Ribosome-binding antibiotics increase bacterial longevity and growth efficiency.

Antibiotics, by definition, reduce bacterial growth rates in optimal culture conditions; however, the real-world environments bacteria inhabit see rapid growth punctuated by periods of low nutrient availability. How antibiotics mediate population decline during these periods is poorly understood. Bacteria cannot optimize for all environmental conditions because a growth-longevity tradeoff predicts faster growth results in faster population decline, and since bacteriostatic antibiotics slow growth, they should also mediate longevity. We quantify how antibiotics, their targets, and resistance mechanisms influence longevity using populations of Escherichia coli and, as the tradeoff predicts, populations are maintained for longer if they encounter ribosome-binding antibiotics doxycycline and erythromycin, a finding that is not observed using antibiotics with alternative cellular targets. This tradeoff also predicts resistance mechanisms that increase growth rates during antibiotic treatment could be detrimental during nutrient stresses, and indeed, we find resistance by ribosomal protection removes benefits to longevity provided by doxycycline. We therefore liken ribosomal protection to a "Trojan horse" because it provides protection from an antibiotic but, during nutrient stresses, it promotes the demise of the bacteria. Seeking mechanisms to support these observations, we show doxycycline promotes efficient metabolism and reduces the concentration of reactive oxygen species. Seeking generality, we sought another mechanism that affects longevity and we found the number of doxycycline targets, namely, the ribosomal RNA operons, mediates growth and longevity even without antibiotics. We conclude that slow growth, as observed during antibiotic treatment, can help bacteria overcome later periods of nutrient stress.

Meet the Metaorganism: A web-based learning app for undergraduate and graduate biology students.

Meet the Metaorganism is a web-based learning app that combines three fundamental biological concepts (coevolution, community dynamics, and immune system) with latest scientific findings using the metaorganism as a central case study. In a transdisciplinary team of scientists, information designers, programmers, science communicators, and educators, we conceptualized and developed the app according to the latest didactic and scientific findings and aimed at setting new standards in visual design, digital knowledge transfer, and online education. A content management system allows continuous integration of new findings, which enables us to expand the app with the dynamics of the research field. Students can thus gain a close insight and connection to current research, and at the same time learn that knowledge is not static but grows dynamically. Especially in the realm of the easily accessible metaorganism research, visualization plays an essential role to keep complex processes understandable and memorable. Meet the Metaorganism is freely available online and can be accessed here: www.metaorganism.app.

The ESKAPE mobilome contributes to the spread of antimicrobial resistance and CRISPR-mediated conflict between mobile genetic elements.

Mobile genetic elements (MGEs) mediate the shuffling of genes among organisms. They contribute to the spread of virulence and antibiotic resistance (AMR) genes in human pathogens, such as the particularly problematic group of ESKAPE pathogens. Here, we performed the first systematic analysis of MGEs, including plasmids, prophages, and integrative and conjugative/mobilizable elements (ICEs/IMEs), across all ESKAPE pathogens. We found that different MGE types are asymmetrically distributed across these pathogens, and that most horizontal gene transfer (HGT) events are restricted by phylum or genus. We show that the MGEs proteome is involved in diverse functional processes and distinguish widespread proteins within the ESKAPE context. Moreover, anti-CRISPRs and AMR genes are overrepresented in the ESKAPE mobilome. Our results also underscore species-specific trends shaping the number of MGEs, AMR, and virulence genes across pairs of conspecific ESKAPE genomes with and without CRISPR-Cas systems. Finally, we observed that CRISPR spacers found on prophages, ICEs/IMEs, and plasmids have different targeting biases: while plasmid and prophage CRISPRs almost exclusively target other plasmids and prophages, respectively, ICEs/IMEs CRISPRs preferentially target prophages. Overall, our study highlights the general importance of the ESKAPE mobilome in contributing to the spread of AMR and mediating conflict among MGEs.

On the evolutionary origins of host-microbe associations.

Many microorganisms with high prevalence in host populations are beneficial to the host and maintained by specialized transmission mechanisms. Although microbial promotion of host fitness and specificity of the associations undoubtedly enhance microbial prevalence, it is an open question whether these symbiotic traits are also a prerequisite for the evolutionary origin of prevalent microbial taxa. To address this issue, we investigate how processes without positive microbial effects on host fitness or host choice can influence the prevalence of certain microbes in a host population. Specifically, we develop a theoretical model to assess the conditions under which particular microbes can become enriched in animal hosts even when they are not providing a specific benefit to a particular host. We find increased prevalence of specific microbes in a host when both show some overlap in their lifecycles, and especially when both share dispersal routes across a patchy habitat distribution. Our results emphasize that host enrichment per se is not a reliable indicator of beneficial host-microbe interactions. The resulting increase in time spent associated with a host may nevertheless give rise to new selection conditions, which can favor microbial adaptations toward a host-associated lifestyle, and, thus, it could be the foundation for subsequent evolution of mutually beneficial coevolved symbioses.

The intricate triangular interaction between protective microbe, pathogen and host determines fitness of the metaorganism.

The microbiota shapes host biology in numerous ways. One example is protection against pathogens, which is likely critical for host fitness in consideration of the ubiquity of pathogens. The host itself can affect abundance of microbiota or pathogens, which has usually been characterized in separate studies. To date, however, it is unclear how the host influences the interaction with both simultaneously and how this triangular interaction determines fitness of the host-microbe assemblage, the so-called metaorganism. To address this current knowledge gap, we focused on a triangular model interaction, consisting of the nematode Caenorhabditis elegans, its protective symbiont Pseudomonas lurida MYb11 and its pathogen Bacillus thuringiensis Bt679. We combined the two microbes with C. elegans mutants with altered immunity and/or microbial colonization, and found that (i) under pathogen stress, immunocompetence has a larger influence on metaorganism fitness than colonization with the protective microbe; (ii) in almost all cases, MYb11 still improves fitness; and (iii) disruption of p38 MAPK signalling, which contributes centrally to immunity against Bt679, completely reverses the protective effect of MYb11, which further reduces nematode survival and fitness upon infection with Bt679. Our study highlights the complex interplay between host, protective microbe and pathogen in shaping metaorganism biology.

Effector and regulator: Diverse functions of C. elegans C-type lectin-like domain proteins.

In C. elegans, 283 clec genes encode a highly diverse family of C-type lectin-like domain (CTLD) proteins. Since vertebrate CTLD proteins have characterized functions in defense responses against pathogens and since expression of C. elegans clec genes is pathogen-dependent, it is generally assumed that clec genes function in C. elegans immune defenses. However, little is known about the relative contribution and exact function of CLEC proteins in C. elegans immunity. Here, we focused on the C. elegans clec gene clec-4, whose expression is highly upregulated by pathogen infection, and its paralogs clec-41 and clec-42. We found that, while mutation of clec-4 resulted in enhanced resistance to the Gram-positive pathogen Bacillus thuringiensis MYBt18247 (Bt247), inactivation of clec-41 and clec-42 by RNAi enhanced susceptibility to Bt247. Further analyses revealed that enhanced resistance of clec-4 mutants to Bt247 was due to an increase in feeding cessation on the pathogen and consequently a decrease in pathogen load. Moreover, clec-4 mutants exhibited feeding deficits also on non-pathogenic bacteria that were in part reflected in the clec-4 gene expression profile, which overlapped with gene sets affected by starvation or mutation in nutrient sensing pathways. However, loss of CLEC-4 function only mildly affected life-history traits such as fertility, indicating that clec-4 mutants are not subjected to dietary restriction. While CLEC-4 function appears to be associated with the regulation of feeding behavior, we show that CLEC-41 and CLEC-42 proteins likely function as bona fide immune effector proteins that have bacterial binding and antimicrobial capacities. Together, our results exemplify functional diversification within clec gene paralogs.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa.

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

The Antibiotic Dosage of Fastest Resistance Evolution: Gene Amplifications Underpinning the Inverted-U.

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin's minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an "inverted-U" pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

The C. elegans GATA transcription factor elt-2 mediates distinct transcriptional responses and opposite infection outcomes towards different Bacillus thuringiensis strains.

The nematode Caenorhabditis elegans has been extensively used as a model for the study of innate immune responses against bacterial pathogens. While it is well established that the worm mounts distinct transcriptional responses to different bacterial species, it is still unclear in how far it can fine-tune its response to different strains of a single pathogen species, especially if the strains vary in virulence and infection dynamics. To rectify this knowledge gap, we systematically analyzed the C. elegans response to two strains of Bacillus thuringiensis (Bt), MYBt18247 (Bt247) and MYBt18679 (Bt679), which produce different pore forming toxins (PFTs) and vary in infection dynamics. We combined host transcriptomics with cytopathological characterizations and identified both a common and also a differentiated response to the two strains, the latter comprising almost 10% of the infection responsive genes. Functional genetic analyses revealed that the AP-1 component gene jun-1 mediates the common response to both Bt strains. In contrast, the strain-specific response is mediated by the C. elegans GATA transcription factor ELT-2, a homolog of Drosophila SERPENT and vertebrate GATA4-6, and a known master regulator of intestinal responses in the nematode. elt-2 RNAi knockdown decreased resistance to Bt679, but remarkably, increased survival on Bt247. The elt-2 silencing-mediated increase in survival was characterized by reduced intestinal tissue damage despite a high pathogen burden and might thus involve increased tolerance. Additional functional genetic analyses confirmed the involvement of distinct signaling pathways in the C. elegans defense response: the p38-MAPK pathway acts either directly with or in parallel to elt-2 in mediating resistance to Bt679 infection but is not required for protection against Bt247. Our results further suggest that the elt-2 silencing-mediated increase in survival on Bt247 is multifactorial, influenced by the nuclear hormone receptors NHR-99 and NHR-193, and may further involve lipid metabolism and detoxification. Our study highlights that the nematode C. elegans with its comparatively simple immune defense system is capable of generating a differentiated response to distinct strains of the same pathogen species. Importantly, our study provides a molecular insight into the diversity of biological processes that are influenced by a single master regulator and jointly determine host survival after pathogen infection.

Neutrality in the Metaorganism.

Almost all animals and plants are inhabited by diverse communities of microorganisms, the microbiota, thereby forming an integrated entity, the metaorganism. Natural selection should favor hosts that shape the community composition of these microbes to promote a beneficial host-microbe symbiosis. Indeed, animal hosts often pose selective environments, which only a subset of the environmentally available microbes are able to colonize. How these microbes assemble after colonization to form the complex microbiota is less clear. Neutral models are based on the assumption that the alternatives in microbiota community composition are selectively equivalent and thus entirely shaped by random population dynamics and dispersal. Here, we use the neutral model as a null hypothesis to assess microbiata composition in host organisms, which does not rely on invoking any adaptive processes underlying microbial community assembly. We show that the overall microbiota community structure from a wide range of host organisms, in particular including previously understudied invertebrates, is in many cases consistent with neutral expectations. Our approach allows to identify individual microbes that are deviating from the neutral expectation and are therefore interesting candidates for further study. Moreover, using simulated communities, we demonstrate that transient community states may play a role in the deviations from the neutral expectation. Our findings highlight that the consideration of neutral processes and temporal changes in community composition are critical for an in-depth understanding of microbiota-host interactions.

Experimental evolution of immunological specificity.

Memory and specificity are hallmarks of the adaptive immune system. Contrary to prior belief, innate immune systems can also provide forms of immune memory, such as immune priming in invertebrates and trained immunity in vertebrates. Immune priming can even be specific but differs remarkably in cellular and molecular functionality from the well-studied adaptive immune system of vertebrates. To date, it is unknown whether and how the level of specificity in immune priming can adapt during evolution in response to natural selection. We tested the evolution of priming specificity in an invertebrate model, the beetle Tribolium castaneum Using controlled evolution experiments, we selected beetles for either specific or unspecific immune priming toward the bacteria Pseudomonas fluorescens, Lactococcus lactis, and 4 strains of the entomopathogen Bacillus thuringiensis After 14 generations of host selection, specificity of priming was not universally higher in the lines selected for specificity, but rather depended on the bacterium used for priming and challenge. The insect pathogen B. thuringiensis induced the strongest priming effect. Differences between the evolved populations were mirrored in the transcriptomic response, revealing involvement of immune, metabolic, and transcription-modifying genes. Finally, we demonstrate that the induction strength of a set of differentially expressed immune genes predicts the survival probability of the evolved lines upon infection. We conclude that high specificity of immune priming can evolve rapidly for certain bacteria, most likely due to changes in the regulation of immune genes.

The genomic basis of Red Queen dynamics during rapid reciprocal host-pathogen coevolution.

Red Queen dynamics, involving coevolutionary interactions between species, are ubiquitous, shaping the evolution of diverse biological systems. To date, information on the underlying selection dynamics and the involved genome regions is mainly available for bacteria-phage systems or only one of the antagonists of a eukaryotic host-pathogen interaction. We add to our understanding of these important coevolutionary interactions using an experimental host-pathogen model, which includes the nematode Caenorhabditis elegans and its pathogen Bacillus thuringiensis We combined experimental evolution with time-shift experiments, in which a focal host or pathogen is tested against a coevolved antagonist from the past, present, or future, followed by genomic analysis. We show that (i) coevolution occurs rapidly within few generations, (ii) temporal coadaptation at the phenotypic level is found in parallel across replicate populations, consistent with antagonistic frequency-dependent selection, (iii) genomic changes in the pathogen match the phenotypic pattern and include copy number variations of a toxin-encoding plasmid, and (iv) host genomic changes do not match the phenotypic pattern and likely involve selective responses at more than one locus. By exploring the dynamics of coevolution at the phenotypic and genomic level for both host and pathogen simultaneously, our findings demonstrate a more complex model of the Red Queen, consisting of distinct selective processes acting on the two antagonists during rapid and reciprocal coadaptation.

Population size impacts host-pathogen coevolution.

Ongoing host-pathogen interactions are characterized by rapid coevolutionary changes forcing species to continuously adapt to each other. The interacting species are often defined by finite population sizes. In theory, finite population size limits genetic diversity and compromises the efficiency of selection owing to genetic drift, in turn constraining any rapid coevolutionary responses. To date, however, experimental evidence for such constraints is scarce. The aim of our study was to assess to what extent population size influences the dynamics of host-pathogen coevolution. We used Caenorhabditus elegans and its pathogen Bacillus thuringiensis as a model for experimental coevolution in small and large host populations, as well as in host populations which were periodically forced through a bottleneck. By carefully controlling host population size for 23 host generations, we found that host adaptation was constrained in small populations and to a lesser extent in the bottlenecked populations. As a result, coevolution in large and small populations gave rise to different selection dynamics and produced different patterns of host-pathogen genotype-by-genotype interactions. Our results demonstrate a major influence of host population size on the ability of the antagonists to co-adapt to each other, thereby shaping the dynamics of antagonistic coevolution.

Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model.

The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction-irrespective of whether combinations were compared at the same level of inhibition or not-while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution.

Cellular hysteresis as a principle to maximize the efficacy of antibiotic therapy.

Antibiotic resistance has become one of the most dramatic threats to global health. While novel treatment options are urgently required, most attempts focus on finding new antibiotic substances. However, their development is costly, and their efficacy is often compromised within short time periods due to the enormous potential of microorganisms for rapid adaptation. Here, we developed a strategy that uses the currently available antibiotics. Our strategy exploits cellular hysteresis, which is the long-lasting, transgenerational change in cellular physiology that is induced by one antibiotic and sensitizes bacteria to another subsequently administered antibiotic. Using evolution experiments, mathematical modeling, genomics, and functional genetic analysis, we demonstrate that sequential treatment protocols with high levels of cellular hysteresis constrain the evolving bacteria by (i) increasing extinction frequencies, (ii) reducing adaptation rates, and (iii) limiting emergence of multidrug resistance. Cellular hysteresis is most effective in fast sequential protocols, in which antibiotics are changed within 12 h or 24 h, in contrast to the less frequent changes in cycling protocols commonly implemented in hospitals. We found that cellular hysteresis imposes specific selective pressure on the bacteria that disfavors resistance mutations. Instead, if bacterial populations survive, hysteresis is countered in two distinct ways, either through a process related to antibiotic tolerance or a mechanism controlled by the previously uncharacterized two-component regulator CpxS. We conclude that cellular hysteresis can be harnessed to optimize antibiotic therapy, to achieve both enhanced bacterial elimination and reduced resistance evolution.

How long do Red Queen dynamics survive under genetic drift? A comparative analysis of evolutionary and eco-evolutionary models.

BACKGROUND: Red Queen dynamics are defined as long term co-evolutionary dynamics, often with oscillations of genotype abundances driven by fluctuating selection in host-parasite systems. Much of our current understanding of these dynamics is based on theoretical concepts explored in mathematical models that are mostly (i) deterministic, inferring an infinite population size and (ii) evolutionary, thus ecological interactions that change population sizes are excluded. Here, we recall the different mathematical approaches used in the current literature on Red Queen dynamics. We then compare models from game theory (evo) and classical theoretical ecology models (eco-evo), that are all derived from individual interactions and are thus intrinsically stochastic. We assess the influence of this stochasticity through the time to the first loss of a genotype within a host or parasite population. RESULTS: The time until the first genotype is lost ("extinction time"), is shorter when ecological dynamics, in the form of a changing population size, is considered. Furthermore, when individuals compete only locally with other individuals extinction is even faster. On the other hand, evolutionary models with a fixed population size and competition on the scale of the whole population prolong extinction and therefore stabilise the oscillations. The stabilising properties of intra-specific competitions become stronger when population size is increased and the deterministic part of the dynamics gain influence. In general, the loss of genotype diversity can be counteracted with mutations (or recombination), which then allow the populations to recurrently undergo negative frequency-dependent selection dynamics and selective sweeps. CONCLUSION: Although the models we investigated are equal in their biological motivation and interpretation, they have diverging mathematical properties both in the derived deterministic dynamics and the derived stochastic dynamics. We find that models that do not consider intraspecific competition and that include ecological dynamics by letting the population size vary, lose genotypes - and thus Red Queen oscillations - faster than models with competition and a fixed population size.

Community assembly of the native C. elegans microbiome is influenced by time, substrate and individual bacterial taxa.

Microbiome communities are complex assemblages of bacteria. The dissection of their assembly dynamics is challenging because it requires repeated sampling of both host and source communities. We used the nematode Caenorhabditis elegans as a model to study these dynamics. We characterized microbiome variation from natural worm populations and their substrates for two consecutive years using 16S rDNA amplicon sequencing. We found conservation in microbiome composition across time at the genus, but not amplicon sequencing variant (ASV) level. Only three ASVs were consistently present across worm samples (Comamonas ASV10859, Pseudomonas ASV7162 and Cellvibrio ASV9073). ASVs were more diverse in worms from different rather than the same substrates, indicating an influence of the source community on microbiome assembly. Surprisingly, almost 50% of worm-associated ASVs were absent in corresponding substrates, potentially due to environmental filtering. Ecological network analysis revealed strong effects of bacteria-bacteria interactions on community composition: While a dominant Erwinia strain correlated with decreased alpha-diversity, predatory bacteria of the Bdellovibrio and like organisms associated with increased alpha-diversity. High alpha-diversity was further linked to high worm population growth, especially on species-poor substrates. Our results highlight that microbiomes are individually shaped and sensitive to dramatic community shifts in response to particular competitive species.

Bdellovibrio and Like Organisms Are Predictors of Microbiome Diversity in Distinct Host Groups.

Biodiversity is generally believed to be a main determinant of ecosystem functioning. This principle also applies to the microbiome and could consequently contribute to host health. According to ecological theory, communities are shaped by top predators whose direct and indirect interactions with community members cause stability and diversity. Bdellovibrio and like organisms (BALOs) are a neglected group of predatory bacteria that feed on Gram-negative bacteria and can thereby influence microbiome composition. We asked whether BALOs can predict biodiversity levels in microbiomes from distinct host groups and environments. We demonstrate that genetic signatures of BALOs are commonly found within the 16S rRNA reads from diverse host taxa. In many cases, their presence, abundance, and especially richness are positively correlated with overall microbiome diversity. Our findings suggest that BALOs can act as drivers of microbial alpha-diversity and should therefore be considered candidates for the restoration of microbiomes and the prevention of dysbiosis.

A multi-parent recombinant inbred line population of C. elegans allows identification of novel QTLs for complex life history traits.

BACKGROUND: The nematode Caenorhabditis elegans has been extensively used to explore the relationships between complex traits, genotypes, and environments. Complex traits can vary across different genotypes of a species, and the genetic regulators of trait variation can be mapped on the genome using quantitative trait locus (QTL) analysis of recombinant inbred lines (RILs) derived from genetically and phenotypically divergent parents. Most RILs have been derived from crossing two parents from globally distant locations. However, the genetic diversity between local C. elegans populations can be as diverse as between global populations and could thus provide means of identifying genetic variation associated with complex traits relevant on a broader scale. RESULTS: To investigate the effect of local genetic variation on heritable traits, we developed a new RIL population derived from 4 parental wild isolates collected from 2 closely located sites in France: Orsay and Santeuil. We crossed these 4 genetically diverse parental isolates to generate a population of 200 multi-parental RILs and used RNA-seq to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the 4 genotypes with an average spacing of 11 kb, doubling the mapping resolution relative to currently available RIL panels for many loci. The SNPs were used to construct a genetic map to facilitate QTL analysis. We measured life history traits such as lifespan, stress resistance, developmental speed, and population growth in different environments, and found substantial variation for most traits. We detected multiple QTLs for most traits, including novel QTLs not found in previous QTL analysis, including those for lifespan and pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping. CONCLUSION: Taken together, we show that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates facilitates the detection of QTLs. The use of multi-parent RIL populations can further enhance our understanding of local adaptation and life history trade-offs.

aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.

BACKGROUND: Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices. RESULTS: We here introduce a new tool for estimating differential expression in noisy real-life data. It employs a novel normalization procedure (qtotal), which takes account of the overall distribution of read counts for data standardization enhancing reliable identification of differential gene expression, especially in case of asymmetrical distributions of up- and downregulated genes. The tool then introduces a polynomial algorithm (aFold) to model the uncertainty of read counts across treatments and genes. We extensively benchmark aFold on a variety of simulated and validated real-life data sets (e.g. ABRF, SEQC and MAQC-II) and show a higher ability to correctly identify differentially expressed genes under most tested conditions. aFold infers fold change values that are comparable across experiments, thereby facilitating data clustering, visualization, and other downstream applications. CONCLUSIONS: We here present a new transcriptomics analysis tool that includes both a data normalization method and a differential expression analysis approach. The new tool is shown to enhance reliable identification of significant differential expression across distinct data distributions. It outcompetes alternative procedures in case of asymmetrical distributions of up- versus down-regulated genes and also the presence of outliers, all common to real data sets.