Institute for Integrative Systems Biology

The remarkable capacity of some viruses to adapt to new hosts and environments is highly dependent on their ability to generate de novo diversity in a short period of time. Rates of spontaneous mutation vary amply among viruses. RNA viruses mutate faster than DNA viruses, single-stranded viruses mutate faster than double-strand virus, and genome size appears to correlate negatively with mutation rate. Viral mutation rates are modulated at different levels, including polymerase fidelity, sequence context, template secondary structure, cellular microenvironment, replication mechanisms, proofreading, and access to post-replicative repair. Additionally, massive numbers of mutations can be introduced by some virus-encoded diversity-generating elements, as well as by host-encoded cytidine/adenine deaminases. Our current knowledge of viral mutation rates indicates that viral genetic diversity is determined by multiple virus- and host-dependent processes, and that viral mutation rates can evolve in response to specific selective pressures.

BACKGROUND: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. RESULTS: Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user's sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. CONCLUSION: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/reactome/ ).

The drastic development of polymeric materials for a wide range of biomedical and biomaterial applications has been explored in the last few decades. Among these materials, a new class of 'smart' or 'intelligent' biomaterial has been developed, and these materials are highly responsive to slight changes in their environments. Due to their dynamically alterable properties, smart materials allow for smart biomaterials to be developed. This review presents smart thermo-responsive polymers and discusses how they may be used as smart biomaterials. We describe typical thermo-responsive polymers that are either lower critical solution temperature-type, upper critical solution temperature-type, or thermo-induced shape-memory polymers. The basic mechanisms of the thermo-response processes will also be described. The applications of smart biomaterials with various forms, such as smart fibres, surfaces and hydrogels, will also be introduced.

Gut microbiota-related metabolites are potential clinical biomarkers for cardiovascular disease (CVD). Circulating succinate, a metabolite produced by both microbiota and the host, is increased in hypertension, ischemic heart disease, and type 2 diabetes. We aimed to analyze systemic levels of succinate in obesity, a major risk factor for CVD, and its relationship with gut microbiome. We explored the association of circulating succinate with specific metagenomic signatures in cross-sectional and prospective cohorts of Caucasian Spanish subjects. Obesity was associated with elevated levels of circulating succinate concomitant with impaired glucose metabolism. This increase was associated with specific changes in gut microbiota related to succinate metabolism: a higher relative abundance of succinate-producing Prevotellaceae (P) and Veillonellaceae (V), and a lower relative abundance of succinate-consuming Odoribacteraceae (O) and Clostridaceae (C) in obese individuals, with the (P + V/O + C) ratio being a main determinant of plasma succinate. Weight loss intervention decreased (P + V/O + C) ratio coincident with the reduction in circulating succinate. In the spontaneous evolution after good dietary advice, alterations in circulating succinate levels were linked to specific metagenomic signatures associated with carbohydrate metabolism and energy production with independence of body weight change. Our data support the importance of microbe-microbe interactions for the metabolite signature of gut microbiome and uncover succinate as a potential microbiota-derived metabolite related to CVD risk.

Microalgae have been used for centuries to provide nourishment to humans and animals, only very recently they have become much more widely cultured and harvested at large industrial scale. This paper reviews the potential health benefits and nutrition provided by microalgae whose benefits are contributing to expand their market. We also point out several key challenges that remain to be addressed in this field.

Following its emergence in late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic resulting in unprecedented efforts to reduce transmission and develop therapies and vaccines (WHO Emergency Committee, 2020; Zhu et al., 2020). Rapidly generated viral genome sequences have allowed the spread of the virus to be tracked via phylogenetic analysis (Worobey et al., 2020; Hadfield et al., 2018; Pybus et al., 2020). While the virus spread globally in early 2020 before borders closed, intercontinental travel has since been greatly reduced, allowing continent-specific variants to emerge. However, within Europe travel resumed in the summer of 2020, and the impact of this travel on the epidemic is not well understood. Here we report on a novel SARS-CoV-2 variant, 20E (EU1), that emerged in Spain in early summer, and subsequently spread to multiple locations in Europe. We find no evidence of increased transmissibility of this variant, but instead demonstrate how rising incidence in Spain, resumption of travel across Europe, and lack of effective screening and containment may explain the variant’s success. Despite travel restrictions and quarantine requirements, we estimate 20E (EU1) was introduced hundreds of times to countries across Europe by summertime travellers, likely undermining local efforts to keep SARS-CoV-2 cases low. Our results demonstrate how a variant can rapidly become dominant even in absence of a substantial transmission advantage in favorable epidemiological settings. Genomic surveillance is critical to understanding how travel can impact SARS-CoV-2 transmission, and thus for informing future containment strategies as travel resumes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the first pandemic where the spread of a viral pathogen has been globally tracked in near real-time using phylogenetic analysis of viral genome sequences (Worobey et al., 2020; Hadfield et al., 2018; Pybus et al., 2020). SARS-CoV-2 genomes continue to be generated at a rate far greater than for any other pathogen and more than 500,000 full genomes are available on GISAID as of February 2020 (Shu and McCauley, 2017). In addition to tracking the viral spread, these genome sequences have been used to monitor mutations which might change the transmission, pathogenesis, or anti-genic properties of the virus. One mutation in particular, D614G in the spike protein, has received much attention. This variant (Nextstrain clade 20A) seeded large outbreaks in Europe in early 2020 and subsequently dominated the outbreaks in the Americas, thereby largely replacing previously circulating lineages. This rapid rise led to the suggestion that this variant is more transmissible, which has since been corroborated by phylogenetic (Korber et al., 2020; Volz et al., 2020) and experimental evidence (Plante et al., 2020; Yurkovetskiy et al., 2020). Following the global dissemination of SARS-CoV-2 in early 2020 (Worobey et al., 2020), intercontinental travel dropped dramatically. Within Europe, however, travel and in particular holiday travel resumed in summer (though at lower levels than in previous years) with largely uncharacterized effects on the pandemic. Here we report on a novel SARS-CoV-2 variant 20E (EU1) ( S:A222V ) that emerged in early summer 2020, presumably in Spain, and subsequently spread to multiple locations in Europe. Over the summer, it rose in frequency in parallel in multiple countries. As we report here, this variant, 20E (EU1), and a second variant 20A.EU2 with mutation S477N in the spike protein accounted for the majority of sequences in Europe in the autumn of 2020.

Dysbiosis in the genital tract or gut microbiome can be associated with endometriosis. We sampled vaginal, cervical and gut microbiota from 14 women with histology proven stage 3/4 endometriosis and 14 healthy controls. The V3 and V4 regions of the 16S rRNA gene were amplified following the 16S Metagenomic Sequencing Library Preparation. Despite overall similar vaginal, cervical and intestinal microbiota composition between stage 3/4 endometriosis group and controls, we observed differences at genus level. The complete absence of Atopobium in the vaginal and cervical microbiota of the stage 3/4 endometriosis group was noteworthy. In the cervical microbiota, Gardnerella, Streptococcus, Escherichia, Shigella, and Ureoplasma, all of which contain potentially pathogenic species, were increased in stage 3/4 endometriosis. More women in the stage 3/4 endometriosis group had Shigella/Escherichia dominant stool microbiome. Further studies can clarify whether the association is causal, and whether dysbiosis leads to endometriosis or endometriosis leads to dysbiosis.

Grapevine is one of the most economically important crops worldwide. However, the previous versions of the grapevine reference genome tipically consist of thousands of fragments with missing centromeres and telomeres, limiting the accessibility of the repetitive sequences, the centromeric and telomeric regions, and the study of inheritance of important agronomic traits in these regions. Here, we assembled a telomere-to-telomere (T2T) gap-free reference genome for the cultivar PN40024 using PacBio HiFi long reads. The T2T reference genome (PN_T2T) is 69 Mb longer with 9018 more genes identified than the 12X.v0 version. We annotated 67% repetitive sequences, 19 centromeres and 36 telomeres, and incorporated gene annotations of previous versions into the PN_T2T assembly. We detected a total of 377 gene clusters, which showed associations with complex traits, such as aroma and disease resistance. Even though PN40024 derives from nine generations of selfing, we still found nine genomic hotspots of heterozygous sites associated with biological processes, such as the oxidation-reduction process and protein phosphorylation. The fully annotated complete reference genome therefore constitutes an important resource for grapevine genetic studies and breeding programs.

Background Cryptococcal meningitis is a leading cause of human immunodeficiency virus (HIV)–related death in sub-Saharan Africa. Whether a treatment regimen that includes a single high dose of liposomal amphotericin B would be efficacious is not known. Methods In this phase 3 randomized, controlled, noninferiority trial conducted in five African countries, we assigned HIV-positive adults with cryptococcal meningitis in a 1:1 ratio to receive either a single high dose of liposomal amphotericin B (10 mg per kilogram of body weight) on day 1 plus 14 days of flucytosine (100 mg per kilogram per day) and fluconazole (1200 mg per day) or the current World Health Organization–recommended treatment, which includes amphotericin B deoxycholate (1 mg per kilogram per day) plus flucytosine (100 mg per kilogram per day) for 7 days, followed by fluconazole (1200 mg per day) for 7 days (control). The primary end point was death from any cause at 10 weeks; the trial was powered to show noninferiority at a 10-percentage-point margin. Results A total of 844 participants underwent randomization; 814 were included in the intention-to-treat population. At 10 weeks, deaths were reported in 101 participants (24.8%; 95% confidence interval [CI], 20.7 to 29.3) in the liposomal amphotericin B group and 117 (28.7%; 95% CI, 24.4 to 33.4) in the control group (difference, −3.9 percentage points); the upper boundary of the one-sided 95% confidence interval was 1.2 percentage points (within the noninferiority margin; P<0.001 for noninferiority). Fungal clearance from cerebrospinal fluid was −0.40 log10 colony-forming units (CFU) per milliliter per day in the liposomal amphotericin B group and −0.42 log10 CFU per milliliter per day in the control group. Fewer participants had grade 3 or 4 adverse events in the liposomal amphotericin B group than in the control group (50.0% vs. 62.3%). Conclusions Single-dose liposomal amphotericin B combined with flucytosine and fluconazole was noninferior to the WHO-recommended treatment for HIV-associated cryptococcal meningitis and was associated with fewer adverse events.

Biofilms are clusters of bacteria that live in association with surfaces. Their main characteristic is that the bacteria inside the biofilms are attached to other bacterial cells and to the surface by an extracellular polymeric matrix. Biofilms are capable of adhering to a wide variety of surfaces, both biotic and abiotic, including human tissues, medical devices, and other materials. On these surfaces, biofilms represent a major threat causing infectious diseases and economic losses. In addition, current antibiotics and common disinfectants have shown limited ability to remove biofilms adequately, and phage-based treatments are proposed as promising alternatives for biofilm eradication. This review analyzes the main advantages and challenges that phages can offer for the elimination of biofilms, as well as the most important factors to be taken into account in order to design effective phage-based treatments.

Human tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto , as well as two lineages (L5 and L6) traditionally referred to as Mycobacterium africanum . Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known of their genomic diversity, phylogeography and evolution. Here, we analysed the genomes of 350 L5 and 320 L6 strains, isolated from patients from 21 African countries, plus 5 related genomes that had not been classified into any of the known MTBC lineages. Our population genomic and phylogeographical analyses showed that the unclassified genomes belonged to a new group that we propose to name MTBC lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure and genetic diversity. Finally, we could not confirm the previous association of drug-resistance markers with lineage and sublineages. Instead, our results indicate that the association of drug resistance with lineage is most likely driven by sample bias or geography. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum , and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of TB in Africa and globally.

Tuberculosis (TB) affects humans and other animals and is caused by bacteria from the Mycobacterium tuberculosis complex (MTBC). Previous studies have shown that there are at least nine members of the MTBC infecting animals other than humans; these have also been referred to as ecotypes. However, the ecology and the evolution of these animal-adapted MTBC ecotypes are poorly understood. Here we screened 12,886 publicly available MTBC genomes and newly sequenced 17 animal-adapted MTBC strains, gathering a total of 529 genomes of animal-adapted MTBC strains. Phylogenomic and comparative analyses confirm that the animal-adapted MTBC members are paraphyletic with some members more closely related to the human-adapted Mycobacterium africanum Lineage 6 than to other animal-adapted strains. Furthermore, we identified four main animal-adapted MTBC clades that might correspond to four main host shifts; two of these clades are proposed to reflect independent cattle domestication events. Contrary to what would be expected from an obligate pathogen, MTBC nucleotide diversity was not positively correlated with host phylogenetic distances, suggesting that host tropism in the animal-adapted MTBC seems to be driven more by contact rates and demographic aspects of the host population rather than host relatedness. By combining phylogenomics with ecological data, we propose an evolutionary scenario in which the ancestor of Lineage 6 and all animal-adapted MTBC ecotypes was a generalist pathogen that subsequently adapted to different host species. This study provides a new phylogenetic framework to better understand the evolution of the different ecotypes of the MTBC and guide future work aimed at elucidating the molecular mechanisms underlying host specificity

Despite their long success for more than half a century, antibiotics are currently under the spotlight due to the emergence of multidrug-resistant bacteria. The development of new alternative treatments is of particular interest in the fight against bacterial resistance. Bacteriophages (phages) are natural killers of bacteria and are an excellent tool due to their specificity and ecological safety. Here, we highlight some of their advantages and drawbacks as potential therapeutic agents. Interestingly, phages are not only attractive from a clinical point of view, but other areas, such as agriculture, food control, or industry, are also areas for their potential application. Therefore, we propose phages as a real alternative to current antibiotics.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Convergent evolution of SARS-CoV-2 Omicron BA.2, BA.4, and BA.5 lineages has led to the emergence of several new subvariants, including BA.2.75.2, BA.4.6. and BQ.1.1. The subvariant BQ.1.1 became predominant in many countries in December 2022. The subvariants carry an additional and often redundant set of mutations in the spike, likely responsible for increased transmissibility and immune evasion. Here, we established a viral amplification procedure to easily isolate Omicron strains. We examined their sensitivity to 6 therapeutic monoclonal antibodies (mAbs) and to 72 sera from Pfizer BNT162b2-vaccinated individuals, with or without BA.1/BA.2 or BA.5 breakthrough infection. Ronapreve (Casirivimab and Imdevimab) and Evusheld (Cilgavimab and Tixagevimab) lose antiviral efficacy against BA.2.75.2 and BQ.1.1, whereas Xevudy (Sotrovimab) remaine weakly active. BQ.1.1 is also resistant to Bebtelovimab. Neutralizing titers in triply vaccinated individuals are low to undetectable against BQ.1.1 and BA.2.75.2, 4 months after boosting. A BA.1/BA.2 breakthrough infection increases these titers, which remains about 18-fold lower against BA.2.75.2 and BQ.1.1, than against BA.1. Reciprocally, a BA.5 breakthrough infection increases more efficiently neutralization against BA.5 and BQ.1.1 than against BA.2.75.2. Thus, the evolution trajectory of novel Omicron subvariants facilitates their spread in immunized populations and raises concerns about the efficacy of most available mAbs.

Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies. Multi-omics studies have been increasingly used to better understand biological samples and infer molecular interactions. Nevertheless, a number of challenges must still be addressed to take full advantage of multi-omics data and to avoid reaching potentially incorrect conclusions.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Viruses are involved in various interactions both within and between infected cells. Social evolution theory offers a conceptual framework for how virus-virus interactions, ranging from conflict to cooperation, have evolved. A critical examination of these interactions could expand our understanding of viruses and be exploited for epidemiological and medical interventions. Viruses are involved in various interactions both within and between infected cells. Social evolution theory offers a conceptual framework for how virus-virus interactions, ranging from conflict to cooperation, have evolved. A critical examination of these interactions could expand our understanding of viruses and be exploited for epidemiological and medical interventions. Virus-virus interactions are pervasive and highly diverse (DaPalma et al., 2010DaPalma T. Doonan B.P. Trager N.M. Kasman L.M. Virus Res. 2010; 149: 1-9Crossref PubMed Scopus (138) Google Scholar; Figure 1). Some viruses need another, “helper” virus to complete their infection cycle, and other viruses are commonly activated or suppressed by the presence of secondary viral infections. Viral proteins can mix and produce mosaic-like viral particles (pseudotypes) when a cell is coinfected with two different viruses. Viral coinfection of microbes is widespread (Díaz-Muñoz, 2017Díaz-Muñoz S.L. Virus Evol. 2017; 3: vex011Crossref PubMed Scopus (45) Google Scholar), and viruses have mechanisms enabling multiple viral genomes to be cotransmitted in the same infectious unit (reviewed in Sanjuán, 2017Sanjuán R. Trends Microbiol. 2017; 25: 402-412Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar). Coinfecting viral genomes can be distinct, variants of the same virus, or even genetically identical, suggesting different types of functional interplay. Furthermore, bacteriophages use a form of communication to regulate lysis of the infected cell (Erez et al., 2017Erez Z. Steinberger-Levy I. Shamir M. Doron S. Stokar-Avihail A. Peleg Y. Melamed S. Leavitt A. Savidor A. Albeck S. et al.Nature. 2017; 541: 488-493Crossref PubMed Scopus (317) Google Scholar). Finally, virus-virus interactions in the absence of cellular coinfection can also be mediated by changes at the host level, such as immune responses. Despite this growing body of empirical evidence suggesting virus-virus interactions, we lack a well-founded conceptual framework that provides an understanding of how these interactions have evolved and how they could shape viral pathogenesis. Social evolution theory was originally developed to explain animal behavior, but has since been extended to microorganisms, including bacteria and unicellular eukaryotes. Yet this social perspective has not been embraced in the study of viruses. Potential misgivings with this social-evolution approach include the idea that viruses are too simple to interact socially, or that to infer traits like cooperation would be anthropomorphic. On the flip side, social interactions have driven the evolution of life at all levels of complexity and thus could have a role in viral evolution as well: genes cooperate to form genomes, cells cooperate to form multicellular organisms, and multicellular organisms form complex cooperative societies. Conflict is also ubiquitous: “selfish” genes drive their transmission at a cost to the genome, cells exploit collectively produced goods like nutrient-scavenging molecules, and organisms compete with each other for resources. Since no complex phenotypes are necessary for the evolution of social traits, the appearance of anthropomorphism or the “simple” organization of viruses may not pose problems for the application of sociobiology models in virology. We argue for a social evolution approach to understand and predict virus-virus interactions. This framework can clarify unexplained phenomena in virus-virus interactions by identifying evolved viral social traits, their genetic and mechanistic basis, and the selective pressures underlying these traits. We outline how this approach could lead to new breakthroughs in both fundamental and applied virology. Social interactions take place when the traits of one individual influence the fitness of another individual. As such, evolutionary analysis of social interactions relies fundamentally on natural selection. The only major difference with non-social models is that two or more interacting individuals are involved. For example, cooperative traits, which benefit others, will only be selected for by natural selection if they provide a fitness benefit to the individual performing them (Figure 2A). Cooperative traits cannot evolve merely because they provide a benefit to a population. Furthermore, the evolution of cooperation does not require the ability of individuals to foresee the consequences of their actions. In other words, cooperation and social evolution can be explained by, and are not at odds with, individual-based natural selection. One way cooperation can be evolutionarily favored is when it is directed toward genetically identical individuals or relatives. From an evolutionary perspective, helping a genetically identical individual reproduce is the same as reproducing yourself. By extension, helping relatives reproduce provides a fitness benefit to the actor, as relatives share a fraction of the actor’s genes. This process by which individuals increase their fitness by helping relatives reproduce is termed kin selection. An important potential difficulty in applying social evolution to virology resides in the definition of an individual. One may consider the virion as an individual. Yet in some cases, including paramyxoviruses, birnaviruses, filoviruses, retroviruses, and inoviruses, a virion can carry multiple genome copies (n-ploidy). Functionally, these n-ploid virions should be similar to multiple virions entering the same cell, because they would both lead to infections with multiple viral genomes. As such, we suggest that the definition of an individual should be set at the level of the single infectious viral genome. Another potential complication stems from the fact that some viruses, notably RNA viruses, show extremely high mutation rates, leading to the suggestion that the minimal level at which an individual RNA virus can be defined is a sequence cloud, or quasispecies (Andino and Domingo, 2015Andino R. Domingo E. Virology. 2015; 479-480: 46-51Crossref PubMed Scopus (270) Google Scholar). In this case, the individual could be redefined as the consensus (or predominant) sequence of the quasispecies. If, however, functional social interactions are established among genetic variants within such clouds, then it remains useful to keep the definition of the individual at the single viral genome level, because this allows identifying and analyzing such interactions. If we think of a genome as an individual, then the natural history of viruses is filled with opportunities for social interactions (Figure 1), even though experimental demonstration is still lacking in many cases. A single viral genome entering a cell needs to accomplish both replication and gene expression (transcription and translation). The mRNAs and proteins resulting from viral gene expression can provide a collective benefit to genomes in the cell, such as generating capsids and proteins that block host immunity. The fact that these factors act as public goods permits, but does not guarantee, cooperation (Chao and Elena, 2017Chao L. Elena S.F. Proc. Biol. Sci. 2017; 284: 20170228-20170229Crossref PubMed Scopus (16) Google Scholar). Cooperation is more likely to evolve if most interacting genomes are identical, since such high relatedness allows kin selection to operate (Turner and Chao, 1999Turner P.E. Chao L. Nature. 1999; 398: 441-443Crossref PubMed Scopus (460) Google Scholar). Studied with this social evolution lens, even the infection and replication of a single viral genome within a single cell is an inherently social process (Figure 1A). Evidence in favor of viruses acting as cooperative social agents is, paradoxically, provided by the occasional spread of uncooperative “cheats.” If individuals are not genetically identical, they will not necessarily have the same evolutionary interests, creating the potential for conflict. Social evolution theory predicts that if multiple genomes infect a cell, one that invests less in cooperative traits such as transcription and invests more in its own replication will be favored, because it will benefit from cooperation without paying as much of the cost (Chao and Elena, 2017Chao L. Elena S.F. Proc. Biol. Sci. 2017; 284: 20170228-20170229Crossref PubMed Scopus (16) Google Scholar). This is analogous to the tragedy of the commons in humans, where cooperation breaks down due to selfish interests, even though everyone could do better in the long run by cooperating. Two important variables determine the extent to which multiple genomes infect a cell: the multiplicity of infection (MOI) and the rate of spontaneous mutation. High mutation rates such as those exhibited by RNA viruses result in extremely diverse populations, whereas a high MOI can bring genomes from different lineages into the same host cells. These variables can enable genomes to interact in coinfection, as long as there is sharing of viral products within cells. A clear experimental test of the association between MOI and cheater evolution was generated with bacteriophage phi6 (Turner and Chao, 1999Turner P.E. Chao L. Nature. 1999; 398: 441-443Crossref PubMed Scopus (460) Google Scholar). Serial passage at high MOI led to the evolution of a cheater virus optimized to increase its fitness in coinfection by favoring its replication over other coinfecting viruses, reducing average population fitness. In contrast, passage at low MOI selected a virus optimized to grow efficiently in monoinfection and to high population fitness. Thus, coinfection generates conditions ripe for the emergence of cheats. Defective interfering particles (DIPs) appear to be “cheats.” DIPs usually have a large portion of their genome deleted, including essential proteins, and hence can only reproduce in the presence of completely functional “helper” viruses. However, their smaller genomes provide a replication advantage, outcompeting helper viruses in mixed infections of cells and severely reducing viral population fitness. In the laboratory, artificially high MOIs are well-known to promote the emergence of DIPs (Marriott and Dimmock, 2010Marriott A.C. Dimmock N.J. Rev. Med. Virol. 2010; 20: 51-62Crossref PubMed Scopus (91) Google Scholar). However, a pending challenge is to measure MOIs in vivo and to determine the ensuing levels of intra-host genetic relatedness in viruses. In turn, the association between mutation rate and cheating has been shown in experimental populations of RNA viruses treated with base analog mutagens. This selected for a fraction of viral genomes with DIP-like behavior, greatly reducing population fitness (reviewed in Andino and Domingo, 2015Andino R. Domingo E. Virology. 2015; 479-480: 46-51Crossref PubMed Scopus (270) Google Scholar). Finally, evidence that viral proteins can function as public goods inside the cell is supported by well-known processes in virology, such as pseudotyping, in which a viral particle contains the envelope proteins of a different virus. However, many plus-strand RNA viruses replicate in well-defined subcellular structures. This situation suggests that, in some cases, intracellular viral product sharing might be restricted, which would tend to prevent the spread of cheaters. Thus, the details of viral population structure at the intracellular level and the identification and mechanistic characterization of public goods will be crucial milestones to reach in the sociovirology field. As detailed above, kin selection theory predicts that high coinfection rates select against cooperation, because they reduce genetic relatedness. However, mutually beneficial cooperation involving genetically distinct individuals (“heterotypic cooperation”) can also evolve. All that is required is that the individuals have some shared interest, such that they have higher fitness if they cooperate. Cooperation between different genetic variants is a familiar concept in virology, but rigorous evidence supporting it remains scarce. The finding that population genetic diversity correlated with the ability of poliovirus to cause disease in mice led to the suggestion of cooperation, with similar processes postulated for hepatitis B virus, among others (reviewed in Andino and Domingo, 2015Andino R. Domingo E. Virology. 2015; 479-480: 46-51Crossref PubMed Scopus (270) Google Scholar). Measuring genetic diversity (e.g., using quasispecies theory or mutation-selection balance) is a first step to establish that interactions potentially exist, but does not provide information on the nature of the interactions and their fitness outcomes. Genetic complementation, whereby the genetic defects of the interacting viruses are mutually compensated, is not intrinsically cooperation, but represents a possible mechanism for heterotypic cooperation. A potential example comes from influenza viruses, when one strain has a more efficient hemagglutinin, which mediates virus attachment to host cells, and another has a more efficient neuraminidase that facilitates the release of virions from infected cells. In cell culture, these viruses reproduce better together than they do independently, because one is advantaged in cell entry and the other is advantaged in cell exit (Xue et al., 2016Xue K.S. Hooper K.A. Ollodart A.R. Dingens A.S. Bloom J.D. Elife. 2016; 5: e13974Crossref PubMed Scopus (50) Google Scholar). Multiple genome infections also lead to higher viral growth in measles virus and even gain of functions, such as extended cell tropism (Shirogane et al., 2012Shirogane Y. Watanabe S. Yanagi Y. Nat. Commun. 2012; 3: 1235-1237Crossref PubMed Scopus (60) Google Scholar). A possible reason why interacting genomes may surpass wild-type fitness is that some beneficial mutations show strong negative epistasis when combined in the same viral genome. Adaptive immunity evasion in hepatitis C virus provides another suggested instance of cooperation. Some variants may encode dominant antigens to monopolize immune responses and favor other viruses, leading to a complex network of cross-immunoreactivity that mitigates the immune pressure acting on certain variants (Skums et al., 2015Skums P. Bunimovich L. Khudyakov Y. Proc. Natl. Acad. Sci. USA. 2015; 112: 6653-6658Crossref PubMed Scopus (23) Google Scholar). As opposed to the above examples, this may not be a mutually beneficial interaction, because the variants encoding the flag antigen would play an altruistic role (Figure 1F). To what extent this outcome is evolutionarily stable is unclear because, as discussed above, altruistic cooperative behavior is critically dependent on genetic relatedness and kin selection. As a note of caution, many of the above examples of distinct viral genomes engaging in positive or mutually beneficial interactions are not necessarily cooperation. Whereas cooperation is colloquially used as a synonym for “helping,” in evolutionary terms it requires that a trait provides a benefit to another individual (mutual benefit or altruism) and that the trait has evolved at least partially due to this benefit (West et al., 2007West S.A. Griffin A.S. Gardner A. J. Evol. Biol. 2007; 20: 415-432Crossref PubMed Scopus (890) Google Scholar). This latter clause is required because we are interested in whether the benefit to others is an adaptation (Figure 2), and not just a byproduct of an otherwise selfish trait. For example, when an elephant produces dung, this is beneficial to the elephant, but also beneficial to a dung beetle that uses that dung. However, elephants have clearly not evolved defecation to help dung beetles, and so although defecation is mutually beneficial, it is not cooperation. Thus, the challenge resides in explaining cooperative traits that evolved to benefit others, which becomes difficult with complex interactions. Intercellular virus-virus interactions mediated through changes in the host, such as breakage of physical barriers or immune suppression, could easily fall into the elephant-dung beetle category. Similarly, some of these synergistic interactions reported as heterotypic cooperation between virus variants may be transient. The designation of byproduct rather than cooperation does not imply that the observed phenomena are not important. Rather, they raise different questions about why they are favored by natural selection, which can be crucial for understanding and manipulating these viral social interactions. The utility of identifying cooperation or cheating, as opposed to a transient interaction, is that they represent evolved social interactions. This identification has two consequences. First, we can uncover the genetic underpinnings of the trait. For instance, the specific mechanism underlying cheating by DIPs has led to DIP-derived treatments that depend only on the gene, rather than the entire viral particle. Second, we can establish the environmental and social pressures that led to selection of that trait. This knowledge of the selective regime can lead to generalizable principles, such as low-MOI vaccine propagation to avoid the appearance of DIPs. In practice, the key first step will be to test whether evolved social interactions are really occurring between strains, and what form they take (Figure 2B). First, one can measure the fitness of each partner alone and in combination (Bordería et al., 2015Bordería A.V. Isakov O. Moratorio G. Henningsson R. Agüera-González S. Organtini L. Gnädig N.F. Blanc H. Alcover A. Hafenstein S. et al.PLoS Pathog. 2015; 11 (e1004838–e20)Crossref PubMed Scopus (83) Google Scholar). This will show whether a given interaction is beneficial or detrimental, and whether it can be exploited by potential cheats. Second, to show an evolved social interaction, as opposed to a transient interaction, the genetic mechanisms underlying the interaction can be uncovered and exploited in experimental manipulations. For instance, one can engineer strains with and without the trait to test whether a beneficial trait is cooperation or a detrimental trait is cheating. This experimentation can falsify alternative, non-adaptive explanations such as incidental interactions. Finally, if the conditions favoring a cooperative or cheating interaction are known or hypothesized, evolutionary in which these conditions are can provide evidence for an evolved social The whereby bacteriophages produce that are by specific in cells, provides a test (Erez et al., 2017Erez Z. Steinberger-Levy I. Shamir M. Doron S. Stokar-Avihail A. Peleg Y. Melamed S. Leavitt A. Savidor A. Albeck S. et al.Nature. 2017; 541: 488-493Crossref PubMed Scopus (317) Google Scholar). This could be a cooperative because it population and Thus, viruses the to produce more to prevent the of host cells. If as above, determine is an evolved social this knowledge can be for instance, to that the better can be a social trait. In the growth will increase but also increase host viral transmission can be at an growth where the host is with the an virus provides a potential example of in the field. virus a disease known as in the and was as a to populations in and However, of natural the virus evolved toward rates and host opportunities for both and transmission et al., J. I. E. 2015; PubMed Scopus Google Scholar). Virus or host can also influence the social evolution of a population with of both virus and In this case, if a viral strain then it can the of and hence transmission of that strain in the long models predict that such populations will select for and hence more host as first in an virus and M. M. 2007; PubMed Scopus Google Scholar). In to the above example, low can also evolve in to relatedness. host relies on and cooperation between individual genomes, coinfection can select for leading to the of cooperation growth and (Turner and Chao, 1999Turner P.E. Chao L. Nature. 1999; 398: 441-443Crossref PubMed Scopus (460) Google Scholar). A social perspective on virus evolution can new and Social can viruses in a new and fundamental to virology. Similarly, by on virus-virus and we may be to uncover new in our own The role of genetic relatedness and kin selection in the evolution of cooperation can help better understand key processes in virology, such as which the cell to entry of potentially genomes, and why some viruses tend to be as infectious The social evolution perspective also to certain For example, have that may not populations (reviewed in Sanjuán, 2017Sanjuán R. Trends Microbiol. 2017; 25: 402-412Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar). Social evolution theory can which types of viruses are more likely to be by this and its on The social evolution perspective also the to of better viral by our knowledge of social using DIPs could be generated by using social underlying the emergence of (Marriott and Dimmock, 2010Marriott A.C. Dimmock N.J. Rev. Med. Virol. 2010; 20: 51-62Crossref PubMed Scopus (91) Google Scholar). on cheating and how it population fitness could be used in to predict the to viral On the other knowledge of can be used to increase viral in vaccine or to increase viral gene knowledge of evolved interactions between bacteriophages could lead to In there is growing evidence that virus-virus interactions are pervasive and that viruses have mechanisms to these interactions. We suggest that these traits are and using a social evolution As such, sociovirology for breakthroughs in fundamental and applied virology. was by was by

Some specialist insects feed on plants rich in secondary compounds, which pose a major selective pressure on both the phytophagous and the gut microbiota. However, microbial communities of toxic plant feeders are still poorly characterized. Here, we show the bacterial communities of the gut of two specialized Lepidoptera, Hyles euphorbiae and Brithys crini, which exclusively feed on latex-rich Euphorbia sp. and alkaloid-rich Pancratium maritimum, respectively. A metagenomic analysis based on high-throughput sequencing of the 16S rRNA gene revealed that the gut microbiota of both insects is dominated by the phylum Firmicutes, and especially by the common gut inhabitant Enterococcus sp. Staphylococcus sp. are also found in H. euphorbiae though to a lesser extent. By SEM, we found a dense ring-shaped bacterial biofilm in the hindgut of H. euphorbiae, and identified the most prominent bacterium in the biofilm as Enterococcus casseliflavus through molecular techniques. Interestingly, this species has previously been reported to contribute to the immobilization of latex-like molecules in the larvae of Spodoptera litura, a highly polyphagous lepidopteran. The E. casseliflavus strain was isolated from the gut and its ability to tolerate natural latex was tested under laboratory conditions. This fact, along with the identification of less frequent bacterial species able to degrade alkaloids and/or latex, suggest a putative role of bacterial communities in the tolerance of specialized insects to their toxic diet.

BACKGROUND: Gonadal steroid hormones have been suggested as the underlying mechanism responsible for the sexual dimorphism observed in metabolic diseases. Animal studies have also evidenced a causal role of the gut microbiome and metabolic health. However, the role of sexual dimorphism in the gut microbiota and the potential role of the microbiome in influencing sex steroid hormones and shaping sexually dimorphic susceptibility to disease have been largely overlooked. Although there is some evidence of sex-specific differences in the gut microbiota diversity, composition, and functionality, the results are inconsistent. Importantly, most of these studies have not taken into account the gonadal steroid status. Therefore, we investigated the gut microbiome composition and functionality in relation to sex, menopausal status, and circulating sex steroids. RESULTS: No significant differences were found in alpha diversity indices among pre- and post-menopausal women and men, but beta diversity differed among groups. The gut microbiota from post-menopausal women was more similar to men than to pre-menopausal women. Metagenome functional analyses revealed no significant differences between post-menopausal women and men. Gonadal steroids were specifically associated with these differences. Hence, the gut microbiota of pre-menopausal women was more enriched in genes from the steroid biosynthesis and degradation pathways, with the former having the strongest fold change among all associated pathways. Microbial steroid pathways also had significant associations with the plasma levels of testosterone and progesterone. In addition, a specific microbiome signature was able to predict the circulating testosterone levels at baseline and after 1-year follow-up. In addition, this microbiome signature could be transmitted from humans to antibiotic-induced microbiome-depleted male mice, being able to predict donor's testosterone levels 4 weeks later, implying that the microbiota profile of the recipient mouse was influenced by the donor's gender. Finally, obesity eliminated most of the differences observed among non-obese pre-menopausal women, post-menopausal women, and men in the gut microbiota composition (Bray-Curtis and weighted unifrac beta diversity), functionality, and the gonadal steroid status. CONCLUSIONS: The present findings evidence clear differences in the gut microbial composition and functionality between men and women, which is eliminated by both menopausal and obesity status. We also reveal a tight link between the gut microbiota composition and the circulating levels of gonadal steroids, particularly testosterone. Video Abstract.