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We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmm-scan and GODomainMiner to assign initial functions to proteins at large evolutionary distances; our clusters then enable us to newly assign functions to uncharacterized proteins through “function by association.” We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. 

Abstract The application of established cell viability assays such as the commonly used trypan blue staining method to coral cells is not straightforward due to different culture parameters and different cellular features specific to mammalian cells compared to marine invertebrates. Using Pocillopora damicornis as a model, we characterized the autofluorescence and tested different fluorescent dye pair combinations to identify alternative viability indicators. The cytotoxicity of different representative molecules, namely small organic molecules, proteins and nanoparticles (NP), was measured after 24 h of exposure using the fluorescent dye pair Hoechst 33342 and SYTOX orange. Our results show that this dye pair can be distinctly measured in the presence of fluorescent proteins plus chlorophyll. P. damicornis cells exposed for 24 h to Triton-X100, insulin or titanium dioxide (TiO 2 ) NPs, respectively, at concentrations ranging from 0.5 to 100 µg/mL, revealed a LC50 of 0.46 µg/mL for Triton-X100, 6.21 µg/mL for TiO 2 NPs and 33.9 µg/mL for insulin. This work presents the approach used to customize dye pairs for membrane integrity-based cell viability assays considering the species- and genotype-specific autofluorescence of scleractinian corals, namely: endogenous fluorescence characterization followed by the selection of dyes that do not overlap with endogenous signals. 

Abstract Coral reef ecosystems support significant biological activities and harbor huge diversity, but they are facing a severe crisis driven by anthropogenic activities and climate change. An important behavioral trait of the coral holobiont is coral motion, which may play an essential role in feeding, competition, reproduction, and thus survival and fitness. Therefore, characterizing coral behavior through motion analysis will aid our understanding of basic biological and physical coral functions. However, tissue motion in the stony scleractinian corals that contribute most to coral reef construction are subtle and may be imperceptible to both the human eye and commonly used imaging techniques. Here we propose and apply a systematic approach to quantify and visualize subtle coral motion across a series of light and dark cycles in the scleractinian coral Montipora capricornis . We use digital image correlation and optical flow techniques to quantify and characterize minute coral motions under different light conditions. In addition, as a visualization tool, motion magnification algorithm magnifies coral motions in different frequencies, which explicitly displays the distinctive dynamic modes of coral movement. Specifically, our assessment of displacement, strain, optical flow, and mode shape quantify coral motion under different light conditions, and they all show that M. capricornis exhibits more active motions at night compared to day. Our approach provides an unprecedented insight into micro-scale coral movement and behavior through macro-scale digital imaging, thus offering a useful empirical toolset for the coral research community. 

Pseudomonas aeruginosa utilizes the quorum sensing (QS) system to strategically coordinate virulence and biofilm formation. Targeting QS pathways may be a potential anti-infective approach to treat P. aeruginosa infections. In the present study, we define cephalosporins’ anti-QS activity using Chromobacterium violaceum CV026 for screening and QS-regulated mutants of P. aeruginosa for validation. We quantified the effects of three cephalosporins, cefepime, ceftazidime, and ceftriaxone, on (1) pyocyanin production using spectrophotometric assay, (2) bacterial motility using agar plate assay, and (3) biofilm formation using scanning electron microscopy. We also studied isogenic QS mutant strains of PAO1 (Δ lasR ,Δ rhlR ,Δ pqsA , and Δ pqsR) to compare and distinguish QS-mediated effects on the motility phenotypes and bacterial growth with and without sub-MIC concentrations of antibiotics. Results showed that cephalosporins have anti-QS activity and reduce bacterial motility, pyocyanin production, and biofilm formation for CV026 and PAO1. Also, sub-MICs of cefepime increased aminoglycosides’ antimicrobial activity against P. aeruginosa PAO1, suggesting the advantage of combined anti-QS and antibacterial treatment. To correlate experimentally observed anti-QS effects with the interactions between cephalosporins and QS receptors, we performed molecular docking with ligand binding sites of quorum sensing receptors using Autodock Vina. Molecular docking predicted cephalosporins’ binding affinities to the ligand-binding pocket of QS receptors (CviR, LasR, and PqsR). To validate our results using an infection model, we quantified the survival rate of C aenorhabditis elegans following P. aeruginosa PAO1 challenge at concentrations less than the minimum inhibitory concentration (MIC) of antibiotics. C. elegans infected with PAO1 without antibiotics showed 0% survivability after 72 h. In contrast, PAO1-infected C. elegans showed 65 ± 5%, 58 ± 4%, and 49 ± 8% survivability after treatment with cefepime, ceftazidime, and ceftriaxone, respectively. We determined the survival rates of C. elegans infected by QS mutant strains Δ lasR (32 ± 11%), Δ rhlR (27 ± 8%), Δ pqsA (27 ± 10%), and Δ pqsR (37 ± 6%), which suggest essential role of QS system in virulence. In summary, cephalosporins at sub-MIC concentrations show anti-QS activity and enhance the antibacterial efficacy of aminoglycosides, a different class of antibiotics. Thus, cephalosporins at sub-MIC concentrations in combination with other antibiotics are potential candidates for developing therapies to combat infections caused by P. aeruginosa. 

Abstract Background 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. Results 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. Conclusions 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.