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Equine influenza viruses (EIVs) of H3N8 subtype are culprits of severe acute respiratory infections in horses, and are still responsible for significant outbreaks worldwide. Adaptability of influenza viruses to a particular host is significantly influenced by their codon usage preference, due to an absolute dependence on the host cellular machinery for their replication. In the present study, we analyzed genome-wide codon usage patterns in 92 EIV strains, including both H3N8 and H7N7 subtypes by computing several codon usage indices and applying multivariate statistical methods. Relative synonymous codon usage (RSCU) analysis disclosed bias of preferred synonymous codons towards A/U-ended codons. The overall codon usage bias in EIVs was slightly lower, and mainly affected by the nucleotide compositional constraints as inferred from the RSCU and effective number of codon (ENc) analysis. Our data suggested that codon usage pattern in EIVs is governed by the interplay of mutation pressure, natural selection from its hosts and undefined factors. The H7N7 subtype was found less fit to its host (horse) in comparison to H3N8, by possessing higher codon bias, lower mutation pressure and much less adaptation to tRNA pool of equine cells. To the best of our knowledge, this is the first report describing the codon usage analysis of the complete genomes of EIVs. The outcome of our study is likely to enhance our understanding of factors involved in viral adaptation, evolution, and fitness towards their hosts.

The LINE-1 (L1) retrotransposon is an ancient genetic parasite that has written around one-third of the human genome through a ‘copy and paste’ mechanism catalysed by its multifunctional enzyme, open reading frame 2 protein (ORF2p) 1 . ORF2p reverse transcriptase (RT) and endonuclease activities have been implicated in the pathophysiology of cancer 2 , 3 , autoimmunity 4 , 5 and ageing 6 , 7 , making ORF2p a potential therapeutic target. However, a lack of structural and mechanistic knowledge has hampered efforts to rationally exploit it. We report structures of the human ORF2p ‘core’ (residues 238–1061, including the RT domain) by X-ray crystallography and cryo-electron microscopy in several conformational states. Our analyses identified two previously undescribed folded domains, extensive contacts to RNA templates and associated adaptations that contribute to unique aspects of the L1 replication cycle. Computed integrative structural models of full-length ORF2p show a dynamic closed-ring conformation that appears to open during retrotransposition. We characterize ORF2p RT inhibition and reveal its underlying structural basis. Imaging and biochemistry show that non-canonical cytosolic ORF2p RT activity can produce RNA:DNA hybrids, activating innate immune signalling through cGAS/STING and resulting in interferon production 6 – 8 . In contrast to retroviral RTs, L1 RT is efficiently primed by short RNAs and hairpins, which probably explains cytosolic priming. Other biochemical activities including processivity, DNA-directed polymerization, non-templated base addition and template switching together allow us to propose a revised L1 insertion model. Finally, our evolutionary analysis demonstrates structural conservation between ORF2p and other RNA- and DNA-dependent polymerases. We therefore provide key mechanistic insights into L1 polymerization and insertion, shed light on the evolutionary history of L1 and enable rational drug development targeting L1. X-ray crystallography, cryo-electron microscopy, structural modelling, biochemistry, cell biology, and evolutionary analysis enable characterization of ORF2p, the reverse transcriptase of the ancient ‘parasitic’ LINE-1 retrotransposon that has written around one-third of the human genome.

The relationship of SARS-CoV-2 pulmonary infection and severity of disease is not fully understood. Here we show analysis of autopsy specimens from 24 patients who succumbed to SARS-CoV-2 infection using a combination of different RNA and protein analytical platforms to characterize inter-patient and intra-patient heterogeneity of pulmonary virus infection. There is a spectrum of high and low virus cases associated with duration of disease. High viral cases have high activation of interferon pathway genes and a predominant M1-like macrophage infiltrate. Low viral cases are more heterogeneous likely reflecting inherent patient differences in the evolution of host response, but there is consistent indication of pulmonary epithelial cell recovery based on napsin A immunohistochemistry and RNA expression of surfactant and mucin genes. Using a digital spatial profiling platform, we find the virus corresponds to distinct spatial expression of interferon response genes demonstrating the intra-pulmonary heterogeneity of SARS-CoV-2 infection. Understanding the pathology in the lungs of patients with COVID-19 might provide clues as to the susceptibility of patients and how the SARS-CoV-2 virus can be fatal. Here the authors analyze cadaveric pulmonary tissue and show one group with high viral load, early death, inflammation and inflammatory damage, and another with low viral load, longer duration of disease, and more M2-like polarization and fibrotic lung damage.

It is well known that the dinucleotide CpG is under-represented in the genomic DNA of many vertebrates. This is commonly thought to be due to the methylation of cytosine residues in this dinucleotide and the corresponding high rate of deamination of 5-methycytosine, which lowers the frequency of this dinucleotide in DNA. Surprisingly, many single-stranded RNA viruses that replicate in these vertebrate hosts also have a very low presence of CpG dinucleotides in their genomes. Viruses are obligate intracellular parasites and the evolution of a virus is inexorably linked to the nature and fate of its host. One therefore expects that virus and host genomes should have common features. In this work, we compare evolutionary patterns in the genomes of ssRNA viruses and their hosts. In particular, we have analyzed dinucleotide patterns and found that the same patterns are pervasively over- or under-represented in many RNA viruses and their hosts suggesting that many RNA viruses evolve by mimicking some of the features of their host's genes (DNA) and likely also their corresponding mRNAs. When a virus crosses a species barrier into a different host, the pressure to replicate, survive and adapt, leaves a footprint in dinucleotide frequencies. For instance, since human genes seem to be under higher pressure to eliminate CpG dinucleotide motifs than avian genes, this pressure might be reflected in the genomes of human viruses (DNA and RNA viruses) when compared to those of the same viruses replicating in avian hosts. To test this idea we have analyzed the evolution of the influenza virus since 1918. We find that the influenza A virus, which originated from an avian reservoir and has been replicating in humans over many generations, evolves in a direction strongly selected to reduce the frequency of CpG dinucleotides in its genome. Consistent with this observation, we find that the influenza B virus, which has spent much more time in the human population, has adapted to its human host and exhibits an extremely low CpG dinucleotide content. We believe that these observations directly show that the evolution of RNA viral genomes can be shaped by pressures observed in the host genome. As a possible explanation, we suggest that the strong selection pressures acting on these RNA viruses are most likely related to the innate immune response and to nucleotide motifs in the host DNA and RNAs.

Somatic mobilization of LINE-1 (L1) has been implicated in cancer etiology. We analyzed a recent TCGA data release comprised of nearly 5000 pan-cancer paired tumor-normal whole-genome sequencing (WGS) samples and ~9000 tumor RNA samples. We developed TotalReCall an improved algorithm and pipeline for detection of L1 retrotransposition (RT), finding high correlation between L1 expression and “RT burden” per sample. Furthermore, we mathematically model the dual regulatory roles of p53, where mutations in TP53 disrupt regulation of both L1 expression and retrotransposition. We found those with Li-Fraumeni Syndrome (LFS) heritable TP53 pathogenic and likely pathogenic variants bear similarly high L1 activity compared to matched cancers from patients without LFS, suggesting this population be considered in attempts to target L1 therapeutically. Due to improved sensitivity, we detect over 10 genes beyond TP53 whose mutations correlate with L1, including ATRX , suggesting other, potentially targetable, mechanisms underlying L1 regulation in cancer remain to be discovered. LINE-1 activity was quantified in a large, pan-cancer dataset, finding locus-specific heterogeneity and new associations using a computational pipeline. A mathematical mediation model of p53 and L1 interactions was inferred. Somatic retrotransposition was seen in Li-Fraumeni Syndrome with heritable TP53 mutations.

With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.

Recent studies have demonstrated abundant transcription of a set of noncoding RNAs (ncRNAs) preferentially within tumors as opposed to normal tissue. Using an approach from statistical physics, we quantify global transcriptome-wide motif use for the first time, to our knowledge, in human and murine ncRNAs, determining that most have motif use consistent with the coding genome. However, an outlier subset of tumor-associated ncRNAs, typically of recent evolutionary origin, has motif use that is often indicative of pathogenassociated RNA. For instance, we show that the tumor-associated human repeat human satellite repeat II (HSATII) is enriched in motifs containing CpG dinucleotides in AU-rich contexts that most of the human genome and human adapted viruses have evolved to avoid. We demonstrate that a key subset of these ncRNAs functions as immunostimulatory “self-agonists” and directly activates cells of the mononuclear phagocytic system to produce proinflammatory cytokines. These ncRNAs arise from endogenous repetitive elements that are normally silenced, yet are often very highly expressed in cancers. We propose that the innate response in tumors may partially originate from direct interaction of immunogenic ncRNAs expressed in cancer cells with innate pattern recognition receptors, and thereby assign a previously unidentified danger-associated function to a set of dark matter repetitive elements. These findings potentially reconcile several observations concerning the role of ncRNA expression in cancers and their relationship to the tumor microenvironment.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time. In single-cell RNA-seq analyses, it would be critical to measure the relationships between genes. Here, the authors develop a framework for single-cell dimensionality reduction that incorporates gene-specific relationships - GeneVector -, and use it for tasks such as annotating cell types and analysing pathway variation after treatment.

Tandem satellite repeats account for 3% of the human genome. One of them, Human Satellite II (HSATII), is highly expressed in several epithelial cancers and cancer cell lines. Here we report an acute induction of HSATII RNA in human cells infected with two herpes viruses. We show that human cytomegalovirus (HCMV) IE1 and IE2 proteins cooperate to induce HSATII RNA affecting several aspects of the HCMV replication cycle, viral titers and infected-cell processes. HSATII RNA expression in tissue from two chronic HCMV colitis patients correlates with the strength of CMV antigen staining. Thus, endogenous HSATII RNA synthesis after herpesvirus infections appears to have functionally important consequences for viral replication and may provide a novel insight into viral pathogenesis. The HSATII induction seen in both infected and cancer cells suggests possible convergence upon common HSATII-based regulatory mechanisms in these seemingly disparate diseases. The human genome includes a large amount of repetitive sequence, such as human satellite II (HSATII), but their function remains largely unknown. Here, Nogalski et al. show that herpesvirus infection induces HSATII RNA expression, which in turn affects virus replication and cell motility.