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BackgroundRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.FindingsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression is associated with the presence of a high viral load. We further demonstrate that systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 severity. For clinical outcome prediction (e.g. respiratory failure), IFI27 expression displays a high positive (0.83) and negative (0.95) predictive value, outperforming all other known predictors of COVID-19 severity. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 swine influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.InterpretationThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.Research in contextEvidence before this studyWe searched the scientific literature using PubMed to identify studies that used the IFI27 biomarker to predict outcomes in COVID-19 patients. We used the search terms “IFI27”, “COVID-19, “gene expression” and “outcome prediction”. We did not identify any study that investigated the role of IFI27 biomarker in outcome prediction. Although ten studies were identified using the general terms of “gene expression” and “COVID-19”, IFI27 was only mentioned in passing as one of the identified genes. All these studies addressed the broader question of the host response to COVID-19; none focused solely on using IFI27 to improve the risk stratification of infected patients in a pandemic.Added value of this studyHere, we present the findings of a multi-cohort study of the IFI27 biomarker in COVID-19 patients. Our findings show that the host response, as reflected by blood IFI27 gene expression, accurately predicts COVID-19 disease progression (positive and negative predictive values; 0.83 and 0.95, respectively), outperforming age, comorbidity, C-reactive protein and all other known risk factors. The strong association of IFI27 with disease severity occurs not only in SARS-CoV-2 infection, but also in other respiratory viruses with pandemic potential, such as the influenza virus. These findings suggest that host response biomarkers, such as IFI27, could help identify high-risk COVID-19 patients - those who are more likely to develop infection complications - and therefore may help improve patient triage in a pandemic.Implications of all the available evidenceThis is the first systemic study of the clinical role of IFI27 in the current COVID-19 pandemic and its possible future application in other respiratory virus pandemics. The findings not only could help improve the current management of COVID-19 patients but may also improve future pandemic preparedness.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

Although discriminating self from nonself is a cardinal animal trait, metazoan allorecognition genes do not appear to be homologous. Here, we characterize the Aggregation Factor (AF) gene family, which encodes putative allorecognition factors in the demosponge Amphimedon queenslandica, and trace its evolution across 24 sponge (Porifera) species. The AF locus in Amphimedon is comprised of a cluster of five similar genes that encode Calx-beta and Von Willebrand domains and a newly defined Wreath domain, and are highly polymorphic. Further AF variance appears to be generated through individualistic patterns of RNA editing. The AF gene family varies between poriferans, with protein sequences and domains diagnostic of the AF family being present in Amphimedon and other demosponges, but absent from other sponge classes. Within the demosponges, AFs vary widely with no two species having the same AF repertoire or domain organization. The evolution of AFs suggests that their diversification occurs via high allelism, and the continual and rapid gain, loss and shuffling of domains over evolutionary time. Given the marked differences in metazoan allorecognition genes, we propose the rapid evolution of AFs in sponges provides a model for understanding the extensive diversification of self-nonself recognition systems in the animal kingdom.

Sponges, despite their simple body plan, discriminate between self and nonself with remarkable specificity. Sponge grafting experiments simulate the effects of natural self or nonself contact under laboratory conditions. Here we take a transcriptomic approach to investigate the temporal response to self and nonself grafts in the marine demosponge Amphimedon queenslandica. Auto- and allografts were established, observed and sampled over a period of three days, over which time the grafts either rejected or accepted, depending on the identity of the paired individuals, in a replicable and predictable manner. Fourteen transcriptomes were generated that spanned the auto- and allograft responses. Self grafts fuse completely in under three days, and the process appears to be controlled by relatively few genes. In contrast, nonself grafting results in a complete lack of fusion after three days, and appears to involve a broad downregulation of normal biological processes, rather than the mounting of an intense defensive response.

The multicellular condition cannot be maintained without safeguards protecting the integrity of the individual. Tissue contact and fusion with other conspecific individuals may threaten this integrity, as genetically non-identical cells may shirk their somatic duties and gain disproportionate access to the germ line. As sessile invertebrates that commonly inhabit crowded benthic environments, sponges are particularly reliant on a molecular self-nonself defense system in order to resist loss of habitat space, chimerism and possible germ line parasitism by neighbouring conspecific sponges. Sponge allorecognition appears to be, at least in part, under the control of extracellular proteoglycans called aggregation factors (AFs), which were first discovered based on their role in the species-specific reaggregation of dissociated sponge cells. Although the AFs have been extensively studied for over fifty years, the majority of this work has involved biochemical, rather than genetic approaches, and has focussed on the role of the glycan subunits associated with the AFs. In the present work, I investigate the genetic properties underlying the AF protein backbone, to better understand the functions and evolution of these putative allorecognition molecules. Using newly-available genomic and transcriptomic data, I surveyed the phylum Porifera for novel putative AF sequences, to explore the evolutionary origins of this gene family. I conclude that the AFs are a demosponge and hexactinellid-specific innovation. I then performed an in-depth characterisation of the six AF genes from the model demosponge species, Amphimedon queenslandica. The six genes display a highly modular intron/exon organisation. However, as expected of putative allorecognition genes, the AFs are greatly diversified between individuals, with nucleotide polymorphism (and possible positive selection) and intron retention events distributed across the six genes. The AFs are very highly expressed across sponge development and in response to alloimmune challenge, and undergo a particular spike in gene expression levels after the onset of sponge metamorphosis. The AF genes also exhibit expression patterns across development that are significantly correlated with those of other, developmentally important genes with roles in various cell signalling pathways. I conclude that the AFs play a novel developmental role, in addition to their putative allorecognition capabilities.

The multicellular condition cannot be maintained without safeguards protecting the integrity of the individual. Tissue contact and fusion with other conspecific individuals may threaten this integrity, as genetically non-identical cells may shirk their somatic duties and gain disproportionate access to the germ line. Allorecognition capabilities appear to be widespread amongst metazoans. However, although similar functional mechanisms underlie the different metazoan allorecognition systems, all evidence to date rejects the idea of homology and a shared evolutionary history.