Complex Organisms Research Articles

Effects of different vaccination regimes on the immunodiagnosis of tuberculosis in goats and evaluation of defined antigens

Tuberculosis (TB) in goats is a chronic infectious disease caused by Mycobacterium tuberculosis complex (MTBC) organisms that pose a great health and economic challenge for the caprine industry in some European and developing countries. It is also a zoonotic disease posing a risk for public health. The control programs of the disease are based on a test-and-slaughter strategy, and vaccination is not feasible with available vaccines due to its interferences with the current TB immunodiagnosis. There is still a need for the development of an effective TB vaccine and, concurrently, diagnostic methods that allow differentiation between infected and vaccinated animals (DIVA approach). In this study, we investigated the interferences caused by the tuberculin (PPD)-based TB diagnostic tests in goats immunized by different mucosal and parenteral vaccination strategies: three single-dose strategies based on intranasal administration of BCG and two heat-inactivated M. bovis (HIMB) vaccines, and two prime-boost strategies based on parenteral BCG or HIMB priming and intranasal HIMB boosting. In addition, the defined antigens ESAT-6, CPF10, and EspC were evaluated as alternative diagnostic reagents to PPDs. At week 14 after prime vaccination of the animals, skin tests, IFN-γ release assay, and antibody detection assays were performed. The two prime-boosted and the single-dose intranasal BCG groups displayed greater cell-mediated immune responses to PPDs than the two single-dose intranasal HIMB vaccines. However, the use of reagents based on the defined antigens eliminated or reduced the vaccine-induced diagnostic interferences in all groups. Based on these results, the use of defined antigens in the current immunodiagnostic tests appears to be suitable in a future goat TB vaccination scenario.

Caenorhabditis nematodes influence microbiome and metabolome characteristics of their natural apple substrates over time.

The microbiomes of host organisms and their direct source environments are closely linked and key for shaping microbial community dynamics. The relationship between these linked dynamics is largely unexplored because source substrates are usually unavailable. To address this current knowledge gap, we employed bacteriovorous Caenorhabditis nematodes as a unique model system, for which source substrates like rotting apples can be easily collected. We compared single host microbiomes with their corresponding apple source substrates, as well as nematode-free substrates, over a 2-year sampling period in the botanical garden in Kiel, Germany. We found that single worms have unique microbiomes, which overlap most strongly with nematodes from the same source apple. A comparison to previous, related work revealed that variation in microbiome composition of natural Caenorhabditis isolates is significantly influenced by the substrate type, from which worms were obtained (e.g., fruits or compost). Our current sampling further showed that microbiome assembly is mostly driven by dispersal limitation. Importantly, two independent analysis approaches consistently suggest that worm microbiomes significantly influence characteristics of the apple microbiomes, possibly indicating niche construction by nematodes. Moreover, combining apple microbiome and metabolome data, we identified individual microbes and specific compounds indicative of fruit ripening that are significantly associated with nematode presence. In conclusion, our study elucidates the complex relationship between host microbiomes and their directly connected substrate microbiomes. Our analyses underscore the significant influence of nematode microbiomes on shaping the apple microbiome and, consequently, the fruit's metabolic capacity, thereby enhancing our general understanding of host-microbiome interactions in their natural habitat.IMPORTANCEAlmost all complex organisms are host to a microbial community, the microbiome. This microbiome can influence diverse host functions, such as food processing, protection against parasites, or development. The relationship between host and microbiome critically depends on the assembly of the microbial community, which may be shaped by microbes in the directly linked environment, the source microbiome. This assembly process is often not well understood because of the unavailability of source substrates. Here, we used Caenorhabditis nematodes as a model system that facilitates a direct comparison of host and source microbiomes. Based on a 2-year sampling period, we identified (i) a clear link between assembly dynamics of host and source microbiomes, (ii) a significant influence of nematode microbiomes on apple microbiomes, and (iii) specific microbes and compounds that are associated with the presence of nematodes in the sampled substrates. Overall, our study enhances our understanding of microbiome assembly dynamics and resulting functions.

Transferrin Modified Gold Nanoclusters-Based Biosensing Nanoplatform for High-Precision Multimodal Bioimaging of Tumor Cells.

Bioimaging technology has been broadly used in biomedicine, and the growth of multimodal imaging technology based on synergistic advantages can overcome the shortcomings of traditional single-modal bioimaging methods and attain high specificity and sensitivity in the fields of bioimaging and biosensing. The analysis of low-abundance microRNAs (miRNAs) in complex organisms is of high importance for early-stage diagnosis and clinical treatment of tumors. In our current study, a biosensing nanoplatform based on Tf-AuNCs and MnO2 nanosheets was developed for multimodal imaging of tumor cells. First, oxidizable MnO2 nanosheets provided a smart tool for use as nanocarriers and contrast agents for intracellular glutathione (GSH)-activated magnetic resonance imaging (MRI). Then, MnO2 nanosheets delivered Tf-AuNC-based biosensing nanoplatforms into cells through endocytosis. Endogenous GSH degraded MnO2 nanosheets into Mn2+, and the released functional nucleic acid probes can perform specific biosensing responses to miR-21 exhibiting multimodal imaging including two-photon near-infrared fluorescence imaging (TP-NIRFI), fluorescence lifetime imaging (FLIM), and MRI. Finally, the biosensing nanoplatform achieved satisfactory results in tumor cells and tissues by TP-NIRFI (300.0 μm penetration depth), FLIM (τ ≈ 50.0 ns), and MRI. Therefore, biosensing nanoplatforms based on Tf-AuNCs and MnO2 nanosheets show great potential for multimodal detection and imaging in tumor cells.

Evaluation of the efficacy of herbal (neem) and chemical (chlorhexidine) chips as local drug delivery agents in the treatment of chronic periodontitis: A clinical and microbiological study

Context: Herbal remedies can be effective in controlling periodontal disease. Neem is gaining attention as a reliable alternative to pharmaceuticals. Aims: The purpose of this study was to compare the efficacy of chlorhexidine (CHX) and neem chips (NCs) when used with scaling and root planing (SRP). Settings and Design: The study design involves interventional prospective study. Methods: A total of 45 periodontal pockets were divided into three groups of 15 sites each. Included both males and females of age group 35–55 years. Group I–SRP alone, Group II–SRP and CHXC, and Group III–SRP and NC. Parameters evaluated were plaque index (PI), gingival index (GI), probing pocket depth (PPD), and clinical attachment level (CAL). A qualitative multiplex polymerase chain reaction assay aided in detecting red complex organisms. Statistical Analysis Used: Evaluated at baseline, 10th day, and 1 month. Kruskal–Wallis test and Mann–Whitney U-test were used for the analysis. Results: There was a noticeable variation in the PPD at the start between the SRP, NC, and CHX groups. However, no significant differences were observed in other aspects such as the PI, GI, CAL, and the presence of Porphyromonas, Treponema, and Tannerella bacteria at the baseline, 10th day, and 30th day. Conclusions: All groups improved clinically, but no statistical difference was found in all clinical and microbiological parameters from baseline to day 30. Trial registration: https://ctri.nic.in/Clinicaltrials/login.php.Date registered 21/10/2020.reg no.CTRI/2020/10/028532.

How a Cosmology of a Nonlocally Unified, Meaningfully in-formed, Holographically Manifested and Purposefully Evolving Universe Engenders Planetary Systems as Gravitational Environments for the Emergence of Biological and Biomechanical Complexity

The emergent cosmology of a nonlocally unified, meaningfully in-formed, holographically manifested and purposefully evolving Universe, treats gravity as an emergent consequence of the in-formational and holographic nature of space-time and describes it as the consequence of the informational content, or intropy, associated with positions in space-time of massive bodies. In doing so, the evolutionary impulse inherently embodied in the Universe’s finite lifecycle engenders solar systems and planetary homes for the eventual evolution and complexity of biological organisms. Showing that planetary gravitational fields are required to enable cellular differentiation and subsequent development of a range of biomechanical strategies for building complex morphology, describes how such complex organisms such as human beings could eventually develop

SALC as a Community: Perspectives on Heavy-Recurrent Users

The purpose of this paper is first to develop a more complex understanding of students in the English Lounge, a self-access learning center (SALC) in northern Japan, by applying a sociological lens to their actions and experiences. The impetus for this project came from the desire, based on both personal experience and by analyzing sign-in data, to consider SALC users as a multiplicity rather than a monolith. While much energy has been expended at this institution toward addressing the needs of the broadest profile—that of the completely, or near-completely disengaged user—there is also potential for learning from the most extreme cases of heavy-recurrent users. Also, while the institution assumes that the primary beneficiaries of the SALC’s resources are Japanese undergraduates, the facility is a more complex organism with a variety of stakeholders. Thus, in addition to surveying heavy-recurrent users’ motivational histories, images of the SALC, and patterns of use, this paper also incorporates the results of a small-scale survey of international students hired to work as conversation partners. Keywords: student-led language learning community, autonomy-supportive leadership development, reflective practice

Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network

Essential genes are those whose presence is vital for a cell’s survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically modified microorganisms, and identifying targets for treatment. When essential genes are expressed, they give rise to essential proteins. Identifying these genes, especially in complex organisms like Plasmodium falciparum, which causes malaria, is challenging due to the cost and time associated with experimental methods. Thus, computational approaches have emerged. Early research in this area prioritised the study of less intricate organisms, inadvertently neglecting the complexities of metabolite transport in metabolic networks. To overcome this, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The proposed approach substantially improved gene essentiality predictions as it considered the weighted and directed nature of metabolic networks and utilised network-based features, achieving a high accuracy rate of 0.85 and an AuROC of 0.7. Furthermore, this study enhanced the understanding of metabolic networks and their role in determining gene essentiality in Plasmodium falciparum. Notably, our model identified 9 genes previously considered non-essential in the Ogee database but now predicted to be essential, with some of them potentially serving as drug targets for malaria treatment, thereby opening exciting research avenues.

Organismal mucosal immunology: A perspective through the eyes of game theory.

In complex organisms, functional units must interact cohesively to maintain homeostasis, especially within mucosal barriers that house diverse, specialized cell exposed to constant environmental challenges. Understanding how homeostasis at mucosal barriers is maintained and how its disruption can lead to autoimmune diseases or cancer, requires a holistic view. Although omics approaches and systems immunology have become powerful tools, they are not without limitations; interpretations may reflect researchers' assumptions, even if other explanations exist. In this perspective, I propose that applying game theory concepts to mucosal immunology could help interpret complex data, offering fresh perspectives and supporting the exploration of alternative scenarios. By framing the mucosal immune system as a network of strategic interactions with multiple possible outcomes, game theory, which analyzes strategic interactions and decision-making processes, could illuminate novel cell types and functions, cell interactions, and responses to pathogens and commensals, leading to a more comprehensive understanding of immune homeostasis and diseases. In addition, game theory might encourage researchers to consider a broader range of possibilities, reduce the risk of myopic thinking, and ultimately enable a more refined and comprehensive understanding of the complexity of the immune system at mucosal barriers. This perspective aims to introduce game theory as a complementary framework for mucosal immunologists, encouraging them to incorporate these concepts into data interpretation and system modeling.

PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms

Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by learning amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder. Extensive experimental validation of 24 positive predictions in cellulo shows an overall ~82% accuracy regardless of the structural disorder content of the tested proteins. While increasing disorder content is associated with organismal complexity, our analysis of 26 species reveals no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we present a machine learning classifier to interrogate condensate components at whole-proteome levels across the tree of life.

Toxicological Analysis of the Arylnaphthalene Lignan Justicidin B Using a Caenorhabditis elegans Model.

The screening of plant-derived compounds with anti-cancer properties is a promising strategy to meet the growing need for new, safe and effective anti-cancer drugs. Justicidin B is a plants secondary metabolite that displays anti-cancer properties in several tumor cells. Therefore, it represents a good candidate. We used the 3R-compliant organism Caenorhabditis elegans to evaluate the safety of justicidin B produced by in vitro-grown adventitious roots of Linum lewisii. We showed that a dose of 100 µg/mL justicidin B does not affect worm vitality in either short-term or chronic administration; in contrast, the 200 µg/mL dose induces a lifespan reduction, but only in short-term daily treatment. We attributed this effect to its accumulation in lipofuscin granules in the pharynx as observed through confocal analysis. HPLC analysis confirmed the higher accumulation justicidin B with a 200 µg/mL dose but also revealed the presence of metabolic derivatives that could be responsible for the toxicity. We also demonstrated that the 100 µg/mL dose does not affect worm fertility or development. Our results highlight the safety of justicidin B, supporting its employment in cancer therapy, and encourage the use of a C. elegans model as an appropriate tool to assess compounds' toxicity before moving to more complex organisms.

Influence of Uric Acid on Vascular and Cognitive Functions: Evidence for an Ambivalent Relationship.

The growing recognition of the public health impact of cognitive impairment and dementia has sparked a global initiative to identify risk factors and develop strategies to prevent or slow the progression of these cognitive disorders. Uric acid, the end product of the metabolism of purine nucleotides, has been reported as a key factor of many conditions potentially involved in cognitive dysfunction/dementia. In addition, some studies support the hypothesis that elevated uric acid levels could reduce the risk of Alzheimer's disease, slow down the decline of cognition, and delay the progression of Alzheimer's disease, while other evidence achieves opposite positions. These discrepancies might reflect a biological ambivalence for uric acid depending on a very complex interplay of factors that include its concentrations achieved in biological fluids, the nature, and concentration of free radicals, the presence and concentration of other antioxidant molecules, potentially responsible for bi-directional effects of uric acid on brain health/functioning. In this narrative review, we attempt to elucidate the influential role of uric acid metabolism in cognitive functioning by discussing pathophysiological mechanisms putatively involved, being well aware that none of them can be considered one-sided due to the complexity of the human organism.

START domains generate paralog-specific regulons from a single network architecture

Functional divergence of transcription factors (TFs) has driven cellular and organismal complexity throughout evolution, but its mechanistic drivers remain poorly understood. Here we test for new mechanisms using CORONA (CNA) and PHABULOSA (PHB), two functionally diverged paralogs in the CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) family of TFs. We show that virtually all genes bound by PHB ( ~ 99%) are also bound by CNA, ruling out occupation of distinct sets of genes as a mechanism of functional divergence. Further, genes bound and regulated by both paralogs are almost always regulated in the same direction, ruling out opposite regulation of shared targets as a mechanistic driver. Functional divergence of CNA and PHB instead results from differential usage of shared binding sites, with hundreds of uniquely regulated genes emerging from a commonly bound genetic network. Regulation of a given gene by CNA or PHB is thus a function of whether a bound site is considered ‘responsive’ versus ‘non-responsive’ by each paralog. Discrimination between responsive and non-responsive sites is controlled, at least in part, by their lipid binding START domain. This suggests a model in which HD-ZIPIII TFs use information integrated by their START domain to generate paralog-specific transcriptional outcomes from a shared network architecture. Taken together, our study identifies a mechanism of HD-ZIPIII TF paralog divergence and proposes the ubiquitously distributed START evolutionary module as a driver of functional divergence.

A multicellular developmental program in a close animal relative.

All animals develop from a single-celled zygote into a complex multicellular organism through a series of precisely orchestrated processes1,2. Despite the remarkable conservation of early embryogenesis across animals, the evolutionary origins of how and when this process first emerged remain elusive. Here, by combining time-resolved imaging and transcriptomic profiling, we show that single cells of the ichthyosporean Chromosphaera perkinsii-a close relative that diverged from animals about 1 billion years ago3,4-undergo symmetry breaking and develop through cleavage divisions to produce a prolonged multicellular colony with distinct co-existing cell types. Our findings about the autonomous and palintomic developmental program of C. perkinsii hint that such multicellular development either is much older than previously thought or evolved convergently in ichthyosporeans.

Comprehensive Exploration of CRISPR and Gene Editing Technologies: Applications, Ethical Considerations, and Future Implications in Genetic Research

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and other gene-editing technologies have revolutionized genetic research by enabling precise, targeted modifications of DNA sequences. This paper provides a comprehensive exploration of CRISPR technology, detailing its development, mechanism of action, and versatility in diverse applications. From advancements in medicine, including therapeutic interventions for genetic disorders, to innovations in agriculture aimed at enhancing crop resilience and yield, CRISPR's transformative potential is vast. However, the rapid evolution of gene editing presents significant ethical and societal challenges, particularly concerning human germline editing, ecological impacts, and issues of accessibility and equity. This paper examines these ethical considerations, emphasizing the need for robust regulatory frameworks and responsible scientific practices. It also projects the future trajectory of gene editing technologies, speculating on emerging trends, possible breakthroughs, and the global implications of CRISPR in fields such as personalized medicine, synthetic biology, and biotechnology. By critically analyzing current applications and addressing ethical concerns, this study aims to provide a balanced perspective on CRISPR's potential to reshape genetic research while advocating for ethical governance and public engagement in its ongoing development. CRISPR’s ability to target specific genes with high accuracy has made it an invaluable tool not only in research laboratories but also in clinical settings, where it shows promise in treating previously incurable diseases. Recent advancements have extended CRISPR’s applications beyond simple gene knockout, allowing for base editing, prime editing, and epigenetic modifications that expand the possibilities for genetic correction and enhancement. As scientists explore using CRISPR in complex organisms, the precision and control required for safe and effective treatments become a key focus, particularly in addressing off-target effects that could lead to unintended genetic consequences.

Determining bad actors: A linear mixed effects model approach to elucidate behavioral toxicity of metal mixtures in drinking water

Mixtures of chemical contaminants can pose a significant health risk to humans and wildlife, even at levels considered safe for each individual chemical. There is a critical need to develop statistical methods to evaluate the drivers of toxic effects in chemical mixtures (i.e., bad actors) from exposure studies. Here, we develop a hierarchical modeling framework to disentangle the toxicity of complex metal mixtures from a screening study of 92 drinking well water samples containing multiple metal elements from Maine and New Hampshire, USA. In order to screen for neurodevelopmental impacts from exposure to these drinking water samples, we use a larval zebrafish (Danio rerio) behavioral assay. Zebrafish are an advantageous toxicological model organism due to combining the complexity of a vertebrate organism and higher-throughput exposure methods. We formulate a linear mixed modeling approach that captures intrinsic complexity in a common larval behavioral assay in order to improve its sensitivity and rigor and identify drivers of behavioral toxicity from the metal mixtures within the drinking water samples. Our analysis identifies lead (Pb), cadmium (Cd), nickel (Ni), copper (Cu), barium (Ba), and uranium (U) as metals that consistently impact larval locomotor activity, individually and across nine pairs of those metals. Our model also elucidates three distinct clusters of metal mixture components that drive behavioral effects: (Ba:Cu:U), (Ni:Pb:U), (Ba:Pb:U). Having identified a set of “bad-actor” metals from the water samples, we conduct exposure experiments for each individual metal (Pb, Cd, Ni, Cu, and Ba) at levels considered safe by the US Environmental Protection Agency drinking water regulatory limits and validate Pb, Ni, Cu, and Ba as behavioral toxicants at these concentrations. Collectively, our modeling approach estimates the impact of metal elements on a complex behavioral outcome in a statistically robust manner and establishes an approach to capture “bad actors” and key chemical interactions in a complex mixture.

Influence of toxic heavy metals on growth characteristics of Paraclostridium benzoelyticum isolated from subsurface sediment samples of Sonebil – the second largest wetland of Asia

Contaminants like heavy metals, including lead, mercury, cadmium and zinc are inherent in natural systems. One of the biggest environmental issues affecting our world now is heavy metal contamination, for which there is an urgent need for remedies. There have been various reports of the health hazards of toxic heavy metals, such as lead, cadmium, copper and zinc. An increase in the concentration of heavy metals can have an impact on the development and metabolism of certain native microorganisms and thus reducing their diversity. The simplest to most complex organisms. can be harmful to heavy metals due to their high reactivity, which varies depending on their oxidation states. Numerous specific interactions between microorganisms and metals are possible. The present study aimed at the growth study of Paraclostridium benzoelyticum, an anaerobic and sulfate-reducing bacteria isolated from the subsurface sediments of Sonebil, in the presence of the four most toxic heavy metals reported mainly from the soil, namely lead, cadmium, copper and zinc. It has been observed that the strain has higher tolerance to metal lead and zinc followed by cadmium and copper. The minimum inhibitory concentration of Paraclostridium benzoelyticum was observed to be 12 µg/ml for zinc and lead, 11.5 µg/ml for cadmium and 10 µg/ml for copper. Furthermore, the growth curve outline was explored in various concentrations of lead, cadmium, copper and zinc.

In situ visualization of endothelial cell-derived extracellular vesicle formation in steady state and malignant conditions.

Endothelial cells are integral components of all vasculature within complex organisms. As they line the blood vessel wall, endothelial cells are constantly exposed to a variety of molecular factors and shear force that can induce cellular damage and stress. However, how endothelial cells are removed or eliminate unwanted cellular contents, remains unclear. The generation of large extracellular vesicles (EVs) has emerged as a key mechanism for the removal of cellular waste from cells that are dying or stressed. Here, we used intravital microscopy of the bone marrow to directly measure the kinetics of EV formation from endothelial cells in vivo under homoeostatic and malignant conditions. These large EVs are mitochondria-rich, expose the 'eat me' signal phosphatidylserine, and can interact with immune cell populations as a potential clearance mechanism. Elevated levels of circulating EVs correlates with degradation ofthe bone marrow vasculature caused by acute myeloid leukaemia. Together, our study provides in vivo spatio-temporal characterization of EV formation in the murine vasculature and suggests that circulating, large endothelial cell-derived EVs can provide a snapshot of vascular damage at distal sites.

Biological Activities and Potential Applications of Lichens

Lichens are complex organisms that are formed by symbiotic associations between fungi and photosynthetic organisms that can be algae or cyanobacteria. Most are terrestrial, although a few species are aquatic. Lichens have been studied for their production of secondary metabolites with biological activity such as antimicrobial, antifungal, antiviral, anticancer, antioxidant, among others; they have also been used in the cosmetics industry, as a source of natural dyes, they have been considered bioindicators and have been useful in some bioremediation processes, in addition, they are a potential source of enzymes. This review mentions some bioactive compounds of lichens as well as their potential uses, emphasizing some of their biological activities.

Proteome-scale structural prediction of the giant Marseillevirus reveals conserved folds and putative homologs of the hypothetical proteins.

A significant proportion of the highly divergent and novel proteins of giant viruses are termed "hypothetical" due to the absence of detectable homologous sequences in the existing databases. The quality of genome and proteome annotations often relies on the identification of signature sequences and motifs in order to assign putative functions to the gene products. These annotations serve as the first set of information for researchers to develop workable hypotheses for further experimental research. The structure-function relationship of proteins suggests that proteins with similar functions may also exhibit similar folding patterns. Here, we report the first proteome-wide structure prediction of the giant Marseillevirus. We use AlphaFold-predicted structures and their comparative analysis with the experimental structures in the PDB database to preliminarily annotate the viral proteins. Our work highlights the conservation of structural folds in proteins with highly divergent sequences and reveals potentially paralogous relationships among them. We also provide evidence for gene duplication and fusion as contributing factors to giant viral genome expansion and evolution. With the easily accessible AlphaFold and other advanced bioinformatics tools for high-confidence de novo structure prediction, we propose a combined sequence and predicted-structure-based proteome annotation approach for the initial characterization of novel and complex organisms or viruses.

The ageing virus hypothesis: Epigenetic ageing beyond the Tree of Life.

A recent thought-provoking theory argues that complex organisms using epigenetic information for their normal development and functioning must irreversibly age as a result of epigenetic signal loss. Importantly, the scope of this theory could be considerably expanded, with scientific benefits, by analyzing epigenetic ageing beyond the borders of the Tree of Life. Viruses that use epigenetic signals for their normal functioning may also age, that is, present an increasing risk of failing to complete their individual life cycle and to disappear with time. As viruses are ancient, abundant, and infect a considerable diversity of hosts, the ageing virus hypothesis, if verified, would have important consequences for many fields of the Life sciences. Uncovering ageing viruses would integrate the most abundant and biologically central entities on Earth into theories of ageing, enhance virology, gerontology, evolutionary biology, molecular ecology, genomics, and possibly medicine through the development of new therapies manipulating viral ageing.