). Overall, these results suggest that socially driven plasticity in the expression of territorial behavior is accommodated in the central nervous system by rapid changes in functional connectivity between nodes of relevant networks (SDMN) rather than by localized changes of activity in specific brain nuclei.

A theoretical framework to identify invariant thresholds in infectious disease epidemiology.

Setting global strategies and targets for disease prevention and control often involves mathematical models. Model structure is typically subject to intense scrutiny, such as confrontation with empirical data and alternative formulations, while a less frequently challenged aspect is the widely adopted reduction of parameters to their average values. Focusing on endemic diseases, we use a general transmission model to explain how mean field approximations decrease the estimated R0 from prevalence data, while threshold phenomena - such as the epidemic and reinfection thresholds - remain invariant. This results in an underestimation of the effort required to control disease, which may be particularly severe when the approximation inappropriately places transmission estimates below important thresholds. These concepts are widely applicable across endemic pathogen systems.

Assessment of fight outcome is needed to activate socially driven transcriptional changes in the zebrafish brain.

Group living animals must be able to express different behavior profiles depending on their social status. Therefore, the same genotype may translate into different behavioral phenotypes through socially driven differential gene expression. However, how social information is translated into a neurogenomic response and what are the specific cues in a social interaction that signal a change in social status are questions that have remained unanswered. Here, we show for the first time, to our knowledge, that the switch between status-specific neurogenomic states relies on the assessment of fight outcome rather than just on self- or opponent-only assessment of fighting ability. For this purpose, we manipulated the perception of fight outcome in male zebrafish and measured its impact on the brain transcriptome using a zebrafish whole genome gene chip. Males fought either a real opponent, and a winner and a loser were identified, or their own image on a mirror, in which case, despite expressing aggressive behavior, males did not experience either a victory or a defeat. Massive changes in the brain transcriptome were observed in real opponent fighters, with losers displaying both a higher number of differentially expressed genes and of coexpressed gene modules than winners. In contrast, mirror fighters expressed a neurogenomic state similar to that of noninteracting fish. The genes that responded to fight outcome included immediate early genes and genes involved in neuroplasticity and epigenetic modifications. These results indicate that, even in cognitively simple organisms such as zebrafish, neurogenomic responses underlying changes in social status rely on mutual assessment of fighting ability.

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish.

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance.

CodABC: a computational framework to coestimate recombination, substitution, and molecular adaptation rates by approximate Bayesian computation.

The estimation of substitution and recombination rates can provide important insights into the molecular evolution of protein-coding sequences. Here, we present a new computational framework, called "CodABC," to jointly estimate recombination, substitution and synonymous and nonsynonymous rates from coding data. CodABC uses approximate Bayesian computation with and without regression adjustment and implements a variety of codon models, intracodon recombination, and longitudinal sampling. CodABC can provide accurate joint parameter estimates from recombining coding sequences, often outperforming maximum-likelihood methods based on more approximate models. In addition, CodABC allows for the inclusion of several nuisance parameters such as those representing codon frequencies, transition matrices, heterogeneity across sites or invariable sites. CodABC is freely available from http://code.google.com/p/codabc/, includes a GUI, extensive documentation and ready-to-use examples, and can run in parallel on multicore machines.

Unveiling time in dose-response models to infer host susceptibility to pathogens.

The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions.

Social instability promotes hormone-behavior associated patterns in a cichlid fish.

Androgens are known to respond to social challenges and to control the expression of social behavior and reproductive traits, such as gonadal maturation and sperm production, expression of secondary sex characters and reproductive behaviors. According to the challenge hypothesis variation in androgen levels above a breeding baseline should be explained by the regime of social challenges faced by the individual considering the trade-offs of androgens with other traits (e.g. parental care). One prediction that can be derived from the challenge hypothesis is that androgen levels should increase in response to social instability. Moreover, considering that a tighter association of relevant traits is expected in periods of environmental instability, we also predict that in unstable environments the degree of correlations among different behaviors should increase and hormones and behavior should be associated. These predictions were tested in a polygamous cichlid fish (Mozambique tilapia, Oreochromis mossambicus) with exclusive maternal care. Social instability was produced by swapping dominant males among groups. Stable treatment consisted in removing and placing back dominant males in the same group, in order to control for handling stress. Cortisol levels were also measured to monitor stress levels involved in the procedure and their relation to the androgen patterns and behavior. As predicted androgen levels increased in males in response to the establishment of a social hierarchy and presence of receptive females. However, there were no further differential increases in androgen levels over the social manipulation phase between social stable and social unstable groups. As predicted behaviors were significantly more correlated among themselves in the unstable than in the stable treatment and an associated hormone-behavior pattern was only observed in the unstable treatment.

Compositional biases among synonymous substitutions cause conflict between gene and protein trees for plastid origins.

Archaeplastida (=Kingdom Plantae) are primary plastid-bearing organisms that evolved via the endosymbiotic association of a heterotrophic eukaryote host cell and a cyanobacterial endosymbiont approximately 1,400 Ma. Here, we present analyses of cyanobacterial and plastid genomes that show strongly conflicting phylogenies based on 75 plastid (or nuclear plastid-targeted) protein-coding genes and their direct translations to proteins. The conflict between genes and proteins is largely robust to the use of sophisticated data- and tree-heterogeneous composition models. However, by using nucleotide ambiguity codes to eliminate synonymous substitutions due to codon-degeneracy, we identify a composition bias, and dependent codon-usage bias, resulting from synonymous substitutions at all third codon positions and first codon positions of leucine and arginine, as the main cause for the conflicting phylogenetic signals. We argue that the protein-coding gene data analyses are likely misleading due to artifacts induced by convergent composition biases at first codon positions of leucine and arginine and at all third codon positions. Our analyses corroborate previous studies based on gene sequence analysis that suggest Cyanobacteria evolved by the early paraphyletic splitting of Gloeobacter and a specific Synechococcus strain (JA33Ab), with all other remaining cyanobacterial groups, including both unicellular and filamentous species, forming the sister-group to the Archaeplastida lineage. In addition, our analyses using better-fitting models suggest (but without statistically strong support) an early divergence of Glaucophyta within Archaeplastida, with the Rhodophyta (red algae), and Viridiplantae (green algae and land plants) forming a separate lineage.

SNP typing reveals similarity in Mycobacterium tuberculosis genetic diversity between Portugal and Northeast Brazil.

Human tuberculosis is an infectious disease caused by bacteria from the Mycobacterium tuberculosis complex (MTBC). Although spoligotyping and MIRU-VNTR are standard methodologies in MTBC genetic epidemiology, recent studies suggest that Single Nucleotide Polymorphisms (SNP) are advantageous in phylogenetics and strain group/lineages identification. In this work we use a set of 79 SNPs to characterize 1987 MTBC isolates from Portugal and 141 from Northeast Brazil. All Brazilian samples were further characterized using spolygotyping. Phylogenetic analysis against a reference set revealed that about 95% of the isolates in both populations are singly attributed to bacterial lineage 4. Within this lineage, the most frequent strain groups in both Portugal and Brazil are LAM, followed by Haarlem and X. Contrary to these groups, strain group T showed a very different prevalence between Portugal (10%) and Brazil (1.5%). Spoligotype identification shows about 10% of mis-matches compared to the use of SNPs and a little more than 1% of strains unidentifiability. The mis-matches are observed in the most represented groups of our sample set (i.e., LAM and Haarlem) in almost the same proportion. Besides being more accurate in identifying strain groups/lineages, SNP-typing can also provide phylogenetic relationships between strain groups/lineages and, thus, indicate cases showing phylogenetic incongruence. Overall, the use of SNP-typing revealed striking similarities between MTBC populations from Portugal and Brazil.

How host heterogeneity governs tuberculosis reinfection?

Recurrent episodes of tuberculosis (TB) can be due to relapse of latent infection or exogenous reinfection, and discrimination is crucial for control planning. Molecular genotyping of Mycobacterium tuberculosis isolates offers concrete opportunities to measure the relative contribution of reinfection in recurrent disease. Here, a mathematical model of TB transmission is fitted to data from 14 molecular epidemiology studies, enabling the estimation of relevant epidemiological parameters. Meta-analysis reveals that rates of reinfection after successful treatment are higher than rates of new TB, raising an important question about the underlying mechanism. We formulate two alternative mechanisms within our model framework: (i) infection increases susceptibility to reinfection or (ii) infection affects individuals differentially, thereby recruiting high-risk individuals to the group at risk for reinfection. The second mechanism is better supported by the fittings to the data, suggesting that reinfection rates are inflated through a population phenomenon that occurs in the presence of heterogeneity in individual risk of infection. As a result, rates of reinfection are higher when measured at the population level even though they might be lower at the individual level. Finally, differential host recruitment is modulated by transmission intensity, being less pronounced when incidence is high.

PopABC: a program to infer historical demographic parameters.

UNLABELLED: PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at http://www.reading.ac.uk/ approximately sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems.