The phylogenetic range of bacterial and viral pathogens of vertebrates

Making sense of the virome in light of evolution and ecology

Understanding the patterns and drivers of viral prevalence and abundance is of key importance for understanding pathogen emergence. Over the last decade, metagenomic sequencing has exponentially expanded our knowledge of the diversity and evolution of viruses associated with all domains of life. However, as most of these 'virome' studies are primarily descriptive, our understanding of the predictors of virus prevalence, abundance and diversity, and their variation in space and time, remains limited. For example, we do not yet understand the relative importance of ecological predictors (e.g. seasonality and habitat) versus evolutionary predictors (e.g. host and virus phylogenies) in driving virus prevalence and diversity. Few studies are set up to reveal the factors that predict the virome composition of individual hosts, populations or species. In addition, most studies of virus ecology represent a snapshot of single species viromes at a single point in time and space. Fortunately, recent studies have begun to use metagenomic data to directly test hypotheses about the evolutionary and ecological factors which drive virus prevalence, sharing and diversity. By synthesizing evidence across studies, we present some over-arching ecological and evolutionary patterns in virome composition, and illustrate the need for additional work to quantify the drivers of virus prevalence and diversity.

Socioeconomic and Eco-Environmental Drivers Differentially Trigger and Amplify Bacterial and Viral Outbreaks of Zoonotic Pathogens

The frequency of infectious disease outbreaks and pandemics is rising, demanding an understanding of their drivers. Common wisdom suggests that increases in outbreak frequency are driven by socioeconomic factors such as globalization and urbanization, yet, the majority of disease outbreaks are caused by zoonotic pathogens that can be transmitted from animals to humans, suggesting the important role of ecological and environmental drivers. Previous studies of outbreak drivers have also failed to quantify the differences between major classes of pathogens, such as bacterial and viral pathogens. Here, we reconsider the observed drivers of a global sample of 300 zoonotic outbreaks, including the 100 largest outbreaks that occurred between 1977 and 2017. We show that socioeconomic factors more often trigger outbreaks of bacterial pathogens, whereas ecological and environmental factors trigger viral outbreaks. However, socioeconomic factors also act as amplifiers of viral outbreaks, with higher case numbers in viral outbreaks driven by a larger proportion of socioeconomic factors. Our results demonstrate that it is useful to consider the drivers of global disease patterns in aggregate due to commonalities that cross disease systems. However, our work also identifies important differences between the driver profiles of bacterial and viral diseases in aggregate.

Limited similarity in microbial composition among coral reef fishes from the Great Barrier Reef, Australia

Reef fishes exhibit enormous biodiversity within a highly interactive ecosystem. Relatively little is known about the diversity and evolution of microbial species associated with reef fish, even though this may provide valuable insights into the factors that shape microbial communities. Through metatranscriptomic sequencing, we characterized the viruses, bacteria, and single-celled eukaryotes from 126 reef fish species inhabiting Lizard Island and Orpheus Island on the Great Barrier Reef, Australia. We assessed whether microbial communities differed between islands that are separated by 450 km, and to what extent fish viruses emerge in new hosts. Despite strong ecological interactions within the species-rich reef environment, and the presence of the same families of viruses on both islands, there was minimal evidence for the presence of individual viruses shared among fish species, reflecting low levels of cross-species transmission. Among bacteria, we identified the opportunistic bacterial pathogen Photobacterium damselae in apparently healthy cardinalfish species from both islands, indicating that these fish species are natural reservoirs. These data suggest that reef fishes have microbial-host associations that arose prior to the formation of the Great Barrier Reef, likely leading to strong host barriers to cross-species transmission and hence infectious disease emergence.

Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza

Motivation

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales.

Results

We developed a KG to demonstrate the method's utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen-host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

Availability and implementation

The data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.

Genomic diversity and evolutionary patterns of Edwardsiella ictaluri affecting farmed striped catfish (Pangasianodon hypophthalmus) in Vietnam over 20 years

Edwardsiella ictaluri continues to pose a significant risk to the health and production of striped catfish (Pangasianodon hypophthalmus) in Vietnam. Whilst recent advances in genomic sequencing provide an insight into the global genomic diversity of this important fish pathogen, genome-wide analysis of Vietnamese isolates recovered over time is lacking. In this study, we used a whole-genome sequencing approach to compare the genomes of 31 E. ictaluri isolates recovered over a 20-year period (2001-2021) and performed comparative genomic analysis to explore temporal changes in genome diversity, population structure and mechanisms driving pathogenesis and antimicrobial resistance. Our findings revealed an open pan-genome with 4148 genes and a core genome (3 060 genes) accounting for over two-thirds of the genome. Moreover, we found the genomes sequenced to classify into two distinct lineages and estimated the ancestral origin of these lineages within Vietnam to date back to the 1950s. Plasmids were highly prevalent in Vietnamese E. ictaluri, with isolates harbouring up to four plasmids within their genome. Further, a diverse mobilome was observed with nine different plasmid types detected across the genome collection. Exploration of putative plasmids revealed a diverse set of antimicrobial resistance genes (ARGs) against key antibiotics used in Vietnamese aquaculture and virulence genes associated with protein secretion systems. Correlation analysis revealed the total number of ARGs detected in genomes to increase with isolate recovery time. Whilst the number of virulence genes remained relatively stable, temporal variation was noted in several virulence factors related to motility and immune system modulation. Findings from this study highlight the need for continued genomic surveillance to monitor changes in antimicrobial resistance and pathogenesis, to help inform the development of disease control and management strategies.

Viral diseases and the environment relationship

Viral diseases have been present throughout human history, with early examples including influenza (1500 B.C.), smallpox (1000 B.C.), and measles (200 B.C.). The term "virus" was first used in the late 1800s to describe microorganisms smaller than bacteria, and significant milestones include the discovery of the polio virus and the development of its vaccine in the mid-1900s, and the identification of HIV/AIDS in the latter part of the 20th century. The 21st century has seen the emergence of new viral diseases such as West Nile Virus, Zika, SARS, MERS, and COVID-19. Human activities, including crowding, travel, poor sanitation, and environmental changes like deforestation and climate change, significantly influence the spread of these diseases. Conversely, viral diseases can impact the environment by polluting water resources, contributing to deforestation, and reducing biodiversity. These environmental impacts are exacerbated by disruptions in global supply chains and increased demands for resources. This review highlights the intricate relationship between viral diseases and environmental factors, emphasizing how human activities and viral disease progression influence each other. The findings underscore the need for integrated approaches to address the environmental determinants of viral diseases and mitigate their impacts on both health and ecosystems.

Pathogenesis-Associated Bacterial Amyloids: The Network of Interactions

Amyloids are protein fibrils with a characteristic cross-β structure that is responsible for the unusual resistance of amyloids to various physical and chemical factors, as well as numerous pathogenic and functional consequences of amyloidogenesis. The greatest diversity of functional amyloids was identified in bacteria. The majority of bacterial amyloids are involved in virulence and pathogenesis either via facilitating formation of biofilms and adaptation of bacteria to colonization of a host organism or through direct regulation of toxicity. Recent studies have shown that, beside their commonly known activity, amyloids may be involved in the spatial regulation of proteome by modulating aggregation of other amyloidogenic proteins with multiple functional or pathological effects. Although the studies on the role of microbiome-produced amyloids in the development of amyloidoses in humans and animals have only been started, it is clear that humans as holobionts contain amyloids encoded not only by the host genome, but also by microorganisms that constitute the microbiome. Amyloids acquired from external sources (e.g., food) can interact with holobiont amyloids and modulate the effects of bacterial and host amyloids, thus adding another level of complexity to the holobiont-associated amyloid network. In this review, we described bacterial amyloids directly or indirectly involved in disease pathogenesis in humans and discussed the significance of bacterial amyloids in the three-component network of holobiont-associated amyloids.

Features that matter: Evolutionary signatures can predict viral transmission routes

Routes of virus transmission between hosts are key to understanding viral epidemiology. Different routes have large effects on viral ecology, and likelihood and rate of transmission; for example, respiratory and vector-borne viruses together encompass the majority of rapid outbreaks and high-consequence animal and plant epidemics. However, determining the specific transmission route(s) can take months to years, delaying mitigation efforts. Here, we identify the viral features and evolutionary signatures which are predictive of viral transmission routes and use them to predict potential routes for fully-sequenced viruses in silico and rapidly, for both viruses with no observed routes, as well as viruses with missing routes. This was achieved by compiling a dataset of 24,953 virus-host associations with 81 defined transmission routes, constructing a hierarchy of virus transmission encompassing those routes and 42 higher-order modes, and engineering 446 predictive features from three complementary perspectives. We integrated those data and features to train 98 independent ensembles of LightGBM classifiers. We found that all features contributed to the prediction for at least one of the routes and/or modes of transmission, demonstrating the utility of our broad multi-perspective approach. Our framework achieved ROC-AUC = 0.991, and F1-score = 0.855 across all included transmission routes and modes, and was able to achieve high levels of predictive performance for high-consequence respiratory (ROC-AUC = 0.990, and F1-score = 0.864) and vector-borne transmission (ROC-AUC = 0.997, and F1-score = 0.921). Our framework ranks the viral features in order of their contribution to prediction, per transmission route, and hence identifies the genomic evolutionary signatures associated with each route. Together with the more matured field of viral host-range prediction, our predictive framework could: provide early insights into the potential for, and pattern of viral spread; facilitate rapid response with appropriate measures; and significantly triage the time-consuming investigations to confirm the likely routes of transmission.

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

The evolutionary drivers and correlates of viral host jumps

Most emerging and re-emerging infectious diseases stem from viruses that naturally circulate in non-human vertebrates. When these viruses cross over into humans, they can cause disease outbreaks, epidemics and pandemics. While zoonotic host jumps have been extensively studied from an ecological perspective, little attention has gone into characterizing the evolutionary drivers and correlates underlying these events. To address this gap, we harnessed the entirety of publicly available viral genomic data, employing a comprehensive suite of network and phylogenetic analyses to investigate the evolutionary mechanisms underpinning recent viral host jumps. Surprisingly, we find that humans are as much a source as a sink for viral spillover events, insofar as we infer more viral host jumps from humans to other animals than from animals to humans. Moreover, we demonstrate heightened evolution in viral lineages that involve putative host jumps. We further observe that the extent of adaptation associated with a host jump is lower for viruses with broader host ranges. Finally, we show that the genomic targets of natural selection associated with host jumps vary across different viral families, with either structural or auxiliary genes being the prime targets of selection. Collectively, our results illuminate some of the evolutionary drivers underlying viral host jumps that may contribute to mitigating viral threats across species boundaries.

Viral community distribution, assembly mechanism, and associated hosts in an industrial park wastewater treatment plant

Viruses manipulate bacterial community composition and impact wastewater treatment efficiency. Some viruses pose threats to the environment and human populations through infection. Improving the efficiency of wastewater treatment and ensuring the health of the effluent and receptor pools requires an understanding of how viral communities assemble and interact with hosts in wastewater treatment plants (WWTPs). We used metagenomic analysis to study the distribution, assembly mechanism, and sensitive hosts for the viral communities in raw water, anaerobic tanks, and returned activated sludge units of a large-scale industrial park WWTP. Uroviricota (53.42% ± 0.14%) and Nucleocytoviricota (26.1% ± 0.19%) were dominant in all units. Viral community composition significantly differed between units, as measured by β diversity (P = 0.005). Compared to raw water, the relative viral abundance decreased by 29.8% in the anaerobic tank but increased by 9.9% in the activated sludge. Viral community assembly in raw water and anaerobic tanks was predominantly driven by deterministic processes (MST <0.5) versus stochastic processes (MST >0.5) in the activated sludge, indicating that differences in diffusion limits may fundamentally alter the assembly mechanisms of viral communities between the solid and liquid-phase environments. Acidobacteria was identified as the sensitive host contributing to viral abundance, exhibiting strong interactions and a mutual dependence (degree = 59). These results demonstrate the occurrence and prevalence of viruses in WWTPs, their different assembly mechanism, and sensitive hosts. These observations require further study of the mechanisms of viral community succession, ecological function, and roles in the successive wastewater treatment units.

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Range area and the fast-slow continuum of life history traits predict pathogen richness in wild mammals

Surveillance of pathogen richness in wildlife is needed to identify host species with a high risk of zoonotic disease spillover. While several predictors of pathogen richness in wildlife hosts have been proposed, their relative importance has not been formally examined. This hampers our ability to identify potential disease reservoirs, particularly in remote areas with limited surveillance efforts. Here we analyzed 14 proposed predictors of pathogen richness using ensemble modeling and a dataset of 1040 host species to identify the most important predictors of pathogen richness in wild mammal species. After controlling for research effort, larger species geographic range area was identified to be associated with higher pathogen richness. We found evidence of duality in the relationship between the fast-slow continuum of life-history traits and pathogen richness, where pathogen richness increases near the extremities. Taxonomic orders Carnivora, Proboscidea, Artiodactyla, and Perissodactyla were predicted to host high pathogen richness. The top three species with the highest pathogen richness predicted by our ensemble model were Canis lupus, Sus scrofa, and Alces alces. Our results can help support evidence-informed pathogen surveillance and disease reservoir management to prevent the emergence of future zoonotic diseases.

Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset

Incomplete information on parasites, their associated hosts, and their precise geographical location hampers the ability to predict disease emergence in Brazil, a continental-sized country characterised by significant regional disparities. Here, we demonstrate how the NCBI Nucleotide and GBIF databases can be used as complementary databases to study spatially georeferenced parasite-host associations. We also provide a comprehensive dataset of parasites associated with mammal species that occur in Brazil, the Brazilian Mammal Parasite Occurrence Data (BMPO). This dataset integrates wild mammal species' morphological and life-history traits, zoonotic parasite status, and zoonotic microparasite transmission modes. Through meta-networks, comprising interconnected host species linked by shared zoonotic microparasites, we elucidate patterns of zoonotic microparasite dissemination. This approach contributes to wild animal and zoonoses surveillance, identifying and targeting host species accountable for disproportionate levels of parasite sharing within distinct biomes. Moreover, our novel dataset contributes to the refinement of models concerning disease emergence and parasite distribution among host species.

Presence of human adenovirus 36 in visceral fat tissue, viral load, and analysis of its genetic variability

It has been proposed that infection by adipogenic viruses constitutes a "low risk" factor for obesity. Here, we report the presence of adenovirus 36 (Ad36) and its viral load copy number in fat tissue of participants with obesity and normal weight; phylogenetic analysis was performed to describe their relationship and genetic variability among viral haplotypes. Adipose tissue obtained from 105 adult patients with obesity (cases) and 26 normal-weight adult participants as controls were analyzed by quantitative polymerase chain reaction (qPCR) amplifying the partial Ad36 E1a gene. The amplicons were examined by melting curves and submitted to sequencing. Then, genetic diversity and phylogenetic inferences were performed. Ad36 was identified at rates of 82% and 46% in the case and control groups, respectively (p = 1.1 × 10-4 , odds ratio = 5.28); viral load copies were also significantly different between both groups, being 25% higher in the case group. Melting curve analysis showed clear amplification among positive samples. Phylogenetic inferences and genetic diversity analyses showed that the Ad36 E1a gene exhibits low genetic variability and differentiation with strong gene flow due to an expanding process. Our results suggest that the phenomenon of infectobesity by Ad36 might not be a low-risk factor, as has been previously argued by other authors.

Network embedding unveils the hidden interactions in the mammalian virome

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.

Investigating the outcomes of virus coinfection within and across host species

Interactions between coinfecting pathogens have the potential to alter the course of infection and can act as a source of phenotypic variation in susceptibility between hosts. This phenotypic variation may influence the evolution of host-pathogen interactions within host species and interfere with patterns in the outcomes of infection across host species. Here, we examine experimental coinfections of two Cripaviruses-Cricket Paralysis Virus (CrPV), and Drosophila C Virus (DCV)-across a panel of 25 Drosophila melanogaster inbred lines and 47 Drosophilidae host species. We find that interactions between these viruses alter viral loads across D. melanogaster genotypes, with a ~3 fold increase in the viral load of DCV and a ~2.5 fold decrease in CrPV in coinfection compared to single infection, but we find little evidence of a host genetic basis for these effects. Across host species, we find no evidence of systematic changes in susceptibility during coinfection, with no interaction between DCV and CrPV detected in the majority of host species. These results suggest that phenotypic variation in coinfection interactions within host species can occur independently of natural host genetic variation in susceptibility, and that patterns of susceptibility across host species to single infections can be robust to the added complexity of coinfection.

Mapping human pathogens in wastewater using a metatranscriptomic approach

The monitoring of cities' wastewaters for the detection of potentially pathogenic viruses and bacteria has been considered a priority during the COVID-19 pandemic to monitor public health in urban environments. The methodological approaches frequently used for this purpose include deoxyribonucleic acid (DNA)/Ribonucleic acid (RNA) isolation followed by quantitative polymerase chain reaction (qPCR) and reverse transcription (RT)‒qPCR targeting pathogenic genes. More recently, the application of metatranscriptomic has opened opportunities to develop broad pathogenic monitoring workflows covering the entire pathogenic community within the sample. Nevertheless, the high amount of data generated in the process requires an appropriate analysis to detect the pathogenic community from the entire dataset. Here, an implementation of a bioinformatic workflow was developed to produce a map of the detected pathogenic bacteria and viruses in wastewater samples by analysing metatranscriptomic data. The main objectives of this work was the development of a computational methodology that can accurately detect both human pathogenic virus and bacteria in wastewater samples. This workflow can be easily reproducible with open-source software and uses efficient computational resources. The results showed that the used algorithms can predict potential human pathogens presence in the tested samples and that active forms of both bacteria and virus can be identified. By comparing the computational method implemented in this study to other state-of-the-art workflows, the implementation analysis was faster, while providing higher accuracy and sensitivity. Considering these results, the processes and methods to monitor wastewater for potential human pathogens can become faster and more accurate. The proposed workflow is available at https://github.com/waterpt/watermonitor and can be implemented in currently wastewater monitoring programs to ascertain the presence of potential human pathogenic species.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Homo medicus: The transition to meat eating increased pathogen pressure and the use of pharmacological plants in Homo

The human lineage transitioned to a more carnivorous niche 2.6 mya and evolved a large body size and slower life history, which likely increased zoonotic pathogen pressure. Evidence for this increase includes increased zoonotic infections in modern hunter-gatherers and bushmeat hunters, exceptionally low stomach pH compared to other primates, and divergence in immune-related genes. These all point to change, and probably intensification, in the infectious disease environment of Homo compared to earlier hominins and other apes. At the same time, the brain, an organ in which immune responses are constrained, began to triple in size. We propose that the combination of increased zoonotic pathogen pressure and the challenges of defending a large brain and body from pathogens in a long-lived mammal, selected for intensification of the plant-based self-medication strategies already in place in apes and other primates. In support, there is evidence of medicinal plant use by hominins in the middle Paleolithic, and all cultures today have sophisticated, plant-based medical systems, add spices to food, and regularly consume psychoactive plant substances that are harmful to helminths and other pathogens. We propose that the computational challenges of discovering effective plant-based treatments, the consequent ability to consume more energy-rich animal foods, and the reduced reliance on energetically-costly immune responses helped select for increased cognitive abilities and unique exchange relationships in Homo. In the story of human evolution, which has long emphasized hunting skills, medical skills had an equal role to play.

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream.

Predicting primate-parasite associations using exponential random graph models

Ecological associations between hosts and parasites are influenced by host exposure and susceptibility to parasites, and by parasite traits, such as transmission mode. Advances in network analysis allow us to answer questions about the causes and consequences of traits in ecological networks in ways that could not be addressed in the past. We used a network-based framework (exponential random graph models or ERGMs) to investigate the biogeographic, phylogenetic and ecological characteristics of hosts and parasites that affect the probability of interactions among nonhuman primates and their parasites. Parasites included arthropods, bacteria, fungi, protozoa, viruses and helminths. We investigated existing hypotheses, along with new predictors and an expanded host-parasite database that included 213 primate nodes, 763 parasite nodes and 2319 edges among them. Analyses also investigated phylogenetic relatedness, sampling effort and spatial overlap among hosts. In addition to supporting some previous findings, our ERGM approach demonstrated that more threatened hosts had fewer parasites, and notably, that this effect was independent of hosts also having a smaller geographic range. Despite having fewer parasites, threatened host species shared more parasites with other hosts, consistent with loss of specialist parasites and threat arising from generalist parasites that can be maintained in other, non-threatened hosts. Viruses, protozoa and helminths had broader host ranges than bacteria, or fungi, and parasites that infect non-primates had a higher probability of infecting more primate species. The value of the ERGM approach for investigating the processes structing host-parasite networks provided a more complete view on the biogeographic, phylogenetic and ecological traits that influence parasite species richness and parasite sharing among hosts. The results supported some previous analyses and revealed new associations that warrant future research, thus revealing how hosts and parasites interact to form ecological networks.

Evaluating the transmission feasibility of SARS-CoV-2 Omicron (B.1.1.529) variant to 143 mammalian hosts: insights from S protein RBD and host ACE2 interaction studies

In comparison to previously known severe respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the newly emerged Omicron (B.1.1.529) variant shows higher infectivity in humans. Exceptionally high infectivity of this variant raises concern of its possible transmission via other intermediate hosts. The SARS-CoV-2 infectivity is established via the association of spike (S) protein receptor binding domain (RBD) with host angiotensin I converting enzyme 2 (hACE2) receptor. In the course of this study, we investigated the interaction between Omicron S protein RBD with the ACE2 receptor of 143 mammalian hosts including human by protein-protein interaction analysis. The goal of this study was to forecast the likelihood that the virus may infect other mammalian species that coexist with or are close to humans in the household, rural, agricultural, or zoological environments. The Omicron RBD was found to interact with higher binding affinity with the ACE2 receptor of 122 mammalian hosts via different amino acid residues from the human ACE2 (hACE2). The rat (Rattus rattus) ACE2 was found to show the strongest interaction with Omicron RBD with a binding affinity of -1393.6 kcal/mol. These distinct strong binding affinity of RBD of Omicron with host ACE2 indicates a greater potential of new host transmissibility and infection via intermediate hosts. Though expected but the phylogenetic position of the mammalian species may not dictate the Omicron RBD binding to the host ACE2 receptor suggesting an involvement of multiple factors in guiding host divergence of the variant.

A comprehensive list of bacterial pathogens infecting humans

There exists an enormous diversity of bacteria capable of human infection, but no up-to-date, publicly accessible list is available. Combining a pragmatic definition of pathogenicity with an extensive search strategy, we report 1513 bacterial pathogens known to infect humans described pre-2021. Of these, 73 % were regarded as established (have infected at least three persons in three or more references) and 27 % as putative (fewer than three known cases). Pathogen species belong to 10 phyla and 24 classes scattered throughout the bacterial phylogeny. We show that new human pathogens are discovered at a rapid rate. Finally, we discuss how our results could be expanded to a database, which could provide a useful resource for microbiologists. Our list is freely available and archived on GitHub and Zenodo and we have provided walkthroughs to facilitate access and use.

Molecular epidemiology of the citrus bacterial pathogen Xanthomonas citri pv. citri from the Arabian Peninsula reveals a complex structure of specialist and generalist strains

Molecular epidemiology studies are essential to refine our understanding of migrations of phytopathogenic bacteria, the major determining factor in their emergence, and to understand the factors that shape their population structure. Microsatellite and minisatellite typing are useful techniques for deciphering the population structure of Xanthomonas citri pv. citri, the causal agent of Asiatic citrus canker. This paper presents a molecular epidemiology study, which has improved our understanding of the history of the pathogen's introductions into the Arabian Peninsula, since it was first reported in the 1980s. An unexpectedly high genetic diversity of the pathogen was revealed. The four distinct genetic lineages within X. citri pv. citri, which have been reported throughout the world, were identified in the Arabian Peninsula, most likely as the result of multiple introductions. No copper-resistant X. citri pv. citri strains were identified. The pathogen's population structure on Mexican lime (their shared host species) was closely examined in two countries, Saudi Arabia and Yemen. We highlighted the marked prevalence of specialist pathotype A* strains in both countries, which suggests that specialist strains of X. citri pv. citri may perform better than generalist strains when they occur concomitantly in this environment. Subclade 4.2 was the prevailing lineage identified. Several analyses (genetic structure deciphered by discriminant analysis of principal components, RST-based genetic differentiation, geographic structure) congruently suggested the role of human activities in the pathogen's spread. We discuss the implications of these results on the management of Asiatic citrus canker in the region.

Demographic Expansions and the Emergence of Host Specialization in Genetically Distinct Ecotypes of the Tick-Transmitted Bacterium Anaplasma phagocytophilum

In Europe, genetically distinct ecotypes of the tick-vectored bacterium Anaplasma phagocytophilum circulate among mammals in three discrete enzootic cycles. To date, potential ecological factors that contributed to the emergence of these divergent ecotypes have been poorly studied. Here, we show that the ecotype that predominantly infects roe deer (Capreolus capreolus) is evolutionarily derived. Its divergence from a host generalist ancestor occurred after the last glacial maximum as mammal populations, including roe deer, recolonized the European mainland from southern refugia. We also provide evidence that this host specialist ecotype's effective population size (N_e) has tracked changes in the population of its roe deer host. Specifically, both host and bacterium have undergone substantial increases in N_e over the past 1,500 years. In contrast, we show that while it appears to have undergone a major population expansion starting ~3,500 years ago, in the past 500 years, the contemporary host generalist ecotype has experienced a substantial reduction in genetic diversity levels, possibly as a result of reduced opportunities for transmission between competent hosts. IMPORTANCE The findings of this study reveal specific events important for the evolution of host specialization in a naturally occurring, obligately intracellular bacterial pathogen. Specifically, they show that host range shifts and the emergence of host specialization may occur during periods of population growth in a generalist ancestor. Our results also demonstrate the close correlation between demographic patterns in host and pathogen for a specialist system. These findings have important relevance for understanding the evolution of host range diversity. They may inform future work on host range dynamics, and they provide insights for understanding the emergence of pathogens that have human and veterinary health implications.

The past, current and future epidemiological dynamic of SARS-CoV-2

SARS-CoV-2, the agent of the COVID-19 pandemic, emerged in late 2019 in China, and rapidly spread throughout the world to reach all continents. As the virus expanded in its novel human host, viral lineages diversified through the accumulation of around two mutations a month on average. Different viral lineages have replaced each other since the start of the pandemic, with the most successful Alpha, Delta and Omicron variants of concern (VoCs) sequentially sweeping through the world to reach high global prevalence. Neither Alpha nor Delta was characterized by strong immune escape, with their success coming mainly from their higher transmissibility. Omicron is far more prone to immune evasion and spread primarily due to its increased ability to (re-)infect hosts with prior immunity. As host immunity reaches high levels globally through vaccination and prior infection, the epidemic is expected to transition from a pandemic regime to an endemic one where seasonality and waning host immunization are anticipated to become the primary forces shaping future SARS-CoV-2 lineage dynamics. In this review, we consider a body of evidence on the origins, host tropism, epidemiology, genomic and immunogenetic evolution of SARS-CoV-2 including an assessment of other coronaviruses infecting humans. Considering what is known so far, we conclude by delineating scenarios for the future dynamic of SARS-CoV-2, ranging from the good-circulation of a fifth endemic 'common cold' coronavirus of potentially low virulence, the bad-a situation roughly comparable with seasonal flu, and the ugly-extensive diversification into serotypes with long-term high-level endemicity.

Urban-adapted mammal species have more known pathogens

The world is rapidly urbanizing, inviting mounting concern that urban environments will experience increased zoonotic disease risk. Urban animals could have more frequent contact with humans, therefore transmitting more zoonotic parasites; however, this relationship is complicated by sampling bias and phenotypic confounders. Here we test whether urban mammal species host more zoonotic parasites, investigating the underlying drivers alongside a suite of phenotypic, taxonomic and geographic predictors. We found that urban-adapted mammals have more documented parasites and more zoonotic parasites: despite comprising only 6% of investigated species, urban mammals provided 39% of known host-parasite combinations. However, contrary to predictions, much of the observed effect was attributable to parasite discovery and research effort rather than to urban adaptation status, and urban-adapted species in fact hosted fewer zoonotic parasites than expected on the basis of their total parasite richness. We conclude that extended historical contact with humans has had a limited impact on zoonotic parasite richness in urban-adapted mammals; instead, their greater observed zoonotic richness probably reflects sampling bias arising from proximity to humans, supporting a near-universal conflation between zoonotic risk, research effort and synanthropy. These findings underscore the need to resolve the mechanisms linking anthropogenic change, sampling bias and observed wildlife disease dynamics.

A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface

Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.

Parasite-associated mortality in birds: the roles of specialist parasites and host evolutionary distance

The factors that influence whether a parasite is likely to cause death in a given host species are not well known. Generalist parasites with high local abundances, broad distributions and the ability to infect a wide phylogenetic diversity of hosts are often considered especially dangerous for host populations, though comparatively little research has been done on the potential for specialist parasites to cause host mortality. Here, using a novel database of avian mortality records, we tested whether phylogenetic host specialist or host generalist haemosporidian blood parasites were associated with avian host deaths based on infection records from over 81 000 examined hosts. In support of the hypothesis that host specialist parasites can be highly virulent in novel hosts, we found that the parasites that were associated with avian host mortality predominantly infected more closely related host species than expected under a null model. Hosts that died tended to be distantly related to the host species that a parasite lineage typically infects, illustrating that specialist parasites can cause death outside of their limited host range. Overall, this study highlights the overlooked potential for host specialist parasites to cause host mortality despite their constrained ecological niches.

Biological invasions facilitate zoonotic disease emergences

Outbreaks of zoonotic diseases are accelerating at an unprecedented rate in the current era of globalization, with substantial impacts on the global economy, public health, and sustainability. Alien species invasions have been hypothesized to be important to zoonotic diseases by introducing both existing and novel pathogens to invaded ranges. However, few studies have evaluated the generality of alien species facilitating zoonoses across multiple host and parasite taxa worldwide. Here, we simultaneously quantify the role of 795 established alien hosts on the 10,473 zoonosis events across the globe since the 14th century. We observe an average of ~5.9 zoonoses per alien zoonotic host. After accounting for species-, disease-, and geographic-level sampling biases, spatial autocorrelation, and the lack of independence of zoonosis events, we find that the number of zoonosis events increase with the richness of alien zoonotic hosts, both across space and through time. We also detect positive associations between the number of zoonosis events per unit space and climate change, land-use change, biodiversity loss, human population density, and PubMed citations. These findings suggest that alien host introductions have likely contributed to zoonosis emergences throughout recent history and that minimizing future zoonotic host species introductions could have global health benefits.

The Global Virome in One Network (VIRION): an Atlas of Vertebrate-Virus Associations

Data that catalogue viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open sharing, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static data sets and integration of dynamically updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus “species” (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically valid organisms. Together, these data cover roughly a quarter of mammal diversity, a 10th of bird diversity, and ∼6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to test hypotheses about microbiology, ecology, and evolution and make suggestions for best practices that address the unique mix of evidence that coexists in these data.

Highlight of potential impact of new viral genotypes of SARS-CoV-2 on vaccines and anti-viral therapeutics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of the coronavirus disease (COVID-19) pandemic, has infected millions of people globally. Genetic variation and selective pressures lead to the accumulation of single nucleotide polymorphism (SNP) within the viral genome that may affect virulence, transmission rate, viral recognition and the efficacy of prophylactic and interventional measures. To address these concerns at the genomic level, we assessed the phylogeny and SNPs of the SARS-CoV-2 mutant population collected to date in Iran in relation to globally reported variants. Phylogenetic analysis of mutant strains revealed the occurrence of the variants known as B.1.1.7 (Alpha), B.1.525 (Eta), and B.1.617 (Delta) that appear to have delineated independently in Iran. SNP analysis of the Iranian sequences revealed that the mutations were predominantly positioned within the S protein-coding region, with most SNPs localizing to the S1 subunit. Seventeen S1-localizing SNPs occurred in the RNA binding domain that interacts with ACE2 of the host cell. Importantly, many of these SNPs are predicted to influence the binding of antibodies and anti-viral therapeutics, indicating that the adaptive host response appears to be imposing a selective pressure that is driving the evolution of the virus in this closed population through enhancing virulence. The SNPs detected within these mutant cohorts are addressed with respect to current prophylactic measures and therapeutic interventions.

Infectious disease in an era of global change

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.

Mammal virus diversity estimates are unstable due to accelerating discovery effort

Host-virus association data underpin research into the distribution and eco-evolutionary correlates of viral diversity and zoonotic risk across host species. However, current knowledge of the wildlife virome is inherently constrained by historical discovery effort, and there are concerns that the reliability of ecological inference from host-virus data may be undermined by taxonomic and geographical sampling biases. Here, we evaluate whether current estimates of host-level viral diversity in wild mammals are stable enough to be considered biologically meaningful, by analysing a comprehensive dataset of discovery dates of 6571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are either constant or accelerating, with little evidence of declines towards viral richness asymptotes, even in highly sampled hosts. Consequently, inference of relative viral richness across host species has been unstable over time, particularly in bats, where intensified surveillance since the early 2000s caused a rapid rearrangement of species' ranked viral richness. Our results illustrate that comparative inference of host-level virus diversity across mammals is highly sensitive to even short-term changes in sampling effort. We advise caution to avoid overinterpreting patterns in current data, since it is feasible that an analysis conducted today could draw quite different conclusions than one conducted only a decade ago.

A simple model for how the risk of pandemics from different virus families depends on viral and human traits

Different virus families, like influenza or corona viruses, exhibit characteristic traits such as typical modes of transmission and replication as well as specific animal reservoirs in which each family of viruses circulate. These traits of genetically related groups of viruses influence how easily an animal virus can adapt to infect humans, how well novel human variants can spread in the population, and the risk of causing a global pandemic. Relating the traits of virus families to their risk of causing future pandemics, and identification of the key time scales within which public health interventions can control the spread of a new virus that could cause a pandemic, are obviously significant. We address these issues using a minimal model whose parameters are related to characteristic traits of different virus families. A key trait of viruses that "spillover" from animal reservoirs to infect humans is their ability to propagate infection through the human population (fitness). We find that the risk of pandemics emerging from virus families characterized by a wide distribution of the fitness of spillover strains is much higher than if such strains were characterized by narrow fitness distributions around the same mean. The dependences of the risk of a pandemic on various model parameters exhibit inflection points. We find that these inflection points define informative thresholds. For example, the inflection point in variation of pandemic risk with time after the spillover represents a threshold time beyond which global interventions would likely be too late to prevent a pandemic.

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface

The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife–human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host–pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.

The science of the host-virus network

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

Predictions of primate-parasite coextinction

Future biodiversity loss threatens the integrity of complex ecological associations, including among hosts and parasites. Almost half of primate species are threatened with extinction, and the loss of threatened hosts could negatively impact parasite associations and ecosystem functions. If endangered hosts are highly connected in host-parasite networks, then future host extinctions will also drive parasite extinctions, destabilizing ecological networks. If threatened hosts are not highly connected, however, then network structure should not be greatly affected by the loss of threatened hosts. Networks with high connectance, modularity, nestedness and robustness are more resilient to perturbations such as the loss of interactions than sparse, nonmodular and non-nested networks. We analysed the interaction network involving 213 primates and 763 parasites and removed threatened primates (114 species) to simulate the effects of extinction. Our analyses revealed that connections to 23% of primate parasites (176 species) may be lost if threatened primates go extinct. In addition, measures of network structure were affected, but in varying ways because threatened hosts have fewer parasite interactions than non-threatened hosts. These results reveal that host extinctions will perturb the host-parasite network and potentially lead to secondary extinctions of parasites. The ecological consequences of these extinctions remain unclear. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Pre-existing T cell-mediated cross-reactivity to SARS-CoV-2 cannot solely be explained by prior exposure to endemic human coronaviruses

T-cell-mediated immunity to SARS-CoV-2-derived peptides in individuals unexposed to SARS-CoV-2 has been previously reported. This pre-existing immunity was suggested to largely derive from prior exposure to 'common cold' endemic human coronaviruses (HCoVs). To test this, we characterised the sequence homology of SARS-CoV-2-derived T-cell epitopes reported in the literature across the full proteome of the Coronaviridae family. 54.8% of these epitopes had no homology to any of the HCoVs. Further, the proportion of SARS-CoV-2-derived epitopes with any level of sequence homology to the proteins encoded by any of the coronaviruses tested is well-predicted by their alignment-free phylogenetic distance to SARS-CoV-2 (Pearson's r = -0.958). No coronavirus in our dataset showed a significant excess of T-cell epitope homology relative to the proportion of expected random matches, given their genetic similarity to SARS-CoV-2. Our findings suggest that prior exposure to human or animal-associated coronaviruses cannot completely explain the T-cell repertoire in unexposed individuals that recognise SARS-CoV-2 cross-reactive epitopes.

Challenges in modeling the emergence of novel pathogens

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.

Between virus correlations in the outcome of infection across host species: Evidence of virus by host species interactions

Virus host shifts are a major source of outbreaks and emerging infectious diseases, and predicting the outcome of novel host and virus interactions remains a key challenge for virus research. The evolutionary relationships between host species can explain variation in transmission rates, virulence, and virus community composition between hosts, but it is unclear if correlations exist between related viruses in infection traits across novel hosts. Here, we measure correlations in viral load of four Cripavirus isolates across experimental infections of 45 Drosophilidae host species. We find positive correlations between every pair of viruses tested, suggesting that some host clades show broad susceptibility and could act as reservoirs and donors for certain types of viruses. Additionally, we find evidence of virus by host species interactions, highlighting the importance of both host and virus traits in determining the outcome of virus host shifts. Of the four viruses tested here, those that were more closely related tended to be more strongly correlated, providing tentative evidence that virus evolutionary relatedness may be a useful proxy for determining the likelihood of novel virus emergence, which warrants further research.

Evolution of pathogen tolerance and emerging infections: A missing experimental paradigm

Researchers worldwide are repeatedly warning us against future zoonotic diseases resulting from humankind's insurgence into natural ecosystems. The same zoonotic pathogens that cause severe infections in a human host frequently fail to produce any disease outcome in their natural hosts. What precise features of the immune system enable natural reservoirs to carry these pathogens so efficiently? To understand these effects, we highlight the importance of tracing the evolutionary basis of pathogen tolerance in reservoir hosts, while drawing implications from their diverse physiological and life-history traits, and ecological contexts of host-pathogen interactions. Long-term co-evolution might allow reservoir hosts to modulate immunity and evolve tolerance to zoonotic pathogens, increasing their circulation and infectious period. Such processes can also create a genetically diverse pathogen pool by allowing more mutations and genetic exchanges between circulating strains, thereby harboring rare alive-on-arrival variants with extended infectivity to new hosts (i.e., spillover). Finally, we end by underscoring the indispensability of a large multidisciplinary empirical framework to explore the proposed link between evolved tolerance, pathogen prevalence, and spillover in the wild.

Metagenomic evidence for a polymicrobial signature of sepsis

Our understanding of the host component of sepsis has made significant progress. However, detailed study of the microorganisms causing sepsis, either as single pathogens or microbial assemblages, has received far less attention. Metagenomic data offer opportunities to characterize the microbial communities found in septic and healthy individuals. In this study we apply gradient-boosted tree classifiers and a novel computational decontamination technique built upon SHapley Additive exPlanations (SHAP) to identify microbial hallmarks which discriminate blood metagenomic samples of septic patients from that of healthy individuals. Classifiers had high performance when using the read assignments to microbial genera [area under the receiver operating characteristic (AUROC=0.995)], including after removal of species ‘culture-confirmed’ as the cause of sepsis through clinical testing (AUROC=0.915). Models trained on single genera were inferior to those employing a polymicrobial model and we identified multiple co-occurring bacterial genera absent from healthy controls. While prevailing diagnostic paradigms seek to identify single pathogens, our results point to the involvement of a polymicrobial community in sepsis. We demonstrate the importance of the microbial component in characterising sepsis, which may offer new biological insights into the aetiology of sepsis, and ultimately support the development of clinical diagnostic or even prognostic tools.

Difference of Intrahost Dynamics of the Second Human Pegivirus and Hepatitis C Virus in HPgV-2/HCV-Coinfected Patients

Background

The second human pegivirus (HPgV-2) and hepatitis C virus (HCV) belong to the Flaviviridae family and share some common genome features. However, the two viruses exhibit significantly different genetic diversity. The comparison of intrahost dynamics of HPgV-2 and HCV that mainly reflect virus-host interactions is needed to elucidate their intrahost difference of genetic diversity and the possible mechanisms.

Methods

Intrahost single nucleotide variations (iSNVs) were identified by means of next-generation sequencing from both cross-sectional and longitudinal samples from HPgV-2- and HCV-coinfected patients. The levels of human cytokines were quantified in the patient before and after HCV elimination by the treatment of direct-acting antivirals (DAA).

Results

Unlike HCV, the viral sequences of HPgV-2 are highly conserved among HPgV-2-infected patients. However, iSNV analysis confirmed the intrahost variation or quasispecies of HPgV-2. Almost all iSNVs of HPgV-2 did not accumulate or transmit within host over time, which may explain the highly conserved HPgV-2 consensus sequence. Intrahost variation of HPgV-2 mainly causes nucleotide transition in particular at the 3rd codon position and synonymous substitutions, indicating purifying or negative selection posed by host immune system. Cytokine data further indicate that HPgV-2 infection alone may not efficiently stimulate innate immune responses since proinflammatory cytokine expression dramatically decreased with elimination of HCV.

Conclusion

This study provided new insights into the intrahost genomic variations and evolutionary dynamics of HPgV-2 as well as the impact of host immune selection and virus polymerase on virus evolution. The different genetic diversity of HPgV-2 and HCV makes HPgV-2 a potential new model to investigate RNA virus diversity and the mechanism of viral polymerase in modulating virus replication.

Data Proliferation, Reconciliation, and Synthesis in Viral Ecology

The fields of viral ecology and evolution are rapidly expanding, motivated in part by concerns around emerging zoonoses. One consequence is the proliferation of host–virus association data, which underpin viral macroecology and zoonotic risk prediction but remain fragmented across numerous data portals. In the present article, we propose that synthesis of host–virus data is a central challenge to characterize the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open database of mammal host–virus associations that reconciles four published data sets. We show that this offers a substantially richer view of the known virome than any individual source data set but also that databases such as these risk becoming out of date as viral discovery accelerates. We argue for a shift in practice toward the development, incremental updating, and use of synthetic data sets in viral ecology, to improve replicability and facilitate work to predict the structure and dynamics of the global virome.

Anthropogenic disturbance favours generalist over specialist parasites in bird communities: Implications for risk of disease emergence

Niche theory predicts specialists which will be more sensitive to environmental perturbation compared to generalists, a hypothesis receiving broad support in free-living species. Based on their niche breadth, parasites can also be classified as specialists and generalists, with specialists infecting only a few and generalists a diverse array of host species. Here, using avian haemosporidian parasites infecting wild bird populations inhabiting the Western Ghats, India as a model system, we elucidate how climate, habitat and human disturbance affects parasite prevalence both directly and indirectly via their effects on host diversity. Our data demonstrate that anthropogenic disturbance acts to reduce the prevalence of specialist parasite lineages, while increasing that of generalist lineages. Thus, as in free-living species, disturbance favours parasite communities dominated by generalist versus specialist species. Because generalist parasites are more likely to cause emerging infectious diseases, such biotic homogenisation of parasite communities could increase disease emergence risk in the Anthropocene.

Human Coronavirus: Envelope Protein Evolution

Envelope protein of human coronavirus play significant role in an evolution and mutation of the virus life cycle. In the present research, the author evaluated amino acid sequences, its abundance and GC content of the genes that corresponds to envelope proteins’ of the human coronaviruses, identified from year 2003-2019. It includes SARS-CoV (2003), HCoV-NL-63 (2003), HCoV-HKU-1 (2004), MERS-CoV (2013), and SARS-CoV-2 (2019). The present research findings illustrated a point mutation in D2 location of SARS-CoV-2 (2019), representing Arginine in the place of Glutamic acid (SARS-CoV, 2003) where glycine was found deleted. SARS-CoV-2 (2019) coronavirus revealed increased abundance of Glutamic acid (100%), Asparagine (67%), Serine (300%) and Valine (85.7%) in comparison with HCoV-NL-63 (2003). We observed lower GC content in the SARS-CoV-2 (2019) among SARS-CoV (2003) and MERS-CoV (2013). The present findings have evolutionary significance and indicate SARS-CoV-2 (2019) adaptation in human.