CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins.

BACKGROUND: CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. RESULTS: CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. CONCLUSION: CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA-protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.

Resistance of Echovirus 11 to ClO2 Is Associated with Enhanced Host Receptor Use, Altered Entry Routes, and High Fitness.

Waterborne viruses can exhibit resistance to common water disinfectants, yet the mechanisms that allow them to tolerate disinfection are poorly understood. Here, we generated echovirus 11 (E11) with resistance to chlorine dioxide (ClO2) by experimental evolution, and we assessed the associated genotypic and phenotypic traits. ClO2 resistance emerged after E11 populations were repeatedly reduced (either by ClO2-exposure or by dilution) and then regrown in cell culture. The resistance was linked to an improved capacity of E11 to bind to its host cells, which was further attributed to two potential causes: first, the resistant E11 populations possessed mutations that caused amino acid substitutions from ClO2-labile to ClO2-stable residues in the viral proteins, which likely increased the chemical stability of the capsid toward ClO2. Second, resistant E11 mutants exhibited the capacity to utilize alternative cell receptors for host binding. Interestingly, the emergence of ClO2 resistance resulted in an enhanced replicative fitness compared to the less resistant starting population. Overall this study contributes to a better understanding of the mechanism underlying disinfection resistance in waterborne viruses, and processes that drive resistance development.

Influenza virus drug resistance: a time-sampled population genetics perspective.

The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

Suppression of Toll-Like Receptor 4 Dimerization by 1-[5-Methoxy-2-(2-nitrovinyl)phenyl]pyrrolidine.

Toll-like receptor 4 (TLR4) recognizes lipopolysaccharide (LPS) and triggers the activation of myeloid differention factor 88 (MyD88) and the Toll/interleukin-1 receptor domain-containing adapter, inducing interferon-ß (TRIF)-dependent major downstream signaling pathways. To evaluate the therapeutic potential of 1-[5-methoxy-2-(2-nitrovinyl)phenyl]pyrrolidine (MNP), previously synthesized in our laboratory, its effect on signal transduction via the TLR signaling pathways was examined. Here, we investigated whether MNP modulates the TLR4 signaling pathways and which anti-inflammatory target in TLR4 signaling is regulated by MNP. MNP inhibited the activation of nuclear factor-κB (NF-κB) induced by LPS (TLR4 agonist), and it also inhibited the expression of cyclooxygenase-2 and inducible nitric oxide synthase. MNP inhibited LPS-induced NF-κB activation by targeting TLR4 dimerization in addition to IKKß. These results suggest that MNP can modulate the TLR4 signaling pathway at the receptor level to decrease inflammatory gene expression.

Three Innovations of Next-Generation Antibiotics: Evolvability, Specificity, and Non-Immunogenicity.

Antimicrobial resistance is a silent pandemic exacerbated by the uncontrolled use of antibiotics. Since the discovery of penicillin, we have been largely dependent on microbe-derived small molecules to treat bacterial infections. However, the golden era of antibiotics is coming to an end, as the emergence and spread of antimicrobial resistance against these antibacterial compounds are outpacing the discovery and development of new antibiotics. The current antibiotic market suffers from various shortcomings, including the absence of profitability and investment. The most important underlying issue of traditional antibiotics arises from the inherent properties of these small molecules being mostly broad-spectrum and non-programmable. As the scientific knowledge of microbes progresses, the scientific community is starting to explore entirely novel approaches to tackling antimicrobial resistance. One of the most prominent approaches is to develop next-generation antibiotics. In this review, we discuss three innovations of next-generation antibiotics compared to traditional antibiotics as specificity, evolvability, and non-immunogenicity. We present a number of potential antimicrobial agents, including bacteriophage-based therapy, CRISPR-Cas-based antimicrobials, and microbiome-derived antimicrobial agents. These alternative antimicrobial agents possess innovative properties that may overcome the inherent shortcomings of traditional antibiotics, and some of these next-generation antibiotics are not merely far-fetched ideas but are currently in clinical development. We further discuss some related issues and challenges such as infection diagnostics and regulatory frameworks that still need to be addressed to bring these next-generation antibiotics to the antibiotic market as viable products to combat antimicrobial resistance using a diversified set of strategies.

Investigating the Genomic Background of CRISPR-Cas Genomes for CRISPR-Based Antimicrobials.

CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that the diversity and frequency of these pathways differ by the CRISPR-Cas type. The interplay between CRISPR-Cas systems and DNA repair pathways is essential for the acquisition of new spacers in CRISPR arrays. We conduct simulation studies to demonstrate that the efficiency of these DNA repair pathways may be inferred from the time-series patterns in the RNA structure of CRISPR repeats. This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to consider multifaceted interactions between different genes and systems, to design effective CRISPR-based antimicrobials that can specifically target drug-resistant bacteria in natural microbial communities.

Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins.

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches, such as AlphaFold2, opens new opportunities to exploit the complexity of these macro-biomolecules for highly specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of anti-CRISPR proteins which are structurally similar to the recently discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials.

RNA-protein interactions are crucial for diverse biological processes. In prokaryotes, RNA-protein interactions enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements through crRNA-mediated nuclease activities of Cas proteins. Thanks to the programmability and specificity of CRISPR-Cas systems, CRISPR-based antimicrobials have the potential to be repurposed as new types of antibiotics. Unlike traditional antibiotics, these CRISPR-based antimicrobials can be designed to target specific bacteria and minimize detrimental effects on the human microbiome during antibacterial therapy. In this study, we explore the potential of CRISPR-based antimicrobials by optimizing the RNA-protein interactions of crRNAs and Cas13 proteins. CRISPR-Cas13 systems are unique as they degrade specific foreign RNAs using the crRNA template, which leads to non-specific RNase activities and cell cycle arrest. We show that a high proportion of the Cas13 systems have no colocalized CRISPR arrays, and the lack of direct association between crRNAs and Cas proteins may result in suboptimal RNA-protein interactions in the current tools. Here, we investigate the RNA-protein interactions of the Cas13-based systems by curating the validation dataset of Cas13 protein and CRISPR repeat pairs that are experimentally validated to interact, and the candidate dataset of CRISPR repeats that reside on the same genome as the currently known Cas13 proteins. To find optimal CRISPR-Cas13 interactions, we first validate the 3-D structure prediction of crRNAs based on their experimental structures. Next, we test a number of RNA-protein interaction programs to optimize the in silico docking of crRNAs with the Cas13 proteins. From this optimized pipeline, we find a number of candidate crRNAs that have comparable or better in silico docking with the Cas13 proteins of the current tools. This study fully automatizes the in silico optimization of RNA-protein interactions as an efficient preliminary step for designing effective CRISPR-Cas13-based antimicrobials.

Diverse ATPase Proteins in Mobilomes Constitute a Large Potential Sink for Prokaryotic Host ATP.

Prokaryote mobilome genomes rely on host machineries for survival and replication. Given that mobile genetic elements (MGEs) derive their energy from host cells, we investigated the diversity of ATP-utilizing proteins in MGE genomes to determine whether they might be associated with proteins that could suppress related host proteins that consume energy. A comprehensive search of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase domains. For example, ATPase proteins constitute ∼3% of the genomes of Lak phages with ∼550 kbp genomes that occur in the microbiomes of humans and other animals. Statistical analysis shows the number of ATPase proteins increases linearly with genome length, consistent with a large sink for host ATP during replication of megaphages. Using metagenomic data from diverse environments, we found 505 mobilome proteins with ATPase domains fused to diverse functional domains. Among these composite ATPase proteins, 61.6% have known functional domains that could contribute to host energy diversion during the mobilome infection cycle. As many have domains that are known to interact with nucleic acids and proteins, we infer that numerous ATPase proteins are used during replication and for protection from host immune systems. We found a set of uncharacterized ATPase proteins with nuclease and protease activities, displaying unique domain architectures that are energy intensive based on the presence of multiple ATPase domains. In many cases, these composite ATPase proteins genomically co-localize with small proteins in genomic contexts that are reminiscent of toxin-antitoxin systems and phage helicase-antibacterial helicase systems. Small proteins that function as inhibitors may be a common strategy for control of cellular processes, thus could inspire future biochemical experiments for the development of new nucleic acid and protein manipulation tools, with diverse biotechnological applications.

Qualification of Non-Halogenated Organic Solvents Applied to Microsphere Manufacturing Process.

As a non-halogenated dispersed solvent, ethyl acetate has been most commonly used for the manufacturing of poly-d,l-lactide-co-glycolide (PLGA) microspheres. However, ethyl acetate-based microencapsulation processes face several limitations. This study was aimed at proposing ethyl formate as an alternative. Evaluated in this study was the solvent qualification of ethyl formate and ethyl acetate for microencapsulation of a hydrophobic drug into PLGA microspheres. An oil-in-water emulsion solvent extraction technique was developed to load progesterone into PLGA microspheres. Briefly, right after emulsion droplets were temporarily stabilized, they were subject to primary solvent extraction. Appearing semisolid, embryonic microspheres were completely hardened through subsequent secondary solvent extraction. Changes in process parameters of the preparative technique made it possible to manipulate the properties of emulsion droplets, progesterone behavior, and microsphere quality. Despite the two solvents showing comparable Hansen solubility parameter distances toward PLGA, ethyl formate surpassed ethyl acetate in relation to volatility and water miscibility. These features served as advantages in the microsphere manufacturing process, helping produce PLGA microspheres with better quality in terms of drug crystallization, drug encapsulation efficiency, microsphere size homogeneity, and residual solvent content. The present ethyl formate-based preparative technique could be an attractive method of choice for the production of drug-loaded PLGA microspheres.

Assessment of Residual Solvent and Drug in PLGA Microspheres by Derivative Thermogravimetry.

Thermogravimetry does not give specific information on residual organic solvents in polymeric matrices unless it is hyphenated with the so-called evolved gas analysis. The purpose of this study was to apply, for the first time, derivative thermogravimetry (DTG) to characterize a residual solvent and a drug in poly-d,l-lactide-co-glycolide (PLGA) microspheres. Ethyl formate, an ICH class 3 solvent, was used to encapsulate progesterone into microspheres. DTG provided a distinct peak, displaying the onset and end temperatures at which ethyl formate started to evolve from to where it completely escaped out of the microspheres. DTG also gave the area and height of the solvent peak, as well as the temperature of the highest mass change rate of the microspheres. These derivative parameters allowed for the measurement of the amount of residual ethyl formate in the microspheres. Interestingly, progesterone affected not only the residual solvent amount but also these derivative parameters. Another intriguing finding was that there was a linear relationship between progesterone content and the peak height of ethyl formate. The residual solvent data calculated by DTG were quite comparable to those measured by gas chromatography. In summary, DTG could be an efficient and practical quality control tool to evaluate residual solvents and drugs in various polymeric matrices.

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network.

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO2) from the experimental evolution data. Results from the training using a new open-source software TensorFlow show that the learned distributed vectors can be clustered using principal component analysis and hierarchical clustering to reveal a list of non-synonymous mutations that arise on the structural protein VP1 in connection to the candidate mutation for ClO2 adaptation. Furthermore, this method can account for recombination rates by setting the extent of interactions as a biological hyper-parameter, and the results show that the most realistic scenario of mid-range interactions across the genome is most consistent with the previous studies.

Differential regulation of MyD88- and TRIF-dependent signaling pathways of Toll-like receptors by cardamonin.

Toll-like receptors (TLRs) play a crucial role in the induction of innate immune response against bacterial and viral infections. TLRs induce downstream signaling via MyD88- and TRIF-dependent pathways. Cardamonin is a naturally occurring chalcone from Alpinia species exhibiting anti-inflammatory effects. However, the principal molecular mechanisms remain unclear. The objective of this study was to investigate the role of cardamonin in TLR signaling pathways. Cardamonin inhibited NF-κB activation as well as COX-2 expression induced by TLR agonists. Cardamonin inhibited the activation of IRF3 and the expression of interferon-inducible protein-10 (IP-10) induced by TLR3 or TLR4 agonists. Cardamonin also inhibited ligand-independent NF-κB activation overexpressed by MyD88, IKKß, or p65 and IRF3 activation overexpressed by TRIF, TBK1, or IRF3. However, cardamonin had no effect on TBK1 kinase activity in vitro. These results suggest that cardamonin modulates both the MyD88- and TRIF-dependent pathways of TLRs and represents a potentially new anti-inflammatory candidate.

Andrographolide suppresses TRIF-dependent signaling of toll-like receptors by targeting TBK1.

Toll-like receptors (TLRs) play a crucial role in danger recognition and induction of innate immune response against bacterial and viral infections. The TLR adaptor molecule, toll-interleukin-1 receptor domain-containing adapter inducing interferon-ß (TRIF), facilitates TLR3 and TLR4 signaling, leading to the activation of the transcription factor, NF-κB and interferon regulatory factor 3 (IRF3). Andrographolide, the active component of Andrographis paniculata, exerts anti-inflammatory effects; however, the principal molecular mechanisms remain unclear. The objective of this study was to investigate the role of andrographolide in TLR signaling pathways. Andrographolide suppressed NF-κB activation as well as COX-2 expression induced by TLR3 or TLR4 agonists. Andrographolide also suppressed the activation of IRF3 and the expression of interferon inducible protein-10 (IP-10) induced by TLR3 or TLR4 agonists. Andrographolide attenuated ligand-independent activation of IRF3 following overexpression of TRIF, TBK1, or IRF3. Furthermore, andrographolide inhibited TBK1 kinase activity in vitro. These results indicate that andrographolide modulates the TRIF-dependent pathway of TLRs by targeting TBK1 and represents a potential new anti-inflammatory candidate.

Experimental adaptation of human echovirus 11 to ultraviolet radiation leads to resistance to disinfection and ribavirin.

Ultraviolet light in the UVC range is a commonly used disinfectant to control viruses in clinical settings and water treatment. However, it is currently unknown whether human viral pathogens may develop resistance to such stressor. Here, we investigate the adaptation of an enteric pathogen, human echovirus 11, to disinfection by UVC, and characterized the underlying phenotypic and genotypic changes. Repeated exposure to UVC lead to a reduction in the UVC inactivation rate of approximately 15 per cent compared to that of the wild-type and the control populations. Time-series next-generation sequencing data revealed that this adaptation to UVC was accompanied by a decrease in the virus mutation rate. The inactivation efficiency of UVC was additionally compromised by a shift from first-order to biphasic inactivation kinetics, a form of 'viral persistence' present in the UVC resistant and control populations. Importantly, populations with biphasic inactivation kinetics also exhibited resistance to ribavirin, an antiviral drug that, as UVC, interferes with the viral replication. Overall, the ability of echovirus 11 to adapt to UVC is limited, but it may have relevant consequences for disinfection in clinical settings and water treatment plants.

Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.

During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright suggested fluctuating selection as a potential explanation for the observed change in allele frequencies. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider nonrandom changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory. The simulation studies of this method reveal the combinations of parameter ranges and input values that optimize performance, thus indicating optimal experimental design strategies. We apply this approach to both the historical dataset of P. dominula in order to shed light on this historical debate, as well as to whole-genome time-serial data from influenza virus in order to identify sites with changing selection intensities in response to drug treatment.

Suppression of TLRs signaling pathways by 1-[5-methoxy-2-(2-nitrovinyl)phenyl]pyrrolidine.

Toll-like receptors (TLRs) play significant roles in recognizing the pathogen-associated molecular patterns that induce innate immunity, and subsequently, acquired immunity. In general, TLRs have two downstream signaling pathways, the myeloid differential factor 88 (MyD88)-dependent and toll-interleukin-1 receptor domain-containing adapter-inducing interferon-ß (TRIF)-dependent pathways, which lead to the activation of nuclear factor-kappa B (NF-κB) and interferon regulatory factor 3 (IRF3). 1-[5-methoxy-2-(2-nitrovinyl)phenyl]pyrrolidine (MNP) has been previously synthesized in our laboratory. To evaluate the therapeutic potential of MNP, its effect on signal transduction via the TLR signaling pathways was examined. MNP was shown to inhibit the activation of NF-κB and IRF3 induced by TLR agonists, as well as to inhibit the expression of cyclooxygenase-2, inducible nitric oxide synthase, and interferon inducible protein-10. MNP also inhibited the activation of NF-κB and IRF3 induced by the overexpression of downstream signaling components of the MyD88- or TRIF-dependent signaling pathways. These results suggest that MNP can modulate MyD88- and TRIF-dependent signaling pathways of TLRs, leading to decreased inflammatory gene expression.

An experimental evaluation of drug-induced mutational meltdown as an antiviral treatment strategy.

The rapid evolution of drug resistance remains a critical public health concern. The treatment of influenza A virus (IAV) has proven particularly challenging, due to the ability of the virus to develop resistance against current antivirals and vaccines. Here, we evaluate a novel antiviral drug therapy, favipiravir, for which the mechanism of action in IAV involves an interaction with the viral RNA-dependent RNA polymerase resulting in an effective increase in the viral mutation rate. We used an experimental evolution framework, combined with novel population genetic method development for inference from time-sampled data, to evaluate the effectiveness of favipiravir against IAV. Evaluating whole genome polymorphism data across 15 time points under multiple drug concentrations and in controls, we present the first evidence for the ability of IAV populations to effectively adapt to low concentrations of favipiravir. In contrast, under high concentrations, we observe population extinction, indicative of mutational meltdown. We discuss the observed dynamics with respect to the evolutionary forces at play and emphasize the utility of evolutionary theory to inform drug development.

WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data.

With novel developments in sequencing technologies, time-sampled data are becoming more available and accessible. Naturally, there have been efforts in parallel to infer population genetic parameters from these data sets. Here, we compare and analyse four recent approaches based on the Wright-Fisher model for inferring selection coefficients (s) given effective population size (N(e)), with simulated temporal data sets. Furthermore, we demonstrate the advantage of a recently proposed approximate Bayesian computation (ABC)-based method that is able to correctly infer genomewide average N(e) from time-serial data, which is then set as a prior for inferring per-site selection coefficients accurately and precisely. We implement this ABC method in a new software and apply it to a classical time-serial data set of the medionigra genotype in the moth Panaxia dominula. We show that a recessive lethal model is the best explanation for the observed variation in allele frequency by implementing an estimator of the dominance ratio (h).