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Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction.

We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents.

We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

 

The gut of the European honey bee (Apis mellifera)possesses a relatively simple bacterial community, but little is known about its community of prophages (temperate bacteriophages integrated into the bacterial genome). Although prophages may eventually begin replicating and kill their bacterial hosts, they can also sometimes be beneficial for their hosts by conferring protection from other phage infections or encoding genes in metabolic pathways and for toxins. In this study, we explored prophages in 17 species of core bacteria in the honey bee gut and two honey bee pathogens. Out of the 181 genomes examined, 431 putative prophage regions were predicted. Among core gut bacteria, the number of prophages per genome ranged from zero to seven and prophage composition (the compositional percentage of each bacterial genome attributable to prophages) ranged from 0 to 7%.Snodgrassella alviandGilliamella apicolahad the highest median prophages per genome (3.0 ± 1.46; 3.0 ± 1.59), as well as the highest prophage composition (2.58% ± 1.4; 3.0% ± 1.59). The pathogenPaenibacillus larvaehad a higher median number of prophages (8.0 ± 5.33) and prophage composition (6.40% ± 3.08) than the pathogenMelissococcus plutoniusor any of the core bacteria. Prophage populations were highly specific to their bacterial host species, suggesting most prophages were acquired recently relative to the divergence of these bacterial groups. Furthermore, functional annotation of the predicted genes encoded within the prophage regions indicates that some prophages in the honey bee gut encode additional benefits to their bacterial hosts, such as genes in carbohydrate metabolism. Collectively, this survey suggests that prophages within the honey bee gut may contribute to the maintenance and stability of the honey bee gut microbiome and potentially modulate specific members of the bacterial community, particularlyS. alviandG. apicola.

 

Abstract Lactobacillaceae are an important family of lactic acid bacteria that play key roles in the gut microbiome of many animal species. In the honey bee (Apis mellifera) gut microbiome, many species of Lactobacillaceae are found, and there is functionally important strain-level variation in the bacteria. In this study, we completed whole-genome sequencing of 3 unique Lactobacillaceae isolates collected from hives in Virginia, USA. Using 107 genomes of known bee-associated Lactobacillaceae and Limosilactobacillus reuteri as an outgroup, the phylogenetics of the 3 isolates was assessed, and these isolates were identified as novel strains of Apilactobacillus kunkeei, Lactobacillus kullabergensis, and Bombilactobacillus mellis. Genome rearrangements, conserved orthologous genes (COG) categories and potential prophage regions were identified across the 3 novel strains. The new A. kunkeei strain was enriched in genes related to replication, recombination and repair, the L. kullabergensis strain was enriched for carbohydrate transport, and the B. mellis strain was enriched in transcription or transcriptional regulation and in some genes with unknown functions. Prophage regions were identified in the A. kunkeei and L. kullabergensis isolates. These new bee-associated strains add to our growing knowledge of the honey bee gut microbiome, and to Lactobacillaceae genomics more broadly. 

Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a’s interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2. 

Abstract Motivation Nearly 40% of the genes in sequenced genomes have no experimentally or computationally derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally based functional annotations and systematically transfer them to newly sequenced organisms on a genome-wide scale. However, the large sizes of such networks pose a challenge for the scalability of current methods. Results We develop a label propagation algorithm called FastSinkSource. By formally bounding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under 3 min for 200 bacterial species. Availability and implementation An implementation of our framework and all data used in this research are available at https://github.com/Murali-group/multi-species-GOA-prediction. Supplementary information Supplementary data are available at Bioinformatics online.