With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.

High throughput sequencing allowed the discovery of many new viruses and viral organizations increasing our comprehension of virus origin and evolution. Most RNA viruses are currently characterized through similarity searches of annotated virus databases. This approach limits the possibility to detect completely new virus-encoded proteins with no detectable similarities to existing ones, i.e. ORFan proteins. A strong indication of the ORFan viral origin in a metatranscriptome is the lack of DNA corresponding to an assembled RNA sequence in the biological sample. Furthermore, sequence homology among ORFans and evidence of co-occurrence of these ORFans in specific host individuals provides further indication of a viral origin. Here, we use this theoretical framework to report the finding of three conserved clades of protein-coding RNA segments without a corresponding DNA in fungi. Protein sequence and structural alignment suggest these proteins are distantly related to viral RNA-dependent RNA polymerases (RdRP). In these new putative viral RdRP clades, no GDD catalytic triad is present, but the most common putative catalytic triad is NDD and a clade with GDQ, a triad previously unreported at that site. SDD, HDD, and ADD are also represented. For most members of these three clades, we were able to associate a second genomic segment, coding for a protein of unknown function. We provisionally named this new group of viruses ormycovirus. Interestingly, all the members of one of these sub-clades (gammaormycovirus) accumulate more minus sense RNA than plus sense RNA during infection.

The bacterial DNA gyrase complex (GyrA/GyrB) plays a crucial role during DNA replication and serves as a target for multiple antibiotics, including the fluoroquinolones. Despite it being a valuable antibiotics target, resistance emergence by pathogens including Pseudomonas aeruginosa are proving problematic. Here, we describe Igy, a peptide inhibitor of gyrase, encoded by Pseudomonas bacteriophage LUZ24 and other members of the Bruynoghevirus genus. Igy (5.6 kDa) inhibits in vitro gyrase activity and interacts with the P. aeruginosa GyrB subunit, possibly by DNA mimicry, as indicated by a de novo model of the peptide and mutagenesis. In vivo, overproduction of Igy blocks DNA replication and leads to cell death also in fluoroquinolone-resistant bacterial isolates. These data highlight the potential of discovering phage-inspired leads for antibiotics development, supported by co-evolution, as Igy may serve as a scaffold for small molecule mimicry to target the DNA gyrase complex, without cross-resistance to existing molecules.

Cryphonectria parasitica, the causal agent of chestnut blight is controlled in Europe through natural spread of Cryphonectria hypovirus 1 (CHV1), a mycovirus able to induce hypovirulence to the host. In recent years C. parasitica was reported infecting Azerbaijani population of chestnut, but the presence of CHV1 still needs to be confirmed. Aim of this work was to investigate fifty-five C. parasitica isolates collected in Azerbaijan to describe the associated viruses. Our work found i) the first negative-sense ssRNA virus known to infect C. parasitica naturally for which we propose the name Cryphonectria parasitica sclerotimonavirus 1 (CpSV1) and ii) an RNA sequence showing peculiar features suggesting a viral nature for which we propose the name Cryphonectria parasitica ambivirus 1 (CpaV1). The discovery of CpaV1 expands our knowledge of the RNA virosphere suggesting the existence of a new lineage that cannot presently be reliably associated to the monophyletic Riboviria.

Here we report the discovery of Yaravirus, a lineage of amoebal virus with a puzzling origin and evolution. Yaravirus presents 80-nm-sized particles and a 44,924-bp dsDNA genome encoding for 74 predicted proteins. Yaravirus genome annotation showed that none of its genes matched with sequences of known organisms at the nucleotide level; at the amino acid level, six predicted proteins had distant matches in the nr database. Complimentary prediction of three-dimensional structures indicated possible function of 17 proteins in total. Furthermore, we were not able to retrieve viral genomes closely related to Yaravirus in 8,535 publicly available metagenomes spanning diverse habitats around the globe. The Yaravirus genome also contained six types of tRNAs that did not match commonly used codons. Proteomics revealed that Yaravirus particles contain 26 viral proteins, one of which potentially representing a divergent major capsid protein (MCP) with a predicted double jelly-roll domain. Structure-guided phylogeny of MCP suggests that Yaravirus groups together with the MCPs of Pleurochrysis endemic viruses. Yaravirus expands our knowledge of the diversity of DNA viruses. The phylogenetic distance between Yaravirus and all other viruses highlights our still preliminary assessment of the genomic diversity of eukaryotic viruses, reinforcing the need for the isolation of new viruses of protists.

Orphan genes (also known as ORFans [i.e., orphan open reading frames]) are new genes that enable an organism to adapt to its specific living environment. Our focus in this study is to compare ORFans between pathogens (P) and nonpathogens (NP) of the same genus. Using the pangenome idea, we have identified 130,169 ORFans in nine bacterial genera (505 genomes) and classified these ORFans into four groups: (i) SS-ORFans (P), which are only found in a single pathogenic genome; (ii) SS-ORFans (NP), which are only found in a single nonpathogenic genome; (iii) PS-ORFans (P), which are found in multiple pathogenic genomes; and (iv) NS-ORFans (NP), which are found in multiple nonpathogenic genomes. Within the same genus, pathogens do not always have more genes, more ORFans, or more pathogenicity-related genes (PRGs)-including prophages, pathogenicity islands (PAIs), virulence factors (VFs), and horizontal gene transfers (HGTs)-than nonpathogens. Interestingly, in pathogens of the nine genera, the percentages of PS-ORFans are consistently higher than those of SS-ORFans, which is not true in nonpathogens. Similarly, in pathogens of the nine genera, the percentages of PS-ORFans matching the four types of PRGs are also always higher than those of SS-ORFans, but this is not true in nonpathogens. All of these findings suggest the greater importance of PS-ORFans for bacterial pathogenicity. IMPORTANCE Recent pangenome analyses of numerous bacterial species have suggested that each genome of a single species may have a significant fraction of its gene content unique or shared by a very few genomes (i.e., ORFans). We selected nine bacterial genera, each containing at least five pathogenic and five nonpathogenic genomes, to compare their ORFans in relation to pathogenicity-related genes. Pathogens in these genera are known to cause a number of common and devastating human diseases such as pneumonia, diphtheria, melioidosis, and tuberculosis. Thus, they are worthy of in-depth systems microbiology investigations, including the comparative study of ORFans between pathogens and nonpathogens. We provide direct evidence to suggest that ORFans shared by more pathogens are more associated with pathogenicity-related genes and thus are more important targets for development of new diagnostic markers or therapeutic drugs for bacterial infectious diseases.

The family Tectiviridae comprises a group of tailless, icosahedral, membrane-containing bacteriophages that can be divided into two groups by their hosts, either Gram-negative or Gram-positive bacteria. While the first group is composed of PRD1 and nearly identical well-characterized lytic viruses, the second one includes more variable temperate phages, like GIL16 or Bam35, whose hosts are Bacillus cereus and related Gram-positive bacteria. In the genome of Bam35, nearly half of the 32 annotated open reading frames (ORFs) have no homologs in databases (ORFans), being putative proteins of unknown function, which hinders the understanding of their biology. With the aim of increasing knowledge about the viral proteome, we carried out a comprehensive yeast two-hybrid analysis of all the putative proteins encoded by the Bam35 genome. The resulting protein interactome comprised 76 unique interactions among 24 proteins, of which 12 have an unknown function. These results suggest that the P17 protein is the minor capsid protein of Bam35 and P24 is the penton protein, with the latter finding also being supported by iterative threading protein modeling. Moreover, the inner membrane transglycosylase protein P26 could have an additional structural role. We also detected interactions involving nonstructural proteins, such as the DNA-binding protein P1 and the genome terminal protein (P4), which was confirmed by coimmunoprecipitation of recombinant proteins. Altogether, our results provide a functional view of the Bam35 viral proteome, with a focus on the composition and organization of the viral particle.IMPORTANCE Tailless viruses of the family Tectiviridae can infect commensal and pathogenic Gram-positive and Gram-negative bacteria. Moreover, they have been proposed to be at the evolutionary origin of several groups of large eukaryotic DNA viruses and self-replicating plasmids. However, due to their ancient origin and complex diversity, many tectiviral proteins are ORFans of unknown function. Comprehensive protein-protein interaction (PPI) analysis of viral proteins can eventually disclose biological mechanisms and thus provide new insights into protein function unattainable by studying proteins one by one. Here we comprehensively describe intraviral PPIs among tectivirus Bam35 proteins determined using multivector yeast two-hybrid screening, and these PPIs were further supported by the results of coimmunoprecipitation assays and protein structural models. This approach allowed us to propose new functions for known proteins and hypothesize about the biological role of the localization of some viral ORFan proteins within the viral particle that will be helpful for understanding the biology of tectiviruses infecting Gram-positive bacteria.

Predicted open reading frames (ORFs) that lack detectable homology to known proteins are termed ORFans. Despite their prevalence in metagenomes, the extent to which ORFans encode real proteins, the degree to which they can be annotated, and their functional contributions, remain unclear. To gain insights into these questions, we applied sensitive remote-homology detection methods to functionally analyze ORFans from soil, marine, and human gut metagenome collections. ORFans were identified, clustered into sequence families, and annotated through profile-profile comparison to proteins of known structure. We found that a considerable number of metagenomic ORFans (73,896 of 484,121, 15.3%) exhibit significant remote homology to structurally characterized proteins, providing a means for ORFan functional profiling. The extent of detected remote homology far exceeds that obtained for artificial protein families (1.4%). As expected for real genes, the predicted functions of ORFans are significantly similar to the functions of their gene neighbors (p < 0.001). Compared to the functional profiles predicted through standard homology searches, ORFans show biologically intriguing differences. Many ORFan-enriched functions are virus-related and tend to reflect biological processes associated with extreme sequence diversity. Each environment also possesses a large number of unique ORFan families and functions, including some known to play important community roles such as gut microbial polysaccharide digestion. Lastly, ORFans are a valuable resource for finding novel enzymes of interest, as we demonstrate through the identification of hundreds of novel ORFan metalloproteases that all possess a signature catalytic motif despite a general lack of similarity to known proteins. Our ORFan functional predictions are a valuable resource for discovering novel protein families and exploring the boundaries of protein sequence space. All remote homology predictions are available at http://doxey.uwaterloo.ca/ORFans.

ORFans are hypothetical proteins lacking any significant sequence similarity with other proteins. Here, we highlighted by quantitative proteomics the TGAM_1934 ORFan from the hyperradioresistant Thermococcus gammatolerans archaeon as one of the most abundant hypothetical proteins. This protein has been selected as a priority target for structure determination on the basis of its abundance in three cellular conditions. Its solution structure has been determined using multidimensional heteronuclear NMR spectroscopy. TGAM_1934 displays an original fold, although sharing some similarities with the 3D structure of the bacterial ortholog of frataxin, CyaY, a protein conserved in bacteria and eukaryotes and involved in iron-sulfur cluster biogenesis. These results highlight the potential of structural proteomics in prioritizing ORFan targets for structure determination based on quantitative proteomics data. The proteomic data and structure coordinates have been deposited to the ProteomeXchange with identifier PXD000402 (http://proteomecentral.proteomexchange.org/dataset/PXD000402) and Protein Data Bank under the accession number 2mcf, respectively.

One consistent finding among studies using shotgun metagenomics to analyze whole viral communities is that most viral sequences show no significant homology to known sequences. Thus, bioinformatic analyses based on sequence collections such as GenBank nr, which are largely comprised of sequences from known organisms, tend to ignore a majority of sequences within most shotgun viral metagenome libraries. Here we describe a bioinformatic pipeline, the Viral Informatics Resource for Metagenome Exploration (VIROME), that emphasizes the classification of viral metagenome sequences (predicted open-reading frames) based on homology search results against both known and environmental sequences. Functional and taxonomic information is derived from five annotated sequence databases which are linked to the UniRef 100 database. Environmental classifications are obtained from hits against a custom database, MetaGenomes On-Line, which contains 49 million predicted environmental peptides. Each predicted viral metagenomic ORF run through the VIROME pipeline is placed into one of seven ORF classes, thus, every sequence receives a meaningful annotation. Additionally, the pipeline includes quality control measures to remove contaminating and poor quality sequence and assesses the potential amount of cellular DNA contamination in a viral metagenome library by screening for rRNA genes. Access to the VIROME pipeline and analysis results are provided through a web-application interface that is dynamically linked to a relational back-end database. The VIROME web-application interface is designed to allow users flexibility in retrieving sequences (reads, ORFs, predicted peptides) and search results for focused secondary analyses.